Transmission of COVID-19 was slower but more sustained in warm climate

Transmission of COVID-19 was slower but more sustained in warm climate | 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 Research Article Transmission of COVID-19 was slower but more sustained in warm climate Xinru Wan, Chaoyuan Cheng, Zhibin Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2085190/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The COVID-19 novel virus has caused huge damage to public health around the world. Revealing the influencing factors affecting the transmission rate of COVID-19 is essential to take effective control measures. However, the association between transmission of COVID-19 and climate factors remains elusive with high uncertainty. Methods By using an extensive global dataset covering 617 time series from China, USA, Europe, and the rest of the world during 1/1–31/12 2020, we estimated the transmission parameters of COVID-19 and modeled the effects of the human and climate factors on COVID-19 transmission. Results We demonstrate that the transmission rate of COVID-19 was lower in warm climate in China, Europe, USA and the world, and in wet climate in China and Europe after excluding the confounding factors. The maximum transmission rate of COVID-19 seemed to have a peak temperature around 11.2°C in China and the world. The control efficiency (i.e. decreasing speed of transmission rate) in China, USA and the world was lower in warm and wet condition. Conclusions Our study suggested that in summer seasons, the transmission risk of COVID-19 would increase in the high-latitude or high-altitude regions but decrease in low-latitude or low-altitude regions. The area with the 7.8°C isocline between October and January which overlap with the major epicenters of COVID-19 should be investigated as a priority in searching for the natural hosts of COVID-19 and their habitats and movement. COVID-19 transmission ability control efficiency temperature precipitation disease prevention and control Figures Figure 1 Figure 2 Figure 3 Background Recently, a novel coronavirus (defined as SARS-CoV-2 by the International Committee on Taxonomy of Viruses) is spreading rapidly in the world. It has caused incredible damage to public health around the world. By 14 March 2021, a total of 119,212,530 confirmed cases of COVID-19 over 200 countries or regions in the world were reported. There is an urgent need to contain the fast-expansion of COVID-19 in the world. Revealing the influencing factors on the spread of COVID-19 is extremely important to take effective control measures. There is evidence that human movement could facilitate the spread of COVID-19 around the world [ 1 ], thus, the lockdown of the epicenter, social distancing and isolation of infected patients have been widely adopted to prevent and control COVID-19 [ 2 ]. However, the knowledge about the impacts of climate on the spread of COVID-19 remains limited. Some studies suggested that the spread of COVID-19 was associated with temperature and/or humidity [ 3 – 9 ], while many other studies did not find such associations [ 10 – 12 ]. An extensive review by the National Academy of Sciences, Engineering and Medicine of the United States of America indicated that conclusions on associations between climate and COVID-19 were elusive with high uncertainty due to significant caveats in most previous studies such as vague definition of transmission ability, limitation in time and space, data quality and confounding factors [ 13 ]. Therefore, it is necessary to reveal the impacts of climate factors on the spread of COVID-19 by using a more extensive global dataset covering a large geographic and climatic variation, and by excluding the impacts of human factors and spatial autocorrelation. Here, by using global data of COVID-19 cumulative cases released by WHO or national healthy committee or institutions (Fig. 1 ), we estimated the maximum daily increase rate of cumulative cases (representing the early transmission rate without or with little human intervention), the average daily increase rate of cumulative cases (representing late transmission rate under human intervention), and the regression slope of daily increase rate with cumulative cases (representing the control efficiency, or the decreasing speed of transmission rate) (see Methods). We analyzed associations of these three parameters with both human factors (represented by the founding population size of the reported patients within the first one week and population size of a location) and climate factors (represented by the air temperature and precipitation) in China, USA, Europe, and the world. The analysis was first conducted by using data covering the period from 1 January to 4 April 2020 [ 14 ], this analysis was updated by using more extensive data covering the period from 1 January to 31 December 2020 in this study. The results of the two analyses were similar, but with minor differences, likely caused by the scope of COVID-19 data and the climate data resources. Methods Epidemic data We obtained data of cumulative cases of COVID-19 in cities and prefectures in China from 1 January to 31 December 2020 from daily reports or announcements by each provincial or prefectural health commission (making up 99.23%), the World Health Organization (making up 0.61%), and news from official media such as the CCTV news channel (making up 0.10%), and announcements by local governments (0.06%). Data consisted of the following information: reference, date, province, prefecture, coordinates, and cumulative case. The latitude and longitude coordinates of geographical locations were assigned by their capital site using a Baidu map (lbsyun.baidu.com). The data covered each prefecture of 27 provinces and autonomous regions, each district or county of 4 central municipal cities (i.e. Beijing, Tianjin, Shanghai, and Chongqing), 2 special administrative regions (Hong Kong and Macau SAR) and Taiwan. Cumulative cases in China after 11 March were not used because the daily increase rates after 11 March were all smaller than 0.01, indicating the approximate end of an epidemic. This data selection (the same as the other time series below) avoided biased estimation on the average, maximum daily increase rate, and control efficiency with excessive data when the epidemic was close to an end. We obtained the cumulative cases of COVID-19 of states, federal district, and self-governing territories of the United States and other countries, territories, or regions from 22 January to 31 December 2020 from Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU) ( https://www.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6 ) [ 15 ]. Data consisted of date, country/territory/region, coordinates, cumulative cases, and transmission category. The coordinates were assigned by referring to the capital city of each country or region using Google maps ( www.google.com/maps/ ). For China, Japan, South Korea, and Thailand, there are some missing cumulative case values (making up 0.48%); we assigned these missing values with those of the previous day. The model analysis was conducted separately for China, the USA, Europe, and the rest of the world, which represent the incidence of COVID-19 of three large epicenters of the early transmission and the world (Fig. 1 , Table 1 , and electronic supplementary material, Table S1). Similarly, data at the end of an early transmission was not used if the daily increase rates were all smaller than 0.01 (avoiding to use data when first transmission wave is over), or lasted for > 100 days (avoiding to use data of second more transmission waves). Cumulative cases were normalized by (average value-minimum value)/range of the value for easily demonstrating the growth patterns of different locations in Fig. 1 , whereas the original data of cumulative cases were used for modelling analysis, human population and precipitation were log-transformed to avoid extreme values. Notably, our data had various spatial resolutions from prefecture to state or countries. However, spatial resolution was relatively comparable within China, USA, and Europe. Anthropogenic And Climate Proxy Data The human population size ( \(H\) ) of a city or prefecture was obtained from the China Population & Employment Statistics Yearbook 2018 complied by the Population and Employment Statistics Division, National Bureau of Statistics of China. The human population size of countries outside China was obtained from the World Bank ( https://data.worldbank.org/indicator/SP.POP.TOTL ). Gridded human population density was obtained from the Socioeconomic Data and Applications Center (SEDAC) ( https://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11 ) [ 16 ]. Human population size was log-transformed (with base = e) to make the data normally distributed. The daily average air temperature and 20:00–20:00 cumulative precipitation in 2020 from Chinese surface meteorological stations in China were obtained from the dataset of daily surface observation values ( http://data.cma.cn/data/cdcdetail/dataCode/SURF_CLI_CHN_MUL_DAY_V3.0.html ) [ 17 ], which was derived from a daily report by 699 meteorological stations. The temperature and precipitation of each location (a sum of 334 locations in this study) in China was assigned by the temperature and precipitation of the nearest meteorological station. The average daily air temperature ( \(T\) ) and average daily precipitation ( \(P\) ) of an epidemic of a location in China were calculated by assigning the date of incidence of COVID-19 to the date of the corresponding day in 2020. The updated climate data represents the true climate COVID-19 virus experienced, and thus is more accurate than we used in previous analysis using the average monthly values during 2010–2019 (Wan et al 2020). The daily average daytime land surface temperature in 2020 was obtained from NASA Earth Observations (NEO, neo.sci.gsfc.nasa.gov), which was measured from space using instruments carried on satellites [ 18 ]. The daily precipitation in 2020 was obtained from CPC GIS DATA of the Climate Prediction Center's (CPC, cpc.ncep.noaa.gov), which was defined by Optimal Interpolation (OI) of gauge observations [ 19 ]. The raster of temperature and precipitation in the world was upscaled to 0.5 × 0.5 degrees (a total of 259, 200 grids), the temperature and precipitation were extracted from the raster at the location of each country, region, or city outside China. We obtained monthly average temperature and precipitation (during 1970–2000) with a spatial resolution of 5 minutes from Worldclim 2 as climate proxy for seasonal projection of partial effects of climate (Fig. 3 , S1) [ 20 ]. We have to use this dataset because it is hard for us to obtain the observatory climate data of the other countries except for China. Estimation Of The Transmission Rate And Control Efficiency Of Covid-19 We used a logistic model to estimate the transmission parameters of COVID-19 by following our previous study on SARS [ 21 ]. The number of cumulative cases of COVID-19 can be well fitted as follow: \({N}_{t}\) was the number of cumulative cases at day \(t\) , \(K\) was the maximum cumulative cases of COVID-19 patients. The daily increase rate ( \({r}_{t}\) ) of the number of cumulative cases of patients was defined as follow: Thus, the daily increase rate, which represents the transmission rate, should be negatively associated with the number of cumulative cases of patients under human intervention: Here, \(a\) , \(b\) are constants, and all > 0. a represents the maximum daily increase rate ( \({r}_{m}\) ) without human intervention or before further human intervention in the beginning or early stage of disease transmission, \(b\) represents the control efficiency under human intervention, or the decreasing speed of the daily increase rate. The early transmission rate does not necessarily coincide with the transmission rate without human intervention. Countries which saw a late introduction of COVID prepared in advanced or at least the awareness of the population was different. Under this situation, the maximum daily increase rate only represents the initial transmission rate before further control measures are taken after introduction of COVID-19. Because the mean incubation period of COVID-19 patients was estimated to be 5.2 days [ 22 ], we defined the number of cumulative cases of COVID-19 of a location in the first week as the founding population size ( \(F\) ) of COVID-19 patients due to reported cases from source sites. We estimated the early and late transmission parameters by using Eq. 1 and data of cumulative cases of an epidemic of a location covering the period from the 7th day to the date of last observation when the daily increase rates were all less than 0.01 or to the 100th day. A total of 617 time series of cumulative cases of COVID-19 from China (n = 314, 1 January- 11 March 2020), USA (n = 55, 22 January − 31 December 2020), Europe (n = 43, 25 January − 31 December 2020) and the rest of world (n = 205, 20 January − 31 December 2020) was constructed for estimating the transmission parameters (Fig. 1 ). Statistical Analysis We assumed that the transmission parameters ( \({r}_{i}\) , \({a}_{i}\) , \({b}_{i}\) ) should be determined by the founding population size ( \({F}_{i}\) ), human population size of a location ( \({H}_{i}\) ), air temperature ( \({T}_{i}\) ), and precipitation ( \({P}_{i}\) ) in an \(i\) th location. \({r}_{i}\) , \({a}_{i}\) , and \({b}_{i}\) represents the average daily increase rate of cumulative cases, maximum daily increase rate, and control efficiency of COVID-19, respectively. \({r}_{i}\) represents the average transmission rate of COVID-19 under human intervention. \({a}_{i}\) represents the maximum transmission rate ( \({r}_{m}\) ) without human intervention or before further human intervention. \({b}_{i}\) represents the control efficiency under human intervention. GAMs were used to model the effects of the founding population size ( \({F}_{i}\) ), human population size ( \({H}_{i}\) ), and climate factors (air temperature \({T}_{i}\) and precipitation \({P}_{i}\) ) on the average daily increase rate ( \({r}_{i}\) ), maximum daily increase rate ( \({a}_{i}\) ), and control efficiency ( \({b}_{i}\) ) in the \(i\) th location by following Wood, 2011 [ 23 ]. A Gaussian GAMs was firstly fitted by using a linear regression formula: Here, \(\text{Y}\) represents the three transmission parameters ( \({r}_{i}\) , \({a}_{i}\) , \({b}_{i}\) ) separately. To examine the potential nonlinear effect of climate factors, we fitted the data using the following model: Here, \({s(T}_{i})\) , \({s(P}_{i})\) , and \(s\left(Lon, Lat\right)\) were 2D smooth function (with k value, a dimension of the basis = 4) for removing the effects of spatial autocorrelation. \({\epsilon }_{i}\) was uncorrelated random errors of zero mean and finite variance. Pearson’s correlation analysis was introduced to detect significant correlations among variables (electronic supplementary material, Figure S2-S5). Loess regression was introduced to show the changing trend of cumulative cases of COVID-19 in Fig. 1 , S6-7. The relation of average or daily increase rate and control efficiency with climate factors were shown in electronic supplementary material, Figure S6-7 (not partial relation). For variables with strong and significant correlations ( r 0.6; p < 0.05) with the other variables, only one variable with the largest correlation coefficient to transmission parameters was selected to avoid the potential collinearity effect in model analysis. GAM was carried out using the mgcv library (v. 1.8–15) [ 23 ] in R (v. 3.6.1). Associations of the average daily increase rate or the number of cumulative cases with environmental variables of each city or prefecture were analyzed by using raster (v.2.9–22) and rgdal (v.1.4-4) libraries in R (v. 3.6.1) [ 24 ]. Correlation analysis, linear regression, and loess regression were performed with the stats library (v. 3.6.1) in R (v. 3.6.1) [ 25 ]. Results And Discussion Impacts of climate factor Analysis using the linear model (Eq. 2) of generalized additive models (GAM) indicated that air temperature showed a consistent, significant and negative association with the average daily increase rate ( \({r}_{i}\) ) of cumulative cases in China, USA, Europe, and the world, the maximum daily increase rate ( \({a}_{i}\) ) of cumulative cases in China, UAS, and the world (Table 1 and electronic supplementary material, Table S1), indicating high temperature significantly reduced the transmission rate of COVID-19 in both early and late stages of COVID-19 transmission. These results are similar with our previous analysis [ 14 ], except that the statistical tests for the peak temperature was not significant in the current study. Our results are also consistent with some observations of previous studies using different parameters of transmission severity such as incident cases [ 26 – 29 ], the reproductive number [ 30 , 31 ] or mortality [ 9 ]; but not with many others, e.g. Briz-Redón & Serrano-Aroca 2020; Jüni et al. 2020; Yao et al. 2020 [ 10 – 12 ]. Some studies have found the association between climate and COVID-19 transmission. For example, using niche models, tropical climates were less vulnerable to the spread of the virus than temperate climates [ 4 ]. The temperature was positively associated with COVID-19 mortality in Wuhan [ 9 ]. Triplett (2020) found a downward trend of COVID-19 cases with a maximum temperature above 22.5°C [ 32 ]. Wang et al. (2020) reported that high temperature and high humidity significantly reduced the effective reproduction number of COVID-19 in China [ 30 ]. Qi et al. (2020) reported the negative association between the incidence of COVID-19 and temperature or relative humidity in China [ 29 ]. However, many other studies did not show a significant association between COVID-19 and climate. For example, no association was found between the basic reproduction number of COVID-19 and temperature or UV radiation in Chinese cities [ 10 ]. The epidemic growth of COVID-19 showed no association with temperature, but a weak association with humidity [ 12 ] in the world. No evidence of a significant relationship between COVID-19 cases and the temperature was also found in Spain [ 11 ], and the world [ 33 ]. Analysis using the nonlinear model (Eq. 3) of GAM (Fig. 2 and electronic supplementary material, Table S1) was very similar to those using the linear model, temperature showed consistent non-linear but overall negative association with daily increase rate ( \({r}_{i}\) ), maximum daily increase rate ( \({a}_{i}\) ) (Fig. 2 A-H, Table S1). The daily increase rate ( \({r}_{i}\) ) showed a peak around 11.2°C (Fig. 2 H, S6H), similar to our previous study [ 14 ]. Some previous studies also reported the nonlinear association of temperature with transmission severity of COVID-19 using incidence cases [ 3 , 5 , 8 , 34 ] or growth rate [ 6 , 35 ], but the results were inconsistent. A few studies indicated that the optimal temperature for SARS-CoV2 incidence of new cases was at 8.07°C [ 3 ] or 8.72°C around the world [ 8 ], and 13 ~ 19°C in China [ 34 ]. The growth rates peaked at about 5°C in temperate regions in the Northern Hemisphere during the outbreak month, while they decreased in warmer and colder regions [ 6 ]. Bannister-Tyrrell et al . (2020) found COVID-19 incidence had a belled-shaped association around 1°C [ 5 ]. Notari (2020) reported that temperature had a negative association with early exponential growth with a weak peak at about 7.7 ± 3.6°C [ 35 ]. In our updated study, the climate proxy data was updated to the daily resolution in 2020 in China, rather than 10 years average (2010–2019), which may attributed to the difference in estimation of optimal temperature for transmission rate between this study (peaked 11.2°C) and previous one (6.3°C) [ 14 ]. Besides, we found precipitation showed a significant and negative association with daily increase rate ( \({r}_{i}\) ) in Europe and maximum daily increase rate ( \({a}_{i}\) ) in China using linear and nonlinear model, suggesting a wet climate would decrease the transmission ability of COVID-19 (Table 1 , S1, Fig. 2 M-T), which is consistent with a previous study [ 9 ]. Table 1 Associations of the average daily increase rate (r i ) of cumulative cases, the maximum daily increase rate (a i ), and the control efficiency (b i ) with the founding population size of COVID-19 patients during the initial 7 days after the first reported patient (F i ), human population size of a location ( H i ), climate factors (temperature, T i , and precipitation, P i ), and spatial autocorrelation based on analyses using Equation 2 (linear model). Region Parameters Founding population Human population Temperature Precipitation Spatial auto correlation Variance explained Sample size China \({r}_{i}\) -0.00000095 0.01 * -0.0038 *** -0.0036 * 7.01% 324 USA \({r}_{i}\) -0.00014 * 0.011 *** -0.0024 *** -0.015 ** 53.88% 52 Europe \({r}_{i}\) -0.00079 *** 0.0071 ** -0.0047 *** -0.025 * *** 60.18% 42 Global \({r}_{i}\) -1.4e-06 * 0.0062 *** -0.001 *** -0.0017 NS 5.53% 575 China \({a}_{i}\) 6.1e-06 -0.042 * -0.021 *** -0.065 * ** 9.40% 259 USA \({a}_{i}\) 0.00015 0.0098 -0.0045 ** -0.019 NS 25.63% 52 Europe \({a}_{i}\) -0.00084 -0.0065 -0.0058 -0.0065 NS 33.30% 42 Global \({a}_{i}\) -0.00000097 -0.016 ** -0.0042 *** -0.022 * 16.77% 502 China \({b}_{i}\) -1.7e-06 * 0.011 ** 0.0036 *** 0.014 * ** 9.75% 259 USA \({b}_{i}\) 0.00000017 4.9e-05 ** 1.6e-05 *** 9.3e-05 * *** 81.11% 52 Europe \({b}_{i}\) 0.000012 3e-04 *** 0.000029 0.00026 NS 51.25% 42 Global \({b}_{i}\) -0.000000062 0.0025 * 4e-04 * 0.0057 * NS 7.87% 502 The dome-shaped relation of organisms with environmental factors is reasonable based on the Law of Tolerance [ 36 ]. Chin et al. (2020) reported that the virus of COVID-19 was highly stable with only a 0.6-log unit reduction at 4°C in 14 days, with a 3-log unit reduction at 22°C after 7 days and no detection at 14 days, with a 3-log unit reduction at 37°C after 1 day and no virus detected afterward [ 37 ]. This observation supports ours on the potential peak temperature of 11.2°C of the virus of COVID-19. Although the virus could be well preserved in cold conditions, the lower transmission ability under 11.2°C was likely caused by human behaviors. In cold conditions, people are not as active as in warm conditions, which did not favor the person to person transmission of COVID-19. Besides, in cold conditions, droplets can freeze, which prevent their spreading in the air. But, the statistical test on the optimal temperature of COVID-19 is not significant, more data and further analysis is needed to draw solid conclusion. The impacts of climate on control efficiency have never been assessed before. We found air temperature showed significant and positive associations with the control efficiency ( \({b}_{i}\) ) in China, USA, and the world (Table 1 , Fig. 2 I-L), and significant nonlinear associations in China and USA (Fig. 2 I-J and electronic supplementary material, Table S1). Precipitation showed significant and positive associations with the control efficiency in China, USA, and the world (Table 1 , Fig. 2 U-X), and showed significant non-linear associations in China and USA (Fig. 2 U-V and electronic supplementary material, Table S1). These results indicated that warm and wet climate could decrease the control efficiency or increase the time of COVID-19 transmission (Note: smaller \({b}_{i}\) indicates better control efficiency or fast decrease of transmission rate) on COVID-19. This is likely that in warm and wet climate, the transmission rate is relatively low, further reduction of transmission rate is more difficult when the infection rate is low. Our results suggest that cold and dry season facilitates the transmission rate of COVID-19. Human immunity might be lower under cold conditions, which makes them more susceptible to the virus [ 38 ]. The virus appears unstable with high UV irradiation and high temperatures [ 39 ], but cold and dry conditions would benefit the virus to survive or spread in cough droplets of infected patients or on the surfaces of contaminated goods. It is notable that some countries in warm climate zone (e.g., India, Brazil) suffered heavy infections of COVID-19, mainly caused by the low control efficiency not by high transmission rate. In Brazil, the maximum daily increase rate of cumulative COVID-19 cases and control efficiency is 0.17 and − 0.00000033; in India, they are 0.12 and − 0.0000014, respectively, relative to the maximum daily increase rate (0.31) and control efficiency (-0.019) in China. Thus, the heavy infection of COVID-19 in some countries located in the warm climate does not contradict our observation that the transmission rate is negatively associated with air temperature. Impacts Of Human Factors The effects of human factors on the spread of COVID-19 and control efficiency have been widely modeled in previous studies (e.g. Gilbert et al. 2020 [ 40 ]), but quantitative analysis using empirical data is still limited. In this study, we found the founding population size of reported COVID-19 patients showed a consistent negative association with the daily increase rate ( \({r}_{i}\) ) in USA, Europe, and the world (Table 1 ), suggesting that the poor detecting capacity represented by the number of COVID-19 patients in first week may result in a larger transmission rate of COVID-19. The population size showed a consistent, significant and positive association with the average daily increase rate ( \({r}_{i}\) ), but showed negative association with maximum daily increase rate ( \({a}_{i}\) ) in China, and the world (Table 1 ), suggesting countries or regions with large susceptible population suffered an average high infection of COVID-19. The maximum transmission rate at early stage is higher in counties or regions with small population size, likely due to poor capacity of early detecting of COVID-19 in small countries or regions. The control efficiency indicator showed a significant and positive association with population size in China, USA, Europe, and the world (Table 1 ), suggesting the control efficiency or deceasing speed of transmission rate of COVID-19 were lower for countries or regions with larger susceptible population (Note: smaller \({b}_{i}\) indicated the higher control efficiency), probably due to difficulty in managing the large number of infected patients and lock down of travel in a large country or region. The control efficiency indicator showed a significant and a negative association with the founding population size of COVID-19 in China, indicating early detection of COVID-19 benefit control efficiency of COVID-19. In summary, in this study, we found consistent evidence that both maximum and average transmission rates of COVID-19 was reduced in warm climate. There are some evidence the transmission rate of COVID-19 and control efficiency or decreasing speed of transmission rate under human intervention are decreased in warm or wet condition. Human factors may also attribute to the transmission rate and control efficiency of COVID-19, which is more likely related to capacity of detecting and control the SARS-CoV-2 virus in different countries or regions. Implications For Prevention Our study suggests that air temperature has a predominant association with the transmission rate of COVID-19. Based on our results in Fig. 2 D, H, we projected the influences of seasonal change of temperature on the contagious risk of early transmission without or before further human intervention (Fig. 3 A-D), and the late transmission under human intervention (electronic supplementary material, Figure S1) in the world. Summer seasons would decrease the early transmission risk of COVID-19 from low-latitude or low-altitude regions but increase the transmission risk in the high-latitude or high-altitude regions. The summer season would decrease the late transmission rate of the northern hemisphere but increase that of the southern hemisphere. Because both human and climate factors played a significant role in the spread of COVID-19, it is not wise to rely upon climate factors to control this dangerous virus. Human intervention, such as lockdown and travel restrictions at the epicenter, as well as identification and isolation of infected patients or people with close contact, have been demonstrated to be successful in preventing the spread of COVID-19 [ 2 ]. Thus, human intervention such as early detection and high control efficiency is essential to containing the rapid expansion of COVID-19 around the world. More efforts and collaboration are urgently needed in containing the spread of COVID-19 around the world. Conclusions The SARS-CoV-2 virus has caused incredible damage to the world, revealing the influencing factors affecting the transmission of COVID-19 is essential to take effective control measures. Here the associations of COVID-19 transmission with both human factors and climate factors were analyzed. The findings showed the transmission rate of COVID-19 as well as control efficiency are significantly and negatively associated with high temperature and/or precipitation. The transmission rate was negatively associated with the early detected cases and but positively associated with population size of some countries or regions. This study suggests that in summer seasons, the transmission risk of COVID-19 would increase in the high-latitude or high-altitude regions but decrease in low-latitude or low-altitude regions; human intervention is essential in containing the spread of COVID-19 around the world. Abbreviations COVID-19 Coronavirus disease 2019 SARS-CoV-2 Severe acute respiratory syndrome coronavirus 2 WHO World Health Organization GAMs Generalized Additive Models. Declarations Consent for publication Not applicable. Ethics approval and consent to participate Not applicable. Availability of data and materials The dataset supporting the conclusions of this study from Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU) (https://www.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6) are included as a supplementary file. Competing interests The author(s) declare that they have no conflict of interest. Funding This work was supported by the National Key Research and Development Program of China (2021YFC0863400), project of studying transmissions of COVID-19, the key program of Chinese Academy of Sciences (KJZD-SW-L11), ANSO Project of Chinese Academy of Science (ANSO-CR-KP-2020-08), the Young Elite Scientists Sponsorship Program by CAST and ISZS (2021QNRC001, ISZS-YESS Program), and risk assessments based on big data analysis (E0517111) supported by the Institute of Zoology, Chinese Academy of Sciences. Authors' contributions ZZ designed the study, XW and CC collected the data. XW did the data analysis, ZZ and XW wrote the first draft of the manuscript, and all authors contributed intellectually to the manuscript. All authors read and approved the final manuscript. 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Early transmission of COVID - 19 has an optimal temperature but late transmission decreases in warm climate . medRxiv 2020:2020.2005.2014.20102459. Dong E, Du H, Gardner L. An interactive web - based dashboard to track COVID - 19 in real time . The Lancet Infectious Diseases . Center for International Earth Science Information Network CCU. Gridded Population of the World , Version 4 ( GPWv4 ): Administrative Unit Center Points with Population Estimates , Revision 11 . In. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC); 2018. Ren Z, Zou F, Yu Y, Wang G, Zhang Z, Fan S, Zhang Z, Sun C: Daily Dataset of China Surface Climate Data . In. Edited by Center CMDS. Beijing: National Meteorological Information Center; 2020. NASA Earth Observations. Land surface temperature [day] (1 day - TERRA/MODIS) . In.; 2021. Climate Prediction Center. The CPC Unified Global Daily Precipitation Analysis . In.; 2021. Fick SE, Hijmans RJ. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int J Climatol. 2017;37(12):4302–15. Zhang ZB, Sheng CF, Ma ZF, Li DM. The outbreak pattern of the SARS cases in Asia. Chin Sci Bull. 2004;49(17):1819–23. Li Q, Guan X, Wu P, Wang X, Zhou L, Tong Y, Ren R, Leung KSM, Lau EHY, Wong JY, et al. Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus–Infected Pneumonia. N Engl J Med. 2020;382(13):1199–207. Wood SN. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J Royal Stat Soc Ser B-Statistical Methodol. 2011;73:3–36. Bivand R, Keitt T, Rowlingson B: Rgdal : bindings for the geospatial data abstraction library . R package version 11 – 10 2016. Team RC: R : a language and environment for statistical computing . In: R Foundation for Statistical Computing. 2019. Sobral MFF, Duarte GB, da Penha Sobral AIG, Marinho MLM, de Souza Melo A. Association between climate variables and global transmission oF SARS-CoV-2. Sci Total Environ. 2020;729:138997. Wu Y, Jing W, Liu J, Ma Q, Yuan J, Wang Y, Du M, Liu M. Effects of temperature and humidity on the daily new cases and new deaths of COVID-19 in 166 countries. Sci Total Environ. 2020;729:139051. Prata DN, Rodrigues W, Bermejo PH. Temperature significantly changes COVID-19 transmission in (sub)tropical cities of Brazil. Sci Total Environ. 2020;729:138862. Qi H, Xiao S, Shi R, Ward MP, Chen Y, Tu W, Su Q, Wang W, Wang X, Zhang Z. COVID-19 transmission in Mainland China is associated with temperature and humidity: A time-series analysis . Science of The Total Environment 2020:138778. Wang M, Jiang A, Gong L, Luo L, Guo W, Li C, Zheng J, Li C, Yang B, Zeng J, et al: Temperature significant change COVID-19 Transmission in 429 cities . medRxiv 2020:2020.2002.2022.20025791. Merow C, Urban MC. Seasonality and uncertainty in COVID - 19 growth rates . medRxiv 2020:2020.2004.2019.20071951. Triplett M. Evidence that higher temperatures are associated with lower incidence of COVID - 19 in pandemic state , cumulative cases reported up to March 27 , 2020 . medRxiv 2020:2020.2004.2002.20051524. Jamil T, Alam I, Gojobori T, Duarte CM. No Evidence for Temperature-Dependence of the COVID-19 Epidemic. Cold Spring Harbor Laboratory ; 2020. Bu J, Peng D-D, Xiao H, Yue Q, Han Y, Lin Y, Hu G, Chen J. Analysis of meteorological conditions and prediction of epidemic trend of 2019-nCoV infection in 2020 . medRxiv 2020:2020.2002.2013.20022715. Notari A. Temperature dependence of COVID - 19 transmission . medRxiv 2020:2020.2003.2026.20044529. Shelford VE. Some Concepts of Bioecology. Ecology. 1931;12(3):455–67. Chin AWH, Chu JTS, Perera MRA, Hui KPY, Yen H-L, Chan MCW, Peiris M, Poon LLM. Stability of SARS-CoV-2 in different environmental conditions . The Lancet Microbe 2020. Kudo E, Song E, Yockey LJ, Rakib T, Wong PW, Homer RJ, Iwasaki A: Low ambient humidity impairs barrier function and innate resistance against influenza infection . Proceedings of the National Academy of Sciences 2019, 116 (22):10905–10910. Lowen AC, Steel J. Roles of humidity and temperature in shaping influenza seasonality. J Virol. 2014;88(14):7692–5. Gilbert M, Pullano G, Pinotti F, Valdano E, Poletto C, Boëlle P-Y, D'Ortenzio E, Yazdanpanah Y, Eholie SP, Altmann M, et al. Preparedness and vulnerability of African countries against importations of COVID-19: a modelling study. The Lancet. 2020;395(10227):871–7. Additional Declarations No competing interests reported. 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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-2085190","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":142733153,"identity":"3eca37cf-0bb3-44dd-8a5b-5f4e537e1f48","order_by":0,"name":"Xinru Wan","email":"","orcid":"","institution":"Chinese Academy of Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xinru","middleName":"","lastName":"Wan","suffix":""},{"id":142733154,"identity":"9c872158-26e3-4722-a642-aead457cce53","order_by":1,"name":"Chaoyuan Cheng","email":"","orcid":"","institution":"Chinese Academy of Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Chaoyuan","middleName":"","lastName":"Cheng","suffix":""},{"id":142733155,"identity":"82ae9984-781d-45dd-ac1a-c263b2af063f","order_by":2,"name":"Zhibin Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIiWNgGAWjYDACCTB5QI6NZC3GpGtJbCBah/zs5mcPv/y5k97H3v6A4UcNg7w5IS2Mc46ZG8vwPMtt4zmQwNhzjMFwJyH7mCUSzKQlJA7ntkkkHGDgbWBIMDhAQAubRPo3aQmDw+lsEokNjH+J0cIjkWMm+SHhcAKbRDIDM1G2SEjklEkzHHhm2MZzjOGwzDEJww2EtMjPSN8m+ePPHXn59vaHD9/U2MgTtAUEmHmgjAOwaCIIGH8Qp24UjIJRMApGKgAA3x874uXXZGwAAAAASUVORK5CYII=","orcid":"","institution":"Chinese Academy of Sciences","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Zhibin","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2022-09-20 13:44:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2085190/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2085190/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":27749027,"identity":"3da34733-4a5c-41dc-bce3-a12c894f7967","added_by":"auto","created_at":"2022-10-13 22:18:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4074990,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe normalized time series of cumulative cases, temperature, and precipitation of COVID-19 in China (A, E, I), USA (B, F, J), Europe (C, G, K), and the world (D, H, L).\u003c/strong\u003e Dashed grey lines represents time series of the normalized cumulative cases of different cities or prefectures in China, different states in USA, various countries in Europe, and all countries, regions, or locations in the world. The solid red line indicates loess regression with span = 0.25. The date 2020/1/1 was set to zero.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-2085190/v1/b9d0fafa07a33502af691082.png"},{"id":27749025,"identity":"bc64810a-90a6-48d9-a9b6-ba66a12754a9","added_by":"auto","created_at":"2022-10-13 22:18:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1267804,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-2085190/v1/1a2c4ff40efb4951d73c070b.png"},{"id":27749109,"identity":"bdb87e09-9c2d-4d81-a131-aa3da289f6ae","added_by":"auto","created_at":"2022-10-13 22:23:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1011655,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSeasonal projected partial effects of air temperature on the maximum daily increase rate (\u003c/strong\u003e ai\u003cstrong\u003e) of COVID-19 with the peak transmission rate in January (A), April (B), July (C), and October(D).\u003c/strong\u003e Colors (green to red: negative to positive) in Panel A - D indicate the temperature effects on maximum daily increase rate ( ai) in different months.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-2085190/v1/bfbb1c8397f8c57d6ae0b9cc.png"},{"id":28050325,"identity":"e8ba76af-0c0b-4f62-a4a5-76ec2768c082","added_by":"auto","created_at":"2022-10-20 16:59:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2619342,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2085190/v1/8f244d85-bc6f-4b65-be03-a31424e87ac5.pdf"},{"id":27749029,"identity":"45a90a57-2235-4a58-969b-d076ac97c09d","added_by":"auto","created_at":"2022-10-13 22:18:10","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":65062,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterialData.docx","url":"https://assets-eu.researchsquare.com/files/rs-2085190/v1/fe35a0967c5b1daf4d13e53e.docx"},{"id":27749110,"identity":"297b8f9f-a48a-4b06-9028-5ef0409f4627","added_by":"auto","created_at":"2022-10-13 22:23:10","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":2080227,"visible":true,"origin":"","legend":"","description":"","filename":"SupportingInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-2085190/v1/fb09db0e900ed84130feabde.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Transmission of COVID-19 was slower but more sustained in warm climate","fulltext":[{"header":"Background","content":"\u003cp\u003eRecently, a novel coronavirus (defined as SARS-CoV-2 by the International Committee on Taxonomy of Viruses) is spreading rapidly in the world. It has caused incredible damage to public health around the world. By 14 March 2021, a total of 119,212,530 confirmed cases of COVID-19 over 200 countries or regions in the world were reported. There is an urgent need to contain the fast-expansion of COVID-19 in the world.\u003c/p\u003e \u003cp\u003eRevealing the influencing factors on the spread of COVID-19 is extremely important to take effective control measures. There is evidence that human movement could facilitate the spread of COVID-19 around the world [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], thus, the lockdown of the epicenter, social distancing and isolation of infected patients have been widely adopted to prevent and control COVID-19 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, the knowledge about the impacts of climate on the spread of COVID-19 remains limited. Some studies suggested that the spread of COVID-19 was associated with temperature and/or humidity [\u003cspan additionalcitationids=\"CR4 CR5 CR6 CR7 CR8\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], while many other studies did not find such associations [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. An extensive review by the National Academy of Sciences, Engineering and Medicine of the United States of America indicated that conclusions on associations between climate and COVID-19 were elusive with high uncertainty due to significant caveats in most previous studies such as vague definition of transmission ability, limitation in time and space, data quality and confounding factors [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Therefore, it is necessary to reveal the impacts of climate factors on the spread of COVID-19 by using a more extensive global dataset covering a large geographic and climatic variation, and by excluding the impacts of human factors and spatial autocorrelation.\u003c/p\u003e \u003cp\u003eHere, by using global data of COVID-19 cumulative cases released by WHO or national healthy committee or institutions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), we estimated the maximum daily increase rate of cumulative cases (representing the early transmission rate without or with little human intervention), the average daily increase rate of cumulative cases (representing late transmission rate under human intervention), and the regression slope of daily increase rate with cumulative cases (representing the control efficiency, or the decreasing speed of transmission rate) (see Methods). We analyzed associations of these three parameters with both human factors (represented by the founding population size of the reported patients within the first one week and population size of a location) and climate factors (represented by the air temperature and precipitation) in China, USA, Europe, and the world. The analysis was first conducted by using data covering the period from 1 January to 4 April 2020 [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], this analysis was updated by using more extensive data covering the period from 1 January to 31 December 2020 in this study. The results of the two analyses were similar, but with minor differences, likely caused by the scope of COVID-19 data and the climate data resources.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eEpidemic data\u003c/h2\u003e \u003cp\u003eWe obtained data of cumulative cases of COVID-19 in cities and prefectures in China from 1 January to 31 December 2020 from daily reports or announcements by each provincial or prefectural health commission (making up 99.23%), the World Health Organization (making up 0.61%), and news from official media such as the CCTV news channel (making up 0.10%), and announcements by local governments (0.06%). Data consisted of the following information: reference, date, province, prefecture, coordinates, and cumulative case. The latitude and longitude coordinates of geographical locations were assigned by their capital site using a Baidu map (lbsyun.baidu.com). The data covered each prefecture of 27 provinces and autonomous regions, each district or county of 4 central municipal cities (i.e. Beijing, Tianjin, Shanghai, and Chongqing), 2 special administrative regions (Hong Kong and Macau SAR) and Taiwan. Cumulative cases in China after 11 March were not used because the daily increase rates after 11 March were all smaller than 0.01, indicating the approximate end of an epidemic. This data selection (the same as the other time series below) avoided biased estimation on the average, maximum daily increase rate, and control efficiency with excessive data when the epidemic was close to an end.\u003c/p\u003e \u003cp\u003eWe obtained the cumulative cases of COVID-19 of states, federal district, and self-governing territories of the United States and other countries, territories, or regions from 22 January to 31 December 2020 from Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6\u003c/span\u003e\u003cspan address=\"https://www.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Data consisted of date, country/territory/region, coordinates, cumulative cases, and transmission category. The coordinates were assigned by referring to the capital city of each country or region using Google maps (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"https://www.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6\" target=\"_blank\"\u003ewww.google.com/maps/\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.google.com/maps/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). For China, Japan, South Korea, and Thailand, there are some missing cumulative case values (making up 0.48%); we assigned these missing values with those of the previous day. The model analysis was conducted separately for China, the USA, Europe, and the rest of the world, which represent the incidence of COVID-19 of three large epicenters of the early transmission and the world (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, and electronic supplementary material, Table S1). Similarly, data at the end of an early transmission was not used if the daily increase rates were all smaller than 0.01 (avoiding to use data when first transmission wave is over), or lasted for \u0026gt;\u0026thinsp;100 days (avoiding to use data of second more transmission waves). Cumulative cases were normalized by (average value-minimum value)/range of the value for easily demonstrating the growth patterns of different locations in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, whereas the original data of cumulative cases were used for modelling analysis, human population and precipitation were log-transformed to avoid extreme values. Notably, our data had various spatial resolutions from prefecture to state or countries. However, spatial resolution was relatively comparable within China, USA, and Europe.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAnthropogenic And Climate Proxy Data\u003c/h3\u003e\n\u003cp\u003eThe human population size (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(H\\)\u003c/span\u003e\u003c/span\u003e) of a city or prefecture was obtained from the China Population \u0026amp; Employment Statistics Yearbook 2018 complied by the Population and Employment Statistics Division, National Bureau of Statistics of China. The human population size of countries outside China was obtained from the World Bank (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.worldbank.org/indicator/SP.POP.TOTL\u003c/span\u003e\u003cspan address=\"https://data.worldbank.org/indicator/SP.POP.TOTL\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Gridded human population density was obtained from the Socioeconomic Data and Applications Center (SEDAC) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11\u003c/span\u003e\u003cspan address=\"https://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Human population size was log-transformed (with base\u0026thinsp;=\u0026thinsp;e) to make the data normally distributed.\u003c/p\u003e \u003cp\u003eThe daily average air temperature and 20:00\u0026ndash;20:00 cumulative precipitation in 2020 from Chinese surface meteorological stations in China were obtained from the dataset of daily surface observation values (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://data.cma.cn/data/cdcdetail/dataCode/SURF_CLI_CHN_MUL_DAY_V3.0.html\u003c/span\u003e\u003cspan address=\"http://data.cma.cn/data/cdcdetail/dataCode/SURF_CLI_CHN_MUL_DAY_V3.0.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], which was derived from a daily report by 699 meteorological stations. The temperature and precipitation of each location (a sum of 334 locations in this study) in China was assigned by the temperature and precipitation of the nearest meteorological station. The average daily air temperature (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(T\\)\u003c/span\u003e\u003c/span\u003e) and average daily precipitation (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(P\\)\u003c/span\u003e\u003c/span\u003e) of an epidemic of a location in China were calculated by assigning the date of incidence of COVID-19 to the date of the corresponding day in 2020. The updated climate data represents the true climate COVID-19 virus experienced, and thus is more accurate than we used in previous analysis using the average monthly values during 2010\u0026ndash;2019 (Wan et al 2020).\u003c/p\u003e \u003cp\u003eThe daily average daytime land surface temperature in 2020 was obtained from NASA Earth Observations (NEO, neo.sci.gsfc.nasa.gov), which was measured from space using instruments carried on satellites [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The daily precipitation in 2020 was obtained from CPC GIS DATA of the Climate Prediction Center's (CPC, cpc.ncep.noaa.gov), which was defined by Optimal Interpolation (OI) of gauge observations [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The raster of temperature and precipitation in the world was upscaled to 0.5 \u0026times; 0.5 degrees (a total of 259, 200 grids), the temperature and precipitation were extracted from the raster at the location of each country, region, or city outside China. We obtained monthly average temperature and precipitation (during 1970\u0026ndash;2000) with a spatial resolution of 5 minutes from Worldclim 2 as climate proxy for seasonal projection of partial effects of climate (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, S1) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. We have to use this dataset because it is hard for us to obtain the observatory climate data of the other countries except for China.\u003c/p\u003e\n\u003ch3\u003eEstimation Of The Transmission Rate And Control Efficiency Of Covid-19\u003c/h3\u003e\n\u003cp\u003eWe used a logistic model to estimate the transmission parameters of COVID-19 by following our previous study on SARS [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The number of cumulative cases of COVID-19 can be well fitted as follow:\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003cbr\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({N}_{t}\\)\u003c/span\u003e \u003c/span\u003e was the number of cumulative cases at day \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(t\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(K\\)\u003c/span\u003e\u003c/span\u003e was the maximum cumulative cases of COVID-19 patients. The daily increase rate (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({r}_{t}\\)\u003c/span\u003e\u003c/span\u003e) of the number of cumulative cases of patients was defined as follow:\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003cbr\u003e\u003c/p\u003e\u003c/p\u003e \u003cp\u003eThus, the daily increase rate, which represents the transmission rate, should be negatively associated with the number of cumulative cases of patients under human intervention:\u003c/p\u003e \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003cbr\u003e\u003c/p\u003e \u003cp\u003eHere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(a\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(b\\)\u003c/span\u003e\u003c/span\u003e are constants, and all \u0026gt;\u0026thinsp;0. \u003cem\u003ea\u003c/em\u003e represents the maximum daily increase rate (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({r}_{m}\\)\u003c/span\u003e\u003c/span\u003e) without human intervention or before further human intervention in the beginning or early stage of disease transmission, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(b\\)\u003c/span\u003e\u003c/span\u003e represents the control efficiency under human intervention, or the decreasing speed of the daily increase rate. The early transmission rate does not necessarily coincide with the transmission rate without human intervention. Countries which saw a late introduction of COVID prepared in advanced or at least the awareness of the population was different. Under this situation, the maximum daily increase rate only represents the initial transmission rate before further control measures are taken after introduction of COVID-19.\u003c/p\u003e \u003cp\u003eBecause the mean incubation period of COVID-19 patients was estimated to be 5.2 days [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], we defined the number of cumulative cases of COVID-19 of a location in the first week as the founding population size (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(F\\)\u003c/span\u003e\u003c/span\u003e) of COVID-19 patients due to reported cases from source sites. We estimated the early and late transmission parameters by using Eq.\u0026nbsp;1 and data of cumulative cases of an epidemic of a location covering the period from the 7th day to the date of last observation when the daily increase rates were all less than 0.01 or to the 100th day. A total of 617 time series of cumulative cases of COVID-19 from China (n\u0026thinsp;=\u0026thinsp;314, 1 January- 11 March 2020), USA (n\u0026thinsp;=\u0026thinsp;55, 22 January \u0026minus;\u0026thinsp;31 December 2020), Europe (n\u0026thinsp;=\u0026thinsp;43, 25 January \u0026minus;\u0026thinsp;31 December 2020) and the rest of world (n\u0026thinsp;=\u0026thinsp;205, 20 January \u0026minus;\u0026thinsp;31 December 2020) was constructed for estimating the transmission parameters (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eWe assumed that the transmission parameters (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({r}_{i}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({a}_{i}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({b}_{i}\\)\u003c/span\u003e\u003c/span\u003e) should be determined by the founding population size (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({F}_{i}\\)\u003c/span\u003e\u003c/span\u003e), human population size of a location (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({H}_{i}\\)\u003c/span\u003e\u003c/span\u003e), air temperature (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({T}_{i}\\)\u003c/span\u003e\u003c/span\u003e), and precipitation (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({P}_{i}\\)\u003c/span\u003e\u003c/span\u003e) in an\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(i\\)\u003c/span\u003e\u003c/span\u003e\u003csup\u003eth\u003c/sup\u003e location. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({r}_{i}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({a}_{i}\\)\u003c/span\u003e\u003c/span\u003e, and\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({b}_{i}\\)\u003c/span\u003e\u003c/span\u003e represents the average daily increase rate of cumulative cases, maximum daily increase rate, and control efficiency of COVID-19, respectively. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({r}_{i}\\)\u003c/span\u003e\u003c/span\u003e represents the average transmission rate of COVID-19 under human intervention. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({a}_{i}\\)\u003c/span\u003e\u003c/span\u003e represents the maximum transmission rate (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({r}_{m}\\)\u003c/span\u003e\u003c/span\u003e) without human intervention or before further human intervention. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({b}_{i}\\)\u003c/span\u003e\u003c/span\u003e represents the control efficiency under human intervention.\u003c/p\u003e \u003cp\u003eGAMs were used to model the effects of the founding population size (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({F}_{i}\\)\u003c/span\u003e\u003c/span\u003e), human population size (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({H}_{i}\\)\u003c/span\u003e\u003c/span\u003e), and climate factors (air temperature \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({T}_{i}\\)\u003c/span\u003e\u003c/span\u003e and precipitation \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({P}_{i}\\)\u003c/span\u003e\u003c/span\u003e) on the average daily increase rate (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({r}_{i}\\)\u003c/span\u003e\u003c/span\u003e), maximum daily increase rate (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({a}_{i}\\)\u003c/span\u003e\u003c/span\u003e), and control efficiency (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({b}_{i}\\)\u003c/span\u003e\u003c/span\u003e) in the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(i\\)\u003c/span\u003e\u003c/span\u003e\u003csup\u003eth\u003c/sup\u003e location by following Wood, 2011 [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. A Gaussian GAMs was firstly fitted by using a linear regression formula:\u003c/p\u003e \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003cbr\u003e\u003c/p\u003e \u003cp\u003eHere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{Y}\\)\u003c/span\u003e\u003c/span\u003e represents the three transmission parameters (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({r}_{i}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({a}_{i}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({b}_{i}\\)\u003c/span\u003e\u003c/span\u003e) separately. To examine the potential nonlinear effect of climate factors, we fitted the data using the following model:\u003c/p\u003e\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003cbr\u003e\u003c/p\u003e \u003cp\u003eHere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({s(T}_{i})\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({s(P}_{i})\\)\u003c/span\u003e\u003c/span\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(s\\left(Lon, Lat\\right)\\)\u003c/span\u003e\u003c/span\u003e were 2D smooth function (with \u003cem\u003ek\u003c/em\u003e value, a dimension of the basis\u0026thinsp;=\u0026thinsp;4) for removing the effects of spatial autocorrelation. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\epsilon }_{i}\\)\u003c/span\u003e\u003c/span\u003e was uncorrelated random errors of zero mean and finite variance.\u003c/p\u003e \u003cp\u003ePearson\u0026rsquo;s correlation analysis was introduced to detect significant correlations among variables (electronic supplementary material, Figure S2-S5). Loess regression was introduced to show the changing trend of cumulative cases of COVID-19 in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, S6-7. The relation of average or daily increase rate and control efficiency with climate factors were shown in electronic supplementary material, Figure S6-7 (not partial relation). For variables with strong and significant correlations (\u003cem\u003er\u003c/em\u003e \u0026lt; -0.6 or \u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.6; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) with the other variables, only one variable with the largest correlation coefficient to transmission parameters was selected to avoid the potential collinearity effect in model analysis. GAM was carried out using the mgcv library (v. 1.8\u0026ndash;15) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] in R (v. 3.6.1). Associations of the average daily increase rate or the number of cumulative cases with environmental variables of each city or prefecture were analyzed by using raster (v.2.9\u0026ndash;22) and rgdal (v.1.4-4) libraries in R (v. 3.6.1) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Correlation analysis, linear regression, and loess regression were performed with the stats library (v. 3.6.1) in R (v. 3.6.1) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Results And Discussion","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eImpacts of climate factor\u003c/h2\u003e \u003cp\u003eAnalysis using the linear model (Eq.\u0026nbsp;2) of generalized additive models (GAM) indicated that air temperature showed a consistent, significant and negative association with the average daily increase rate (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({r}_{i}\\)\u003c/span\u003e\u003c/span\u003e) of cumulative cases in China, USA, Europe, and the world, the maximum daily increase rate (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({a}_{i}\\)\u003c/span\u003e\u003c/span\u003e) of cumulative cases in China, UAS, and the world (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and electronic supplementary material, Table S1), indicating high temperature significantly reduced the transmission rate of COVID-19 in both early and late stages of COVID-19 transmission. These results are similar with our previous analysis [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], except that the statistical tests for the peak temperature was not significant in the current study. Our results are also consistent with some observations of previous studies using different parameters of transmission severity such as incident cases [\u003cspan additionalcitationids=\"CR27 CR28\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], the reproductive number [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] or mortality [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]; but not with many others, e.g. Briz-Red\u0026oacute;n \u0026amp; Serrano-Aroca 2020; J\u0026uuml;ni \u003cem\u003eet al.\u003c/em\u003e 2020; Yao \u003cem\u003eet al.\u003c/em\u003e 2020 [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Some studies have found the association between climate and COVID-19 transmission. For example, using niche models, tropical climates were less vulnerable to the spread of the virus than temperate climates [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The temperature was positively associated with COVID-19 mortality in Wuhan [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Triplett (2020) found a downward trend of COVID-19 cases with a maximum temperature above 22.5\u0026deg;C [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Wang \u003cem\u003eet al.\u003c/em\u003e (2020) reported that high temperature and high humidity significantly reduced the effective reproduction number of COVID-19 in China [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Qi \u003cem\u003eet al.\u003c/em\u003e (2020) reported the negative association between the incidence of COVID-19 and temperature or relative humidity in China [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. However, many other studies did not show a significant association between COVID-19 and climate. For example, no association was found between the basic reproduction number of COVID-19 and temperature or UV radiation in Chinese cities [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The epidemic growth of COVID-19 showed no association with temperature, but a weak association with humidity [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] in the world. No evidence of a significant relationship between COVID-19 cases and the temperature was also found in Spain [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], and the world [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAnalysis using the nonlinear model (Eq.\u0026nbsp;3) of GAM (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and electronic supplementary material, Table S1) was very similar to those using the linear model, temperature showed consistent non-linear but overall negative association with daily increase rate (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({r}_{i}\\)\u003c/span\u003e\u003c/span\u003e), maximum daily increase rate (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({a}_{i}\\)\u003c/span\u003e\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-H, Table S1). The daily increase rate (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({r}_{i}\\)\u003c/span\u003e\u003c/span\u003e) showed a peak around 11.2\u0026deg;C (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH, S6H), similar to our previous study [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Some previous studies also reported the nonlinear association of temperature with transmission severity of COVID-19 using incidence cases [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] or growth rate [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], but the results were inconsistent. A few studies indicated that the optimal temperature for SARS-CoV2 incidence of new cases was at 8.07\u0026deg;C [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] or 8.72\u0026deg;C around the world [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], and 13\u0026thinsp;~\u0026thinsp;19\u0026deg;C in China [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The growth rates peaked at about 5\u0026deg;C in temperate regions in the Northern Hemisphere during the outbreak month, while they decreased in warmer and colder regions [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Bannister-Tyrrell \u003cem\u003eet al\u003c/em\u003e. (2020) found COVID-19 incidence had a belled-shaped association around 1\u0026deg;C [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Notari (2020) reported that temperature had a negative association with early exponential growth with a weak peak at about 7.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u0026deg;C [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In our updated study, the climate proxy data was updated to the daily resolution in 2020 in China, rather than 10 years average (2010\u0026ndash;2019), which may attributed to the difference in estimation of optimal temperature for transmission rate between this study (peaked 11.2\u0026deg;C) and previous one (6.3\u0026deg;C) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBesides, we found precipitation showed a significant and negative association with daily increase rate (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({r}_{i}\\)\u003c/span\u003e\u003c/span\u003e) in Europe and maximum daily increase rate (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({a}_{i}\\)\u003c/span\u003e\u003c/span\u003e) in China using linear and nonlinear model, suggesting a wet climate would decrease the transmission ability of COVID-19 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, S1, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eM-T), which is consistent with a previous study [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \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 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cstrong\u003eAssociations of\u0026nbsp;the average\u0026nbsp;daily increase rate (r\u003csub\u003ei\u003c/sub\u003e\u003c/strong\u003e \u003cstrong\u003e) of cumulative cases, the maximum daily increase rate (a\u003csub\u003ei\u003c/sub\u003e\u003c/strong\u003e \u003cstrong\u003e), and the control efficiency (b\u003csub\u003ei\u003c/sub\u003e\u003c/strong\u003e\u003cstrong\u003e) with the\u0026nbsp;founding population size of COVID-19 patients during the initial 7 days after the first reported patient\u0026nbsp;(F\u003csub\u003ei\u003c/sub\u003e\u003c/strong\u003e\u003cstrong\u003e), human population size of a location (\u003c/strong\u003e\u003cstrong\u003eH\u003csub\u003ei\u003c/sub\u003e\u003c/strong\u003e\u003cstrong\u003e), climate factors (temperature,\u0026nbsp;\u003c/strong\u003e \u003cstrong\u003eT\u003csub\u003ei\u003c/sub\u003e\u003c/strong\u003e\u003cstrong\u003e, and precipitation,\u0026nbsp;\u003c/strong\u003e \u003cstrong\u003eP\u003csub\u003ei\u003c/sub\u003e\u003c/strong\u003e\u003cstrong\u003e), and spatial autocorrelation based on analyses using Equation 2 (linear model).\u003c/strong\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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=\"left\" 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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFounding population\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHuman population\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTemperature\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePrecipitation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSpatial auto correlation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eVariance explained\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSample size\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({r}_{i}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.00000095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0038 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.01%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e324\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({r}_{i}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.00014 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.011 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0024 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e53.88%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEurope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({r}_{i}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.00079 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0071 **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0047 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.025 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e60.18%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlobal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({r}_{i}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.4e-06 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0062 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.001 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.53%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e575\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({a}_{i}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.1e-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.042 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.021 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.065 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9.40%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e259\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({a}_{i}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0045 **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e25.63%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEurope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({a}_{i}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.00084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e33.30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlobal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({a}_{i}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.00000097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.016 **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0042 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e16.77%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e502\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({b}_{i}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.7e-06 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.011 **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0036 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.014 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9.75%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e259\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({b}_{i}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00000017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.9e-05 **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.6e-05 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.3e-05 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e81.11%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEurope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({b}_{i}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3e-04 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e51.25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlobal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({b}_{i}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.000000062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0025 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4e-04 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0057 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.87%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e502\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe dome-shaped relation of organisms with environmental factors is reasonable based on the Law of Tolerance [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Chin \u003cem\u003eet al.\u003c/em\u003e (2020) reported that the virus of COVID-19 was highly stable with only a 0.6-log unit reduction at 4\u0026deg;C in 14 days, with a 3-log unit reduction at 22\u0026deg;C after 7 days and no detection at 14 days, with a 3-log unit reduction at 37\u0026deg;C after 1 day and no virus detected afterward [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. This observation supports ours on the potential peak temperature of 11.2\u0026deg;C of the virus of COVID-19. Although the virus could be well preserved in cold conditions, the lower transmission ability under 11.2\u0026deg;C was likely caused by human behaviors. In cold conditions, people are not as active as in warm conditions, which did not favor the person to person transmission of COVID-19. Besides, in cold conditions, droplets can freeze, which prevent their spreading in the air. But, the statistical test on the optimal temperature of COVID-19 is not significant, more data and further analysis is needed to draw solid conclusion.\u003c/p\u003e \u003cp\u003eThe impacts of climate on control efficiency have never been assessed before. We found air temperature showed significant and positive associations with the control efficiency (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({b}_{i}\\)\u003c/span\u003e\u003c/span\u003e) in China, USA, and the world (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eI-L), and significant nonlinear associations in China and USA (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eI-J and electronic supplementary material, Table S1). Precipitation showed significant and positive associations with the control efficiency in China, USA, and the world (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eU-X), and showed significant non-linear associations in China and USA (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eU-V and electronic supplementary material, Table S1). These results indicated that warm and wet climate could decrease the control efficiency or increase the time of COVID-19 transmission (Note: smaller \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({b}_{i}\\)\u003c/span\u003e\u003c/span\u003e indicates better control efficiency or fast decrease of transmission rate) on COVID-19. This is likely that in warm and wet climate, the transmission rate is relatively low, further reduction of transmission rate is more difficult when the infection rate is low.\u003c/p\u003e \u003cp\u003eOur results suggest that cold and dry season facilitates the transmission rate of COVID-19. Human immunity might be lower under cold conditions, which makes them more susceptible to the virus [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The virus appears unstable with high UV irradiation and high temperatures [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], but cold and dry conditions would benefit the virus to survive or spread in cough droplets of infected patients or on the surfaces of contaminated goods.\u003c/p\u003e \u003cp\u003eIt is notable that some countries in warm climate zone (e.g., India, Brazil) suffered heavy infections of COVID-19, mainly caused by the low control efficiency not by high transmission rate. In Brazil, the maximum daily increase rate of cumulative COVID-19 cases and control efficiency is 0.17 and \u0026minus;\u0026thinsp;0.00000033; in India, they are 0.12 and \u0026minus;\u0026thinsp;0.0000014, respectively, relative to the maximum daily increase rate (0.31) and control efficiency (-0.019) in China. Thus, the heavy infection of COVID-19 in some countries located in the warm climate does not contradict our observation that the transmission rate is negatively associated with air temperature.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eImpacts Of Human Factors\u003c/h3\u003e\n\u003cp\u003eThe effects of human factors on the spread of COVID-19 and control efficiency have been widely modeled in previous studies (e.g. Gilbert \u003cem\u003eet al.\u003c/em\u003e 2020 [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]), but quantitative analysis using empirical data is still limited. In this study, we found the founding population size of reported COVID-19 patients showed a consistent negative association with the daily increase rate (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({r}_{i}\\)\u003c/span\u003e\u003c/span\u003e) in USA, Europe, and the world (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), suggesting that the poor detecting capacity represented by the number of COVID-19 patients in first week may result in a larger transmission rate of COVID-19. The population size showed a consistent, significant and positive association with the average daily increase rate (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({r}_{i}\\)\u003c/span\u003e\u003c/span\u003e), but showed negative association with maximum daily increase rate (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({a}_{i}\\)\u003c/span\u003e\u003c/span\u003e) in China, and the world (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), suggesting countries or regions with large susceptible population suffered an average high infection of COVID-19. The maximum transmission rate at early stage is higher in counties or regions with small population size, likely due to poor capacity of early detecting of COVID-19 in small countries or regions.\u003c/p\u003e \u003cp\u003eThe control efficiency indicator showed a significant and positive association with population size in China, USA, Europe, and the world (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), suggesting the control efficiency or deceasing speed of transmission rate of COVID-19 were lower for countries or regions with larger susceptible population (Note: smaller \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({b}_{i}\\)\u003c/span\u003e\u003c/span\u003e indicated the higher control efficiency), probably due to difficulty in managing the large number of infected patients and lock down of travel in a large country or region. The control efficiency indicator showed a significant and a negative association with the founding population size of COVID-19 in China, indicating early detection of COVID-19 benefit control efficiency of COVID-19.\u003c/p\u003e \u003cp\u003eIn summary, in this study, we found consistent evidence that both maximum and average transmission rates of COVID-19 was reduced in warm climate. There are some evidence the transmission rate of COVID-19 and control efficiency or decreasing speed of transmission rate under human intervention are decreased in warm or wet condition. Human factors may also attribute to the transmission rate and control efficiency of COVID-19, which is more likely related to capacity of detecting and control the SARS-CoV-2 virus in different countries or regions.\u003c/p\u003e\n\u003ch3\u003eImplications For Prevention\u003c/h3\u003e\n\u003cp\u003eOur study suggests that air temperature has a predominant association with the transmission rate of COVID-19. Based on our results in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, H, we projected the influences of seasonal change of temperature on the contagious risk of early transmission without or before further human intervention (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-D), and the late transmission under human intervention (electronic supplementary material, Figure S1) in the world. Summer seasons would decrease the early transmission risk of COVID-19 from low-latitude or low-altitude regions but increase the transmission risk in the high-latitude or high-altitude regions. The summer season would decrease the late transmission rate of the northern hemisphere but increase that of the southern hemisphere.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBecause both human and climate factors played a significant role in the spread of COVID-19, it is not wise to rely upon climate factors to control this dangerous virus. Human intervention, such as lockdown and travel restrictions at the epicenter, as well as identification and isolation of infected patients or people with close contact, have been demonstrated to be successful in preventing the spread of COVID-19 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Thus, human intervention such as early detection and high control efficiency is essential to containing the rapid expansion of COVID-19 around the world. More efforts and collaboration are urgently needed in containing the spread of COVID-19 around the world.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe SARS-CoV-2 virus has caused incredible damage to the world, revealing the influencing factors affecting the transmission of COVID-19 is essential to take effective control measures. Here the associations of COVID-19 transmission with both human factors and climate factors were analyzed. The findings showed the transmission rate of COVID-19 as well as control efficiency are significantly and negatively associated with high temperature and/or precipitation. The transmission rate was negatively associated with the early detected cases and but positively associated with population size of some countries or regions. This study suggests that in summer seasons, the transmission risk of COVID-19 would increase in the high-latitude or high-altitude regions but decrease in low-latitude or low-altitude regions; human intervention is essential in containing the spread of COVID-19 around the world.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCOVID-19\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCoronavirus disease 2019\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSARS-CoV-2\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSevere acute respiratory syndrome coronavirus 2\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWHO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWorld Health Organization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGAMs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGeneralized Additive Models.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset supporting the conclusions of this study from Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU) (https://www.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6) are included as a supplementary file.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Key Research and Development Program of China (2021YFC0863400), project of studying transmissions of COVID-19, the key program of Chinese Academy of Sciences (KJZD-SW-L11), ANSO Project of Chinese\u003c/p\u003e\n\u003cp\u003eAcademy of Science (ANSO-CR-KP-2020-08), the Young Elite Scientists Sponsorship Program by CAST and ISZS (2021QNRC001, ISZS-YESS Program), and risk assessments based on big data analysis (E0517111) supported by the Institute of Zoology, Chinese Academy of Sciences.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZZ designed the study, XW and CC collected the data. XW did the data analysis, ZZ and XW wrote the first draft of the manuscript, and all authors contributed intellectually to the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to Chinese provincial and prefectural health commission, World Health Organization, and Systems Science and Engineering at Johns Hopkins University for providing cumulative cases data of COVID-19.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eYang Z, Zeng Z, Wang K, Wong S-S, Liang W, Zanin M, Liu P, Cao X, Gao Z, Mai Z, et al. Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions. 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The Lancet. 2020;395(10227):871\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"COVID-19, transmission ability, control efficiency, temperature, precipitation, disease prevention and control","lastPublishedDoi":"10.21203/rs.3.rs-2085190/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2085190/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe COVID-19 novel virus has caused huge damage to public health around the world. Revealing the influencing factors affecting the transmission rate of COVID-19 is essential to take effective control measures. However, the association between transmission of COVID-19 and climate factors remains elusive with high uncertainty.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eBy using an extensive global dataset covering 617 time series from China, USA, Europe, and the rest of the world during 1/1\u0026ndash;31/12 2020, we estimated the transmission parameters of COVID-19 and modeled the effects of the human and climate factors on COVID-19 transmission.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe demonstrate that the transmission rate of COVID-19 was lower in warm climate in China, Europe, USA and the world, and in wet climate in China and Europe after excluding the confounding factors. The maximum transmission rate of COVID-19 seemed to have a peak temperature around 11.2\u0026deg;C in China and the world. The control efficiency (i.e. decreasing speed of transmission rate) in China, USA and the world was lower in warm and wet condition.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur study suggested that in summer seasons, the transmission risk of COVID-19 would increase in the high-latitude or high-altitude regions but decrease in low-latitude or low-altitude regions. The area with the 7.8\u0026deg;C isocline between October and January which overlap with the major epicenters of COVID-19 should be investigated as a priority in searching for the natural hosts of COVID-19 and their habitats and movement.\u003c/p\u003e","manuscriptTitle":"Transmission of COVID-19 was slower but more sustained in warm climate","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-10-13 22:18:08","doi":"10.21203/rs.3.rs-2085190/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f62aaee1-f6dd-4263-bf86-5c06b2b30961","owner":[],"postedDate":"October 13th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-10-20T16:59:21+00:00","versionOfRecord":[],"versionCreatedAt":"2022-10-13 22:18:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2085190","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2085190","identity":"rs-2085190","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}