The Response Mechanism of COVID-19 spatial global distribution to Eco-geographic Factors

The Response Mechanism of COVID-19 spatial global distribution to Eco-geographic Factors | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The Response Mechanism of COVID-19 spatial global distribution to Eco-geographic Factors Jing Pan, Arivizhivendhan Kannan Villalan, GuanYing Ni, RenNa Wu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3824333/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract COVID-19 has been massively transmitted for almost three years, and its multiple variants have caused serious health problems and an economic crisis. Our goal was to identify the influencing factors that reduce the threshold of disease transmission and to analyze the epidemiological patterns of COVID-19. This study served as an early assessment of the epidemiological characteristics of COVID-19 using the MaxEnt species distribution algorithm using the maximum entropy model. The transmission of COVID-19 was evaluated based on human factors and environmental variables, including climatic, terrain and vegetation, along with COVID-19 daily confirmed case location data. The results of the SDM model indicate that population density was the major factor influencing the spread of COVID-19. Altitude, land cover and climatic factor showed low impact. We identified a set of practical, high-resolution, multi-factor-based maximum entropy ecological niche risk prediction systems to assess the transmission risk of the COVID-19 epidemic globally. This study provided a comprehensive analysis of various factors influencing the transmission of COVID-19, incorporating both human and environmental variables. These findings emphasize the role of different types of influencing variables in disease transmission, which could have implications for global health regulations and preparedness strategies for future outbreaks. COVID-19 epidemiological characteristics analysis Spatial modeling Maximum entropy population-based studies risk assessment Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Coronavirus Disease 2019 (COVID-19) is a severe acute respiratory syndrome caused by coronavirus type 2 (SARS-CoV-2), which emerged in December 2019. The World Health Organization declared it a global pandemic on March 11, 2020 [ 1 , 2 ] . The coronavirus (CoV) belongs to the family Coronaviridae and is a single-stranded envelope virus with an RNA genome size of approximately 26-32kb [ 3 ] . The term 'coronavirus' is generated by the protrusions of the virus membrane that look like a crown [ 4 , 5 ]. Coronaviruses are a large group of viruses, some of which can cause respiratory diseases in humans and often trigger serious global public health crises. Common clinical manifestations include fever, cough, fatigue, difficulty breathing, diarrhea, nausea, and vomiting [ 6 – 8 ] . SARS-CoV-2 can be transmitted from person to person through droplets, aerosols, and droplets [ 7 ] . Moreover, SARS-CoV-2 infection may lead to long-term lung organ damage and relatively frequent cardiac involvement [ 9 ] . The virus quickly spread worldwide within a few months, leading to global panic and conflicts of interest [ 10 , 11 ] . The spread of COVID-19 was challenging to control and had serious implications for humans, as the first batch of vaccines was introduced only 1.5 years after the discovery of COVID-19 [ 12 ] . As of March 1, 2023, the global confirmed cases of COVID-19 were 676 million, with 6.87 million deaths in 188 countries/regions [ 13 ] . As of 2022, the actual number of deaths was to be as high as 20 million [ 14 , 15 ] . So far, people in many countries have widely received vaccinations against SARS-CoV-2. However, scientists express concern that the persistent unvaccinated population globally may pose a greater risk for the emergence of new mutated strains, such as Omicron [ 16 ] . Although the new generation vaccines and anti-COVID-19 treatment schemes prove helpful in managing acute COVID-19 infection, there are growing concerns about the increasing incidence of post-COVID-19 syndrome [ 17 ] . In some poor countries, the lack of vaccines remains a significant obstacle [ 18 ] . Additionally, highly transmissible variants continue to spread globally, and monitoring of mutated strains remains largely inadequate, with an incomplete understanding of the risk of reinfection [ 19 , 20 ] . Since the SARS-CoV-2 pandemic, a large number of risk and protective factors have been associated with the susceptibility or resistance to COVID-19. Beyond the direct health impact, COVID-19 has profound impact around the world, challenging the food supply chain, livelihood, economy, and animal production system [ 21 ] . COVID-19 has become the most important public health security problem globally at present and even in the next few years. The most dangerous disease in the past decade has not yet ended. Although people's scientific understanding of COVID-19 has improved during the epidemic, their attention to COVID-19 prevention and control measures has declined in the post-epidemic era (H. Wang et al., 2022a). However, many public health experts still believe that COVID-19 is an ongoing health threat (Lazarus et al., 2022). COVID has become a serious chronic disease that constitutes a considerable disease burden, but it still lacks sufficient awareness and appropriate prevention or treatment solutions [ 12 ] . In addition to its direct impact on health, COVID-19 has disrupted economic activity, social interaction, and political processes, affected civil liberties, and disrupted education at all levels [ 12 , 22 ] . Systematic and scientific research on the epidemic characteristics, influencing factors, and transmission risks of newly emerging infectious diseases at the domestic and international level, an important step in understanding the disease to prevent and control future outbreaks. The impact of climate and human factors on the transmission of coronavirus has been a significant question since the beginning of the pandemic [ 23 – 25 ] . Understanding the influence of climate on the transmission of catastrophic threats like COVID-19 a crucial for successful policy implementation and risk management to control the outbreak [ 25 ] . Various meteorological factors, such as temperature and humidity, influenced the infection rate of respiratory viruses and host immunity, leading to variations in the spread of respiratory viruses in different regions [ 24 ] . Sajadi et al., 2020 conducted research on 50 cities throughout the world and found that cities with widespread community transmission were mostly distributed between 30°N-50°N with temperatures ranging from 5–11°C [ 26 ] . Another study, Wu, Y., et al. 2020 investigated 166 countries worldwide and found a negative correlation between temperature and relative humidity based on the number of new cases and deaths per day [ 27 ] . A study conducted in 122 cities in China showed that the influence of temperature on the daily confirmed COVID-19 cases was significantly correlated with the average temperature below 3°C but not above 3°C [ 28 ] . The epidemic transmission trajectory of many countries shows strong seasonal patterns, with fewer cases in summer and more cases in winter [ 29 ] . Although a series of studies have provided empirical evidence for the negative relationship between temperature and infection [ 23 , 30 – 33 ] . Several scholars have come to the opposite conclusion, indicating that the influence of weather varies greatly in terms of impact size, significance level, weather indicators, regions, and time periods [ 34 – 36 ] . However, the impact of meteorological factors on the transmission of COVID-19 is still controversial [ 37 ] . The temperature significantly affects the transmission of COVID-19 in Japan, while there was no significant correlation between temperature and COVID-19 in Indonesia and Spain which gives an opposite conclusion [ 33 , 35 ] . Despite the numerous studies on the impact of meteorological factors on COVID-19 transmission, the relationship between weather conditions and the spread of the virus in a global prospective was relatively limited. The absence of large-scale and accurate research in this domain implies a need for comprehensive assessments to understand the COVID-19 transmission dynamics on a global scale. Currently, evidence-based and globally agreed-upon response standards methods have not been implemented to respond to monitoring, prevention, treatment, and care of COVID-19. This gap emphasized the need for standardized approaches to address the ongoing pandemic at an international level. Risk prediction is an important measure for controlling and preventing outbreaks of infectious diseases. In recent years, with the rapid development of geographic information technology and deep integration in the fields of infectious diseases and ecology. Ecological niche modeling (ENM) methods, such as GARP and MaxEnt, have been widely applied in species distribution modeling when only presence data is available for prediction. The maximum entropy model (MaxEnt), biological population growth model (CLIMEX), bioclimatic and domain models, the niche factor analysis model (ENFA), and the genetic algorithm model (GARP) were commonly used as niche models to validate the influence of environmental variables on disease distribution [ 38 , 39 ] . Among numerous niche models, MaxEnt has been widely used due to its advantages [ 40 , 41 ] . The study highlights the underutilization of geographic information technology and ecological niche modeling (ENM) in large-scale studies on COVID-19. Global COVID-19 case data and SDM, ArcGIS, and SPSS were used to analyze the COVID-19 confirmed case report data with the combined results of major influencing factors for transmission, such as climate data and remote sensing data, to evaluate the global spatial distribution pattern of COVID-19. This article deeply explores the impact of meteorological and cognitive factors on the rapid spread of COVID-19, the potential interaction, and identification of COVID-19 risk areas and hotspots. This study also evaluates the impact of population density and environmental factors on the spread of SARS-CoV-2 worldwide in order to provide guidance for the scientific prevention and control of the COVID-19 outbreak. Results An early assessment of the epidemiological characteristics of SARS-CoV-2 was conducted using the MaxEnt species distribution algorithm to study the future risk distribution of COVID 19 infection risk hotspot. Global map was classified based on geographical regions sourced from Natural Earth ( http://www.naturalearthdata.com/ ) and used for the MaxEnt model (Fig. 1 ). A total of 28142 COVID-19 occurrence points were selected after filtering for application in the MaxEnt to evaluate the future possible risk distribution of COVID-19. The model parameters were optimized and evaluated for the effective prediction of COVID 19 distribution. Rarefying and variables selection The accuracy of SDM model was validated based on AUC values, with the expectation that the best model would have an AUC value about 1. The average output result of the 10-fold cross-validation of the COVID-19 in SDM model demonstrated high training and test AUC values, combined with low standard deviations. The results indicated that the average AUC value of all research areas ranges was from 0.711 to 0.994 (Fig. 2 a). Among the 31 models, only three models such as SDM4, SDM8, and SDM28 had AUC value below 0.8 although they still exceeded 0.7. This suggested that the accuracy of the model was ‘very good’, and the prediction results were reliable, enabling the prediction of COVID-19 distribution. The results of the MaxEnt software simulation output ranged from 0 to 1, where values were closer to 1 corresponded to a higher probability of species existence. The environmental variables and mean range of VIF value for all niche models were provided in the Table 2 . The natural break was used as the minimum distance allowed between training points for the spatially filtered occurrence dataset for spotted knapweed. The application of this minimum distance in spatial filtering led to significant reduction in training sample size (Table 2 ). Table 1 . The environmental predictor variables of layers, sources, categories and variables/proxy used in modelling of COVID-19 distribution. Layers Source Value/categories Variable/proxy Climate a Monthly P (prec1-12) Ibid. 0 to 1201 mm/month Precipitation Monthly mean T (temp1-12) Ibid. -54.9 to 39.2˚C Mean Temperature Monthly min T (tmin1-12) Ibid. -56.5 to 32.3˚C Minimum Temperature Monthly max T (tmax1-12) Ibid. -53.2 to 47.3˚C Maximum Temperature Bioclimatic (bio1-19) Ibid. Annual trends, seasonality, extreme or limiting environmental variables Terrain b Elevation ASTER-GDEM -328 to 4739m a.s.l Climbing distance ISR-spring Ibid. 62.4 to 206.8 wh/m 2 Topo-climate ISR-summer Ibid. 35.5 to 104.2 wh/m 2 Topo-climate ISR-autumn Ibid. 64.1 to 218.2 wh/m 2 Topo-climate ISR-winter Ibid. 58.1 to 184.2 wh/m 2 Topo-climate Vegetation Land cover c ESA Cropland (3), Herbaceous, Tree (9), Shrubland (3), Grassland, Urban areas, Bare areas (2), Mosaic shrub & herbaceous cover, Water bodies, Permanent snow, and ice Human activity venues Human impact Human population d WorldPop 0 to 1,202.6 ind/km 2 Human-Animal interaction a T=temperature; P=precipitation. Source: http://chelsa-climate.org/ b Source: http://www.gscloud.cn/ c Land cover: Cropland, Herbaceous, Tree, Shrubland, Grassland, Urban areas, Bare areas, Water bodies and Permanent snow and ice https://maps.elie.ucl.ac.be/CCI/viewer/ d Source: https://www.worldpop.org/ Table 2 The VIF value of all models and its detailed information of geographical region, COVID-19 occurrence point, elevation and environment variables Models Regions COVID-19 presence points Elevation Natural break Environmental layers VIF SDM-1 North pole 105 Below 1500 10km Population, land cover and maximum temperature of March (tmax3) 1.014–1.742 SDM-2 Middle North America 1980 Below 1500 10km Population and maximum temperature of May (tmax5) 1.213–3.642 SDM-3 Middle North America 1276 Above 1500 10km Minimum temperature of July (tmin7), spring, population and land cover 1.009–2.931 SDM-4 Lower North America 2170 Below 1500 10km Population, elevation, land cover, minimum temperature of November (tmin11) and mean temperature of March (temp3) 1.038–9.558 SDM-5 Lower North America 823 Above 1500 10km Minimum temperature of July (tmin7), spring, population and land cover 1.000 SDM-6 Upper Sorth America 879 Below 1500 10km Population, land cover and autumn 1.000-3.409 SDM-7 Upper South America 11 Above 1500 10km Population, autumn, mean temperature of October (temp10) and land cover 1.0001–5.584 SDM-8 Middle Sorth America 3940 Below 1500 10km Land cover, population, mean temperature of June (temp6) and autum 1.000 SDM-9 Middle South America 11 Above 1500 10km Mean temperature of October (temp10), land cover and elevation 0.965–0.993 SDM-10 Lower Sorth America 1753 Below 1500 10 km Population, land cover and maximum temperature of January (tmax1) 1.000 SDM-11 Western Europe 105 Below 1500 160 km Elevation, population, land cover, winter, maximum temperature of August (tmax8) and Precipitation of Wettest Month (bio13) 1.014–1.078 SDM-12 Western Europe 41 Above 1500 10 km Minimum temperature of July (tmin7), precipitation of June (prec6), winter, land cover and population 1.000 SDM-13 Russia,etc 1224 Below 1500 40 km Population the mean monthly Precipitation of Warmest Quarter (bio18) and summer 1.000 SDM-14 Russia,etc 28 Above 1500 20 km The mean monthly precipitation amount of the wettest quarter (bio16), population and land cove 1.001–6.921 SDM-15 Iran,etc; 103 Above 1500 10 km Population, summer, the Mean Temperature of Wettest Quarter (bio8), and minimum temperature of December (tmin12) 1.001–1.233 SDM-16 Pamirs Plateau 32 Below 1500 50 km Population, land cover, precipitation of December (prec12) and minimum temperature of Octoberber (tmin10) 1.000-1.005 SDM-17 Pamirs Plateau 25 Above 1500 10 km Land cover, population and the precipitation amount of January (prec1) 1.000-1.765 SDM-18 India,etc 85 Below 1500 10 km The mean monthly precipitation amount of the wettest quarter (bio16), population and land cover 1.000 SDM-19 India,etc 7 Above 1500 10 km Bio5, Tmax3 maximum temperature of March, Bio4 1.000 SDM-20 Up Qinling-Huaihe Line 367 Below 1500 10 km Population, land cover, elevation, precipitation of August (prec8) summer 1.000-1.911 SDM-21 Up Qinling-Huaihe Line 18 Above 1500 10km Land cover, spring, maximum temperature of January (tmax1), population 1.000 SDM-22 Blow Qinling-Huaihe Line 717 Below 1500 10 km Population, land cover, elevation 1.000-1.009 SDM-23 Blow Qinling-Huaihe Line 20 Above 1500 10 km Population, elevation, land cover 1.000 SDM-24 Austrilia 289 Below 1500 10 km population, mean temperature of January (temp1) 1.000 SDM-25 Cuba 47 Below 1500 40 km Land cover, population, precipitation amount of June (prec6) and minimum temperature of April (tmin4) 1.007–1.794 SDM-26 England 60 Below 1500 50 km Population, land cover, maximum temperature of June (tmax6), minimum temperature of June (tmin6), summer 1.011–1.116 SDM-27 Japan 58 Below 1500 20 km Population, maximum temperature of January (tmax1), land cover 1.000 SDM-28 The Philippines 69 Below 1500 110 km Maximum temperature of September (tmax9), land cover, precipitation amount of January (prec1), population 1.136–5.862 SDM-29 The Philippines 8 Above 1500 0 km Minimum temperature of June (tmin12), land cover 1.000 SDM-30 Indonisia 163 Below 1500 60 km Population, land cover, maximum temperature of February (tmax2), spring 1.000-2.005 SDM-31 New Zealand 20 Below 1500 10 km Population, maximum temperature of September (tmax9), land cover, mean temperature of September (temp9) 1.000 Influence of population density on COVID-19 The result revealed that population density variables significantly influenced on the transmission of COVID-19 than other variables (Fig. 2 b). The influence of population density on risk distribution areas was notably high in most of the models (Fig. 2 b). The SDM-31 had the highest impact at 93.2%, followed by SDM-20 (92.9%), SDM-22 (91%), SDM-6 (88.5%), SDM-24 (84.3%), SDM-2 (82.5%), SDM-30 (77.9%), SDM-13 (77.7%), SDM-15 (70.3%), SDM-9 (65.2%), SDM-23 (%), SDM-1 (62.2%), SDM-2 (82.5%), SDM-10 (60.1%), SDM-7 (55.2%), SDM-16 (54.3%), SDM-26 (54.2%), SDM-4 (45.9%), SDM-27 (39.6%), SDM-8 (37.8%), SDM-14 (36.6%), SDM-25 (34.5%), SDM-11 (29.5%), SDM-17 (22.9%), SDM-5 (19.3%), SDM-18 (15.4%), SDM-21 (11.9%) (Table 3 and Table 4 ). Out of a total of 31 SDM models, 8 SDM models contributed more than 80% to the specified environmental and geographic variables, and 6 of these SDM models were highly influenced by population density (Fig. 3 ).The population density factor significantly influenced both mainland and island countries in most of the models except for two niche models. According to the MaxEnt response curve of each model predictor were shown in Figures S2-S13. The population density in New Zealand significantly impacts the distribution of SARS-CoV-2, with an estimated contribution of up to 93.2% (Table 3 ). The distribution probability of SARS-CoV-2 becomes stable with the population density reaches 2,000 people/km 2 . Similarly, estimates of contribution above 80% were reported for regions in upper South America, Austrilia, and Middle North America. In most areas below 1500 meters of elevation, such as India and Western Europe, an increase in population density led to a significant reduction in the distribution probability of SARS-CoV-2. The distribution probability of COVID-19 was increased sharply with the increase of population density in most regions when the elevation varied below 1500 meters. Table 3 Percentage contributions of predictor variables to the MaxEnt models blow than 1500m SDM-1 SDM-2 SDM-4 SDM-6 SDM-8 SDM-10 SDM-11 SDM-13 SDM-16 SDM-18 SDM-20 SDM-22 SDM-24 SDM-25 SDM-26 SDM-27 SDM-28 SDM-30 SDM-31 Population 62.2 82.5 45.9 88.5 37.8 60.1 29.5 77.7 54.3 15.4 92.9 91 84.3 34.5 54.2 39.6 6.5 77.9 93.2 Landcover 10.2 8.4 6.6 40.1 23 19.2 27.3 25.7 6 6.3 43.7 38 29 23.4 9.4 2.6 Elevation 42.6 38.8 0.7 2.8 Spring 5.8 2.3 5.8 Summer 8.7 0.1 0.3 Autumn 4.9 19.7 Winter 3.3 Tmax1 16.9 17.9 31.4 Tmax2 6.9 Tmax3 27.6 Tmax5 17.5 Tmax6 4.6 Tmax8 5.9 Tmax9 60.1 3.5 Tmin4 2.9 Tmin6 2.9 Tmin10 5.2 Tmin11 3.2 Temp1 15.7 Temp6 2.4 Temp9 0.7 Prec1 10.1 Prec6 16 Prec8 0.3 Prec12 13.3 Bio12 35.1 Bio13 3.3 Bio18 13.6 Table 4 Percentage contributions of predictor variables to the MaxEnt models above than 1500m SDM-3 SDM-5 SDM-7 SDM-9 SDM-12 SDM-14 SDM-15 SDM-17 SDM-19 SDM-21 SDM-23 SDM-29 Population 19.3 55.2 9.1 36.6 70.3 22.9 11.9 62.8 Landcover 1.7 0.8 25.7 8.8 58 45.7 2.3 3.5 Elevation 9.1 34.9 Spring 65.8 25.6 Summer 84.9 17.2 15.7 Autumn 37.9 Winter 9.2 Tmax1 16.7 Tmax3 15.1 20.9 Tmin7 13.2 54.2 46.3 Tmin12 6.7 96.5 Temp10 6 65.2 Prec1 19.1 Prec6 18.8 Bio4 13.8 Bio5 65.3 Bio8 7.2 Influence of land cover and elevation variables on COVID-19 The probability of COVID-19 distribution was not influenced by population density factor in some regions such as those with elevation greater than 1500m in the Philippines, Middle North America, and India (FigureS2, S8 and S11). In areas with elevations above 1500 meters, the contribution rate of population density was relatively lower (Figure S1 b). The proportion of altitude and landcover showed a significant influence on the probability of COVID-19 distribution (Fig. 2 b). In regions below 1500m altitude, the land cover showed a significant impact on these models, followed by the impact of population density (Figure S1 a). In regions above 1500m altitude, the terrain variables showed a significant impact. The land cover relatively influences the probability of COVID-19 distribution models such as SDM-17 (58%), SDM-21 (45.7%), SDM-25 (43.7%), SDM-8 (40.1%), SDM-26 (38%), SDM-27 (29%), SDM-16 (27.3%), SDM-9 (25.7%), SDM-18 (25.7%), SDM-28 (23.4%), SDM-10 (23%), SDM-11 (19.2%) and SDM-1(10.2%) (Table 3 and Table 4 ). The elevation below 1500m in the Qinling-Huaihe Line exhibited contributions greater than 90% (Table 3 ). Additionally, the distribution probability of SARS-CoV-2 decreased with an increase in population density in the upper part of South America, north of the Qinling Mountains and Huai River, and areas above 1500 meters above elevation. In these regions, the contribution rate of population density was relatively lower, while the proportion of altitude and land cover was significantly increased. Moreover, when elevation was more than 1500m on the Pamirs Plateau and up Qinling-Huaihe Line, land cover also had a quite important impact. The average output result of 10-fold cross-validation of COVID-19 indicates that the land cover was significantly influenced in the Northern Hemisphere. The simulation results further emphasized that land cover was the third important factor influenced the distribution and diffusion of COVID-19 (Fig. 2 b). The results reveal that urban areas with a land cover value of 190 exhibit the highest probability of COVID-19 distribution, which also conformed the actual situation(Figure S2c, S3e, S4a, S5a, S8c, S9ae, S11d, S12ac). Influence of climate variables on COVID-19 In regions above 1500m altitude, the impact of population density decreases, and the impact of climate factors increases (Figure S1 ). Continuous low-probability predictors for COVID-19 include temperature, incident solar radiation, and rainfall. When the altitude is below 1500m, Tmax1 (Maximum temperature of January) (SDM-10, SDM-18 and SDM-27), Tmax9 (Maximum temperature of September) (SDM-28) and Bio12(Annual Precipitation) (SDM-18) were the most important variables influenced the transmission of COVID-19 (Table 3 ). When the altitude is more than 1500m, Tmin7 (Minimum temperature of July) (SDM-12), Tmin12 (Minimum temperature of December) (SDM-29), Bio5 (Max Temperature of Warmest Month) (SDM-19) and Temp10 (Mean temperature of October) (SDM-9) were the most important variables influenced the transmission of COVID-19 (Figure S1 ) (Table 4 ). The Max temperature of the warmest month in India with above 1500 meters elevation emerged as the most influencing variable on the distribution of COVID-19, followed by Tmax3, with temperature Seasonality being the least influential (Table 2 ). The environmental variables (temperature, solar radiation, and precipitation) predominantly influence the occurrence of COVID-19 during spring and summer near the poles of the northern and southern hemispheres. In contrast, solar radiation of autumn and winter were the main influencing environmental variables in the equatorial region (FigureS2-S13 and Table 2 ). Geographical distribution of COVID-19 The impact of demographic factors (population density) and environmental variables (elevation, precipitation, Incoming Solar Radiation, and temperature) on the transmission dynamics of SARS-CoV-2 was assessed with the jackknife analysis (Figure S14-S17). The jackknife analysis, a systamatic form of re-sampling, repeats the process by leaving out a different value and recalculating the test statistic for each time. The model output was reclassified to four types of potential distributions as follows: not suitable area (0–0.2); low suitable area (0.2–0.4); medium suitable area (0.4–0.6); highly suitable area by ArcGIS 10.2.[42, 43]. The Fig. 4 encompasses the global potential distribution mapping of COVID-19, illustrating the comprehensive scope of the virus's potential spread across different regions and locales. The high-risk areas for COVID 19 were located between latitudes 0–50°N and 0–30°S (the central and lower parts of North America, concentrated in the northwest and southeast of the United States, as well as central and southern Mexico. In parts of South America, western Peru, northern Chile, and eastern Brazil. In the Eurasian continent, Northwest and southern Asia, distributed in southern Myanmar, northern and southern Thailand, northeastern Vietnam, and southern China; Southeast Europe, all of Ukraine, northwest Germany, western, northern, and southeastern France; The western part of the Arctic Circle; Ukraine, Belarus, southwestern Russia, northwestern Germany, small areas in southern Guangzhou, southern Harbin, and the entire Changchun region of China;South Korea, Cambodia, southern Myanmar, and southern Vietnam were also showed high-risk. Southeast Oceania; Cuba as a whole; Southeast United Kingdom; Southeast Indonesia; All over the Philippines; Southern Japan and northeastern New Zealand). In North America, most of the central region of the United States and a small portion of the northeast, as well as a small portion of the central northern and southern coastal regions of Mexico; central and eastern Ukraine, central and eastern India, northern and middle eastern Thailand, and Hainan and northeast Harbin of China and all part of Malaysia were predicted as a medium risk region. In North America, southern Canada, northern and southwestern United States, and northern Mexico; in South America, northwest Brazil, Argentina, most of Russia except southwest, most of Mongolia, and Australia except southern region were showed low-risk areas (Fig. 4 ). Discussion The COVID-19 epidemic was certainly destructive, affecting both human health and the global economy [ 44 ] . This research mainly focused on the epidemiology of COVID-19 before the emergence of the Omicron variant. Prior to the outbreak of Omicron, global COVID-19 data statistics were more comprehensive and accurate, enabling a better understanding of the impact of environmental factors on disease transmission and their respective contributions. For niche models, the regional scale prediction model has more advantages in model accuracy [ 45 , 46 ] . Therefore, this study was based on more local scales for modeling. WWF global ecological zoning established for natural conservation purposes (Eco-regions) was adopted as the basic framework for the global ecological geographic zoning knowledge base in this article [ 47 ] . This study provides a method to plot the risk of COVID-19 associated with epidemiological and environmental factors. The MaxEnt model was used to improve variable selection, and its reliability has been confirmed by its good capacity to predict novel presence localities for poorly known species/diseases [ 41 ] . It has been widely used in many diseases, including COVID-19 [ 3 , 48 , 49 ] .. This study mainly focused on identifying the risk areas and hotspots of COVID-19 and the impact of population density and environmental factors on the global spread of SARS-CoV-2. To enhance the accuracy of our analysis, we refined the MaxEnt model and employed it for guiding our variable selection. The MaxEnt model offers a significant advantage by achieving high precision in data processing through calculating CV values. The homology of the city was acceptable, given that the CV values of all variables were less than 15% [ 50 , 51 ] . The geometric center of the city is retained for subsequent MaxEnt modeling. The β parameter of MaxEnt was consistent with the characteristics of overfitting. This implies that the default setting (β = 1) of MaxEnt was correct model, as observed in previous research [ 52 ] . Additionally, a VIF value below 10 indicates low and acceptable multicollinearity [ 53 ] . The average result obtained from the 10-fold cross-validation of the COVID-19 SDM revealed that the average AUC value of 30 areas were above 0.9. This signifies that the MaxEnt model’s performance reached a high level, indicating that it was suitable for simulating the risk areas of COVID-19 on a global scale. The AUC statistic method was commonly used to characterize model performance due to its model accuracy [ 54 ] . Currently, the AUC method was considered as the best criterion for assessing model success for presence/absence data [ 41 ] . The general accepted standard for AUC is above 0.8, which indicates a good model, while an AUC value approaching 1 signifies excellent model performance [ 55 ] . This study conducted an assessment to evaluate the impact of demographic and environmental factors on the transmission of SARS-CoV-2 and to predict the high-risk areas. The results indicate that population density was a core contributor to the model, aligning with various studies highlighting its significance in the spread of SARS-CoV-2. Challenges posed by urbanization and social cohesion complicate efforts to control the global pandemic. The global connectivity of cities and their complex ecosystems facilitates the transmission of the virus from person to person. SARS-CoV-2 was widely distributed in public places, which provided ideal conditions for virus transmission [ 3 , 56 ] . Our findings demonstrate that COVID-19 transmission was predominantly influenced by population density rather than seasonal variation. The population response curve ( illustrated an exponential increase in the impact of population density on SARS-CoV-2 distribution as it exceeds 0. Numerous reports on the distribution of COVID-19 investigations consistently validate our research findings, emphasizing the coherence and reliability of our study in this particular context. [ 57 – 62 ] . Population density emerged as the most influential variable that affects the distribution of SARS-CoV-2 (Fig. 2 b). Among all 31 SDM models, 25 models were significantly influenced by population density. Transmission was more severe in densely populated communities, fostering the spread of SARS-CoV-2 to varying degrees [ 63 , 64 ] . Although some studies have described a positive correlation between altitude and respiratory disease mortality [ 65 , 66 ] . However, the effect of altitude on mortality in COVID-19 showed an opposite result because altitude may be protective or a risk factor for mortality [ 67 ] . Consequentially, SARS-CoV-2 was negatively correlated with population density in the upper part of South America above an altitude of 1500 meters as well as in the north region of the Qinling Mountains-Huaihe River. The population density has reached 6000 people/square kilometer in India and south of the Tropic of Cancer in China. The mortality rate has increased due to limited medical conditions, leading to a decrease in the distribution of SARS-CoV-2 [ 68 ] . Our model revealed that climate variables were the least influential factor in the transmission of COVID 19. While weather conditions can also be considered an influencing factor for the human-to-human transmission of pathogens. The viability of infectious viruses depends on environmental factors such as temperature and humidity [ 69 , 70 ] . Sobral et al. (2020) reported that temperature and humanity had negatively correlated between temperature and the number of SARS-CoV and MERS-CoV. Prolonged exposure of the SAR-CoV virus to low temperatures extends its half-life and survival ability. While low temperature and low humidity enhance the stability of droplet transmission in the nasal mucosa and damage local innate immunity. The temperature of the hottest month and the driest quarter have a negative impact on the transmission of SARS-CoV-2. Weather can affect the transmission of the virus in two different ways, such as from an epidemiological and behavioral perspective. The survival and transmission of viruses depend on the temperature of their environment, with high temperatures damaging the virus's lipid cortex [ 24 , 71 ] . Higher temperatures severely impair the survival ability of the SARS coronavirus [ 72 ] . In behavioral perspective, weather can alter levels of action, social distance, and social gathering locations, thereby influencing the spread of the virus among individuals [ 73 ] . A slight increase in temperature increases the probability of SARS-CoV-2 distribution, while a significant increase in temperature reduced its probability. Additionally, considering that the transmission of coronavirus was similar to influenza, influenza virus was more transmissible at lower temperatures because cold weather can weaken the host's immune system, thereby increasing infection susceptibility. MaxEnt results indicated that the response curve, land cover was the third major factor influencing the spread of COVID-19 (Fig. 2 b). The acceleration of threats to biodiversity over the past 40 years has been attributed to the alarming pace of local land cover change, and it is anticipated that this rate may persist in the near future [ 74 ] . The majority of landscapes globally undergo changes in energy utilization, production of exotic species, land use, and various other activities [ 75 ] . Land cover played a synergetic role in affecting human populations and the spread of terrestrial species [ 76 – 78 ] . The epidemiological characteristics and factors influencing the transmission of COVID-19 at a global scale using the MaxEnt species distribution algorithm. Our predictions for future COVID-19 potential distribution highlighted high-risk areas, providing valuable insights for targeted intervention strategies. This study was conducted during the early stages, predating the emergence of the Omicron variant. This ensures a more comprehensive understanding of the evolving landscape of COVID-19, encompassing the latest evaluations and insights into infection origins. The analysis underscored the critical role of population density as a core contributor to the model, emphasizing the challenges posed by urbanization and social cohesion in controlling the pandemic. Land cover emerged as the second major factor influencing the spread of COVID-19, contributing to biodiversity threats. The MaxEnt model demonstrated high accuracy, with an average AUC value exceeding 0.9, signifying its suitability for simulating global COVID-19 risk areas. Surprisingly, climate variables, particularly temperature, exhibited minimal impact on transmission dynamics. This finding contributes to the understanding of COVID-19 transmission dynamics, emphasizing the significant influence of demographic, geographic, and environmental factors. The findings hold implications for public health strategies and underscore the need for comprehensive, localized modeling to effectively address the global challenges posed by infectious diseases like COVID-19. Methods Differentiation of prediction areas We conducted an analysis of the epidemiological patterns of COVID-19 worldwide based on every region of the COVID-19 occurrence report, except the Africa region, due to the unavailable official data. Eight biogeographic realms, as defined by World Wide Fund for Nature (WWF) ( https://wwf.panda.org ) were considered: Nearctic, Palearctic, Neotropical, Afrotropic, Indo-Malay, Australasia, Oceania, and Antarctic [ 79 ] . The stimulation was performed separately for six island countries, i.e., Japan, Indonesia, New Zealand, the United Kingdom, Ireland, and Cuba. The epidemiological characteristics of SARS-CoV-2 were accurately analyzed in the above-mentioned landscapes. Breifly, the regional study on the global continent according to the altitude, topography, and climate characteristics of each continent, combined with the global temperature zone [ 80 – 84 ] . Subsequently, MaxEnt was supplied for each region separately (Fig. 1 ). COVID-19 occurrence records and processing The early COVID-19-infected cases, spanning from January 1, 2020, to January 30, 2022 across 173 countries, were sourced from WHO (World Health Organization) [ 13 ] . To enhance the accuracy of the species distribution model (SDM), a meticulous screened process was applied to the COVID-19-point data. Excluding cases from countries or regions lacking transmission results. Furthermore, to address potential data shortages at the local level, we calculated the coefficient of variation values (CV), which calculated by variation and reflect the degree of dispersion between data points [ 50 ] . This method serves to quantify the data within the dataset. To conduct a high-precision analysis, a grid size of 1 km2 within each city was employed. This involved utilizing 67 climate variables to evaluate the CV values for specific cities, thereby identifying and evaluating the lack of data. Processing of environmental variables Environmental predictor variables, including climate, terrain, vegetation, and human impact, were generated for COVID-19 modeling. The current forecasting data was collected from the CHELSA database (Table 1 ) [ 39 , 85 ] . The incoming solar radiation (ISR) values were calculated at 30-minute intervals and aggregated per growing season. The seasonal category of each research area was integrated official data from each country, survey reports and the website of the global seasons division [ 86 ] . All spatial data preprocessing and calculations were done with standard operations in ArcGIS 10.2 and were projected in UTM-WGS-1984 with standard settings or resampling to 30 arc seconds [ 40 , 41 , 87 ] . COVID-19 distribution modeling and evaluation The MaxEnt model stands out as one of the best-performing specialty distribution modeling techniques for analyzing occurrence data. Consequently, we employed MaxEnt model to predict the future distribution of COVID-19 infection using case occurrence data [ 88 ] . This model developed the ecological niche models by employing a machine-learning approach, combining COVID-19 case occurrence data with environmental variables. To explore the risk situation of SARS-CoV-2, the MaxEnt model was applied to the spatial distribution model building. The areas of interest were catagorized into those below and above 1500m asl, according to the elevation standard of the highland climate[41, 89]. Spatial autocorrelation was minimized by filtering all recorded COVID-19 locations data using the SDM Toolbox v1.1c in ArcGIS 10.2 [ 87 ] . Principal component analysis (PCA) and multicollinearity were addressed by excluding factors through variance inflation factor (VIF) analysis [ 53 , 90 ] . The filtered COVID-19 location and predictors served as input data for constructing the COVID-19 model using the MaxEnt algorithm. We divided the selected occurrence records into 70% training and 30% testing portions to build and validate the models based on 10 bootstrap replicates. For the remaining parameters, we maintained the default settings in the pilot study. The final COVID-19 predicted risk maps for low-elevation and high-elevation areas were verlaid using the fuzzy overlay. The Jenks natural break optimization method was employed to classify the model output with smothering and visualize high-risk areas [ 39 , 91 ] . The relative contribution of predictors for modeling was evaluated through the jackknife test and variable response curve. The accuracy of the model was assessed by the area under the receiver operating characteristic (ROC) curve [ 92 ] . Declarations Availability of materials and data The environmental predictor variables have been deposited in the CHELSA (http://chelsa-climate.org/), the terrain predictor variables have been deposited in the Geospatial Data Cloud (http://www.gscloud.cn/), the population destiny was download in (https://www.worldpop.org/), Land cover was download in ESA(https://maps.elie.ucl.ac.be/CCI/viewer/). Materials supporting the findings of this study are available from the corresponding authors upon request. Acknowledgements This study was supported by the ‘COVID-19 Epidemic Emergency Special Project’ attached to the Fundamental Research Funds for the Central Universities, the Ministry of Education (Grant No. 2572020DY01). Author contributions statement WANG X.L. and WU X.D. conceived and supervised the study. PAN J. contributed to the data filtering, analysis, interpretation, cartography, and draft writing. ARI contributed to the language editing and chart drawing in the later stage of the article. WANG X.L., NI G.Y., WU R.N. and SUIi S.F. contributed to the map design, discussion, and manuscript writing. All authors significantly contributed to the final manuscript and gave final approval for publication. Additional information Competing interests: The authors declare no competing interests. References Gao, W., et al., A new study of unreported cases of 2019-nCOV epidemic outbreaks. Chaos Solitons Fractals . 138: p. 109929, DOI:https://doi.org/10.1016/j.chaos.2020.109929 (2020). Cucinotta, D. and M. Vanelli, WHO Declares COVID-19 a Pandemic. Acta Biomed . 91(1): p. 157-160, DOI:https://doi.org/10.23750/abm.v91i1.9397 (2020). Yaro, C. A., P. S. U. Eneche, and D. Abu Anyebe, Risk analysis and hot spots detection of SARS-CoV-2 in Nigeria using demographic and environmental variables: an early assessment of transmission dynamics. International Journal of Environmental Health Research . 32(5): p. 1111-1122, DOI:https://doi.org/10.1080/09603123.2020.1834080 (2022). Sarkar, Pranabesh, et al., COVID 19: An Epidemiological and Host Genetics Appraisal. Asian Journal of Medical Sciences . 11(3): p. 71-76, DOI:https://doi.org/10.3126/ajms.v11i3.28569 (2020). Lu, Roujian, et al., Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding. The Lancet . 395(10224): p. 565-574, DOI:https://doi.org/10.1016/s0140-6736(20)30251-8 (2020). Wu, F., et al., A new coronavirus associated with human respiratory disease in China. Nature . 579(7798): p. 265-269, DOI:https://doi.org/10.1038/s41586-020-2008-3 (2020). Gao, Q. Y., Y. X. Chen, and J. Y. Fang, 2019 Novel coronavirus infection and gastrointestinal tract. J Dig Dis . 21(3): p. 125-126, DOI:https://doi.org/10.1111/1751-2980.12851 (2020). Wang, D., et al., Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus-Infected Pneumonia in Wuhan, China. JAMA . 323(11): p. 1061-1069, DOI:https://doi.org/10.1001/jama.2020.1585 (2020). Visco, V., et al., Post-COVID-19 Syndrome: Involvement and Interactions between Respiratory, Cardiovascular and Nervous Systems. Journal of Clinical Medicine . 11(3), DOI:https://doi.org/10.3390/jcm11030524 (2022). Hosseiny, M., et al., Radiology Perspective of Coronavirus Disease 2019 (COVID-19): Lessons From Severe Acute Respiratory Syndrome and Middle East Respiratory Syndrome. AJR Am J Roentgenol . 214(5): p. 1078-1082, DOI:https://doi.org/10.2214/AJR.20.22969 (2020). Memish, Z. A., et al., Middle East respiratory syndrome coronavirus in bats, Saudi Arabia. Emerg Infect Dis . 19(11): p. 1819-23, DOI:https://doi.org/10.3201/eid1911.131172 (2013). Lazarus, Jeffrey V., et al., A multinational Delphi consensus to end the COVID-19 public health threat. Nature . 611(7935): p. 332-345, DOI:https://doi.org/10.1038/s41586-022-05398-2 (2022). World Health Organization . Available from: https://www.who.int. Adam, D, The pandemic’s true death toll: millions more than official counts. Nature 601: p. 312–315, DOI:https://doi.org/10.1038/d41586-022-00104-8 ( 2022). Wang, H. D., et al., Estimating excess mortality due to the COVID-19 pandemic: a systematic analysis of COVID-19-related mortality, 2020-21. Lancet . 399(10334): p. 1513-1536, DOI:https://doi.org/10.1016/s0140-6736(21)02796-3 (2022). Njoga, E. O., et al., COVID-19 Vaccine Hesitancy and Determinants of Acceptance among Healthcare Workers, Academics and Tertiary Students in Nigeria. Vaccines (Basel) . 10(4), DOI:https://doi.org/10.3390/vaccines10040626 (2022). Karbalaeimahdi, Mohammad, et al., The Role of Interferons in Long Covid Infection. Journal of interferon & cytokine research : the official journal of the International Society for Interferon and Cytokine Research . 43(2): p. 65-76, DOI:https://doi.org/10.1089/jir.2022.0193 (2023). Mallapaty, S, Researchers fear growing COVID vaccine hesitancy in developing nations. Nature 601: p. 174-175 DOI:https://doi.org/10.1038/d41586-021-03830-7 (2022). Chen, Z., et al., Global landscape of SARS-CoV-2 genomic surveillance and data sharing. Nat Genet . 54(4): p. 499-507, DOI:https://doi.org/10.1038/s41588-022-01033-y (2022). Karthikeyan, S., et al., Wastewater sequencing reveals early cryptic SARS-CoV-2 variant transmission. Nature . 609(7925): p. 101-108, DOI:https://doi.org/10.1038/s41586-022-05049-6 (2022). Rahimi, P., et al., Impact of the COVID-19 pandemic on food production and animal health. Trends Food Sci Technol . 121: p. 105-113, DOI:https://doi.org/10.1016/j.tifs.2021.12.003 (2022). Sachs, J. D., et al., The Lancet Commission on lessons for the future from the COVID-19 pandemic. Lancet . 400(10359): p. 1224-1280, DOI:https://doi.org/10.1016/S0140-6736(22)01585-9 (2022). Prata, D. N., W. Rodrigues, and P. H. Bermejo, Temperature significantly changes COVID-19 transmission in (sub)tropical cities of Brazil. Sci Total Environ . 729: p. 138862, DOI:https://doi.org/10.1016/j.scitotenv.2020.138862 (2020). Moriyama, M., W. J. Hugentobler, and A. Iwasaki, Seasonality of Respiratory Viral Infections. Annu Rev Virol . 7(1): p. 83-101, DOI:https://doi.org/10.1146/annurev-virology-012420-022445 (2020). Del Rio, C. and A. Camacho-Ortiz, Will environmental changes in temperature affect the course of COVID-19? Braz J Infect Dis . 24(3): p. 261-263, DOI:https://doi.org/10.1016/j.bjid.2020.04.007 (2020). Sajadi, Mohammad M., et al., Temperature, Humidity, and Latitude Analysis to Estimate Potential Spread and Seasonality of Coronavirus Disease 2019 (COVID-19). JAMA network open . 3(6): p. e2011834, DOI:https://doi.org/10.1001/jamanetworkopen.2020.11834 (2020). Wu, Y., et al., Effects of temperature and humidity on the daily new cases and new deaths of COVID-19 in 166 countries. Sci Total Environ . 729: p. 139051, DOI:https://doi.org/10.1016/j.scitotenv.2020.139051 (2020). Xie, Jingui and Yongjian Zhu, Association between ambient temperature and COVID-19 infection in 122 cities from China. The Science of the total environment . 724: p. 138201, DOI:https://doi.org/10.1016/j.scitotenv.2020.138201 (2020). Ganslmeier, M., D. Furceri, and J. D. Ostry, The impact of weather on COVID-19 pandemic. Sci Rep . 11(1): p. 22027, DOI:https://doi.org/10.1038/s41598-021-01189-3 (2021). Lolli, S., et al., Impact of meteorological conditions and air pollution on COVID-19 pandemic transmission in Italy. Sci Rep . 10(1): p. 16213, DOI:https://doi.org/10.1038/s41598-020-73197-8 (2020). Shi, P., et al., Impact of temperature on the dynamics of the COVID-19 outbreak in China. Sci Total Environ . 728: p. 138890, DOI:https://doi.org/10.1016/j.scitotenv.2020.138890 (2020). Malki, Z., et al., Association between weather data and COVID-19 pandemic predicting mortality rate: Machine learning approaches. Chaos Solitons Fractals . 138: p. 110137, DOI:https://doi.org/10.1016/j.chaos.2020.110137 (2020). Tosepu, R., et al., Correlation between weather and Covid-19 pandemic in Jakarta, Indonesia. Sci Total Environ . 725: p. 138436, DOI:https://doi.org/10.1016/j.scitotenv.2020.138436 (2020). Yao, Y., et al., No association of COVID-19 transmission with temperature or UV radiation in Chinese cities. Eur Respir J . 55(5), DOI:https://doi.org/10.1183/13993003.00517-2020 (2020). Briz-Redon, A. and A. Serrano-Aroca, A spatio-temporal analysis for exploring the effect of temperature on COVID-19 early evolution in Spain. Sci Total Environ . 728: p. 138811, DOI:https://doi.org/10.1016/j.scitotenv.2020.138811 (2020). Bashir, M. F., et al., Correlation between climate indicators and COVID-19 pandemic in New York, USA. Sci Total Environ . 728: p. 138835, DOI:https://doi.org/10.1016/j.scitotenv.2020.138835 (2020). Sun, Z., et al., Potential Factors Influencing Repeated SARS Outbreaks in China. Int J Environ Res Public Health . 17(5), DOI:https://doi.org/10.3390/ijerph17051633 (2020). Fekede, R. J., et al., Could wild boar be the Trans-Siberian transmitter of African swine fever? Transbound Emerg Dis . 68(3): p. 1465-1475, DOI:https://doi.org/10.1111/tbed.13814 (2021). van Gils, Hein, et al., Where the bears roam in Majella National Park, Italy. Journal for Nature Conservation . 22(1): p. 23-34, DOI:https://doi.org/10.1016/j.jnc.2013.08.001 (2014). Gao, S., et al., Predicting the possibility of African horse sickness (AHS) introduction into China using spatial risk analysis and habitat connectivity of Culicoides. Scientific Reports . 12(1), DOI:https://doi.org/10.1038/s41598-022-07512-w (2022). Zeng, Z., et al., A predictive analysis on the risk of peste des petits ruminants in livestock in the Trans-Himalayan region and validation of its transboundary transmission paths. Plos One . 16(9), DOI:https://doi.org/10.1371/journal.pone.0257094 (2021). Xu, Danping, et al., Modeling the distribution of Zanthoxylum armatum in China with MaxEnt modeling. Global Ecology and Conservation . 19, DOI:https://doi.org/10.1016/j.gecco.2019.e00691 (2019). Çoban, H. Oğuz, Ömer K. Örücü, and E. Seda Arslan, MaxEnt Modeling for Predicting the Current and Future Potential Geographical Distribution of Quercus libani Olivier. Sustainability . 12(7), DOI:https://doi.org/10.3390/su12072671 (2020). Wu, C., et al., Investigation and influencing factors about well-being level of elderly chronic patients during COVID-19 postpandemic period in Beijing. Medicine . 101(9), DOI:https://doi.org/10.1097/md.0000000000028976 (2022). DAWSON, RICHARD G. PEARSON* and TERENCE P., Predicting the impacts of climate change on the distribution of species- are bioclimate envelope models useful? Global Ecology & Biogeography . 12: p. 361–371, (2003). Willis, K.J. & Whittaker, R.J, Species diversity — scale matters. Science . 295: p. 1245–1248, (2002). Olson, David M, et al., Terrestrial Ecoregions of the World: A New Map of Life on EarthA new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity. BioScience . 51(11): p. 933-938, (2001). Lu, W. L. and H. Y. Ren, Diseases spectrum in the field of spatiotemporal patterns mining of infectious diseases epidemics: A bibliometric and content analysis. Frontiers in Public Health . 10, DOI:https://doi.org/10.3389/fpubh.2022.1089418 (2023). Alkhamis, M. A., et al., Novel approaches for Spatial and Molecular Surveillance of Porcine Reproductive and Respiratory Syndrome Virus (PRRSv) in the United States. Scientific Reports . 7, DOI:https://doi.org/10.1038/s41598-017-04628-2 (2017). Satija, R., A. M. Berezhkovskii, and D. E. Makarov, Broad distributions of transition-path times are fingerprints of multidimensionality of the underlying free energy landscapes. Proc Natl Acad Sci U S A . 117(44): p. 27116-27123, DOI:https://doi.org/10.1073/pnas.2008307117 (2020). Roeder, M., et al., The Accuracy of Repeated Sleep Studies in OSA: A Longitudinal Observational Study With 14 Nights of Oxygen Saturation Monitoring. Chest . 159(3): p. 1222-1231, DOI:https://doi.org/10.1016/j.chest.2020.09.098 (2021). Merckx, Bea, et al., Null models reveal preferential sampling, spatial autocorrelation and overfitting in habitat suitability modelling. Ecological Modelling . 222(3): p. 588-597, (2011). Duque-Lazo, J., et al., Transferability of species distribution models: The case of Phytophthora cinnamomi in Southwest Spain and Southwest Australia. Ecological Modelling . 320: p. 62-70, DOI:https://doi.org/10.1016/j.ecolmodel.2015.09.019 (2016). Roberts, M. H., et al., Frailty Among Older Individuals with and without COPD: A Cohort Study of Prevalence and Association with Adverse Outcomes. International Journal of Chronic Obstructive Pulmonary Disease . 17: p. 701-717, DOI:https://doi.org/10.2147/copd.S348714 (2022). Lu, Zhiyuan, et al., Predicting the potential distribution of wintering Asian Great Bustard (Otis tarda dybowskii) in China: Conservation implications. Global Ecology and Conservation . 31, DOI:https://doi.org/10.1016/j.gecco.2021.e01817 (2021). Tan, W., et al., Is returning to work during the COVID-19 pandemic stressful? A study on immediate mental health status and psychoneuroimmunity prevention measures of Chinese workforce. Brain Behav Immun . 87: p. 84-92, DOI:https://doi.org/10.1016/j.bbi.2020.04.055 (2020). Ward, M. P., S. Xiao, and Z. Zhang, Humidity is a consistent climatic factor contributing to SARS-CoV-2 transmission. Transbound Emerg Dis . 67(6): p. 3069-3074, DOI:https://doi.org/10.1111/tbed.13766 (2020). Gatto, M., et al., Spread and dynamics of the COVID-19 epidemic in Italy: Effects of emergency containment measures. Proc Natl Acad Sci U S A . 117(19): p. 10484-10491, DOI:https://doi.org/10.1073/pnas.2004978117 (2020). MengYao，Huang, Social isolation on covid-19 development impact. Journal of Tsinghua University . 61(2): p. 96-103, (2021). Chen, Y. X., et al., Modeling the Spatiotemporal Association Between COVID-19 Transmission and Population Mobility Using Geographically and Temporally Weighted Regression. Geohealth . 5(5), DOI:https://doi.org/10.1029/2021gh000402 (2021). Huang, M, Y., Social isolation on covid_ 19 development impact. Journal of Tsinghua University . 61: p. 96-103, (2021). Bhadra, Arunava, Arindam Mukherjee, and Kabita Sarkar, Impact of population density on Covid-19 infected and mortality rate in India. Modeling earth systems and environment . 7(1): p. 623-629, DOI:https://doi.org/10.1007/s40808-020-00984-7 (2021). Zhang, A., et al., The fine-scale associations between socioeconomic status, density, functionality, and spread of COVID-19 within a high-density city. BMC Infect Dis . 22(1): p. 274, DOI:https://doi.org/10.1186/s12879-022-07274-w (2022). Campos, A., et al., Effect of altitude on COVID-19 mortality in Ecuador: an ecological study. BMC Public Health . 21(1): p. 2079, DOI:https://doi.org/10.1186/s12889-021-12162-0 (2021). Hwang, J., et al., Positive association between moderate altitude and chronic lower respiratory disease mortality in United States counties. PLoS One . 13(7): p. e0200557, DOI:https://doi.org/10.1371/journal.pone.0200557 (2018). Perez-Padilla, R. and F. Franco-Marina, The impact of altitude on mortality from tuberculosis and pneumonia. International Journal of Tuberculosis and Lung Disease . 8(11): p. 1315-1320, (2004). Martinez-Briseno, D., et al., The Impact of Altitude on Mortality Rates From COVID-19 in Mexico. Arch Bronconeumol . 58(12): p. 830-833, DOI:https://doi.org/10.1016/j.arbres.2022.03.022 (2022). Jiang, Z. H., et al., Identifying the High-Risk Population for COVID-19 Transmission in Hong Kong Leveraging Explainable Machine Learning. Healthcare . 10(9), DOI:https://doi.org/10.3390/healthcare10091624 (2022). Sk. Nafiz Rahaman, Tanvir Shehzad and Maria Sultana , Effect of Seasonal Land Surface Temperature Variation on COVID-19 Infection Rate- A Google Earth Engine-Based Remote Sensing Approach. Environmental Health Insights . 16: p. 1–4, DOI:https://doi.org/10.1177/11786302221131467 (2022). Piscitelli, P., et al., The role of outdoor and indoor air quality in the spread of SARS-CoV-2: Overview and recommendations by the research group on COVID-19 and particulate matter (RESCOP commission). Environmental Research . 211, DOI:https://doi.org/10.1016/j.envres.2022.113038 (2022). Rosario, D. K. A., et al., Relationship between COVID-19 and weather: Case study in a tropical country. Int J Hyg Environ Health . 229: p. 113587, DOI:https://doi.org/10.1016/j.ijheh.2020.113587 (2020). Chan, K. H., et al., The Effects of Temperature and Relative Humidity on the Viability of the SARS Coronavirus. Adv Virol . 2011: p. 734690, DOI:https://doi.org/10.1155/2011/734690 (2011). Kraemer MUG, et al, The effect of human mobility and control measures on the COVID-19 epidemic in China. Science . 368(6490): p. 493–497, DOI:https://doi.org/10.1101/2020.03.02.20026708 (2020). Zanin, M. and A. L. M. Albernaz, Impacts of Climate Change on Native Landcover: Seeking Future Climatic Refuges. Plos One . 11(9), DOI:https://doi.org/10.1371/journal.pone.0162500 (2016). Guo, W. Y., et al., High exposure of global tree diversity to human pressure. Proceedings of the National Academy of Sciences of the United States of America . 119(25), DOI:https://doi.org/10.1073/pnas.2026733119 (2022). Flesch, A. D., Landcover change and habitat quality mediate impacts of temperature and precipitation on population dynamics of a threatened aridland predator. Animal Conservation . DOI:https://doi.org/10.1111/acv.12836. Greenwald, K. R., J. L. Purrenhage, and W. K. Savage, Landcover predicts isolation in Ambystoma salamanders across region and species. Biological Conservation . 142(11): p. 2493-2500, DOI:https://doi.org/10.1016/j.biocon.2009.05.021 (2009). Breininger, D. R., et al., Landcover characterizations and Florida scrub-jay (Aphelocoma coerulescens) population dynamics. Biological Conservation . 128(2): p. 169-181, DOI:https://doi.org/10.1016/j.biocon.2005.09.026 (2006). Olson, David M., et al., Terrestrial Ecoregions of the World: A New Map of Life on Earth. BioScience . 51(11), DOI:https://doi.org/10.1641/0006-3568(2001)051[0933:Teotwa]2.0.Co;2 (2001). Fan, J. L., et al., Empirical and machine learning models for predicting daily global solar radiation from sunshine duration: A review and case study in China. Renewable & Sustainable Energy Reviews . 100: p. 186-212, DOI:https://doi.org/10.1016/j.rser.2018.10.018 (2019). Yazdandoost, F., et al., Evaluation of CMIP6 precipitation simulations across different climatic zones: Uncertainty and model intercomparison. Atmospheric Research . 250, DOI:https://doi.org/10.1016/j.atmosres.2020.105369 (2021). Solman, S. A., Regional Climate Modeling over South America: A Review. Advances in Meteorology . 2013, DOI:https://doi.org/10.1155/2013/504357 (2013). van Heerwaarden, Chiel C., et al., Record high solar irradiance in Western Europe during first COVID-19 lockdown largely due to unusual weather. Communications Earth & Environment . 2(1), DOI:https://doi.org/10.1038/s43247-021-00110-0 (2021). Lam, J. C., K. K. W. Wan, and L. Yang, Solar radiation modelling using ANNs for different climates in China. Energy Conversion and Management . 49(5): p. 1080-1090, DOI:https://doi.org/10.1016/j.enconman.2007.09.021 (2008). Karger, D. N., et al., Climatologies at high resolution for the earth's land surface areas. Sci Data . 4: p. 170122, DOI:https://doi.org/10.1038/sdata.2017.122 (2017). Seasons of the Year. Fekede, R. J., et al., High probability areas for ASF infection in China along the Russian and Korean borders. Transbound Emerg Dis . 66(2): p. 852-864, DOI:10.1111/tbed.13094 (2019). Hernandez, Pilar A., et al., The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography . 29(5): p. 773-785, DOI:https://doi.org/10.1111/j.0906-7590.2006.04700.x (2006). AJ., Arnfield. Köppen climate classification . [cited Access: 19 January 2023; Available from: https://www.britannica.com/science/Koppen-climate-classification. Chaib, J., et al., Physiological relationships among physical, sensory, and morphological attributes of texture in tomato fruits. Journal of Experimental Botany . 58(8): p. 1915-1925, DOI:https://doi.org/10.1093/jxb/erm046 (2007). Wei, C. H., et al., Incorporating Personality Traits to Assess the Risk Level of Aberrant Driving Behaviors for Truck Drivers. Int J Environ Res Public Health . 18(9), DOI:https://doi.org/10.3390/ijerph18094601 (2021). Lobo, J. M., A. Jimenez-Valverde, and R. Real, AUC: a misleading measure of the performance of predictive distribution models. Global Ecology and Biogeography . 17(2): p. 145-151, DOI:https://doi.org/10.1111/j.1466-8238.2007.00358.x (2008). Additional Declarations No competing interests reported. Supplementary Files supportingdocument.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 18 Feb, 2024 Reviews received at journal 16 Feb, 2024 Reviewers agreed at journal 06 Feb, 2024 Reviewers agreed at journal 06 Feb, 2024 Reviewers invited by journal 06 Feb, 2024 Editor assigned by journal 06 Feb, 2024 Editor invited by journal 02 Jan, 2024 Submission checks completed at journal 01 Jan, 2024 First submitted to journal 30 Dec, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3824333","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":264799701,"identity":"f7eec93f-79d3-405a-9c9d-c4b86fe9d983","order_by":0,"name":"Jing Pan","email":"","orcid":"","institution":"Key Laboratory for Wildlife Diseases and Bio-Security Management of Heilongjiang Province","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Pan","suffix":""},{"id":264799702,"identity":"fa6db777-1206-4355-81b8-0a1d7ef0533e","order_by":1,"name":"Arivizhivendhan Kannan Villalan","email":"","orcid":"","institution":"Key Laboratory for Wildlife Diseases and Bio-Security Management of Heilongjiang Province","correspondingAuthor":false,"prefix":"","firstName":"Arivizhivendhan","middleName":"Kannan","lastName":"Villalan","suffix":""},{"id":264799703,"identity":"2511363b-3f1a-4139-b264-190bd18d2be7","order_by":2,"name":"GuanYing Ni","email":"","orcid":"","institution":"HaiXi Animal Disease Control Center","correspondingAuthor":false,"prefix":"","firstName":"GuanYing","middleName":"","lastName":"Ni","suffix":""},{"id":264799704,"identity":"3896a179-aafc-40e1-9d18-df1f54b3a20f","order_by":3,"name":"RenNa Wu","email":"","orcid":"","institution":"HaiXi Animal Disease Control Center","correspondingAuthor":false,"prefix":"","firstName":"RenNa","middleName":"","lastName":"Wu","suffix":""},{"id":264799705,"identity":"22a2f6f4-16a8-4e2e-b71c-a334a838a3c1","order_by":4,"name":"ShiFeng Sui","email":"","orcid":"","institution":"Zhaoyuan Forest Resources Monitoring and Protection Service Center","correspondingAuthor":false,"prefix":"","firstName":"ShiFeng","middleName":"","lastName":"Sui","suffix":""},{"id":264799706,"identity":"b829aa24-0a72-4fef-b0a3-b14917be4fa9","order_by":5,"name":"XiaoDong Wu","email":"","orcid":"","institution":"China Animal Health and Epidemiology Center","correspondingAuthor":false,"prefix":"","firstName":"XiaoDong","middleName":"","lastName":"Wu","suffix":""},{"id":264799707,"identity":"0cc19524-7990-4976-9db5-412775676a79","order_by":6,"name":"XiaoLong Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYFACxgdAwkLOAMwxsCBGCzNIsYSxAQMzSIsE8VoSN4C1MBChxZy9mU2at00ifTt7/9ENPwokGPjbuxPwarHsOcwmzXNGIncnkHGzB+gwiTNnN+DVYnAj/5h0ToVE7oYbyWw3eIBaDIBs/FruP2aTzjGQSDcAarn5hygtN5jZQLYkgLTcJs6WM8nM1n/OSBhuOHPY7LaMgQQPYb8cP8x4c2abjbzB8cZnN9/8sZHjb+/FrwUD8JCmfBSMglEwCkYBVgAACYNBP1ZCQ+kAAAAASUVORK5CYII=","orcid":"","institution":"Key Laboratory for Wildlife Diseases and Bio-Security Management of Heilongjiang Province","correspondingAuthor":true,"prefix":"","firstName":"XiaoLong","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2023-12-30 15:29:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3824333/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3824333/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49140558,"identity":"1c90b059-c650-403b-af19-5bf1371d2175","added_by":"auto","created_at":"2024-01-03 18:21:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":665432,"visible":true,"origin":"","legend":"\u003cp\u003eGlobal map classification based on geographical regions sourced from Natural Earth (http://www.naturalearthdata.com/).\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3824333/v1/db7a3be98fbd456f1a0cc097.png"},{"id":49140561,"identity":"980df98b-e0aa-4ba2-ad72-fc5e95c4eb52","added_by":"auto","created_at":"2024-01-03 18:21:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":486000,"visible":true,"origin":"","legend":"\u003cp\u003e(a) AUC values of MaxEnt models and (b) contributions of important predictor variables to the model\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3824333/v1/74d105d96d57cad4f2107ac6.png"},{"id":49140560,"identity":"62dbc9dd-532d-4494-b6c2-4c31664bb533","added_by":"auto","created_at":"2024-01-03 18:21:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":179555,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of COVID-19 distribution models' response curves influenced by factors which contribution rate is more than 80%. The models (a) SDM2, (b) SDM3, (c) SDM6, (d) SDM20, (e) SDM22, (f) SDM24, (g) SDM29 and (h) SDM (31).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3824333/v1/d432f38d8566209afb6c135f.png"},{"id":49140559,"identity":"52f908ea-2d05-4b42-9f05-d8a9eb7e51d2","added_by":"auto","created_at":"2024-01-03 18:21:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1472410,"visible":true,"origin":"","legend":"\u003cp\u003ePrediction of the COVID 19 global spatial distribution and potential risk hotspot areas. The map was made in ArcGIS 10.2 using the resulting rasters produced by MaxEnt.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3824333/v1/1b092b4b3141e29f9d065ba8.png"},{"id":49142368,"identity":"379fddf9-f77f-4fba-a50c-632d0b80f966","added_by":"auto","created_at":"2024-01-03 18:37:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2431198,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3824333/v1/c4d97d0a-6038-4213-ba3e-b3143eeccc13.pdf"},{"id":49140562,"identity":"60992268-625f-4344-a27a-5f401f25d953","added_by":"auto","created_at":"2024-01-03 18:21:47","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3198599,"visible":true,"origin":"","legend":"","description":"","filename":"supportingdocument.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3824333/v1/c817040ae5b8d021d89644e7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Response Mechanism of COVID-19 spatial global distribution to Eco-geographic Factors","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCoronavirus Disease 2019 (COVID-19) is a severe acute respiratory syndrome caused by coronavirus type 2 (SARS-CoV-2), which emerged in December 2019. The World Health Organization declared it a global pandemic on March 11, 2020\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. The coronavirus (CoV) belongs to the family Coronaviridae and is a single-stranded envelope virus with an RNA genome size of approximately 26-32kb \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. The term 'coronavirus' is generated by the protrusions of the virus membrane that look like a crown \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e]. Coronaviruses are a large group of viruses, some of which can cause respiratory diseases in humans and often trigger serious global public health crises. Common clinical manifestations include fever, cough, fatigue, difficulty breathing, diarrhea, nausea, and vomiting \u003csup\u003e[\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. SARS-CoV-2 can be transmitted from person to person through droplets, aerosols, and droplets \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Moreover, SARS-CoV-2 infection may lead to long-term lung organ damage and relatively frequent cardiac involvement \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. The virus quickly spread worldwide within a few months, leading to global panic and conflicts of interest \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe spread of COVID-19 was challenging to control and had serious implications for humans, as the first batch of vaccines was introduced only 1.5 years after the discovery of COVID-19 \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. As of March 1, 2023, the global confirmed cases of COVID-19 were 676\u0026nbsp;million, with 6.87\u0026nbsp;million deaths in 188 countries/regions \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. As of 2022, the actual number of deaths was to be as high as 20\u0026nbsp;million \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. So far, people in many countries have widely received vaccinations against SARS-CoV-2. However, scientists express concern that the persistent unvaccinated population globally may pose a greater risk for the emergence of new mutated strains, such as Omicron \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Although the new generation vaccines and anti-COVID-19 treatment schemes prove helpful in managing acute COVID-19 infection, there are growing concerns about the increasing incidence of post-COVID-19 syndrome \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. In some poor countries, the lack of vaccines remains a significant obstacle \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Additionally, highly transmissible variants continue to spread globally, and monitoring of mutated strains remains largely inadequate, with an incomplete understanding of the risk of reinfection \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Since the SARS-CoV-2 pandemic, a large number of risk and protective factors have been associated with the susceptibility or resistance to COVID-19. Beyond the direct health impact, COVID-19 has profound impact around the world, challenging the food supply chain, livelihood, economy, and animal production system \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. COVID-19 has become the most important public health security problem globally at present and even in the next few years. The most dangerous disease in the past decade has not yet ended.\u003c/p\u003e \u003cp\u003eAlthough people's scientific understanding of COVID-19 has improved during the epidemic, their attention to COVID-19 prevention and control measures has declined in the post-epidemic era (H. Wang et al., 2022a). However, many public health experts still believe that COVID-19 is an ongoing health threat (Lazarus et al., 2022). COVID has become a serious chronic disease that constitutes a considerable disease burden, but it still lacks sufficient awareness and appropriate prevention or treatment solutions \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. In addition to its direct impact on health, COVID-19 has disrupted economic activity, social interaction, and political processes, affected civil liberties, and disrupted education at all levels \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Systematic and scientific research on the epidemic characteristics, influencing factors, and transmission risks of newly emerging infectious diseases at the domestic and international level, an important step in understanding the disease to prevent and control future outbreaks.\u003c/p\u003e \u003cp\u003eThe impact of climate and human factors on the transmission of coronavirus has been a significant question since the beginning of the pandemic \u003csup\u003e[\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Understanding the influence of climate on the transmission of catastrophic threats like COVID-19 a crucial for successful policy implementation and risk management to control the outbreak \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Various meteorological factors, such as temperature and humidity, influenced the infection rate of respiratory viruses and host immunity, leading to variations in the spread of respiratory viruses in different regions \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Sajadi et al., 2020 conducted research on 50 cities throughout the world and found that cities with widespread community transmission were mostly distributed between 30\u0026deg;N-50\u0026deg;N with temperatures ranging from 5\u0026ndash;11\u0026deg;C \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Another study, Wu, Y., et al. 2020 investigated 166 countries worldwide and found a negative correlation between temperature and relative humidity based on the number of new cases and deaths per day \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. A study conducted in 122 cities in China showed that the influence of temperature on the daily confirmed COVID-19 cases was significantly correlated with the average temperature below 3\u0026deg;C but not above 3\u0026deg;C \u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. The epidemic transmission trajectory of many countries shows strong seasonal patterns, with fewer cases in summer and more cases in winter \u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. Although a series of studies have provided empirical evidence for the negative relationship between temperature and infection \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan additionalcitationids=\"CR31 CR32\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. Several scholars have come to the opposite conclusion, indicating that the influence of weather varies greatly in terms of impact size, significance level, weather indicators, regions, and time periods \u003csup\u003e[\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHowever, the impact of meteorological factors on the transmission of COVID-19 is still controversial \u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. The temperature significantly affects the transmission of COVID-19 in Japan, while there was no significant correlation between temperature and COVID-19 in Indonesia and Spain which gives an opposite conclusion \u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. Despite the numerous studies on the impact of meteorological factors on COVID-19 transmission, the relationship between weather conditions and the spread of the virus in a global prospective was relatively limited. The absence of large-scale and accurate research in this domain implies a need for comprehensive assessments to understand the COVID-19 transmission dynamics on a global scale. Currently, evidence-based and globally agreed-upon response standards methods have not been implemented to respond to monitoring, prevention, treatment, and care of COVID-19. This gap emphasized the need for standardized approaches to address the ongoing pandemic at an international level.\u003c/p\u003e \u003cp\u003eRisk prediction is an important measure for controlling and preventing outbreaks of infectious diseases. In recent years, with the rapid development of geographic information technology and deep integration in the fields of infectious diseases and ecology. Ecological niche modeling (ENM) methods, such as GARP and MaxEnt, have been widely applied in species distribution modeling when only presence data is available for prediction. The maximum entropy model (MaxEnt), biological population growth model (CLIMEX), bioclimatic and domain models, the niche factor analysis model (ENFA), and the genetic algorithm model (GARP) were commonly used as niche models to validate the influence of environmental variables on disease distribution \u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. Among numerous niche models, MaxEnt has been widely used due to its advantages \u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe study highlights the underutilization of geographic information technology and ecological niche modeling (ENM) in large-scale studies on COVID-19. Global COVID-19 case data and SDM, ArcGIS, and SPSS were used to analyze the COVID-19 confirmed case report data with the combined results of major influencing factors for transmission, such as climate data and remote sensing data, to evaluate the global spatial distribution pattern of COVID-19. This article deeply explores the impact of meteorological and cognitive factors on the rapid spread of COVID-19, the potential interaction, and identification of COVID-19 risk areas and hotspots. This study also evaluates the impact of population density and environmental factors on the spread of SARS-CoV-2 worldwide in order to provide guidance for the scientific prevention and control of the COVID-19 outbreak.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eAn early assessment of the epidemiological characteristics of SARS-CoV-2 was conducted using the MaxEnt species distribution algorithm to study the future risk distribution of COVID 19 infection risk hotspot. Global map was classified based on geographical regions sourced from Natural Earth (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.naturalearthdata.com/\u003c/span\u003e\u003cspan address=\"http://www.naturalearthdata.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and used for the MaxEnt model (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A total of 28142 COVID-19 occurrence points were selected after filtering for application in the MaxEnt to evaluate the future possible risk distribution of COVID-19. The model parameters were optimized and evaluated for the effective prediction of COVID 19 distribution.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eRarefying and variables selection\u003c/h2\u003e \u003cp\u003eThe accuracy of SDM model was validated based on AUC values, with the expectation that the best model would have an AUC value about 1. The average output result of the 10-fold cross-validation of the COVID-19 in SDM model demonstrated high training and test AUC values, combined with low standard deviations. The results indicated that the average AUC value of all research areas ranges was from 0.711 to 0.994 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Among the 31 models, only three models such as SDM4, SDM8, and SDM28 had AUC value below 0.8 although they still exceeded 0.7. This suggested that the accuracy of the model was \u0026lsquo;very good\u0026rsquo;, and the prediction results were reliable, enabling the prediction of COVID-19 distribution. The results of the MaxEnt software simulation output ranged from 0 to 1, where values were closer to 1 corresponded to a higher probability of species existence. The environmental variables and mean range of VIF value for all niche models were provided in the Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The natural break was used as the minimum distance allowed between training points for the spatially filtered occurrence dataset for spotted knapweed. The application of this minimum distance in spatial filtering led to significant reduction in training sample size (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e. The environmental predictor variables of layers, sources, categories and variables/proxy used in modelling of COVID-19 distribution.\u0026nbsp;\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"639\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.078369905956112%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLayers\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.912225705329154%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.32601880877743%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eValue/categories\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.683385579937305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable/proxy\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eClimate\u003c/strong\u003e\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.078369905956112%\" valign=\"top\"\u003e\n \u003cp\u003eMonthly P (prec1-12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.912225705329154%\" valign=\"top\"\u003e\n \u003cp\u003eIbid.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.32601880877743%\" valign=\"top\"\u003e\n \u003cp\u003e0 to 1201 mm/month\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.683385579937305%\" valign=\"top\"\u003e\n \u003cp\u003ePrecipitation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.078369905956112%\" valign=\"top\"\u003e\n \u003cp\u003eMonthly mean T (temp1-12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.912225705329154%\" valign=\"top\"\u003e\n \u003cp\u003eIbid.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.32601880877743%\" valign=\"top\"\u003e\n \u003cp\u003e-54.9 to 39.2˚C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.683385579937305%\" valign=\"top\"\u003e\n \u003cp\u003eMean Temperature\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.078369905956112%\" valign=\"top\"\u003e\n \u003cp\u003eMonthly min T (tmin1-12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.912225705329154%\" valign=\"top\"\u003e\n \u003cp\u003eIbid.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.32601880877743%\" valign=\"top\"\u003e\n \u003cp\u003e-56.5 to 32.3˚C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.683385579937305%\" valign=\"top\"\u003e\n \u003cp\u003eMinimum Temperature\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.078369905956112%\" valign=\"top\"\u003e\n \u003cp\u003eMonthly max T (tmax1-12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.912225705329154%\" valign=\"top\"\u003e\n \u003cp\u003eIbid.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.32601880877743%\" valign=\"top\"\u003e\n \u003cp\u003e-53.2 to 47.3˚C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.683385579937305%\" valign=\"top\"\u003e\n \u003cp\u003eMaximum Temperature\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.078369905956112%\" valign=\"top\"\u003e\n \u003cp\u003eBioclimatic (bio1-19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.912225705329154%\" valign=\"top\"\u003e\n \u003cp\u003eIbid.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.32601880877743%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.683385579937305%\" valign=\"top\"\u003e\n \u003cp\u003eAnnual trends, seasonality, extreme or limiting environmental variables\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTerrain\u003c/strong\u003e\u003csup\u003e\u0026nbsp;b\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.078369905956112%\" valign=\"top\"\u003e\n \u003cp\u003eElevation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.912225705329154%\" valign=\"top\"\u003e\n \u003cp\u003eASTER-GDEM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.32601880877743%\" valign=\"top\"\u003e\n \u003cp\u003e-328 to 4739m a.s.l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.683385579937305%\" valign=\"top\"\u003e\n \u003cp\u003eClimbing distance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.078369905956112%\" valign=\"top\"\u003e\n \u003cp\u003eISR-spring\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.912225705329154%\" valign=\"top\"\u003e\n \u003cp\u003eIbid.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.32601880877743%\" valign=\"top\"\u003e\n \u003cp\u003e62.4 to 206.8 wh/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.683385579937305%\" valign=\"top\"\u003e\n \u003cp\u003eTopo-climate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.078369905956112%\" valign=\"top\"\u003e\n \u003cp\u003eISR-summer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.912225705329154%\" valign=\"top\"\u003e\n \u003cp\u003eIbid.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.32601880877743%\" valign=\"top\"\u003e\n \u003cp\u003e35.5 to 104.2 wh/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.683385579937305%\" valign=\"top\"\u003e\n \u003cp\u003eTopo-climate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.078369905956112%\" valign=\"top\"\u003e\n \u003cp\u003eISR-autumn\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.912225705329154%\" valign=\"top\"\u003e\n \u003cp\u003eIbid.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.32601880877743%\" valign=\"top\"\u003e\n \u003cp\u003e64.1 to 218.2 wh/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.683385579937305%\" valign=\"top\"\u003e\n \u003cp\u003eTopo-climate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.078369905956112%\" valign=\"top\"\u003e\n \u003cp\u003eISR-winter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.912225705329154%\" valign=\"top\"\u003e\n \u003cp\u003eIbid.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.32601880877743%\" valign=\"top\"\u003e\n \u003cp\u003e58.1 to 184.2 wh/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.683385579937305%\" valign=\"top\"\u003e\n \u003cp\u003eTopo-climate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVegetation\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.078369905956112%\" valign=\"top\"\u003e\n \u003cp\u003eLand cover\u003csup\u003e\u0026nbsp;c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.912225705329154%\" valign=\"top\"\u003e\n \u003cp\u003eESA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.32601880877743%\" valign=\"top\"\u003e\n \u003cp\u003eCropland (3), Herbaceous, Tree (9), Shrubland (3), Grassland, Urban areas, Bare areas (2), Mosaic shrub \u0026amp; herbaceous cover, Water bodies, Permanent snow, and ice\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.683385579937305%\" valign=\"top\"\u003e\n \u003cp\u003eHuman activity venues\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHuman impact\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.078369905956112%\" valign=\"top\"\u003e\n \u003cp\u003eHuman population\u003csup\u003e\u0026nbsp;d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.912225705329154%\" valign=\"top\"\u003e\n \u003cp\u003eWorldPop\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.32601880877743%\" valign=\"top\"\u003e\n \u003cp\u003e0 to 1,202.6 ind/km\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.683385579937305%\" valign=\"top\"\u003e\n \u003cp\u003eHuman-Animal interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e T=temperature; P=precipitation. Source: http://chelsa-climate.org/ \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u0026nbsp;\u003c/sup\u003eSource: http://www.gscloud.cn/\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ec\u003c/sup\u003e Land cover: Cropland, Herbaceous, Tree, Shrubland, Grassland, Urban areas, Bare areas, Water bodies and Permanent snow and ice https://maps.elie.ucl.ac.be/CCI/viewer/\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ed\u003c/sup\u003e Source: https://www.worldpop.org/\u003c/p\u003e\n\u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe VIF value of all models and its detailed information of geographical region, COVID-19 occurrence point, elevation and environment variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCOVID-19 presence points\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eElevation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNatural break\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEnvironmental layers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDM-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNorth pole\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBelow 1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePopulation, land cover and maximum temperature\u003c/p\u003e \u003cp\u003eof March (tmax3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.014\u0026ndash;1.742\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDM-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMiddle North America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBelow 1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePopulation and maximum temperature of May (tmax5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.213\u0026ndash;3.642\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDM-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMiddle North America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAbove 1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMinimum temperature of July (tmin7), spring, population and land cover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.009\u0026ndash;2.931\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDM-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLower North America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBelow 1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePopulation, elevation, land cover, minimum temperature of November (tmin11) and mean temperature of March (temp3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.038\u0026ndash;9.558\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDM-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLower North America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAbove 1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMinimum temperature of July (tmin7), spring, population and land cover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDM-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUpper Sorth America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBelow 1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePopulation, land cover and autumn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.000-3.409\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDM-7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUpper South America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAbove 1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePopulation, autumn, mean temperature of October (temp10) and land cover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.0001\u0026ndash;5.584\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDM-8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMiddle Sorth America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBelow 1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLand cover, population, mean temperature of June (temp6) and autum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDM-9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMiddle South America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAbove 1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMean temperature of October (temp10), land cover and elevation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.965\u0026ndash;0.993\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDM-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLower Sorth America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBelow 1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePopulation, land cover and maximum temperature of January (tmax1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDM-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWestern Europe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBelow 1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e160 km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eElevation, population, land cover, winter, maximum temperature of August (tmax8) and Precipitation of Wettest Month (bio13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.014\u0026ndash;1.078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDM-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWestern Europe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAbove 1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMinimum temperature of July (tmin7), precipitation of June (prec6), winter, land cover and population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDM-13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRussia,etc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBelow 1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40 km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePopulation the mean monthly Precipitation of Warmest Quarter (bio18) and summer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDM-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRussia,etc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAbove 1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20 km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eThe mean monthly precipitation amount of the wettest quarter (bio16), population and land cove\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.001\u0026ndash;6.921\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDM-15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIran,etc;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAbove 1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePopulation, summer, the Mean Temperature of Wettest Quarter (bio8), and minimum temperature of December (tmin12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.001\u0026ndash;1.233\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDM-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePamirs Plateau\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBelow 1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50 km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePopulation, land cover, precipitation of December (prec12) and minimum temperature of Octoberber (tmin10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.000-1.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDM-17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePamirs Plateau\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAbove 1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLand cover, population and the precipitation amount of January (prec1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.000-1.765\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDM-18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndia,etc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBelow 1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eThe mean monthly precipitation amount of the wettest quarter (bio16), population and land cover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDM-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndia,etc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAbove 1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBio5, Tmax3 maximum temperature\u003c/p\u003e \u003cp\u003eof March, Bio4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDM-20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUp Qinling-Huaihe Line\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBelow 1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePopulation, land cover, elevation, precipitation of August (prec8) summer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.000-1.911\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDM-21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUp Qinling-Huaihe Line\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAbove 1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLand cover, spring, maximum temperature of January (tmax1), population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDM-22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBlow Qinling-Huaihe Line\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBelow 1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePopulation, land cover, elevation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.000-1.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDM-23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBlow Qinling-Huaihe Line\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAbove 1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePopulation, elevation, land cover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDM-24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAustrilia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBelow 1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003epopulation, mean temperature of January (temp1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDM-25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCuba\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBelow 1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40 km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLand cover, population, precipitation amount of June (prec6) and minimum temperature of April (tmin4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.007\u0026ndash;1.794\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDM-26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEngland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBelow 1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50 km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePopulation, land cover, maximum temperature of June (tmax6), minimum temperature of June (tmin6), summer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.011\u0026ndash;1.116\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDM-27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJapan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBelow 1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20 km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePopulation, maximum temperature of January (tmax1), land cover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDM-28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe Philippines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBelow 1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e110 km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMaximum temperature of September (tmax9), land cover, precipitation amount of January (prec1), population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.136\u0026ndash;5.862\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDM-29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe Philippines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAbove 1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMinimum temperature of June (tmin12), land cover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDM-30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndonisia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBelow 1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60 km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePopulation, land cover, maximum temperature of February (tmax2), spring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.000-2.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDM-31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNew Zealand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBelow 1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePopulation, maximum temperature of September\u003c/p\u003e \u003cp\u003e(tmax9), land cover, mean temperature of September (temp9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eInfluence of population density on COVID-19\u003c/h2\u003e \u003cp\u003eThe result revealed that population density variables significantly influenced on the transmission of COVID-19 than other variables (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). The influence of population density on risk distribution areas was notably high in most of the models (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). The SDM-31 had the highest impact at 93.2%, followed by SDM-20 (92.9%), SDM-22 (91%), SDM-6 (88.5%), SDM-24 (84.3%), SDM-2 (82.5%), SDM-30 (77.9%), SDM-13 (77.7%), SDM-15 (70.3%), SDM-9 (65.2%), SDM-23 (%), SDM-1 (62.2%), SDM-2 (82.5%), SDM-10 (60.1%), SDM-7 (55.2%), SDM-16 (54.3%), SDM-26 (54.2%), SDM-4 (45.9%), SDM-27 (39.6%), SDM-8 (37.8%), SDM-14 (36.6%), SDM-25 (34.5%), SDM-11 (29.5%), SDM-17 (22.9%), SDM-5 (19.3%), SDM-18 (15.4%), SDM-21 (11.9%) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Out of a total of 31 SDM models, 8 SDM models contributed more than 80% to the specified environmental and geographic variables, and 6 of these SDM models were highly influenced by population density (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).The population density factor significantly influenced both mainland and island countries in most of the models except for two niche models. According to the MaxEnt response curve of each model predictor were shown in Figures S2-S13. The population density in New Zealand significantly impacts the distribution of SARS-CoV-2, with an estimated contribution of up to 93.2% (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The distribution probability of SARS-CoV-2 becomes stable with the population density reaches 2,000 people/km\u003csup\u003e2\u003c/sup\u003e. Similarly, estimates of contribution above 80% were reported for regions in upper South America, Austrilia, and Middle North America. In most areas below 1500 meters of elevation, such as India and Western Europe, an increase in population density led to a significant reduction in the distribution probability of SARS-CoV-2. The distribution probability of COVID-19 was increased sharply with the increase of population density in most regions when the elevation varied below 1500 meters.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePercentage contributions of predictor variables to the MaxEnt models blow than 1500m\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"20\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c19\" colnum=\"19\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c20\" colnum=\"20\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSDM-1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSDM-2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSDM-4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSDM-6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSDM-8\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSDM-10\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSDM-11\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSDM-13\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSDM-16\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSDM-18\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eSDM-20\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eSDM-22\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eSDM-24\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003eSDM-25\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c16\"\u003e \u003cp\u003eSDM-26\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c17\"\u003e \u003cp\u003eSDM-27\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c18\"\u003e \u003cp\u003eSDM-28\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c19\"\u003e \u003cp\u003eSDM-30\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c20\"\u003e \u003cp\u003eSDM-31\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e62.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e88.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e37.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e60.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e29.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e77.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e54.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e15.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e92.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e84.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e34.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e54.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e39.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e77.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c20\"\u003e \u003cp\u003e93.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLandcover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e40.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e19.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e27.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e25.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e43.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e23.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c20\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElevation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e38.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSummer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAutumn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWinter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTmax1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e17.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e31.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTmax2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTmax3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTmax5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTmax6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTmax8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTmax9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e60.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c20\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTmin4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTmin6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTmin10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTmin11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemp1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e15.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" 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align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemp9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" 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align=\"char\" char=\".\" colname=\"c20\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrec1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrec6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e 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colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrec8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrec12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e13.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBio12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e35.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBio13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBio18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e13.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePercentage contributions of predictor variables to the MaxEnt models above than 1500m\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSDM-3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSDM-5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSDM-7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSDM-9\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSDM-12\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSDM-14\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSDM-15\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSDM-17\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSDM-19\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSDM-21\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eSDM-23\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eSDM-29\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e36.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e70.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e22.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e11.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e62.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLandcover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e45.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElevation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e34.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e25.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSummer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e84.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e17.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e15.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAutumn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWinter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTmax1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e16.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTmax3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e20.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTmin7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e54.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e46.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTmin12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e96.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemp10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e65.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrec1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e19.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrec6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBio4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e13.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBio5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e65.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBio8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eInfluence of land cover and elevation variables on COVID-19\u003c/h2\u003e \u003cp\u003eThe probability of COVID-19 distribution was not influenced by population density factor in some regions such as those with elevation greater than 1500m in the Philippines, Middle North America, and India (FigureS2, S8 and S11). In areas with elevations above 1500 meters, the contribution rate of population density was relatively lower (Figure\u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eThe proportion of altitude and landcover showed a significant influence on the probability of COVID-19 distribution (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). In regions below 1500m altitude, the land cover showed a significant impact on these models, followed by the impact of population density (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ea). In regions above 1500m altitude, the terrain variables showed a significant impact. The land cover relatively influences the probability of COVID-19 distribution models such as SDM-17 (58%), SDM-21 (45.7%), SDM-25 (43.7%), SDM-8 (40.1%), SDM-26 (38%), SDM-27 (29%), SDM-16 (27.3%), SDM-9 (25.7%), SDM-18 (25.7%), SDM-28 (23.4%), SDM-10 (23%), SDM-11 (19.2%) and SDM-1(10.2%) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The elevation below 1500m in the Qinling-Huaihe Line exhibited contributions greater than 90% (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Additionally, the distribution probability of SARS-CoV-2 decreased with an increase in population density in the upper part of South America, north of the Qinling Mountains and Huai River, and areas above 1500 meters above elevation. In these regions, the contribution rate of population density was relatively lower, while the proportion of altitude and land cover was significantly increased. Moreover, when elevation was more than 1500m on the Pamirs Plateau and up Qinling-Huaihe Line, land cover also had a quite important impact. The average output result of 10-fold cross-validation of COVID-19 indicates that the land cover was significantly influenced in the Northern Hemisphere. The simulation results further emphasized that land cover was the third important factor influenced the distribution and diffusion of COVID-19 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). The results reveal that urban areas with a land cover value of 190 exhibit the highest probability of COVID-19 distribution, which also conformed the actual situation(Figure S2c, S3e, S4a, S5a, S8c, S9ae, S11d, S12ac).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eInfluence of climate variables on COVID-19\u003c/h2\u003e \u003cp\u003eIn regions above 1500m altitude, the impact of population density decreases, and the impact of climate factors increases (Figure\u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Continuous low-probability predictors for COVID-19 include temperature, incident solar radiation, and rainfall. When the altitude is below 1500m, Tmax1 (Maximum temperature of January) (SDM-10, SDM-18 and SDM-27), Tmax9 (Maximum temperature of September) (SDM-28) and Bio12(Annual Precipitation) (SDM-18) were the most important variables influenced the transmission of COVID-19 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). When the altitude is more than 1500m, Tmin7 (Minimum temperature of July) (SDM-12), Tmin12 (Minimum temperature of December) (SDM-29), Bio5 (Max Temperature of Warmest Month) (SDM-19) and Temp10 (Mean temperature of October) (SDM-9) were the most important variables influenced the transmission of COVID-19 (Figure\u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The Max temperature of the warmest month in India with above 1500 meters elevation emerged as the most influencing variable on the distribution of COVID-19, followed by Tmax3, with temperature Seasonality being the least influential (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The environmental variables (temperature, solar radiation, and precipitation) predominantly influence the occurrence of COVID-19 during spring and summer near the poles of the northern and southern hemispheres. In contrast, solar radiation of autumn and winter were the main influencing environmental variables in the equatorial region (FigureS2-S13 and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eGeographical distribution of COVID-19\u003c/h2\u003e \u003cp\u003eThe impact of demographic factors (population density) and environmental variables (elevation, precipitation, Incoming Solar Radiation, and temperature) on the transmission dynamics of SARS-CoV-2 was assessed with the jackknife analysis (Figure S14-S17). The jackknife analysis, a systamatic form of re-sampling, repeats the process by leaving out a different value and recalculating the test statistic for each time. The model output was reclassified to four types of potential distributions as follows: not suitable area (0\u0026ndash;0.2); low suitable area (0.2\u0026ndash;0.4); medium suitable area (0.4\u0026ndash;0.6); highly suitable area by ArcGIS 10.2.[42, 43]. The Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e encompasses the global potential distribution mapping of COVID-19, illustrating the comprehensive scope of the virus's potential spread across different regions and locales. The high-risk areas for COVID 19 were located between latitudes 0\u0026ndash;50\u0026deg;N and 0\u0026ndash;30\u0026deg;S (the central and lower parts of North America, concentrated in the northwest and southeast of the United States, as well as central and southern Mexico. In parts of South America, western Peru, northern Chile, and eastern Brazil. In the Eurasian continent, Northwest and southern Asia, distributed in southern Myanmar, northern and southern Thailand, northeastern Vietnam, and southern China; Southeast Europe, all of Ukraine, northwest Germany, western, northern, and southeastern France; The western part of the Arctic Circle; Ukraine, Belarus, southwestern Russia, northwestern Germany, small areas in southern Guangzhou, southern Harbin, and the entire Changchun region of China;South Korea, Cambodia, southern Myanmar, and southern Vietnam were also showed high-risk. Southeast Oceania; Cuba as a whole; Southeast United Kingdom; Southeast Indonesia; All over the Philippines; Southern Japan and northeastern New Zealand). In North America, most of the central region of the United States and a small portion of the northeast, as well as a small portion of the central northern and southern coastal regions of Mexico; central and eastern Ukraine, central and eastern India, northern and middle eastern Thailand, and Hainan and northeast Harbin of China and all part of Malaysia were predicted as a medium risk region. In North America, southern Canada, northern and southwestern United States, and northern Mexico; in South America, northwest Brazil, Argentina, most of Russia except southwest, most of Mongolia, and Australia except southern region were showed low-risk areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe COVID-19 epidemic was certainly destructive, affecting both human health and the global economy \u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e. This research mainly focused on the epidemiology of COVID-19 before the emergence of the Omicron variant. Prior to the outbreak of Omicron, global COVID-19 data statistics were more comprehensive and accurate, enabling a better understanding of the impact of environmental factors on disease transmission and their respective contributions. For niche models, the regional scale prediction model has more advantages in model accuracy \u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e. Therefore, this study was based on more local scales for modeling. WWF global ecological zoning established for natural conservation purposes (Eco-regions) was adopted as the basic framework for the global ecological geographic zoning knowledge base in this article \u003csup\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e. This study provides a method to plot the risk of COVID-19 associated with epidemiological and environmental factors. The MaxEnt model was used to improve variable selection, and its reliability has been confirmed by its good capacity to predict novel presence localities for poorly known species/diseases \u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. It has been widely used in many diseases, including COVID-19 \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/sup\u003e..\u003c/p\u003e \u003cp\u003eThis study mainly focused on identifying the risk areas and hotspots of COVID-19 and the impact of population density and environmental factors on the global spread of SARS-CoV-2. To enhance the accuracy of our analysis, we refined the MaxEnt model and employed it for guiding our variable selection. The MaxEnt model offers a significant advantage by achieving high precision in data processing through calculating CV values. The homology of the city was acceptable, given that the CV values of all variables were less than 15% \u003csup\u003e[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/sup\u003e. The geometric center of the city is retained for subsequent MaxEnt modeling. The β parameter of MaxEnt was consistent with the characteristics of overfitting. This implies that the default setting (β\u0026thinsp;=\u0026thinsp;1) of MaxEnt was correct model, as observed in previous research \u003csup\u003e[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]\u003c/sup\u003e. Additionally, a VIF value below 10 indicates low and acceptable multicollinearity \u003csup\u003e[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe average result obtained from the 10-fold cross-validation of the COVID-19 SDM revealed that the average AUC value of 30 areas were above 0.9. This signifies that the MaxEnt model\u0026rsquo;s performance reached a high level, indicating that it was suitable for simulating the risk areas of COVID-19 on a global scale. The AUC statistic method was commonly used to characterize model performance due to its model accuracy \u003csup\u003e[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]\u003c/sup\u003e. Currently, the AUC method was considered as the best criterion for assessing model success for presence/absence data \u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. The general accepted standard for AUC is above 0.8, which indicates a good model, while an AUC value approaching 1 signifies excellent model performance \u003csup\u003e[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study conducted an assessment to evaluate the impact of demographic and environmental factors on the transmission of SARS-CoV-2 and to predict the high-risk areas. The results indicate that population density was a core contributor to the model, aligning with various studies highlighting its significance in the spread of SARS-CoV-2. Challenges posed by urbanization and social cohesion complicate efforts to control the global pandemic. The global connectivity of cities and their complex ecosystems facilitates the transmission of the virus from person to person. SARS-CoV-2 was widely distributed in public places, which provided ideal conditions for virus transmission \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]\u003c/sup\u003e. Our findings demonstrate that COVID-19 transmission was predominantly influenced by population density rather than seasonal variation. The population response curve ( illustrated an exponential increase in the impact of population density on SARS-CoV-2 distribution as it exceeds 0. Numerous reports on the distribution of COVID-19 investigations consistently validate our research findings, emphasizing the coherence and reliability of our study in this particular context. \u003csup\u003e[\u003cspan additionalcitationids=\"CR58 CR59 CR60 CR61\" citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]\u003c/sup\u003e. Population density emerged as the most influential variable that affects the distribution of SARS-CoV-2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Among all 31 SDM models, 25 models were significantly influenced by population density. Transmission was more severe in densely populated communities, fostering the spread of SARS-CoV-2 to varying degrees \u003csup\u003e[\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]\u003c/sup\u003e. Although some studies have described a positive correlation between altitude and respiratory disease mortality \u003csup\u003e[\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]\u003c/sup\u003e. However, the effect of altitude on mortality in COVID-19 showed an opposite result because altitude may be protective or a risk factor for mortality \u003csup\u003e[\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]\u003c/sup\u003e. Consequentially, SARS-CoV-2 was negatively correlated with population density in the upper part of South America above an altitude of 1500 meters as well as in the north region of the Qinling Mountains-Huaihe River. The population density has reached 6000 people/square kilometer in India and south of the Tropic of Cancer in China. The mortality rate has increased due to limited medical conditions, leading to a decrease in the distribution of SARS-CoV-2 \u003csup\u003e[\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]\u003c/sup\u003e .\u003c/p\u003e \u003cp\u003eOur model revealed that climate variables were the least influential factor in the transmission of COVID 19. While weather conditions can also be considered an influencing factor for the human-to-human transmission of pathogens. The viability of infectious viruses depends on environmental factors such as temperature and humidity \u003csup\u003e[\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]\u003c/sup\u003e. Sobral et al. (2020) reported that temperature and humanity had negatively correlated between temperature and the number of SARS-CoV and MERS-CoV. Prolonged exposure of the SAR-CoV virus to low temperatures extends its half-life and survival ability. While low temperature and low humidity enhance the stability of droplet transmission in the nasal mucosa and damage local innate immunity. The temperature of the hottest month and the driest quarter have a negative impact on the transmission of SARS-CoV-2. Weather can affect the transmission of the virus in two different ways, such as from an epidemiological and behavioral perspective. The survival and transmission of viruses depend on the temperature of their environment, with high temperatures damaging the virus's lipid cortex \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]\u003c/sup\u003e. Higher temperatures severely impair the survival ability of the SARS coronavirus \u003csup\u003e[\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]\u003c/sup\u003e. In behavioral perspective, weather can alter levels of action, social distance, and social gathering locations, thereby influencing the spread of the virus among individuals \u003csup\u003e[\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]\u003c/sup\u003e. A slight increase in temperature increases the probability of SARS-CoV-2 distribution, while a significant increase in temperature reduced its probability. Additionally, considering that the transmission of coronavirus was similar to influenza, influenza virus was more transmissible at lower temperatures because cold weather can weaken the host's immune system, thereby increasing infection susceptibility. MaxEnt results indicated that the response curve, land cover was the third major factor influencing the spread of COVID-19 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). The acceleration of threats to biodiversity over the past 40 years has been attributed to the alarming pace of local land cover change, and it is anticipated that this rate may persist in the near future \u003csup\u003e[\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]\u003c/sup\u003e. The majority of landscapes globally undergo changes in energy utilization, production of exotic species, land use, and various other activities \u003csup\u003e[\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]\u003c/sup\u003e. Land cover played a synergetic role in affecting human populations and the spread of terrestrial species \u003csup\u003e[\u003cspan additionalcitationids=\"CR77\" citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe epidemiological characteristics and factors influencing the transmission of COVID-19 at a global scale using the MaxEnt species distribution algorithm. Our predictions for future COVID-19 potential distribution highlighted high-risk areas, providing valuable insights for targeted intervention strategies. This study was conducted during the early stages, predating the emergence of the Omicron variant. This ensures a more comprehensive understanding of the evolving landscape of COVID-19, encompassing the latest evaluations and insights into infection origins. The analysis underscored the critical role of population density as a core contributor to the model, emphasizing the challenges posed by urbanization and social cohesion in controlling the pandemic. Land cover emerged as the second major factor influencing the spread of COVID-19, contributing to biodiversity threats. The MaxEnt model demonstrated high accuracy, with an average AUC value exceeding 0.9, signifying its suitability for simulating global COVID-19 risk areas. Surprisingly, climate variables, particularly temperature, exhibited minimal impact on transmission dynamics. This finding contributes to the understanding of COVID-19 transmission dynamics, emphasizing the significant influence of demographic, geographic, and environmental factors. The findings hold implications for public health strategies and underscore the need for comprehensive, localized modeling to effectively address the global challenges posed by infectious diseases like COVID-19.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eDifferentiation of prediction areas\u003c/h2\u003e\n \u003cp\u003eWe conducted an analysis of the epidemiological patterns of COVID-19 worldwide based on every region of the COVID-19 occurrence report, except the Africa region, due to the unavailable official data. Eight biogeographic realms, as defined by World Wide Fund for Nature (WWF) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wwf.panda.org\u003c/span\u003e\u003c/span\u003e) were considered: Nearctic, Palearctic, Neotropical, Afrotropic, Indo-Malay, Australasia, Oceania, and Antarctic \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e79\u003c/span\u003e]\u003c/sup\u003e. The stimulation was performed separately for six island countries, i.e., Japan, Indonesia, New Zealand, the United Kingdom, Ireland, and Cuba. The epidemiological characteristics of SARS-CoV-2 were accurately analyzed in the above-mentioned landscapes. Breifly, the regional study on the global continent according to the altitude, topography, and climate characteristics of each continent, combined with the global temperature zone \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e80\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e84\u003c/span\u003e]\u003c/sup\u003e. Subsequently, MaxEnt was supplied for each region separately (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eCOVID-19 occurrence records and processing\u003c/h2\u003e\n \u003cp\u003eThe early COVID-19-infected cases, spanning from January 1, 2020, to January 30, 2022 across 173 countries, were sourced from WHO (World Health Organization) \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. To enhance the accuracy of the species distribution model (SDM), a meticulous screened process was applied to the COVID-19-point data. Excluding cases from countries or regions lacking transmission results. Furthermore, to address potential data shortages at the local level, we calculated the coefficient of variation values (CV), which calculated by variation and reflect the degree of dispersion between data points \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/sup\u003e. This method serves to quantify the data within the dataset. To conduct a high-precision analysis, a grid size of 1 km2 within each city was employed. This involved utilizing 67 climate variables to evaluate the CV values for specific cities, thereby identifying and evaluating the lack of data.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eProcessing of environmental variables\u003c/h2\u003e\n \u003cp\u003eEnvironmental predictor variables, including climate, terrain, vegetation, and human impact, were generated for COVID-19 modeling. The current forecasting data was collected from the CHELSA database (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e85\u003c/span\u003e]\u003c/sup\u003e. The incoming solar radiation (ISR) values were calculated at 30-minute intervals and aggregated per growing season. The seasonal category of each research area was integrated official data from each country, survey reports and the website of the global seasons division \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e86\u003c/span\u003e]\u003c/sup\u003e. All spatial data preprocessing and calculations were done with standard operations in ArcGIS 10.2 and were projected in UTM-WGS-1984 with standard settings or resampling to 30 arc seconds \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e87\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eCOVID-19 distribution modeling and evaluation\u003c/h2\u003e\n \u003cp\u003eThe MaxEnt model stands out as one of the best-performing specialty distribution modeling techniques for analyzing occurrence data. Consequently, we employed MaxEnt model to predict the future distribution of COVID-19 infection using case occurrence data \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e88\u003c/span\u003e]\u003c/sup\u003e. This model developed the ecological niche models by employing a machine-learning approach, combining COVID-19 case occurrence data with environmental variables. To explore the risk situation of SARS-CoV-2, the MaxEnt model was applied to the spatial distribution model building. The areas of interest were catagorized into those below and above 1500m asl, according to the elevation standard of the highland climate[41, 89]. Spatial autocorrelation was minimized by filtering all recorded COVID-19 locations data using the SDM Toolbox v1.1c in ArcGIS 10.2 \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e87\u003c/span\u003e]\u003c/sup\u003e. Principal component analysis (PCA) and multicollinearity were addressed by excluding factors through variance inflation factor (VIF) analysis \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e90\u003c/span\u003e]\u003c/sup\u003e. The filtered COVID-19 location and predictors served as input data for constructing the COVID-19 model using the MaxEnt algorithm. We divided the selected occurrence records into 70% training and 30% testing portions to build and validate the models based on 10 bootstrap replicates. For the remaining parameters, we maintained the default settings in the pilot study. The final COVID-19 predicted risk maps for low-elevation and high-elevation areas were verlaid using the fuzzy overlay. The Jenks natural break optimization method was employed to classify the model output with smothering and visualize high-risk areas \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e91\u003c/span\u003e]\u003c/sup\u003e. The relative contribution of predictors for modeling was evaluated through the jackknife test and variable response curve. The accuracy of the model was assessed by the area under the receiver operating characteristic (ROC) curve \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e92\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eAvailability of materials and data\u003c/p\u003e\n\u003cp\u003eThe environmental predictor variables have been deposited in the CHELSA (http://chelsa-climate.org/), the terrain predictor variables have been deposited in the Geospatial Data Cloud (http://www.gscloud.cn/), the population destiny was download in (https://www.worldpop.org/), Land cover was download in ESA(https://maps.elie.ucl.ac.be/CCI/viewer/). Materials supporting the findings of this study are available from the corresponding authors upon request.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThis study was supported by the \u0026lsquo;COVID-19 Epidemic Emergency Special Project\u0026rsquo; attached to the Fundamental Research Funds for the Central Universities, the Ministry of Education (Grant No. 2572020DY01).\u003c/p\u003e\n\u003cp\u003eAuthor contributions statement\u003c/p\u003e\n\u003cp\u003eWANG X.L. and WU X.D. conceived and supervised the study. PAN J. contributed to the data filtering, analysis, interpretation, cartography, and draft writing. ARI contributed to the language editing and chart drawing in the later stage of the article. WANG X.L., NI G.Y., WU R.N. and SUIi S.F. contributed to the map design, discussion, and manuscript writing. All authors significantly contributed to the final manuscript and gave final approval for publication.\u003c/p\u003e\n\u003cp\u003eAdditional information\u003c/p\u003e\n\u003cp\u003eCompeting interests: The authors declare no competing interests.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGao, W., et al., A new study of unreported cases of 2019-nCOV epidemic outbreaks. \u003cem\u003eChaos Solitons Fractals\u003c/em\u003e. 138: p. 109929, DOI:https://doi.org/10.1016/j.chaos.2020.109929 (2020).\u003c/li\u003e\n\u003cli\u003eCucinotta, D. and M. Vanelli, WHO Declares COVID-19 a Pandemic. \u003cem\u003eActa Biomed\u003c/em\u003e. 91(1): p. 157-160, DOI:https://doi.org/10.23750/abm.v91i1.9397 (2020).\u003c/li\u003e\n\u003cli\u003eYaro, C. A., P. S. U. Eneche, and D. Abu Anyebe, Risk analysis and hot spots detection of SARS-CoV-2 in Nigeria using demographic and environmental variables: an early assessment of transmission dynamics. \u003cem\u003eInternational Journal of Environmental Health Research\u003c/em\u003e. 32(5): p. 1111-1122, DOI:https://doi.org/10.1080/09603123.2020.1834080 (2022).\u003c/li\u003e\n\u003cli\u003eSarkar, Pranabesh, et al., COVID 19: An Epidemiological and Host Genetics Appraisal. \u003cem\u003eAsian Journal of Medical Sciences\u003c/em\u003e. 11(3): p. 71-76, DOI:https://doi.org/10.3126/ajms.v11i3.28569 (2020).\u003c/li\u003e\n\u003cli\u003eLu, Roujian, et al., Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding. \u003cem\u003eThe Lancet\u003c/em\u003e. 395(10224): p. 565-574, DOI:https://doi.org/10.1016/s0140-6736(20)30251-8 (2020).\u003c/li\u003e\n\u003cli\u003eWu, F., et al., A new coronavirus associated with human respiratory disease in China. \u003cem\u003eNature\u003c/em\u003e. 579(7798): p. 265-269, DOI:https://doi.org/10.1038/s41586-020-2008-3 (2020).\u003c/li\u003e\n\u003cli\u003eGao, Q. Y., Y. X. Chen, and J. Y. Fang, 2019 Novel coronavirus infection and gastrointestinal tract. \u003cem\u003eJ Dig Dis\u003c/em\u003e. 21(3): p. 125-126, DOI:https://doi.org/10.1111/1751-2980.12851 (2020).\u003c/li\u003e\n\u003cli\u003eWang, D., et al., Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus-Infected Pneumonia in Wuhan, China. \u003cem\u003eJAMA\u003c/em\u003e. 323(11): p. 1061-1069, DOI:https://doi.org/10.1001/jama.2020.1585 (2020).\u003c/li\u003e\n\u003cli\u003eVisco, V., et al., Post-COVID-19 Syndrome: Involvement and Interactions between Respiratory, Cardiovascular and Nervous Systems. \u003cem\u003eJournal of Clinical Medicine\u003c/em\u003e. 11(3), DOI:https://doi.org/10.3390/jcm11030524 (2022).\u003c/li\u003e\n\u003cli\u003eHosseiny, M., et al., Radiology Perspective of Coronavirus Disease 2019 (COVID-19): Lessons From Severe Acute Respiratory Syndrome and Middle East Respiratory Syndrome. \u003cem\u003eAJR Am J Roentgenol\u003c/em\u003e. 214(5): p. 1078-1082, DOI:https://doi.org/10.2214/AJR.20.22969 (2020).\u003c/li\u003e\n\u003cli\u003eMemish, Z. A., et al., Middle East respiratory syndrome coronavirus in bats, Saudi Arabia. \u003cem\u003eEmerg Infect Dis\u003c/em\u003e. 19(11): p. 1819-23, DOI:https://doi.org/10.3201/eid1911.131172 (2013).\u003c/li\u003e\n\u003cli\u003eLazarus, Jeffrey V., et al., A multinational Delphi consensus to end the COVID-19 public health threat. \u003cem\u003eNature\u003c/em\u003e. 611(7935): p. 332-345, DOI:https://doi.org/10.1038/s41586-022-05398-2 (2022).\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eWorld Health Organization\u003c/em\u003e. Available from: https://www.who.int.\u003c/li\u003e\n\u003cli\u003eAdam, D, The pandemic\u0026rsquo;s true death toll: millions more than official counts. \u003cem\u003eNature \u003c/em\u003e601: p. 312\u0026ndash;315, DOI:https://doi.org/10.1038/d41586-022-00104-8 ( 2022).\u003c/li\u003e\n\u003cli\u003eWang, H. D., et al., Estimating excess mortality due to the COVID-19 pandemic: a systematic analysis of COVID-19-related mortality, 2020-21. \u003cem\u003eLancet\u003c/em\u003e. 399(10334): p. 1513-1536, DOI:https://doi.org/10.1016/s0140-6736(21)02796-3 (2022).\u003c/li\u003e\n\u003cli\u003eNjoga, E. O., et al., COVID-19 Vaccine Hesitancy and Determinants of Acceptance among Healthcare Workers, Academics and Tertiary Students in Nigeria. \u003cem\u003eVaccines (Basel)\u003c/em\u003e. 10(4), DOI:https://doi.org/10.3390/vaccines10040626 (2022).\u003c/li\u003e\n\u003cli\u003eKarbalaeimahdi, Mohammad, et al., The Role of Interferons in Long Covid Infection. \u003cem\u003eJournal of interferon \u0026amp; cytokine research : the official journal of the International Society for Interferon and Cytokine Research\u003c/em\u003e. 43(2): p. 65-76, DOI:https://doi.org/10.1089/jir.2022.0193 (2023).\u003c/li\u003e\n\u003cli\u003eMallapaty, S, Researchers fear growing COVID vaccine hesitancy in developing nations. \u003cem\u003eNature \u003c/em\u003e601: p. 174-175 DOI:https://doi.org/10.1038/d41586-021-03830-7 (2022).\u003c/li\u003e\n\u003cli\u003eChen, Z., et al., Global landscape of SARS-CoV-2 genomic surveillance and data sharing. \u003cem\u003eNat Genet\u003c/em\u003e. 54(4): p. 499-507, DOI:https://doi.org/10.1038/s41588-022-01033-y (2022).\u003c/li\u003e\n\u003cli\u003eKarthikeyan, S., et al., Wastewater sequencing reveals early cryptic SARS-CoV-2 variant transmission. \u003cem\u003eNature\u003c/em\u003e. 609(7925): p. 101-108, DOI:https://doi.org/10.1038/s41586-022-05049-6 (2022).\u003c/li\u003e\n\u003cli\u003eRahimi, P., et al., Impact of the COVID-19 pandemic on food production and animal health. \u003cem\u003eTrends Food Sci Technol\u003c/em\u003e. 121: p. 105-113, DOI:https://doi.org/10.1016/j.tifs.2021.12.003 (2022).\u003c/li\u003e\n\u003cli\u003eSachs, J. D., et al., The Lancet Commission on lessons for the future from the COVID-19 pandemic. \u003cem\u003eLancet\u003c/em\u003e. 400(10359): p. 1224-1280, DOI:https://doi.org/10.1016/S0140-6736(22)01585-9 (2022).\u003c/li\u003e\n\u003cli\u003ePrata, D. N., W. Rodrigues, and P. H. Bermejo, Temperature significantly changes COVID-19 transmission in (sub)tropical cities of Brazil. \u003cem\u003eSci Total Environ\u003c/em\u003e. 729: p. 138862, DOI:https://doi.org/10.1016/j.scitotenv.2020.138862 (2020).\u003c/li\u003e\n\u003cli\u003eMoriyama, M., W. J. Hugentobler, and A. Iwasaki, Seasonality of Respiratory Viral Infections. \u003cem\u003eAnnu Rev Virol\u003c/em\u003e. 7(1): p. 83-101, DOI:https://doi.org/10.1146/annurev-virology-012420-022445 (2020).\u003c/li\u003e\n\u003cli\u003eDel Rio, C. and A. Camacho-Ortiz, Will environmental changes in temperature affect the course of COVID-19? \u003cem\u003eBraz J Infect Dis\u003c/em\u003e. 24(3): p. 261-263, DOI:https://doi.org/10.1016/j.bjid.2020.04.007 (2020).\u003c/li\u003e\n\u003cli\u003eSajadi, Mohammad M., et al., Temperature, Humidity, and Latitude Analysis to Estimate Potential Spread and Seasonality of Coronavirus Disease 2019 (COVID-19). \u003cem\u003eJAMA network open\u003c/em\u003e. 3(6): p. e2011834, DOI:https://doi.org/10.1001/jamanetworkopen.2020.11834 (2020).\u003c/li\u003e\n\u003cli\u003eWu, Y., et al., Effects of temperature and humidity on the daily new cases and new deaths of COVID-19 in 166 countries. \u003cem\u003eSci Total Environ\u003c/em\u003e. 729: p. 139051, DOI:https://doi.org/10.1016/j.scitotenv.2020.139051 (2020).\u003c/li\u003e\n\u003cli\u003eXie, Jingui and Yongjian Zhu, Association between ambient temperature and COVID-19 infection in 122 cities from China. \u003cem\u003eThe Science of the total environment\u003c/em\u003e. 724: p. 138201, DOI:https://doi.org/10.1016/j.scitotenv.2020.138201 (2020).\u003c/li\u003e\n\u003cli\u003eGanslmeier, M., D. Furceri, and J. D. Ostry, The impact of weather on COVID-19 pandemic. \u003cem\u003eSci Rep\u003c/em\u003e. 11(1): p. 22027, DOI:https://doi.org/10.1038/s41598-021-01189-3 (2021).\u003c/li\u003e\n\u003cli\u003eLolli, S., et al., Impact of meteorological conditions and air pollution on COVID-19 pandemic transmission in Italy. \u003cem\u003eSci Rep\u003c/em\u003e. 10(1): p. 16213, DOI:https://doi.org/10.1038/s41598-020-73197-8 (2020).\u003c/li\u003e\n\u003cli\u003eShi, P., et al., Impact of temperature on the dynamics of the COVID-19 outbreak in China. \u003cem\u003eSci Total Environ\u003c/em\u003e. 728: p. 138890, DOI:https://doi.org/10.1016/j.scitotenv.2020.138890 (2020).\u003c/li\u003e\n\u003cli\u003eMalki, Z., et al., Association between weather data and COVID-19 pandemic predicting mortality rate: Machine learning approaches. \u003cem\u003eChaos Solitons Fractals\u003c/em\u003e. 138: p. 110137, DOI:https://doi.org/10.1016/j.chaos.2020.110137 (2020).\u003c/li\u003e\n\u003cli\u003eTosepu, R., et al., Correlation between weather and Covid-19 pandemic in Jakarta, Indonesia. \u003cem\u003eSci Total Environ\u003c/em\u003e. 725: p. 138436, DOI:https://doi.org/10.1016/j.scitotenv.2020.138436 (2020).\u003c/li\u003e\n\u003cli\u003eYao, Y., et al., No association of COVID-19 transmission with temperature or UV radiation in Chinese cities. \u003cem\u003eEur Respir J\u003c/em\u003e. 55(5), DOI:https://doi.org/10.1183/13993003.00517-2020 (2020).\u003c/li\u003e\n\u003cli\u003eBriz-Redon, A. and A. Serrano-Aroca, A spatio-temporal analysis for exploring the effect of temperature on COVID-19 early evolution in Spain. \u003cem\u003eSci Total Environ\u003c/em\u003e. 728: p. 138811, DOI:https://doi.org/10.1016/j.scitotenv.2020.138811 (2020).\u003c/li\u003e\n\u003cli\u003eBashir, M. F., et al., Correlation between climate indicators and COVID-19 pandemic in New York, USA. \u003cem\u003eSci Total Environ\u003c/em\u003e. 728: p. 138835, DOI:https://doi.org/10.1016/j.scitotenv.2020.138835 (2020).\u003c/li\u003e\n\u003cli\u003eSun, Z., et al., Potential Factors Influencing Repeated SARS Outbreaks in China. \u003cem\u003eInt J Environ Res Public Health\u003c/em\u003e. 17(5), DOI:https://doi.org/10.3390/ijerph17051633 (2020).\u003c/li\u003e\n\u003cli\u003eFekede, R. J., et al., Could wild boar be the Trans-Siberian transmitter of African swine fever? \u003cem\u003eTransbound Emerg Dis\u003c/em\u003e. 68(3): p. 1465-1475, DOI:https://doi.org/10.1111/tbed.13814 (2021).\u003c/li\u003e\n\u003cli\u003evan Gils, Hein, et al., Where the bears roam in Majella National Park, Italy. \u003cem\u003eJournal for Nature Conservation\u003c/em\u003e. 22(1): p. 23-34, DOI:https://doi.org/10.1016/j.jnc.2013.08.001 (2014).\u003c/li\u003e\n\u003cli\u003eGao, S., et al., Predicting the possibility of African horse sickness (AHS) introduction into China using spatial risk analysis and habitat connectivity of Culicoides. \u003cem\u003eScientific Reports\u003c/em\u003e. 12(1), DOI:https://doi.org/10.1038/s41598-022-07512-w (2022).\u003c/li\u003e\n\u003cli\u003eZeng, Z., et al., A predictive analysis on the risk of peste des petits ruminants in livestock in the Trans-Himalayan region and validation of its transboundary transmission paths. \u003cem\u003ePlos One\u003c/em\u003e. 16(9), DOI:https://doi.org/10.1371/journal.pone.0257094 (2021).\u003c/li\u003e\n\u003cli\u003eXu, Danping, et al., Modeling the distribution of Zanthoxylum armatum in China with MaxEnt modeling. \u003cem\u003eGlobal Ecology and Conservation\u003c/em\u003e. 19, DOI:https://doi.org/10.1016/j.gecco.2019.e00691 (2019).\u003c/li\u003e\n\u003cli\u003e\u0026Ccedil;oban, H. Oğuz, \u0026Ouml;mer K. \u0026Ouml;r\u0026uuml;c\u0026uuml;, and E. Seda Arslan, MaxEnt Modeling for Predicting the Current and Future Potential Geographical Distribution of Quercus libani Olivier. \u003cem\u003eSustainability\u003c/em\u003e. 12(7), DOI:https://doi.org/10.3390/su12072671 (2020).\u003c/li\u003e\n\u003cli\u003eWu, C., et al., Investigation and influencing factors about well-being level of elderly chronic patients during COVID-19 postpandemic period in Beijing. \u003cem\u003eMedicine\u003c/em\u003e. 101(9), DOI:https://doi.org/10.1097/md.0000000000028976 (2022).\u003c/li\u003e\n\u003cli\u003eDAWSON, RICHARD G. PEARSON* and TERENCE P., Predicting the impacts of climate change on the distribution of species- are bioclimate envelope models useful? \u003cem\u003eGlobal Ecology \u0026amp; Biogeography\u003c/em\u003e. 12: p. 361\u0026ndash;371, (2003).\u003c/li\u003e\n\u003cli\u003eWillis, K.J. \u0026amp; Whittaker, R.J, Species diversity \u0026mdash; scale matters. \u003cem\u003eScience\u003c/em\u003e. 295: p. 1245\u0026ndash;1248, (2002).\u003c/li\u003e\n\u003cli\u003eOlson, David M, et al., Terrestrial Ecoregions of the World: A New Map of Life on EarthA new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity. \u003cem\u003eBioScience\u003c/em\u003e. 51(11): p. 933-938, (2001).\u003c/li\u003e\n\u003cli\u003eLu, W. L. and H. Y. Ren, Diseases spectrum in the field of spatiotemporal patterns mining of infectious diseases epidemics: A bibliometric and content analysis. \u003cem\u003eFrontiers in Public Health\u003c/em\u003e. 10, DOI:https://doi.org/10.3389/fpubh.2022.1089418 (2023).\u003c/li\u003e\n\u003cli\u003eAlkhamis, M. A., et al., Novel approaches for Spatial and Molecular Surveillance of Porcine Reproductive and Respiratory Syndrome Virus (PRRSv) in the United States. \u003cem\u003eScientific Reports\u003c/em\u003e. 7, DOI:https://doi.org/10.1038/s41598-017-04628-2 (2017).\u003c/li\u003e\n\u003cli\u003eSatija, R., A. M. Berezhkovskii, and D. E. Makarov, Broad distributions of transition-path times are fingerprints of multidimensionality of the underlying free energy landscapes. \u003cem\u003eProc Natl Acad Sci U S A\u003c/em\u003e. 117(44): p. 27116-27123, DOI:https://doi.org/10.1073/pnas.2008307117 (2020).\u003c/li\u003e\n\u003cli\u003eRoeder, M., et al., The Accuracy of Repeated Sleep Studies in OSA: A Longitudinal Observational Study With 14 Nights of Oxygen Saturation Monitoring. \u003cem\u003eChest\u003c/em\u003e. 159(3): p. 1222-1231, DOI:https://doi.org/10.1016/j.chest.2020.09.098 (2021).\u003c/li\u003e\n\u003cli\u003eMerckx, Bea, et al., Null models reveal preferential sampling, spatial autocorrelation and overfitting in habitat suitability modelling. \u003cem\u003eEcological Modelling\u003c/em\u003e. 222(3): p. 588-597, (2011).\u003c/li\u003e\n\u003cli\u003eDuque-Lazo, J., et al., Transferability of species distribution models: The case of Phytophthora cinnamomi in Southwest Spain and Southwest Australia. \u003cem\u003eEcological Modelling\u003c/em\u003e. 320: p. 62-70, DOI:https://doi.org/10.1016/j.ecolmodel.2015.09.019 (2016).\u003c/li\u003e\n\u003cli\u003eRoberts, M. H., et al., Frailty Among Older Individuals with and without COPD: A Cohort Study of Prevalence and Association with Adverse Outcomes. \u003cem\u003eInternational Journal of Chronic Obstructive Pulmonary Disease\u003c/em\u003e. 17: p. 701-717, DOI:https://doi.org/10.2147/copd.S348714 (2022).\u003c/li\u003e\n\u003cli\u003eLu, Zhiyuan, et al., Predicting the potential distribution of wintering Asian Great Bustard (Otis tarda dybowskii) in China: Conservation implications. \u003cem\u003eGlobal Ecology and Conservation\u003c/em\u003e. 31, DOI:https://doi.org/10.1016/j.gecco.2021.e01817 (2021).\u003c/li\u003e\n\u003cli\u003eTan, W., et al., Is returning to work during the COVID-19 pandemic stressful? A study on immediate mental health status and psychoneuroimmunity prevention measures of Chinese workforce. \u003cem\u003eBrain Behav Immun\u003c/em\u003e. 87: p. 84-92, DOI:https://doi.org/10.1016/j.bbi.2020.04.055 (2020).\u003c/li\u003e\n\u003cli\u003eWard, M. P., S. Xiao, and Z. Zhang, Humidity is a consistent climatic factor contributing to SARS-CoV-2 transmission. \u003cem\u003eTransbound Emerg Dis\u003c/em\u003e. 67(6): p. 3069-3074, DOI:https://doi.org/10.1111/tbed.13766 (2020).\u003c/li\u003e\n\u003cli\u003eGatto, M., et al., Spread and dynamics of the COVID-19 epidemic in Italy: Effects of emergency containment measures. \u003cem\u003eProc Natl Acad Sci U S A\u003c/em\u003e. 117(19): p. 10484-10491, DOI:https://doi.org/10.1073/pnas.2004978117 (2020).\u003c/li\u003e\n\u003cli\u003eMengYao，Huang, Social isolation on covid-19 development impact. \u003cem\u003eJournal of Tsinghua University\u003c/em\u003e. 61(2): p. 96-103, (2021).\u003c/li\u003e\n\u003cli\u003eChen, Y. X., et al., Modeling the Spatiotemporal Association Between COVID-19 Transmission and Population Mobility Using Geographically and Temporally Weighted Regression. \u003cem\u003eGeohealth\u003c/em\u003e. 5(5), DOI:https://doi.org/10.1029/2021gh000402 (2021).\u003c/li\u003e\n\u003cli\u003eHuang, M, Y., Social isolation on covid_ 19 development impact. \u003cem\u003eJournal of Tsinghua University\u003c/em\u003e. 61: p. 96-103, (2021).\u003c/li\u003e\n\u003cli\u003eBhadra, Arunava, Arindam Mukherjee, and Kabita Sarkar, Impact of population density on Covid-19 infected and mortality rate in India. \u003cem\u003eModeling earth systems and environment\u003c/em\u003e. 7(1): p. 623-629, DOI:https://doi.org/10.1007/s40808-020-00984-7 (2021).\u003c/li\u003e\n\u003cli\u003eZhang, A., et al., The fine-scale associations between socioeconomic status, density, functionality, and spread of COVID-19 within a high-density city. \u003cem\u003eBMC Infect Dis\u003c/em\u003e. 22(1): p. 274, DOI:https://doi.org/10.1186/s12879-022-07274-w (2022).\u003c/li\u003e\n\u003cli\u003eCampos, A., et al., Effect of altitude on COVID-19 mortality in Ecuador: an ecological study. \u003cem\u003eBMC Public Health\u003c/em\u003e. 21(1): p. 2079, DOI:https://doi.org/10.1186/s12889-021-12162-0 (2021).\u003c/li\u003e\n\u003cli\u003eHwang, J., et al., Positive association between moderate altitude and chronic lower respiratory disease mortality in United States counties. \u003cem\u003ePLoS One\u003c/em\u003e. 13(7): p. e0200557, DOI:https://doi.org/10.1371/journal.pone.0200557 (2018).\u003c/li\u003e\n\u003cli\u003ePerez-Padilla, R. and F. Franco-Marina, The impact of altitude on mortality from tuberculosis and pneumonia. \u003cem\u003eInternational Journal of Tuberculosis and Lung Disease\u003c/em\u003e. 8(11): p. 1315-1320, (2004).\u003c/li\u003e\n\u003cli\u003eMartinez-Briseno, D., et al., The Impact of Altitude on Mortality Rates From COVID-19 in Mexico. \u003cem\u003eArch Bronconeumol\u003c/em\u003e. 58(12): p. 830-833, DOI:https://doi.org/10.1016/j.arbres.2022.03.022 (2022).\u003c/li\u003e\n\u003cli\u003eJiang, Z. H., et al., Identifying the High-Risk Population for COVID-19 Transmission in Hong Kong Leveraging Explainable Machine Learning. \u003cem\u003eHealthcare\u003c/em\u003e. 10(9), DOI:https://doi.org/10.3390/healthcare10091624 (2022).\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eSk. Nafiz Rahaman, Tanvir Shehzad and Maria Sultana\u003c/strong\u003e, Effect of Seasonal Land Surface Temperature Variation on COVID-19 Infection Rate- A Google Earth Engine-Based Remote Sensing Approach. \u003cem\u003eEnvironmental Health Insights\u003c/em\u003e. 16: p. 1\u0026ndash;4, DOI:https://doi.org/10.1177/11786302221131467 (2022).\u003c/li\u003e\n\u003cli\u003ePiscitelli, P., et al., The role of outdoor and indoor air quality in the spread of SARS-CoV-2: Overview and recommendations by the research group on COVID-19 and particulate matter (RESCOP commission). \u003cem\u003eEnvironmental Research\u003c/em\u003e. 211, DOI:https://doi.org/10.1016/j.envres.2022.113038 (2022).\u003c/li\u003e\n\u003cli\u003eRosario, D. K. A., et al., Relationship between COVID-19 and weather: Case study in a tropical country. \u003cem\u003eInt J Hyg Environ Health\u003c/em\u003e. 229: p. 113587, DOI:https://doi.org/10.1016/j.ijheh.2020.113587 (2020).\u003c/li\u003e\n\u003cli\u003eChan, K. H., et al., The Effects of Temperature and Relative Humidity on the Viability of the SARS Coronavirus. \u003cem\u003eAdv Virol\u003c/em\u003e. 2011: p. 734690, DOI:https://doi.org/10.1155/2011/734690 (2011).\u003c/li\u003e\n\u003cli\u003eKraemer MUG, et al, The effect of human mobility and control measures on the COVID-19 epidemic in China.\u003cem\u003e Science\u003c/em\u003e. 368(6490): p. 493\u0026ndash;497, DOI:https://doi.org/10.1101/2020.03.02.20026708 (2020).\u003c/li\u003e\n\u003cli\u003eZanin, M. and A. L. M. Albernaz, Impacts of Climate Change on Native Landcover: Seeking Future Climatic Refuges. \u003cem\u003ePlos One\u003c/em\u003e. 11(9), DOI:https://doi.org/10.1371/journal.pone.0162500 (2016).\u003c/li\u003e\n\u003cli\u003eGuo, W. Y., et al., High exposure of global tree diversity to human pressure. \u003cem\u003eProceedings of the National Academy of Sciences of the United States of America\u003c/em\u003e. 119(25), DOI:https://doi.org/10.1073/pnas.2026733119 (2022).\u003c/li\u003e\n\u003cli\u003eFlesch, A. D., Landcover change and habitat quality mediate impacts of temperature and precipitation on population dynamics of a threatened aridland predator. \u003cem\u003eAnimal Conservation\u003c/em\u003e. DOI:https://doi.org/10.1111/acv.12836.\u003c/li\u003e\n\u003cli\u003eGreenwald, K. R., J. L. Purrenhage, and W. K. Savage, Landcover predicts isolation in Ambystoma salamanders across region and species. \u003cem\u003eBiological Conservation\u003c/em\u003e. 142(11): p. 2493-2500, DOI:https://doi.org/10.1016/j.biocon.2009.05.021 (2009).\u003c/li\u003e\n\u003cli\u003eBreininger, D. R., et al., Landcover characterizations and Florida scrub-jay (Aphelocoma coerulescens) population dynamics. \u003cem\u003eBiological Conservation\u003c/em\u003e. 128(2): p. 169-181, DOI:https://doi.org/10.1016/j.biocon.2005.09.026 (2006).\u003c/li\u003e\n\u003cli\u003eOlson, David M., et al., Terrestrial Ecoregions of the World: A New Map of Life on Earth. \u003cem\u003eBioScience\u003c/em\u003e. 51(11), DOI:https://doi.org/10.1641/0006-3568(2001)051[0933:Teotwa]2.0.Co;2 (2001).\u003c/li\u003e\n\u003cli\u003eFan, J. L., et al., Empirical and machine learning models for predicting daily global solar radiation from sunshine duration: A review and case study in China. \u003cem\u003eRenewable \u0026amp; Sustainable Energy Reviews\u003c/em\u003e. 100: p. 186-212, DOI:https://doi.org/10.1016/j.rser.2018.10.018 (2019).\u003c/li\u003e\n\u003cli\u003eYazdandoost, F., et al., Evaluation of CMIP6 precipitation simulations across different climatic zones: Uncertainty and model intercomparison. \u003cem\u003eAtmospheric Research\u003c/em\u003e. 250, DOI:https://doi.org/10.1016/j.atmosres.2020.105369 (2021).\u003c/li\u003e\n\u003cli\u003eSolman, S. A., Regional Climate Modeling over South America: A Review. \u003cem\u003eAdvances in Meteorology\u003c/em\u003e. 2013, DOI:https://doi.org/10.1155/2013/504357 (2013).\u003c/li\u003e\n\u003cli\u003evan Heerwaarden, Chiel C., et al., Record high solar irradiance in Western Europe during first COVID-19 lockdown largely due to unusual weather. \u003cem\u003eCommunications Earth \u0026amp; Environment\u003c/em\u003e. 2(1), DOI:https://doi.org/10.1038/s43247-021-00110-0 (2021).\u003c/li\u003e\n\u003cli\u003eLam, J. C., K. K. W. Wan, and L. Yang, Solar radiation modelling using ANNs for different climates in China. \u003cem\u003eEnergy Conversion and Management\u003c/em\u003e. 49(5): p. 1080-1090, DOI:https://doi.org/10.1016/j.enconman.2007.09.021 (2008).\u003c/li\u003e\n\u003cli\u003eKarger, D. N., et al., Climatologies at high resolution for the earth\u0026apos;s land surface areas. \u003cem\u003eSci Data\u003c/em\u003e. 4: p. 170122, DOI:https://doi.org/10.1038/sdata.2017.122 (2017).\u003c/li\u003e\n\u003cli\u003eSeasons of the Year.\u003c/li\u003e\n\u003cli\u003eFekede, R. J., et al., High probability areas for ASF infection in China along the Russian and Korean borders. \u003cem\u003eTransbound Emerg Dis\u003c/em\u003e. 66(2): p. 852-864, DOI:10.1111/tbed.13094 (2019).\u003c/li\u003e\n\u003cli\u003eHernandez, Pilar A., et al., The effect of sample size and species characteristics on performance of different species distribution modeling methods. \u003cem\u003eEcography\u003c/em\u003e. 29(5): p. 773-785, DOI:https://doi.org/10.1111/j.0906-7590.2006.04700.x (2006).\u003c/li\u003e\n\u003cli\u003eAJ., Arnfield. \u003cem\u003eK\u0026ouml;ppen climate classification\u003c/em\u003e. [cited Access: 19 January 2023; Available from: https://www.britannica.com/science/Koppen-climate-classification.\u003c/li\u003e\n\u003cli\u003eChaib, J., et al., Physiological relationships among physical, sensory, and morphological attributes of texture in tomato fruits. \u003cem\u003eJournal of Experimental Botany\u003c/em\u003e. 58(8): p. 1915-1925, DOI:https://doi.org/10.1093/jxb/erm046 (2007).\u003c/li\u003e\n\u003cli\u003eWei, C. H., et al., Incorporating Personality Traits to Assess the Risk Level of Aberrant Driving Behaviors for Truck Drivers. \u003cem\u003eInt J Environ Res Public Health\u003c/em\u003e. 18(9), DOI:https://doi.org/10.3390/ijerph18094601 (2021).\u003c/li\u003e\n\u003cli\u003eLobo, J. M., A. Jimenez-Valverde, and R. Real, AUC: a misleading measure of the performance of predictive distribution models. \u003cem\u003eGlobal Ecology and Biogeography\u003c/em\u003e. 17(2): p. 145-151, DOI:https://doi.org/10.1111/j.1466-8238.2007.00358.x (2008).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"COVID-19, epidemiological characteristics analysis, Spatial modeling, Maximum entropy, population-based studies, risk assessment","lastPublishedDoi":"10.21203/rs.3.rs-3824333/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3824333/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCOVID-19 has been massively transmitted for almost three years, and its multiple variants have caused serious health problems and an economic crisis. Our goal was to identify the influencing factors that reduce the threshold of disease transmission and to analyze the epidemiological patterns of COVID-19. This study served as an early assessment of the epidemiological characteristics of COVID-19 using the MaxEnt species distribution algorithm using the maximum entropy model. The transmission of COVID-19 was evaluated based on human factors and environmental variables, including climatic, terrain and vegetation, along with COVID-19 daily confirmed case location data. The results of the SDM model indicate that population density was the major factor influencing the spread of COVID-19. Altitude, land cover and climatic factor showed low impact. We identified a set of practical, high-resolution, multi-factor-based maximum entropy ecological niche risk prediction systems to assess the transmission risk of the COVID-19 epidemic globally. This study provided a comprehensive analysis of various factors influencing the transmission of COVID-19, incorporating both human and environmental variables. 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