Mapping the occasionality of inevitable dengue fever prevalence in China

Mapping the occasionality of inevitable dengue fever prevalence in China | 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 Mapping the occasionality of inevitable dengue fever prevalence in China Hongyan Ren, Nankang Xu, Liang Lu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3810038/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Dengue fever (DF) is an acute mosquito-borne viral infectious disease in the world, and increasing DF outbreaks in China have posed serious impacts on public health in recent years. Thus, comprehensively investigating spatiotemporal features and driving or restrictive factors of DF epidemics is critical for the improvement of intervention capacity against this disease. Methods Two famous dividing lines (Hu Line and Q-H Line) were applied to divide the mainland into four regions for geographically characterizing China’s DF prevalence. We defined the stages with suitable relative humidity, temperature, and precipitation as basic time windows for the mosquito vectors’ activities. The Random Forest (RF) model was employed to fit the relationships between local epidemics and included climatic and socioeconomic factors, quantify these factors’ contribution, and then map the city-level risk of local DF prevalence. Results The situation of China’s DF epidemics was increasingly serious due to ascending intensities of local prevalence triggered by more frequently imported cases. The cities with DF cases, together with their frequencies and intensities presented clear geographical disparities on the city scale, and well matched with the time windows for either DF transmission (95.74%) or mosquito vectors’ activities (83.59%). Among these included factors, the imported cases acted as the driving factor of local epidemics in the region I and III because of not only their strongest association ( r =0.43, P <0.01; r=0.46, P<0.01) but also the largest contribution (24.82% and 31.01%). Moreover, in terms of SHAP values, the imported DF cases possessed a steady promoting impact on local epidemics, while the rest 11 inputs had comprehensive promoting or inhibiting effects with different inflexion values. Besides, the RF models considering the time windows owned higher testing AUC value (0.92) while fitting the relationships between local DF epidemics and potential factors, by which we successfully identified about 96% of the cities with the highest and higher risks of local DF prevalence. Conclusions China is being confronted with increasingly larger intensities of occasionally localized DF epidemics triggered by unavoidable higher frequencies of imported epidemics. This study would supply useful clues for the health authorities improving their intervention capacity against this disease. Inevitability Occasionality Dengue fever China Time windows Random forest Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Highlights China’ DF epidemic was featured by ever-increasing frequency and intensity; Spatial distribution of China’s DF epidemic was divided by Hu Line and Q-H Line; Capability of imported DF case triggering local epidemics is rising; Local DF prevalence is geographically restricted by the city-level time windows; Unavoidable DF outbreak would occasionally occur and expand toward inland cities. Introduction Dengue fever (DF) is an acute infectious disease caused by dengue virus, which is transmitted by Aedes albopictus and Aedes aegypti [ 1 ]. Its natural focuses are widely distributed in global tropical and subtropical areas (e.g., the Southeast Asia, the Western Pacific, and the South Africa) [ 2 ], causing about one-third of the global population exposed to this disease [ 3 ]. Since the globalization and climate change was much deeper and continuous in recent years, the increasing incidence and range DF epidemics have become an important public health concern that should not be underestimated. In China, the DF prevalence is mainly induced by imported cases from the global natural focuses. No DF case was reported during 1949–1977 until a sudden outbreak occurred in the city of Foshan, Guangdong Province in 1978 [ 4 ]. From then on, China’s DF epidemic tended to be intermittent and has been included in the list of notifiable communicable diseases since 1990s [ 5 ]. Due to more and more serious DF epidemic in the world and China’s increasingly important roles in the international economy and trade affairs, local DF prevalence induced by imported cases has ascended substantially in recent years [ 6 , 7 ]. Meanwhile, the DF prevalence presented obvious spatial expansion from southern China (southwest border and southeast coastal areas) toward inland northern regions, such as Jiangxi (Yichun), Chongqing, Henan (Xuchang) and Shandong (Jining) [ 8 , 9 ]. Overall, the DF prevalence in China has shown shorter and shorter time intervals and clear spatial expansion. Numerous studies have been conducted by domestic and foreign scholars on a series of factors influencing the spread and prevalence of this disease at various levels, including the dengue virus, mosquito vectors, susceptible population, environmental conditions, and socioeconomic status [ 10 , 11 ]. Among these influential factors, environmental conditions, like climate, hydrology, vegetation coverage, mainly affect the activity of the dengue virus, as well as the breeding, survival surroundings, and biting power of mosquito vectors [ 12 , 13 ]. Socio-economic factors, such as population density and mobility, land use, convenience level of public transportation, residents’ income level and living habits, chiefly alter the probability of human-mosquito contact [ 14 , 15 ]. In general, the DF transmission and prevalence is a complex and typical geo-ecological process. Earlier studies on the spatial and temporal variations at various scales, and their influencing factors of DF epidemics not only have greatly supported the prevention and control against this disease in the absence of effective clinical vaccines, but also have improved our understanding of the DF prevalence and spread in China [ 16 , 17 ]. However, comprehensive knowledge is needed regarding the current ever-increasing growth of cities with DF outbreaks, the limitations on geographical patterns and the quantitative contributions of potential influencing factors of DF epidemics in China. Therefore, our study was conducted to 1) characterize the frequency and intensity, geographical distribution, 2) use a random forest (RF) model to quantitatively explore the relationships between local DF prevalence and a series of potential influencing factors, and then 3) map the city-level risk of DF prevalence across China. The results of this study will provide important support for strengthening the capacities of interventions against this disease and comprehensively raising the current understandings on China’s DF epidemics. Methods Data collection Dengue cases The dengue cases were sourced from the National Infectious Disease Surveillance System, covering the period from 2003 to 2022 and encompassing confirmed dengue cases in China. The dataset included information on the date of onset, residential address, and reporting address of these cases. Based on the origin source, dengue cases were classified into imported and local cases. Imported cases referred to those who have traveled to a dengue-endemic country or region within 14 days before the onset of the disease. Local cases were those who have not left the city within 14 days before the onset of the disease or those who have left the city within 14 days before the onset of the disease and visited other domestic dengue epidemic cities [ 18 ]. An epidemic outbreak was defined as the occurrence of three or more local cases within the maximum incubation period of 14 days in a city. If no local cases were reported within 14 days after the outbreak, it was considered that the outbreak had ended. The duration from the beginning of the outbreak to its end was referred to the outbreak period for the local DF epidemic. After filtering out isolated local cases occurring outside the outbreak period, imported and local cases were consolidated at the city level based on their reporting address codes. Potential influencing factors According to previous research [ 10 – 15 ], we collected 11 potential influencing factors, which were categorized into two dimensions: socioeconomic factors and natural factors (Table 1 ). Among 6 socioeconomic factors, factors related to the economy and population included 1 km × 1 km gridded gross domestic product (GDP) and population density (Popu). Urbanization-related land-use factors included the percentage of four land use types: cropland (Cropland), forest (Forest), water (Water), and impervious surface (Impervious), which were collected from yearly 30m × 30m gridded data. In addition to the above socioeconomic factors, 5 natural factors including monthly maximum temperature (Tmax), monthly average temperature (Tmean), monthly minimum temperature (Tmin), monthly average relative humidity (RH), and monthly precipitation (Prec) were also considered as potential influencing factors for the spread of the epidemic, which were collected from 1 km × 1 km gridded data at a monthly scale. To facilitate statistical and spatial analysis, the variables of each factor at the city level were extracted using zoning statistics and spatial connectivity tools in ArcGIS 10.6 software. Furthermore, for missing data in certain years, adjacent year data was used as a replacement. Table 1 Data collection and sources in this study Data group Selected variables from previous studies Data unit Time scale Resolution Source Socioeconomic factors Gross domestic product per capita (GDP) [ 10 , 14 ] Million dollars/km 2 Annual 1 km The Scientific Data ( https://doi.org/10.6084/m9.figshare.17004523.v1 ) WorldPop( https://hub.worldpop.org/ ) Earth System Science Data ( https://doi.org/10.5281/zenodo.5816591 ) Population density (Popu) [ 10 , 14 , 15 ] Person/km 2 Annual 1 km Annual average percentage of cropland (Cropland) [ 15 ] % Annual 30 m Annual average percentage of forest (Forest) [ 15 ] % Annual 30 m Annual average percentage of water (Water) [ 15 ] % Annual 30 m Annual average percentage of impervious (Impervious) [ 15 ] % Annual 30 m Natural factors monthly maximum air temperature (Tmax) [ 11 , 12 ] degree Celsius Monthly 1 km Earth System Science Data ( https://doi.org/10.5281/zenodo.5112232 ) National Earth System Science Data Center ( http://www.geodata.cn ) National Tibetan Plateau/Third Pole Environment Data Center ( https://data.tpdc.ac.cn/ ) monthly mean air temperature (Tmean) [ 11 – 13 ] degree Celsius Monthly 1 km monthly minimum air temperature (Tmin) [ 11 , 12 ] degree Celsius Monthly 1 km monthly mean relative humidity (RH) [ 13 ] % Monthly 1 km monthly precipitation (Prec) [ 11 – 13 ] 0.1mm Monthly 1 km Descriptive analysis on frequency and intensity of DF outbreaks in China The yearly counts of local and imported cases were separately aggregated from 2003 to 2022. According to the occurrences of either imported or local epidemics, these cities were classified into four groups including the cities with either imported or local epidemics, the cities with both of them, the cities with imported epidemics only, and the cities with local epidemics only. Furthermore, we analyzed the spatial distribution characteristics of these four groups of cities and determined the correlations between imported and local epidemics by using the Spearman. To further characterize DF epidemics in China in the past two decades, we established statistical indicators of DF epidemics from the perspective of prevalence frequency and intensity (Table 2 ). Subsequently, we analyzed the variations in the frequency and intensity of epidemic prevalence and characterized the spatial distribution of overall frequency and intensity. Table 2 Statistical indicators of DF epidemics Category Definition Formula Frequency Number of months with epidemics within the specified time Number of months with epidemics / the specified time Times of outbreaks within the specified time Times of outbreaks / the specified time Intensity Monthly average number of cases in the months with epidemics Total cases / number of the months with epidemics Average number of cases per outbreak Total cases / times of outbreaks The Retrieval of the time windows for local DF epidemics The suitability of natural conditions such as temperature, humidity and precipitation influenced mosquito breeding and activities, which directly affected the spread of local epidemics. Previous studies have found that it was suitable for the breeding of Aedes mosquito when the monthly minimum temperature was higher than 10 ℃ [ 19 – 21 ], the monthly average temperature was between 15–32 ℃ [ 22 , 23 ], the monthly maximum temperature was lower than 38 ℃ [ 20 , 21 ], the monthly average relative humidity was between 60% and 90% [ 24 , 25 ] and the monthly precipitation was between 60 mm and 650 mm [ 26 , 27 ]. Moreover, we defined the periods meeting all these conditions as the time windows for the mosquito vectors’ activities. Considering the incubation periods of dengue viruses [ 28 ], we extended the time windows of mosquito vectors by one month and defined it as the time windows of local DF prevalence. To further evaluate the effectiveness of the time windows, we defined the match degree as the proportion of the number of outbreaks where the outbreak period overlapped with the time windows to the total number of outbreaks. The higher the match degree, the higher the proportion of outbreaks occurring within the time windows. Identification of the dengue driving forces at the city level We aggregated the number of imported cases within the time windows from 2003 to 2019 as an independent variable at the city level. Meanwhile, we counted the number of local cases in the year and transformed it into a binary variable as the dependent variable according to the occurrence of local epidemics. Additionally, six socioeconomic variables and five environmental variables (Table 1 ) of the average value in the months of the time windows were considered. And preliminary correlation analyses between the number of local cases and input variables were conducted using the Spearman. The Random Forest (RF), the Gradient Boosting Machine (GBM) and the Support Vector Machines (SVM) were powerful machine learning methods for classification and regression, which were typically employed for the analysis of the dengue driving forces [ 29 – 31 ]. We applied these three methods to fit the relation between local epidemic outbreaks and the influencing factors. The data from 2003 to 2018 were divided into the training set (70%) and the test set (30%). In the RF model, the number of trees to grow was the main parameter, and a range of tree numbers (from 100 to 2000 with an interval of 100) were set to select the optimal parameter based on the performances of the model. In the GBM model, the number of trees to grow and the learning rate were the main parameters, and the ranges of tree numbers and learning rates are 100–2000 (step = 100) and 0.01–0.2 (step = 0.01). In the SVM model, we applied ‘rbf’ as the kernel function, and the ranges of regularization parameter and kernel coefficient were 0.1-3 (step = 0.1) and 0.01–0.5 (step = 0.01). In these models, the five-fold cross-validation was performed to boost the stability of modeling. The Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve was used as a measured value of the model predictive ability and a larger AUC indicated better predictive capability of the model [ 32 ]. The model accuracy characterized by the AUC, was as follows: 0.50–0.70, indicating a poor model; 0.70–0.80, suggesting an average model; and 0.80-1.00, reflecting a good model [ 33 , 34 ]. According to the AUC of above three models, we looked for the optimal model. The SHapley Additive exPlanations (SHAP) method, widely applied for interpreting machine learning models [ 35 ], was introduced to quantify the contributions and driving forces of each independent variable to local epidemic outbreak. Globel SHAP values were calculated for each variable in the RF model, with larger absolute values indicating greater contributions to the model. Local SHAP values for each instance reflected the driving force for the epidemic outbreak. Positive SHAP values indicated a positive driving force, with larger values indicating a stronger positive influence. Conversely, negative SHAP values indicated a negative driving force, with smaller values signifying a stronger negative influence [ 36 , 37 ]. To analyze the impact of the time windows on the model, the model without considering the time windows was built. And the model considered the imported cases and the mean values of natural environmental variables in the entire year, while keeping other variables constant. The specific modeling process and evaluation methods were consistent with those described above. By comparing the testing accuracies of different models, we selected the optimal model for mapping and assessing the risk of local DF prevalence in China from 2019 to 2022. In this study, we utilized the sklearn package and shap package in Python for building and interpreting the RF model. Results Current situation of the DF prevalence in China There was total 98,560 DF cases displaying an overall upward trend over the past two decades although the amounts of both imported and local DF cases sharply decreased in 2020–2022, causing total 329 DF outbreaks within 104 months (Fig. 1 a). The proportions of local DF cases in the total were much higher than those of imported cases in 15 years, especially holding a steady situation since 2012 (Fig. 1 b). At the same time, the counts of local DF cases were significantly correlated with those of imported cases on the national scale in 2003–2012 ( r = 0.23, P < 0.01), 2013–2022 ( r = 0.44, P < 0.01), and 2003–2022 ( r = 0.41, P < 0.01). These results indicated that China’s DF epidemic was dominated by the local prevalence and was increasingly closely associated with imported DF epidemics. Meanwhile, there were more and more cities being affected by this disease. Despite of an acute drop in 2020–2022, the amounts of cities with either imported or local epidemics (Fig. 1 c), both of them (Fig. 1 d), and only imported epidemics (Fig. 1 e) displayed similar temporal variations. By contrast, the counts of cities with local epidemics only persisted below 3 except for 2014 with 8 cities (Fig. 1 f), which seemed to be not serious. Even though, the amounts of cities with local epidemics were significantly associated with those of cities with imported epidemics on the nation scale ( r = 0.79, P < 0.01), which further indicated that the local DF prevalence was obviously associated with imported epidemics on the city scale across China. The frequencies of imported DF epidemics were less than 9 months per year before 2006, and then quickly ascended to 11 months per year or higher since 2007 despite of a drop to 9 in 2021–2022 (Fig. 2 a). In comparison, the frequencies of local epidemics possessed a relatively slower and fluctuating uptrend varying from 0 to 8. But, in terms of the intensity, local DF epidemics were more serious than imported epidemics although the latter displayed a steadier and quickly uptrend (Fig. 2 b). Besides, another frequency (i.e., the times of local DF outbreaks per year) also presented clear uptrend and their intensities persisted at relatively higher levels than 100 cases per outbreak since 2012 (Additional file 1). These results indicated that the local DF epidemic was increasingly serious in China over the past two decades. Geographical distribution of the DF epidemic The city-level DF epidemics were spatially featured across China. As illustrated in additional file 1, the vast majorities (96% − 100%) of the cities with either imported or local epidemics were distributed on the right side of Huhuanyong line (Hu Line). Among them, more than 75% was located on the south side of Qin Mountain – Huai River line (Q-H Line) and displayed a downtrend from 2012 (Fig. 1 c), which implied that the DF prevalence expanded towards the north side of Q-H Line. Meanwhile, the overwhelming proportion of the cities with both imported and local epidemics was also distributed on the south side of Q-H Line. In comparison, the cities with only imported epidemics were mainly located on the right side of Hu Line, while fewer cities were infected by only local epidemics. As Hu Line and Q-H Line acted as important geographical dividing lines for China’s DF epidemics, three regions were divided for China’s DF epidemics including region I (the cities on the right side of Hu Line), region II (the cities on the north side of Q-H Line), and region III (the cities on the south side of Q-H Line). These analyses implied that the local DF epidemics tended to be geographically differentiated on the city scale across China. On the city scale, China’s DF epidemics termed by the frequency and intensity also presented geographical disparities. The cities with higher average frequencies of imported epidemics than 1 month per year were mainly located in the region III (Fig. 3 a). Among them, a few inland provincial capitals and several prefecture-level cities in the southwestern border and southeastern coastal regions owned much higher average frequencies than 1.5 months per year. By contrast, fewer cities possessed higher average frequencies of local epidemics than 0.5 months per year and mainly concentrated in the region III (Fig. 3 b). Similarly, the cities with relatively higher intensities of imported epidemics were mainly located in the region III (Fig. 3 c), while those of local epidemics were sparsely distributed here (Fig. 3 d). It should be mentioned that the provincial capitals often possessed much higher frequencies and intensities of imported epidemics, and that some inland cities, like Ji’ an, Yichun, Chongqing, and Hangzhou, owned the highest intensities of local epidemics despite of relatively lower frequencies. These results showed that the region III tended to be easily confronted by the imported epidemics and to be much more heavily affected by local epidemics. Time windows for local DF prevalence In terms of match degrees, the time windows for local DF prevalence performed much better (95.74%) than those of the mosquito vectors’ activities (83.59%). Due to different average beginning or duration on the city scale, the time windows showed obvious spatial differences in the region I (Fig. 4 ), among which the region II possessed relatively later beginnings (June – July) and shorter durations (three - four months) while the region III displayed earlier beginnings (April – May) and longer durations (seven - eight months, Fig. 4 u). As a result, some cities in the region II occasionally closed their time windows or opened for a short period as those in the region III held it for a longer time. Relationships analysis within the time windows As far as the relationships between the local DF epidemics and 12 potential influencing factors within the time windows were concerned, the local DF epidemics termed by city-level case numbers were significantly positively associated with most natural and socioeconomic factors, except for Cropland ( r =-0.14, P < 0.01) (Table 3 ) in the region I , which was similar in the region III . In contrast, the local DF epidemics showed significant correlations only with several factors (Imported Cases, GDP, Popu and Impervious) in region II . Among these inputs, the counts of imported cases consistently possessed the strongest association. Table 3 Correlation coefficients between local DF epidemic and input variables. Variable type Variable name r Region I Region II Region III Imported Cases 0.43** 0.12** 0.46** Socioeconomic variables GDP 0.17** 0.08** 0.25** Popu 0.14** 0.08** 0.18** Cropland -0.14** 0.01 − .09** Forest 0.10** -0.01 0.01 Water 0.13** 0.02 0.11** Impervious 0.06** 0.06** 0.18** Environmental variables Tmax 0.09** 0.02 0.11** Tmean 0.13** 0.02 0.12** Tmin 0.16** 0.02 0.12** RH 0.15** -0.03 0.08** Prec 0.21** -0.03 0.19** Notes: r is the Pearson correlation coefficient. ** indicates this value is significant at the level of 0.01. ImportedCases: The imported DF cases; GDP: Gross domestic product; Popu: Population density; Cropland: The percentages of cropland; Forest: The percentages of forest; Water: The percentages of water; Impervious: The percentages of impervious; Tmax: The average values of maximum temperature; Tmean: Monthly average values of mean temperature; Tmin: Monthly average values of minimum temperature; RH: Monthly average values of relative humidity; Prec: Monthly average values of precipitation. The accuracy of RF, GBM, SVM models was showed in the additional file 1, which showed that the RF model was superior compared to other two models. In terms of AUC values (Table 4 ), the RF models possessed good performances of fitting the relationships between local epidemics and influencing factors in the region I (0.92) and III (0.85). In particular, the RF models considering the time windows of local DF epidemics in the region I tended to possess slightly higher AUC value (0.92) than those without regarding to the time windows (0.90). Accordingly, the RF model considering the time windows could be further used for exploring the factors’ contributions, as well as mapping the city-level risk of local DF prevalence. Table 4 The AUC values derived from the RF modelling Region I Region III Training Testing Prediction Training Testing Model 1 0.87 0.92 0.91 0.88 0.85 Model 2 0.87 0.92 0.88 Model 3 0.89 0.90 Note : Model 1 and Model 2 respectively represented the models with regard for the time windows of local DF prevalence and mosquito vectors. Model 3 represented the model without regard for the time windows. Region I and III were assigned for the regions on the right side of Hu Line and on the south side of Q-H Line. Dominant influencing factors Regarding the contributions to local DF epidemics, the imported cases ranked the first (24.82%) and greatly exceeded the second (Tmin, 16.88%) and the third (Forest, 8.58%) in the region I , while the rest factors possessed smaller contributions (Fig. 5 a). In the region III , it also ranked the first with the highest contribution (31.01%, Fig. 5 b). Besides, top five inputs with relative higher contributions included Tmin, Forest, Popu, Prec and Tmean in the region I , which differed from those in the region III (GDP, Popu, RH, Cropland, and Forest). For this point, the influencing factors’ groups were differentiated among these two regions. In the region I , the natural factors made larger contributions (41.04%) than those of socioeconomic conditions (34.15%) to the local DF epidemics, while the socioeconomic group possessed much higher contributions (44.26%) than those of natural group (24.74%) in the region III . These results implied that the dominant influencing factors of local DF epidemics were geographically differentiated although the local DF prevalence was absolutely induced by the imported cases. According to the SHAP values, these 12 inputs possessed protective or risk effects on the city-level local DF epidemics in the region I . Along with the count of imported cases ascending, its promoting effect on the city-level local epidemics were increasingly powerful before 50 imported cases and then stopped to persist at a high promotion (Fig. 6 a). In comparison with its single promoting effect, the rest factors generally possessed composite promoting and inhibiting impacts, resulting in two groups including inhibiting before promoting (Group 1) and promoting before inhibiting (Group 2). Among top five factors (Fig. 5 a), Tmin, Forest, Popu, and Prec belonged to Group 1 with respective inflexion values of 20℃ (Fig. 6 b), 60% (Fig. 6 c), 1,000 persons per square kilometer (Fig. 6 d), and 185 millimeters (Fig. 6 e) while other one variable (Tmean) in Group 2 turned their promotive effects to inhibitive roles at the value of 21 ℃ (Fig. 6 f). The rest six factors could be attributed into Group 1 (GDP, Impervious and Water) and Group 2 (RH, Cropland and Tmax), respectively. In comparison, included 12 input variables also possessed comprehensively promoting or inhibiting effects on the local epidemics in the region III (Additional file 1), although their inflexion values were slightly different from those in the region I . Above analyses validated that the imported DF cases acted as the trigger of local epidemics, and showed that the local epidemics were comprehensively promoted or inhibited by natural and socioeconomic factors on the city scale across China. Mapping the city-level risk of local DF prevalence Among total 295 cities with time windows opened for the local epidemic in the region I , there were 20, 25, 23, 34, and 193 cities categorized into five levels for their various probabilities of local epidemics in 2019. It was satisfied that 20 cities with the highest risk were exactly identified because the local DF prevalence occurred in these cities indeed, showing a 100% hitting rate. By contrast, among the cities beyond the risk of higher (45 cities), middle (68 cities), lower (102 cities) levels, and the lowest (295 cities), the hitting rates decreased gradually to 95.56%, 79.41%, 67.65%, and 29.15% even though the accumulative counts of identified cities increased to 43, 54, 69, and 86. Although the RF models’ mapping abilities displayed gradual declines in 2020–2022, the hitting rates in 2020 were still acceptable because one city (i.e., Guangzhou) was not only identified for its highest risk but also infected by this disease indeed. Table 5 Assessments on the mapping of the city-level risk of local DF prevalence in the region I . Years Statistics of the cities Probability of local DF epidemics 0.8 ~ 1 0.6 ~ 1 0.4 ~ 1 0.2 ~ 1 0.0 ~ 1 2019 Number of cities identified (NCI) 20 45 68 102 295 Number of cities with actual epidemics (NCA) 20 43 54 69 86 Percentages of NCA in NCI 100.00% 95.56% 79.41% 67.65% 29.15% 2020 Number of cities identified (NCI) 1 2 5 23 295 Number of cities with actual epidemics (NCA) 1 1 2 2 4 Percentages of NCA in NCI 100.00% 50.00% 20.00% 8.70% 1.36% 2021 Number of cities identified (NCI) 0 1 5 17 295 Number of cities with actual epidemics (NCA) 0 0 0 0 1 Percentages of NCA in NCI / 0.00% 0.00% 0.00% 0.34% 2022 Number of cities identified (NCI) 0 1 4 14 295 Number of cities with actual epidemics (NCA) 0 0 1 1 2 Percentages of NCA in NCI / 0.00% 25.00% 7.14% 0.68% Notes : The probabilities, like 0.80–1, 0.60–0.80, 0.40–0.60, 0.20–0.40, and 0–0.20, were respectively categorized as the highest, higher, middle, lower, and the lowest risk levels. According to local DF risk in the region I in 2019 (Fig. 7 ), all of 20 cities with the highest risk identified by the RF models were mainly located in the southwestern border (two cities) and southeastern coastal areas (16 cities). Meanwhile, 25 cities in the southeastern areas and central regions, as well as 23 cities scattered in the region I were also recognized as the areas with higher and middle risks. As a result, above 68 cities were mostly distributed in the region III . Besides, the cities recognized as the areas at various risk levels in 2020–2022 were also mainly located in this region. These results indicated that it was feasible to employ the RF models in mapping the city-level risk of local DF prevalence in China. Discussion Since DF epidemics in China were ever-increasingly serious in recent years, it is urgent to reveal the comprehensive features of DF outbreaks for appropriately mapping its risk. Our study analyzed its frequencies and intensities, and then identified their potential influencing factors for mapping the probability of city-level local DF outbreaks through the RF models. Several notable findings were achieved and would provide some useful clues for making targeted interventions on this disease. Previous studies have pointed out that China experienced an ever-increasingly serious threat enforced by DF epidemics in terms of the incidence rates or other indices on various spatial scales [ 7 , 9 , 16 , 38 ], and that local epidemics were closely correlated with imported epidemics in the past years [ 39 , 40 ]. Similarly, our study found that the DF epidemics in China presented continuous uptrends of DF case amounts, as well as obvious spatial expansions towards many more inland cities with increasing frequencies and intensities of city-level DF epidemics. Moreover, the capability of imported epidemics initiating local DF prevalence was rising due to their increasingly stronger association. As a result, many inland cities like some regional hub cities (e.g., the provincial capitals) tended to possess relatively lower frequencies but higher intensity of local epidemics, as well as the inverse appearance of high frequencies and low intensity of imported epidemics. One reasonable explanation is that there have been increasingly larger counts of imported DF cases from some global endemic countries or territories (e.g., the Southeast of Asia, the Central America, and so on) because of closer and stronger connection between China and these countries/territories in recent years [ 39 , 41 ]. Apart from traditional regions (i.e., the Southeast and Southwest of China) often infected by imported epidemics [ 5 , 6 ], the inland regional hub cities featured by bigger airports and larger export-oriented economy were constantly confronted by ascending numbers of inbound or outbound tourists [ 42 , 43 ]. Another potential reason is the spillover effects of DF cases from either the traditional regions or hub cities to their surrounding inland cities with sustainable environment conditions [ 9 , 44 ]. Hence, it can be concluded that China’s DF epidemics were featured by its ascending frequencies and intensities within much more inland cities. Accordingly, we cautiously suggest that additional attention should be emphatically paid to inland regional hub cities and their surrounding cities, especially in case of ever-growing imported epidemics and their increasing initiating ability. China’s DF epidemics were geographically differentiated around two famous dividing lines (Hu Line and Q-H Line) [ 45 , 46 ]. The local DF prevalence was still geographically restricted within the region I , especially in the region III by far, for which the distribution of city-level time windows for mosquito vectors’ activities (Additional file 1) may be a rational explanation since this disease is transmitted by Aedes species ( Aedes albopictus and Aedes aegypti ) in some specific phases with suitable environmental conditions (i.e., climatic elements) [ 11 , 12 ]. Moreover, the city-level match degrees among the actual occurrences and the time windows for either the mosquito vectors’ activities or local DF transmission were satisfying in the region I . Under this circumstance, it was reasonable that local DF prevalence was occasionally reported in some cities in the region II with late opened and shortly held time windows. That is to say that the time windows were very crucial and non-negligible for local DF prevalence across China. Accordingly, we believe that the time windows would provide helpful information for relative departments implementing timely interventions on this disease. Apart from the spatial differences as mentioned above, the geographical disparities of local DF prevalence were also featured by its dominant influencing factors within the region I and III differing from each other, which may be partially attributed to the different coefficients of variances (CV) of these influencing factors (Additional file 1). This finding was similar to our earlier investigation on the comparison of dominant influencing factors on the DF epidemics in two traditional hotspot regions (the Pearl River Delta and the Border of Yunan and Myanmar) [ 17 ]. Thus, it can be concluded that the local DF prevalence was not only geographically restricted by the time windows but also spatially characterized by regionally differentiated influencing factors across China. Therefore, we suggest that the health authorities of each city should take both the status of time windows and regional attributes into account for either making targeted measures before local epidemic occurring or implementing efficient interventions once imported cases initiating local prevalence. Furthermore, the knowledge on China’s DF epidemics was comprehensively improved. First, a large and increasing number of ceaseless imported DF cases would be undoubtedly foreseen in the future since China is playing more and more important roles in the international economy and trade affairs [ 47 , 48 ], which means that China would be confronted by the inevitable DF prevalence (i.e., either imported or local epidemics). Meanwhile, the domestic loops of socioeconomic development are being accelerated in China so that the spillover of DF cases would be constantly observed among domestic regions [ 49 ], especially within the cities with opening time windows for DF transmission. However, the inland cities hit by local DF prevalence remained uncertain along with imported epidemics expanding from traditional southeastern coastal regions or southwest border areas northwards many inland regional hub cities (i.e., the provincial capitals). In other words, there is somewhat occasionality of the cities hit by local epidemics initiated by imported epidemics in the inland regions with opening time windows. Here, we cautiously advise three proper solutions to the challenges imposed by the inevitability and occasionality of DF prevalence in China. First of all, we need to keep our sensitive eyes on the overseas DF epidemics, by which the inbound tourists from these endemic areas could be timely acquired for judging the situation of imported epidemics in China. Secondly, sufficient surveillance of climatic elements on the city scale should be efficiently utilized for determining the status of time windows across China, especially in the inland regional hub cities and their surrounding areas. The final and the key point is properly and scientifically building a robust and reliable prediction model, like the RF models constructed in this study, by which the health authorities could make targeted measures for preventing and controlling this disease. Of course, there were two prerequisites for our investigation that the worldwide natural focuses of this disease cannot be eliminated in a short term, and that China remains as an unnatural focus of this disease. Several limitations are worth noting. First, the acquisition of meteorological conditions data with a higher temporal resolution (e.g., weekly, ten-days) would be helpful for more finely characterizing the time windows on the city scale, by which the current match degrees between the city-level time windows and actual stages of local DF prevalence may be well increased and then effective interventions could be more precisely and timely implemented. Second, the effectiveness of city-level time windows could be further validated through obtaining synchronous surveillance data of mosquito vectors, by which the capabilities of RF models fitting comprehensive relationships between local epidemics and potential factors within these time windows might be improved for subsequent mapping of the city-level risk for local DF prevalence across China. Finally, as the spillover of DF cases among domestic regions played important role in the DF prevalence across China, an efficient solution should be scientifically proposed to characterize the network of relationships among domestic cities or regions in terms of population flows, economic exchanges, space-time distance, and so on. Conclusion In summary, China is to be confronted by unavoidable DF prevalence in the appearance of ever-increasing frequencies of imported epidemics and stronger intensities of occasional local transmission in the inland cities. It is feasible of the RF model to map the occasionality of local DF prevalence for the cities geographically restricted by the status and duration of their time windows. This study has improved our understanding of the severity of DF prevalence and its influencing factors across China, which would supply useful clues for the health authorities improving their intervention capacity on such disease. Abbreviations DF Dengue fever RF Random Forest GBM Gradient Boosting Machine SVM Support Vector Machine ROC Receiver Operating Characteristic AUC Area Under the Curve SHAP SHapley Additive exPlanations Hu Line Huhuanyong line Q-H Line Qin Mountain – Huai River line Region I the cities on the right side of Hu Line Region II the cities on the north side of Q-H Line Region III the cities on the south side of Q-H Line NCI Number of cities identified NCA Number of cities with actual epidemics CV The coefficients of variances Declarations Availability of data and materials The datasets used and analyzed during the current study are available from the corresponding author on reasonable request. Acknowledgements Not applicable. Funding HY R received the financial support from the National Natural Science Foundation of China (Grant NO.42071136) and the National Key Research and Development Program of China (2023YFC2307502). Author information Authors and Affiliations State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China Hongyan Ren & Nankang Xu College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China Nankang Xu National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China Liang Lu Contributions HR and NX conceived and designed the experimental concept. LL helped collect data. HR and NX analyzed the data and drafted the paper. HR, NX and LL revised the manuscript. All authors read and approved the final manuscript. Corresponding authors Correspondence to Hongyan Ren ( [email protected] ). 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Supplementary Files floatimage1.jpeg Graphical abstract additionalfile1.docx Additional file 1: Fig.S1 The distributions of China at provincial-level administrative divisions. Table S1 The list of the potential driving factors. Fig.S2 Spatial distribution of the ratio of local cases to imported cases in the past two decades. Fig.S3 Distribution of the cities with different occurrences of DF epidemics. Fig.S4 The frequencies and intensities of local DF outbreaks in 2003 – 2022. Fig.S5 Time windows for the city-level mosquito vectors’ activities across China. Table S2 The AUC values derived from RF, GBM, SVM models. Fig.S6 The contributions of input variables in the model without regard for the time windows in the region I . S1.1 Analyses of relationships between inputs and local DF epidemics in the region I. Fig.S7 Relationships between 12 inputs and local DF epidemics by means of SHAP values on the city scale in the region III . S1.2Analyses of relationships between inputs and local DF epidemics in the region III . Table S1 The coefficients of variances (CV) of 12 factors. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major revision 05 Feb, 2024 Reviewers agreed at journal 05 Jan, 2024 Reviewers invited by journal 04 Jan, 2024 Editor assigned by journal 03 Jan, 2024 First submitted to journal 26 Dec, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3810038","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":265312705,"identity":"4bfeb3e7-04d0-4ffa-8a49-975d8425753b","order_by":0,"name":"Hongyan Ren","email":"","orcid":"https://orcid.org/0000-0002-6948-5769","institution":"Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Hongyan","middleName":"","lastName":"Ren","suffix":""},{"id":265312706,"identity":"e8d6f455-d378-41c3-8486-223fa93984b3","order_by":1,"name":"Nankang Xu","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0007-7844-8445","institution":"Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences","correspondingAuthor":true,"prefix":"","firstName":"Nankang","middleName":"","lastName":"Xu","suffix":""},{"id":265312707,"identity":"885bdd7f-85b3-487c-a12f-746e3f3ba364","order_by":2,"name":"Liang Lu","email":"","orcid":"","institution":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Disease","correspondingAuthor":false,"prefix":"","firstName":"Liang","middleName":"","lastName":"Lu","suffix":""}],"badges":[],"createdAt":"2023-12-27 01:38:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3810038/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3810038/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49288946,"identity":"0656d8af-6511-4edf-b13d-1a57ffffc401","added_by":"auto","created_at":"2024-01-08 03:47:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":367826,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTemporal variations of DF epidemics in China over the past two decades. \u003c/strong\u003e(a) Yearly counts of total, imported, and local DF cases; (b) Yearly proportions of imported and local DF cases and the ratio of local cases to imported cases; (c) Cities with either imported or local epidemics; (d) Cities with both imported and local epidemics; (e) Cities with imported epidemics only; (f) Cities with local epidemics only. Region \u003cem\u003eI\u003c/em\u003e: The cities on the right side of Hu Line; Region \u003cem\u003eIII\u003c/em\u003e: The cities on the south side of Q-H Line.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3810038/v1/e0db5ff0b63593b8a5692f4f.png"},{"id":49289132,"identity":"4ae84b2c-a44d-4f00-9eb9-71184f629e22","added_by":"auto","created_at":"2024-01-08 03:55:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":168437,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTemporal variations in the frequencies and intensities of DF epidemics in 2003 – 2022. \u003c/strong\u003e(a) The frequencies of imported epidemics and local epidemics;(b) The intensities of imported epidemics and local epidemics.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3810038/v1/6509918b5bf87738b486ca09.png"},{"id":49288953,"identity":"73cf4ad7-dc98-4d21-8248-9cf331d4c9d5","added_by":"auto","created_at":"2024-01-08 03:47:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":565648,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of the frequencies and intensities of DF epidemics on the city scale. \u003c/strong\u003e(a) The frequencies of imported epidemics; (b) The frequencies of local epidemics; (c) The intensities of imported epidemics; (d) The intensities of local epidemics.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-3810038/v1/d1a929a42faa90dccbe99817.png"},{"id":49288948,"identity":"ee44bc84-2f69-48b1-b457-6faf10841696","added_by":"auto","created_at":"2024-01-08 03:47:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":776246,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTime windows for the city-level local DF prevalence across China. \u003c/strong\u003e(a-t) Yearly time windows from 2003 to 2022; (u) Average values of beginnings and duration of time windows. Region \u003cem\u003eII\u003c/em\u003e: The cities on the north side of Q-H Line; Region \u003cem\u003eIII\u003c/em\u003e: The cities on the south side of Q-H Line.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-3810038/v1/e54ff36de00a21ac3faf4e41.png"},{"id":49288947,"identity":"e59862a9-a37e-4217-a54c-d61e64274ed9","added_by":"auto","created_at":"2024-01-08 03:47:24","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":68785,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe contributions of input variables in the model with regarding the time windows.\u003c/strong\u003e (a) The contributions of input variables in the region \u003cem\u003eI\u003c/em\u003e; (b) The contributions of input variables in the region \u003cem\u003eIII\u003c/em\u003e.\u003cstrong\u003e \u003c/strong\u003eImportedCases: The imported DF cases; Tmin: Monthly average values of minimum temperature; Forest: The percentages of forest; Popu: Population density; Prec: Monthly average values of precipitation; Tmean: Monthly average values of mean temperature; GDP: Gross domestic product; RH: Monthly average values of relative humidity; Cropland: The percentages of cropland; Tmax: The average values of maximum temperature; Impervious: The percentages of impervious; Water: The percentages of water. Region \u003cem\u003eI\u003c/em\u003e and \u003cem\u003eIII\u003c/em\u003ewere assigned for the regions on the right side of Hu Line and on the south side of Q-H Line.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-3810038/v1/43fc99f56a75b12083e82e14.png"},{"id":49288949,"identity":"08190969-08b3-4119-aa66-438f6c8b42d9","added_by":"auto","created_at":"2024-01-08 03:47:24","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":262719,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelationships between 12 inputs and local DF epidemics by means of SHAP values on the city scale in the region \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eI\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e. a \u003c/strong\u003eThe imported DF cases; \u003cstrong\u003eb \u003c/strong\u003eMonthly average values of minimum temperature (Tmin); \u003cstrong\u003ec\u003c/strong\u003eThe percentages of forest (Forest); \u003cstrong\u003ed\u003c/strong\u003ePopulation density (Popu); \u003cstrong\u003ee \u003c/strong\u003eMonthly average values of precipitation (Prec); \u003cstrong\u003ef\u003c/strong\u003eMonthly average values of mean temperature (Tmean); \u003cstrong\u003eg \u003c/strong\u003eGross domestic product (GDP); \u003cstrong\u003eh\u003c/strong\u003e Monthly average values of relative humidity (RH); \u003cstrong\u003ei\u003c/strong\u003e The percentages of cropland (Cropland); \u003cstrong\u003ej \u003c/strong\u003eThe average values of maximum temperature (Tmax); \u003cstrong\u003ek \u003c/strong\u003eThe percentages of impervious (Impervious); \u003cstrong\u003el \u003c/strong\u003eThe percentages of water (Water). The region \u003cem\u003eI\u003c/em\u003e represented the cities on the right side of Hu Line.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-3810038/v1/98574b5c055dc062c903acfa.png"},{"id":49289484,"identity":"719bace2-afa0-43b0-a1ac-f4486be3ce33","added_by":"auto","created_at":"2024-01-08 04:03:24","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":444770,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe city-level risk of local DF epidemics mapped for 2019 (a), 2020 (b), 2021 (c), and 2022 (d). \u003c/strong\u003eThe region \u003cem\u003eI\u003c/em\u003e and \u003cem\u003eIII\u003c/em\u003e respectively represented the cities on the right side of Hu Line and the south side of Q-H Line.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-3810038/v1/bffcd7e950d24e02b65fe05d.png"},{"id":49289651,"identity":"0204f0a7-d8c7-4b97-af66-8755d30bb346","added_by":"auto","created_at":"2024-01-08 04:11:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3615946,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3810038/v1/1803bbd8-5fc0-4e89-84ff-df6c34a58930.pdf"},{"id":49289485,"identity":"0539e750-347b-4789-906a-5213dfc07dab","added_by":"auto","created_at":"2024-01-08 04:03:24","extension":"jpeg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":239439,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical abstract\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3810038/v1/1bb345ba256ce77a35634297.jpeg"},{"id":49288954,"identity":"c9e376da-8c6a-48f1-88b4-07878583aa34","added_by":"auto","created_at":"2024-01-08 03:47:25","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":6032109,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file\u003c/p\u003e\n\u003cp\u003e1:\u003cstrong\u003e Fig.S1 \u003c/strong\u003eThe distributions of China at provincial-level administrative divisions. \u003cstrong\u003eTable S1\u003c/strong\u003e The list of the potential driving factors. \u003cstrong\u003eFig.S2\u003c/strong\u003e Spatial distribution of the ratio of local cases to imported cases in the past two decades. \u003cstrong\u003eFig.S3\u003c/strong\u003e Distribution of the cities with different occurrences of DF epidemics. \u003cstrong\u003eFig.S4\u003c/strong\u003e The frequencies and intensities of local DF outbreaks in 2003 – 2022. \u003cstrong\u003eFig.S5\u003c/strong\u003e Time windows for the city-level mosquito vectors’ activities across China. \u003cstrong\u003eTable S2\u003c/strong\u003e The AUC values derived from RF, GBM, SVM models. \u003cstrong\u003eFig.S6\u003c/strong\u003e The contributions of input variables in the model without regard for the time windows in the region \u003cem\u003eI\u003c/em\u003e. \u003cstrong\u003eS1.1\u003c/strong\u003e Analyses of relationships between inputs and local DF epidemics in the region \u003cem\u003eI. \u003c/em\u003e\u003cstrong\u003eFig.S7\u003c/strong\u003e Relationships between 12 inputs and local DF epidemics by means of SHAP values on the city scale in the region \u003cem\u003eIII\u003c/em\u003e. \u003cstrong\u003eS1.2\u003c/strong\u003eAnalyses of relationships between inputs and local DF epidemics in the region \u003cem\u003eIII\u003c/em\u003e. \u003cstrong\u003eTable S1\u003c/strong\u003e The coefficients of variances (CV) of 12 factors.\u003c/p\u003e","description":"","filename":"additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-3810038/v1/39f186f08e4932d170a8bf20.docx"}],"financialInterests":"","formattedTitle":"Mapping the occasionality of inevitable dengue fever prevalence in China","fulltext":[{"header":"Highlights","content":"\u003cul\u003e\n\u003cli\u003eChina\u0026rsquo; DF epidemic was featured by ever-increasing frequency and intensity;\u003c/li\u003e\n\u003cli\u003eSpatial distribution of China\u0026rsquo;s DF epidemic was divided by Hu Line and Q-H Line;\u003c/li\u003e\n\u003cli\u003eCapability of imported DF case triggering local epidemics is rising;\u003c/li\u003e\n\u003cli\u003eLocal DF prevalence is geographically restricted by the city-level time windows;\u003c/li\u003e\n\u003cli\u003eUnavoidable DF outbreak would occasionally occur and expand toward inland cities.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Introduction","content":"\u003cp\u003eDengue fever (DF) is an acute infectious disease caused by dengue virus, which is transmitted by \u003cem\u003eAedes albopictus\u003c/em\u003e and \u003cem\u003eAedes aegypti\u003c/em\u003e [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Its natural focuses are widely distributed in global tropical and subtropical areas (e.g., the Southeast Asia, the Western Pacific, and the South Africa) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], causing about one-third of the global population exposed to this disease [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Since the globalization and climate change was much deeper and continuous in recent years, the increasing incidence and range DF epidemics have become an important public health concern that should not be underestimated.\u003c/p\u003e \u003cp\u003eIn China, the DF prevalence is mainly induced by imported cases from the global natural focuses. No DF case was reported during 1949\u0026ndash;1977 until a sudden outbreak occurred in the city of Foshan, Guangdong Province in 1978 [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. From then on, China\u0026rsquo;s DF epidemic tended to be intermittent and has been included in the list of notifiable communicable diseases since 1990s [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Due to more and more serious DF epidemic in the world and China\u0026rsquo;s increasingly important roles in the international economy and trade affairs, local DF prevalence induced by imported cases has ascended substantially in recent years [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Meanwhile, the DF prevalence presented obvious spatial expansion from southern China (southwest border and southeast coastal areas) toward inland northern regions, such as Jiangxi (Yichun), Chongqing, Henan (Xuchang) and Shandong (Jining) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Overall, the DF prevalence in China has shown shorter and shorter time intervals and clear spatial expansion.\u003c/p\u003e \u003cp\u003eNumerous studies have been conducted by domestic and foreign scholars on a series of factors influencing the spread and prevalence of this disease at various levels, including the dengue virus, mosquito vectors, susceptible population, environmental conditions, and socioeconomic status [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Among these influential factors, environmental conditions, like climate, hydrology, vegetation coverage, mainly affect the activity of the dengue virus, as well as the breeding, survival surroundings, and biting power of mosquito vectors [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Socio-economic factors, such as population density and mobility, land use, convenience level of public transportation, residents\u0026rsquo; income level and living habits, chiefly alter the probability of human-mosquito contact [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In general, the DF transmission and prevalence is a complex and typical geo-ecological process.\u003c/p\u003e \u003cp\u003eEarlier studies on the spatial and temporal variations at various scales, and their influencing factors of DF epidemics not only have greatly supported the prevention and control against this disease in the absence of effective clinical vaccines, but also have improved our understanding of the DF prevalence and spread in China [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, comprehensive knowledge is needed regarding the current ever-increasing growth of cities with DF outbreaks, the limitations on geographical patterns and the quantitative contributions of potential influencing factors of DF epidemics in China.\u003c/p\u003e \u003cp\u003eTherefore, our study was conducted to 1) characterize the frequency and intensity, geographical distribution, 2) use a random forest (RF) model to quantitatively explore the relationships between local DF prevalence and a series of potential influencing factors, and then 3) map the city-level risk of DF prevalence across China. The results of this study will provide important support for strengthening the capacities of interventions against this disease and comprehensively raising the current understandings on China\u0026rsquo;s DF epidemics.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003e \u003cb\u003eDengue cases\u003c/b\u003e The dengue cases were sourced from the National Infectious Disease Surveillance System, covering the period from 2003 to 2022 and encompassing confirmed dengue cases in China. The dataset included information on the date of onset, residential address, and reporting address of these cases. Based on the origin source, dengue cases were classified into imported and local cases. Imported cases referred to those who have traveled to a dengue-endemic country or region within 14 days before the onset of the disease. Local cases were those who have not left the city within 14 days before the onset of the disease or those who have left the city within 14 days before the onset of the disease and visited other domestic dengue epidemic cities [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. An epidemic outbreak was defined as the occurrence of three or more local cases within the maximum incubation period of 14 days in a city. If no local cases were reported within 14 days after the outbreak, it was considered that the outbreak had ended. The duration from the beginning of the outbreak to its end was referred to the outbreak period for the local DF epidemic. After filtering out isolated local cases occurring outside the outbreak period, imported and local cases were consolidated at the city level based on their reporting address codes.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePotential influencing factors\u003c/b\u003e According to previous research [\u003cspan additionalcitationids=\"CR11 CR12 CR13 CR14\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], we collected 11 potential influencing factors, which were categorized into two dimensions: socioeconomic factors and natural factors (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Among 6 socioeconomic factors, factors related to the economy and population included 1 km \u0026times; 1 km gridded gross domestic product (GDP) and population density (Popu). Urbanization-related land-use factors included the percentage of four land use types: cropland (Cropland), forest (Forest), water (Water), and impervious surface (Impervious), which were collected from yearly 30m \u0026times; 30m gridded data. In addition to the above socioeconomic factors, 5 natural factors including monthly maximum temperature (Tmax), monthly average temperature (Tmean), monthly minimum temperature (Tmin), monthly average relative humidity (RH), and monthly precipitation (Prec) were also considered as potential influencing factors for the spread of the epidemic, which were collected from 1 km \u0026times; 1 km gridded data at a monthly scale.\u003c/p\u003e \u003cp\u003eTo facilitate statistical and spatial analysis, the variables of each factor at the city level were extracted using zoning statistics and spatial connectivity tools in ArcGIS 10.6 software. Furthermore, for missing data in certain years, adjacent year data was used as a replacement.\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\u003eData collection and sources in this study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eData group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelected variables from previous studies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eData unit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTime scale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eResolution\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eSocioeconomic\u003c/p\u003e \u003cp\u003efactors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGross domestic product per capita (GDP) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMillion dollars/km\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnnual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eThe Scientific Data (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.6084/m9.figshare.17004523.v1\u003c/span\u003e\u003cspan address=\"10.6084/m9.figshare.17004523.v1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e\u003c/p\u003e \u003cp\u003eWorldPop(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://hub.worldpop.org/\u003c/span\u003e\u003cspan address=\"https://hub.worldpop.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e\u003c/p\u003e \u003cp\u003eEarth System Science Data (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5281/zenodo.5816591\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.5816591\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePopulation density (Popu) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePerson/km\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnnual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 km\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnnual average percentage of cropland (Cropland) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnnual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30 m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnnual average percentage of forest (Forest) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnnual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30 m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnnual average percentage of water (Water) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnnual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30 m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnnual average percentage of impervious (Impervious) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnnual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30 m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eNatural\u003c/p\u003e \u003cp\u003efactors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emonthly maximum air temperature (Tmax) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003edegree Celsius\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMonthly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eEarth System Science Data\u003c/p\u003e \u003cp\u003e(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5281/zenodo.5112232\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.5112232\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e\u003c/p\u003e \u003cp\u003eNational Earth System Science Data Center\u003c/p\u003e \u003cp\u003e(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.geodata.cn\u003c/span\u003e\u003cspan address=\"http://www.geodata.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e\u003c/p\u003e \u003cp\u003eNational Tibetan Plateau/Third Pole Environment Data Center (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.tpdc.ac.cn/\u003c/span\u003e\u003cspan address=\"https://data.tpdc.ac.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emonthly mean air temperature (Tmean) [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003edegree Celsius\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMonthly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 km\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emonthly minimum air temperature (Tmin) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003edegree Celsius\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMonthly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 km\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emonthly mean relative humidity (RH) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMonthly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 km\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emonthly precipitation (Prec) [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMonthly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 km\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive analysis on frequency and intensity of DF outbreaks in China\u003c/h2\u003e \u003cp\u003eThe yearly counts of local and imported cases were separately aggregated from 2003 to 2022. According to the occurrences of either imported or local epidemics, these cities were classified into four groups including the cities with either imported or local epidemics, the cities with both of them, the cities with imported epidemics only, and the cities with local epidemics only. Furthermore, we analyzed the spatial distribution characteristics of these four groups of cities and determined the correlations between imported and local epidemics by using the Spearman. To further characterize DF epidemics in China in the past two decades, we established statistical indicators of DF epidemics from the perspective of prevalence frequency and intensity (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Subsequently, we analyzed the variations in the frequency and intensity of epidemic prevalence and characterized the spatial distribution of overall frequency and intensity.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStatistical indicators of DF epidemics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDefinition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFormula\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of months with epidemics within the specified time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of months with epidemics / the specified time\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTimes of outbreaks within the specified time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTimes of outbreaks / the specified time\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntensity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMonthly average number of cases in the months with epidemics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal cases / number of the months with epidemics\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage number of cases per outbreak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal cases / times of outbreaks\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eThe Retrieval of the time windows for local DF epidemics\u003c/h2\u003e \u003cp\u003eThe suitability of natural conditions such as temperature, humidity and precipitation influenced mosquito breeding and activities, which directly affected the spread of local epidemics. Previous studies have found that it was suitable for the breeding of Aedes mosquito when the monthly minimum temperature was higher than 10 ℃ [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], the monthly average temperature was between 15\u0026ndash;32 ℃ [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], the monthly maximum temperature was lower than 38 ℃ [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], the monthly average relative humidity was between 60% and 90% [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] and the monthly precipitation was between 60 mm and 650 mm [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Moreover, we defined the periods meeting all these conditions as the time windows for the mosquito vectors\u0026rsquo; activities. Considering the incubation periods of dengue viruses [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], we extended the time windows of mosquito vectors by one month and defined it as the time windows of local DF prevalence. To further evaluate the effectiveness of the time windows, we defined the match degree as the proportion of the number of outbreaks where the outbreak period overlapped with the time windows to the total number of outbreaks. The higher the match degree, the higher the proportion of outbreaks occurring within the time windows.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of the dengue driving forces at the city level\u003c/h2\u003e \u003cp\u003eWe aggregated the number of imported cases within the time windows from 2003 to 2019 as an independent variable at the city level. Meanwhile, we counted the number of local cases in the year and transformed it into a binary variable as the dependent variable according to the occurrence of local epidemics. Additionally, six socioeconomic variables and five environmental variables (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) of the average value in the months of the time windows were considered. And preliminary correlation analyses between the number of local cases and input variables were conducted using the Spearman. The Random Forest (RF), the Gradient Boosting Machine (GBM) and the Support Vector Machines (SVM) were powerful machine learning methods for classification and regression, which were typically employed for the analysis of the dengue driving forces [\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. We applied these three methods to fit the relation between local epidemic outbreaks and the influencing factors. The data from 2003 to 2018 were divided into the training set (70%) and the test set (30%). In the RF model, the number of trees to grow was the main parameter, and a range of tree numbers (from 100 to 2000 with an interval of 100) were set to select the optimal parameter based on the performances of the model. In the GBM model, the number of trees to grow and the learning rate were the main parameters, and the ranges of tree numbers and learning rates are 100\u0026ndash;2000 (step\u0026thinsp;=\u0026thinsp;100) and 0.01\u0026ndash;0.2 (step\u0026thinsp;=\u0026thinsp;0.01). In the SVM model, we applied \u0026lsquo;rbf\u0026rsquo; as the kernel function, and the ranges of regularization parameter and kernel coefficient were 0.1-3 (step\u0026thinsp;=\u0026thinsp;0.1) and 0.01\u0026ndash;0.5 (step\u0026thinsp;=\u0026thinsp;0.01). In these models, the five-fold cross-validation was performed to boost the stability of modeling. The Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve was used as a measured value of the model predictive ability and a larger AUC indicated better predictive capability of the model [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The model accuracy characterized by the AUC, was as follows: 0.50\u0026ndash;0.70, indicating a poor model; 0.70\u0026ndash;0.80, suggesting an average model; and 0.80-1.00, reflecting a good model [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. According to the AUC of above three models, we looked for the optimal model.\u003c/p\u003e \u003cp\u003eThe SHapley Additive exPlanations (SHAP) method, widely applied for interpreting machine learning models [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], was introduced to quantify the contributions and driving forces of each independent variable to local epidemic outbreak. Globel SHAP values were calculated for each variable in the RF model, with larger absolute values indicating greater contributions to the model. Local SHAP values for each instance reflected the driving force for the epidemic outbreak. Positive SHAP values indicated a positive driving force, with larger values indicating a stronger positive influence. Conversely, negative SHAP values indicated a negative driving force, with smaller values signifying a stronger negative influence [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo analyze the impact of the time windows on the model, the model without considering the time windows was built. And the model considered the imported cases and the mean values of natural environmental variables in the entire year, while keeping other variables constant. The specific modeling process and evaluation methods were consistent with those described above. By comparing the testing accuracies of different models, we selected the optimal model for mapping and assessing the risk of local DF prevalence in China from 2019 to 2022. In this study, we utilized the \u003cem\u003esklearn\u003c/em\u003e package and \u003cem\u003eshap\u003c/em\u003e package in Python for building and interpreting the RF model.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCurrent situation of the DF prevalence in China\u003c/h2\u003e \u003cp\u003eThere was total 98,560 DF cases displaying an overall upward trend over the past two decades although the amounts of both imported and local DF cases sharply decreased in 2020\u0026ndash;2022, causing total 329 DF outbreaks within 104 months (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). The proportions of local DF cases in the total were much higher than those of imported cases in 15 years, especially holding a steady situation since 2012 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). At the same time, the counts of local DF cases were significantly correlated with those of imported cases on the national scale in 2003\u0026ndash;2012 (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.23, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), 2013\u0026ndash;2022 (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.44, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and 2003\u0026ndash;2022 (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.41, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). These results indicated that China\u0026rsquo;s DF epidemic was dominated by the local prevalence and was increasingly closely associated with imported DF epidemics.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMeanwhile, there were more and more cities being affected by this disease. Despite of an acute drop in 2020\u0026ndash;2022, the amounts of cities with either imported or local epidemics (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec), both of them (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed), and only imported epidemics (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee) displayed similar temporal variations. By contrast, the counts of cities with local epidemics only persisted below 3 except for 2014 with 8 cities (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ef), which seemed to be not serious. Even though, the amounts of cities with local epidemics were significantly associated with those of cities with imported epidemics on the nation scale (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.79, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), which further indicated that the local DF prevalence was obviously associated with imported epidemics on the city scale across China.\u003c/p\u003e \u003cp\u003eThe frequencies of imported DF epidemics were less than 9 months per year before 2006, and then quickly ascended to 11 months per year or higher since 2007 despite of a drop to 9 in 2021\u0026ndash;2022 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). In comparison, the frequencies of local epidemics possessed a relatively slower and fluctuating uptrend varying from 0 to 8. But, in terms of the intensity, local DF epidemics were more serious than imported epidemics although the latter displayed a steadier and quickly uptrend (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Besides, another frequency (i.e., the times of local DF outbreaks per year) also presented clear uptrend and their intensities persisted at relatively higher levels than 100 cases per outbreak since 2012 (Additional file 1). These results indicated that the local DF epidemic was increasingly serious in China over the past two decades.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eGeographical distribution of the DF epidemic\u003c/h2\u003e \u003cp\u003eThe city-level DF epidemics were spatially featured across China. As illustrated in additional file 1, the vast majorities (96% \u0026minus;\u0026thinsp;100%) of the cities with either imported or local epidemics were distributed on the right side of Huhuanyong line (Hu Line). Among them, more than 75% was located on the south side of Qin Mountain \u0026ndash; Huai River line (Q-H Line) and displayed a downtrend from 2012 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec), which implied that the DF prevalence expanded towards the north side of Q-H Line. Meanwhile, the overwhelming proportion of the cities with both imported and local epidemics was also distributed on the south side of Q-H Line. In comparison, the cities with only imported epidemics were mainly located on the right side of Hu Line, while fewer cities were infected by only local epidemics. As Hu Line and Q-H Line acted as important geographical dividing lines for China\u0026rsquo;s DF epidemics, three regions were divided for China\u0026rsquo;s DF epidemics including region \u003cem\u003eI\u003c/em\u003e (the cities on the right side of Hu Line), region \u003cem\u003eII\u003c/em\u003e (the cities on the north side of Q-H Line), and region \u003cem\u003eIII\u003c/em\u003e (the cities on the south side of Q-H Line). These analyses implied that the local DF epidemics tended to be geographically differentiated on the city scale across China.\u003c/p\u003e \u003cp\u003eOn the city scale, China\u0026rsquo;s DF epidemics termed by the frequency and intensity also presented geographical disparities. The cities with higher average frequencies of imported epidemics than 1 month per year were mainly located in the region \u003cem\u003eIII\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Among them, a few inland provincial capitals and several prefecture-level cities in the southwestern border and southeastern coastal regions owned much higher average frequencies than 1.5 months per year. By contrast, fewer cities possessed higher average frequencies of local epidemics than 0.5 months per year and mainly concentrated in the region \u003cem\u003eIII\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). Similarly, the cities with relatively higher intensities of imported epidemics were mainly located in the region \u003cem\u003eIII\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec), while those of local epidemics were sparsely distributed here (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). It should be mentioned that the provincial capitals often possessed much higher frequencies and intensities of imported epidemics, and that some inland cities, like Ji\u0026rsquo; an, Yichun, Chongqing, and Hangzhou, owned the highest intensities of local epidemics despite of relatively lower frequencies. These results showed that the region \u003cem\u003eIII\u003c/em\u003e tended to be easily confronted by the imported epidemics and to be much more heavily affected by local epidemics.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eTime windows for local DF prevalence\u003c/h2\u003e \u003cp\u003eIn terms of match degrees, the time windows for local DF prevalence performed much better (95.74%) than those of the mosquito vectors\u0026rsquo; activities (83.59%). Due to different average beginning or duration on the city scale, the time windows showed obvious spatial differences in the region \u003cem\u003eI\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), among which the region \u003cem\u003eII\u003c/em\u003e possessed relatively later beginnings (June \u0026ndash; July) and shorter durations (three - four months) while the region \u003cem\u003eIII\u003c/em\u003e displayed earlier beginnings (April \u0026ndash; May) and longer durations (seven - eight months, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eu). As a result, some cities in the region \u003cem\u003eII\u003c/em\u003e occasionally closed their time windows or opened for a short period as those in the region \u003cem\u003eIII\u003c/em\u003e held it for a longer time.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eRelationships analysis within the time windows\u003c/h2\u003e \u003cp\u003eAs far as the relationships between the local DF epidemics and 12 potential influencing factors within the time windows were concerned, the local DF epidemics termed by city-level case numbers were significantly positively associated with most natural and socioeconomic factors, except for Cropland (\u003cem\u003er\u003c/em\u003e=-0.14, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) in the region \u003cem\u003eI\u003c/em\u003e, which was similar in the region \u003cem\u003eIII\u003c/em\u003e. In contrast, the local DF epidemics showed significant correlations only with several factors (Imported Cases, GDP, Popu and Impervious) in region \u003cem\u003eII\u003c/em\u003e. Among these inputs, the counts of imported cases consistently possessed the strongest association.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation coefficients between local DF epidemic and input variables.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRegion \u003cem\u003eI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRegion \u003cem\u003eII\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRegion \u003cem\u003eIII\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImported Cases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.43**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.12**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.46**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eSocioeconomic variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.17**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.08**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.25**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePopu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.14**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.08**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.18**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCropland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.14**\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\u0026minus;\u0026thinsp;.09**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.10**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.13**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.11**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImpervious\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.18**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eEnvironmental variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTmax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.09**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.11**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTmean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.13**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.12**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTmin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.16**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.12**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.15**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.08**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.21**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.19**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eNotes: \u003cem\u003er\u003c/em\u003e is the Pearson correlation coefficient. ** indicates this value is significant at the level of 0.01. ImportedCases: The imported DF cases; GDP: Gross domestic product; Popu: Population density; Cropland: The percentages of cropland; Forest: The percentages of forest; Water: The percentages of water; Impervious: The percentages of impervious; Tmax: The average values of maximum temperature; Tmean: Monthly average values of mean temperature; Tmin: Monthly average values of minimum temperature; RH: Monthly average values of relative humidity; Prec: Monthly average values of precipitation.\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 accuracy of RF, GBM, SVM models was showed in the additional file 1, which showed that the RF model was superior compared to other two models. In terms of AUC values (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), the RF models possessed good performances of fitting the relationships between local epidemics and influencing factors in the region \u003cem\u003eI\u003c/em\u003e (0.92) and \u003cem\u003eIII\u003c/em\u003e (0.85). In particular, the RF models considering the time windows of local DF epidemics in the region \u003cem\u003eI\u003c/em\u003e tended to possess slightly higher AUC value (0.92) than those without regarding to the time windows (0.90). Accordingly, the RF model considering the time windows could be further used for exploring the factors\u0026rsquo; contributions, as well as mapping the city-level risk of local DF prevalence.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe AUC values derived from the RF modelling\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eRegion \u003cem\u003eI\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eRegion \u003cem\u003eIII\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTraining\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTesting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePrediction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTraining\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTesting\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNote\u003c/b\u003e: Model 1 and Model 2 respectively represented the models with regard for the time windows of local DF prevalence and mosquito vectors. Model 3 represented the model without regard for the time windows. Region \u003cem\u003eI\u003c/em\u003e and \u003cem\u003eIII\u003c/em\u003e were assigned for the regions on the right side of Hu Line and on the south side of Q-H Line.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDominant influencing factors\u003c/h2\u003e \u003cp\u003eRegarding the contributions to local DF epidemics, the imported cases ranked the first (24.82%) and greatly exceeded the second (Tmin, 16.88%) and the third (Forest, 8.58%) in the region \u003cem\u003eI\u003c/em\u003e, while the rest factors possessed smaller contributions (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). In the region \u003cem\u003eIII\u003c/em\u003e, it also ranked the first with the highest contribution (31.01%, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). Besides, top five inputs with relative higher contributions included Tmin, Forest, Popu, Prec and Tmean in the region \u003cem\u003eI\u003c/em\u003e, which differed from those in the region \u003cem\u003eIII\u003c/em\u003e (GDP, Popu, RH, Cropland, and Forest). For this point, the influencing factors\u0026rsquo; groups were differentiated among these two regions. In the region \u003cem\u003eI\u003c/em\u003e, the natural factors made larger contributions (41.04%) than those of socioeconomic conditions (34.15%) to the local DF epidemics, while the socioeconomic group possessed much higher contributions (44.26%) than those of natural group (24.74%) in the region \u003cem\u003eIII\u003c/em\u003e. These results implied that the dominant influencing factors of local DF epidemics were geographically differentiated although the local DF prevalence was absolutely induced by the imported cases.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAccording to the SHAP values, these 12 inputs possessed protective or risk effects on the city-level local DF epidemics in the region \u003cem\u003eI\u003c/em\u003e. Along with the count of imported cases ascending, its promoting effect on the city-level local epidemics were increasingly powerful before 50 imported cases and then stopped to persist at a high promotion (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). In comparison with its single promoting effect, the rest factors generally possessed composite promoting and inhibiting impacts, resulting in two groups including inhibiting before promoting (Group 1) and promoting before inhibiting (Group 2). Among top five factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea), Tmin, Forest, Popu, and Prec belonged to Group 1 with respective inflexion values of 20℃ (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb), 60% (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec), 1,000 persons per square kilometer (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed), and 185 millimeters (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ee) while other one variable (Tmean) in Group 2 turned their promotive effects to inhibitive roles at the value of 21 ℃ (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ef). The rest six factors could be attributed into Group 1 (GDP, Impervious and Water) and Group 2 (RH, Cropland and Tmax), respectively. In comparison, included 12 input variables also possessed comprehensively promoting or inhibiting effects on the local epidemics in the region \u003cem\u003eIII\u003c/em\u003e (Additional file 1), although their inflexion values were slightly different from those in the region \u003cem\u003eI\u003c/em\u003e. Above analyses validated that the imported DF cases acted as the trigger of local epidemics, and showed that the local epidemics were comprehensively promoted or inhibited by natural and socioeconomic factors on the city scale across China.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMapping the city-level risk of local DF prevalence\u003c/h2\u003e \u003cp\u003eAmong total 295 cities with time windows opened for the local epidemic in the region \u003cem\u003eI\u003c/em\u003e, there were 20, 25, 23, 34, and 193 cities categorized into five levels for their various probabilities of local epidemics in 2019. It was satisfied that 20 cities with the highest risk were exactly identified because the local DF prevalence occurred in these cities indeed, showing a 100% hitting rate. By contrast, among the cities beyond the risk of higher (45 cities), middle (68 cities), lower (102 cities) levels, and the lowest (295 cities), the hitting rates decreased gradually to 95.56%, 79.41%, 67.65%, and 29.15% even though the accumulative counts of identified cities increased to 43, 54, 69, and 86. Although the RF models\u0026rsquo; mapping abilities displayed gradual declines in 2020\u0026ndash;2022, the hitting rates in 2020 were still acceptable because one city (i.e., Guangzhou) was not only identified for its highest risk but also infected by this disease indeed.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssessments on the mapping of the city-level risk of local DF prevalence in the region \u003cem\u003eI\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eYears\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStatistics of the cities\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003eProbability of local DF epidemics\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8\u0026thinsp;~\u0026thinsp;1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6\u0026thinsp;~\u0026thinsp;1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4\u0026thinsp;~\u0026thinsp;1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2\u0026thinsp;~\u0026thinsp;1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0\u0026thinsp;~\u0026thinsp;1\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of cities identified (NCI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e295\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of cities with actual epidemics (NCA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentages of NCA in NCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95.56%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79.41%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e67.65%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e29.15%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of cities identified (NCI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e295\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of cities with actual epidemics (NCA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentages of NCA in NCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.70%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.36%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of cities identified (NCI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e295\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of cities with actual epidemics (NCA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentages of NCA in NCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.34%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of cities identified (NCI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e295\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of cities with actual epidemics (NCA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentages of NCA in NCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.14%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.68%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNotes\u003c/b\u003e: The probabilities, like 0.80\u0026ndash;1, 0.60\u0026ndash;0.80, 0.40\u0026ndash;0.60, 0.20\u0026ndash;0.40, and 0\u0026ndash;0.20, were respectively categorized as the highest, higher, middle, lower, and the lowest risk levels.\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\u003eAccording to local DF risk in the region \u003cem\u003eI\u003c/em\u003e in 2019 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), all of 20 cities with the highest risk identified by the RF models were mainly located in the southwestern border (two cities) and southeastern coastal areas (16 cities). Meanwhile, 25 cities in the southeastern areas and central regions, as well as 23 cities scattered in the region \u003cem\u003eI\u003c/em\u003e were also recognized as the areas with higher and middle risks. As a result, above 68 cities were mostly distributed in the region \u003cem\u003eIII\u003c/em\u003e. Besides, the cities recognized as the areas at various risk levels in 2020\u0026ndash;2022 were also mainly located in this region. These results indicated that it was feasible to employ the RF models in mapping the city-level risk of local DF prevalence in China.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eSince DF epidemics in China were ever-increasingly serious in recent years, it is urgent to reveal the comprehensive features of DF outbreaks for appropriately mapping its risk. Our study analyzed its frequencies and intensities, and then identified their potential influencing factors for mapping the probability of city-level local DF outbreaks through the RF models. Several notable findings were achieved and would provide some useful clues for making targeted interventions on this disease.\u003c/p\u003e \u003cp\u003ePrevious studies have pointed out that China experienced an ever-increasingly serious threat enforced by DF epidemics in terms of the incidence rates or other indices on various spatial scales [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], and that local epidemics were closely correlated with imported epidemics in the past years [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Similarly, our study found that the DF epidemics in China presented continuous uptrends of DF case amounts, as well as obvious spatial expansions towards many more inland cities with increasing frequencies and intensities of city-level DF epidemics. Moreover, the capability of imported epidemics initiating local DF prevalence was rising due to their increasingly stronger association. As a result, many inland cities like some regional hub cities (e.g., the provincial capitals) tended to possess relatively lower frequencies but higher intensity of local epidemics, as well as the inverse appearance of high frequencies and low intensity of imported epidemics. One reasonable explanation is that there have been increasingly larger counts of imported DF cases from some global endemic countries or territories (e.g., the Southeast of Asia, the Central America, and so on) because of closer and stronger connection between China and these countries/territories in recent years [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Apart from traditional regions (i.e., the Southeast and Southwest of China) often infected by imported epidemics [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], the inland regional hub cities featured by bigger airports and larger export-oriented economy were constantly confronted by ascending numbers of inbound or outbound tourists [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Another potential reason is the spillover effects of DF cases from either the traditional regions or hub cities to their surrounding inland cities with sustainable environment conditions [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Hence, it can be concluded that China\u0026rsquo;s DF epidemics were featured by its ascending frequencies and intensities within much more inland cities. Accordingly, we cautiously suggest that additional attention should be emphatically paid to inland regional hub cities and their surrounding cities, especially in case of ever-growing imported epidemics and their increasing initiating ability.\u003c/p\u003e \u003cp\u003eChina\u0026rsquo;s DF epidemics were geographically differentiated around two famous dividing lines (Hu Line and Q-H Line) [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. The local DF prevalence was still geographically restricted within the region \u003cem\u003eI\u003c/em\u003e, especially in the region \u003cem\u003eIII\u003c/em\u003e by far, for which the distribution of city-level time windows for mosquito vectors\u0026rsquo; activities (Additional file 1) may be a rational explanation since this disease is transmitted by \u003cem\u003eAedes\u003c/em\u003e species (\u003cem\u003eAedes albopictus\u003c/em\u003e and \u003cem\u003eAedes aegypti\u003c/em\u003e) in some specific phases with suitable environmental conditions (i.e., climatic elements) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Moreover, the city-level match degrees among the actual occurrences and the time windows for either the mosquito vectors\u0026rsquo; activities or local DF transmission were satisfying in the region \u003cem\u003eI\u003c/em\u003e. Under this circumstance, it was reasonable that local DF prevalence was occasionally reported in some cities in the region \u003cem\u003eII\u003c/em\u003e with late opened and shortly held time windows. That is to say that the time windows were very crucial and non-negligible for local DF prevalence across China. Accordingly, we believe that the time windows would provide helpful information for relative departments implementing timely interventions on this disease.\u003c/p\u003e \u003cp\u003eApart from the spatial differences as mentioned above, the geographical disparities of local DF prevalence were also featured by its dominant influencing factors within the region \u003cem\u003eI\u003c/em\u003e and \u003cem\u003eIII\u003c/em\u003e differing from each other, which may be partially attributed to the different coefficients of variances (CV) of these influencing factors (Additional file 1). This finding was similar to our earlier investigation on the comparison of dominant influencing factors on the DF epidemics in two traditional hotspot regions (the Pearl River Delta and the Border of Yunan and Myanmar) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Thus, it can be concluded that the local DF prevalence was not only geographically restricted by the time windows but also spatially characterized by regionally differentiated influencing factors across China. Therefore, we suggest that the health authorities of each city should take both the status of time windows and regional attributes into account for either making targeted measures before local epidemic occurring or implementing efficient interventions once imported cases initiating local prevalence.\u003c/p\u003e \u003cp\u003eFurthermore, the knowledge on China\u0026rsquo;s DF epidemics was comprehensively improved. First, a large and increasing number of ceaseless imported DF cases would be undoubtedly foreseen in the future since China is playing more and more important roles in the international economy and trade affairs [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], which means that China would be confronted by the inevitable DF prevalence (i.e., either imported or local epidemics). Meanwhile, the domestic loops of socioeconomic development are being accelerated in China so that the spillover of DF cases would be constantly observed among domestic regions [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], especially within the cities with opening time windows for DF transmission. However, the inland cities hit by local DF prevalence remained uncertain along with imported epidemics expanding from traditional southeastern coastal regions or southwest border areas northwards many inland regional hub cities (i.e., the provincial capitals). In other words, there is somewhat occasionality of the cities hit by local epidemics initiated by imported epidemics in the inland regions with opening time windows. Here, we cautiously advise three proper solutions to the challenges imposed by the inevitability and occasionality of DF prevalence in China. First of all, we need to keep our sensitive eyes on the overseas DF epidemics, by which the inbound tourists from these endemic areas could be timely acquired for judging the situation of imported epidemics in China. Secondly, sufficient surveillance of climatic elements on the city scale should be efficiently utilized for determining the status of time windows across China, especially in the inland regional hub cities and their surrounding areas. The final and the key point is properly and scientifically building a robust and reliable prediction model, like the RF models constructed in this study, by which the health authorities could make targeted measures for preventing and controlling this disease. Of course, there were two prerequisites for our investigation that the worldwide natural focuses of this disease cannot be eliminated in a short term, and that China remains as an unnatural focus of this disease.\u003c/p\u003e \u003cp\u003eSeveral limitations are worth noting. First, the acquisition of meteorological conditions data with a higher temporal resolution (e.g., weekly, ten-days) would be helpful for more finely characterizing the time windows on the city scale, by which the current match degrees between the city-level time windows and actual stages of local DF prevalence may be well increased and then effective interventions could be more precisely and timely implemented. Second, the effectiveness of city-level time windows could be further validated through obtaining synchronous surveillance data of mosquito vectors, by which the capabilities of RF models fitting comprehensive relationships between local epidemics and potential factors within these time windows might be improved for subsequent mapping of the city-level risk for local DF prevalence across China. Finally, as the spillover of DF cases among domestic regions played important role in the DF prevalence across China, an efficient solution should be scientifically proposed to characterize the network of relationships among domestic cities or regions in terms of population flows, economic exchanges, space-time distance, and so on.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, China is to be confronted by unavoidable DF prevalence in the appearance of ever-increasing frequencies of imported epidemics and stronger intensities of occasional local transmission in the inland cities. It is feasible of the RF model to map the occasionality of local DF prevalence for the cities geographically restricted by the status and duration of their time windows. This study has improved our understanding of the severity of DF prevalence and its influencing factors across China, which would supply useful clues for the health authorities improving their intervention capacity on such disease.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eDF\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDengue fever\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eRF\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRandom Forest\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eGBM\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGradient Boosting Machine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSVM\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSupport Vector Machine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eROC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReceiver Operating Characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eAUC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArea Under the Curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSHAP\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSHapley Additive exPlanations\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eHu Line\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHuhuanyong line\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eQ-H Line\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eQin Mountain \u0026ndash; Huai River line\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eRegion\u003c/b\u003e \u003cb\u003eI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ethe cities on the right side of Hu Line\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eRegion\u003c/b\u003e \u003cb\u003eII\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ethe cities on the north side of Q-H Line\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eRegion\u003c/b\u003e \u003cb\u003eIII\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ethe cities on the south side of Q-H Line\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eNCI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNumber of cities identified\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eNCA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNumber of cities with actual epidemics\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCV\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThe coefficients of variances\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHY R received the financial support from the National Natural Science Foundation of China (Grant NO.42071136) and the National Key Research and Development Program of China (2023YFC2307502).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors and Affiliations\u003c/p\u003e\n\u003cp\u003eState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China\u003c/p\u003e\n\u003cp\u003eHongyan Ren \u0026amp; Nankang Xu\u003c/p\u003e\n\u003cp\u003eCollege of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China\u003c/p\u003e\n\u003cp\u003eNankang Xu\u003c/p\u003e\n\u003cp\u003eNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China\u003c/p\u003e\n\u003cp\u003eLiang Lu\u003c/p\u003e\n\u003cp\u003eContributions\u003c/p\u003e\n\u003cp\u003eHR and NX conceived and designed the experimental concept. LL helped collect data. HR and NX analyzed the data and drafted the paper. HR, NX and LL revised the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eCorresponding authors\u003c/p\u003e\n\u003cp\u003eCorrespondence to Hongyan Ren ([email protected]).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBhatt S, Gething PW, Brady OJ, Messina JP, Farlow AW, Moyes CL, et al. The global distribution and burden of dengue. 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Does One Belt One Road initiative promote Chinese overseas direct investment? China Econ Rev. 2018;47:189\u0026ndash;205.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu G, Liu T, Xiao J, Zhang B, Song T, Zhang Y, et al. Effects of human mobility, temperature and mosquito control on the spatiotemporal transmission of dengue. Sci Total Environ. 2019;651:969\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"infectious-diseases-of-poverty","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"idop","sideBox":"Learn more about [Infectious Diseases of Poverty](http://idpjournal.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/idop/default.aspx","title":"Infectious Diseases of Poverty","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Inevitability, Occasionality, Dengue fever, China, Time windows, Random forest","lastPublishedDoi":"10.21203/rs.3.rs-3810038/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3810038/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDengue fever (DF) is an acute mosquito-borne viral infectious disease in the world, and increasing DF outbreaks in China have posed serious impacts on public health in recent years. Thus, comprehensively investigating spatiotemporal features and driving or restrictive factors of DF epidemics is critical for the improvement of intervention capacity against this disease.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTwo famous dividing lines (Hu Line and Q-H Line) were applied to divide the mainland into four regions for geographically characterizing China’s DF prevalence. We defined the stages with suitable relative humidity, temperature, and precipitation as basic time windows for the mosquito vectors’ activities. The Random Forest (RF) model was employed to fit the relationships between local epidemics and included climatic and socioeconomic factors, quantify these factors’ contribution, and then map the city-level risk of local DF prevalence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e The situation of China’s DF epidemics was increasingly serious due to ascending intensities of local prevalence triggered by more frequently imported cases. The cities with DF cases, together with their frequencies and intensities presented clear geographical disparities on the city scale, and well matched with the time windows for either DF transmission (95.74%) or mosquito vectors’ activities (83.59%). Among these included factors, the imported cases acted as the driving factor of local epidemics in the region \u003cem\u003eI\u003c/em\u003e and \u003cem\u003eIII\u003c/em\u003e because of not only their strongest association (\u003cem\u003er\u003c/em\u003e=0.43, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.01; r=0.46, P\u0026lt;0.01) but also the largest contribution (24.82% and 31.01%). Moreover, in terms of SHAP values, the imported DF cases possessed a steady promoting impact on local epidemics, while the rest 11 inputs had comprehensive promoting or inhibiting effects with different inflexion values. Besides, the RF models considering the time windows owned higher testing AUC value (0.92) while fitting the relationships between local DF epidemics and potential factors, by which we successfully identified about 96% of the cities with the highest and higher risks of local DF prevalence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e China is being confronted with increasingly larger intensities of occasionally localized DF epidemics triggered by unavoidable higher frequencies of imported epidemics. This study would supply useful clues for the health authorities improving their intervention capacity against this disease.\u003c/p\u003e","manuscriptTitle":"Mapping the occasionality of inevitable dengue fever prevalence in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-08 03:47:20","doi":"10.21203/rs.3.rs-3810038/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2024-02-05T21:29:57+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-01-05T23:42:49+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-01-04T06:34:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-01-03T07:36:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"Infectious Diseases of Poverty","date":"2023-12-27T02:48:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"infectious-diseases-of-poverty","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"idop","sideBox":"Learn more about [Infectious Diseases of Poverty](http://idpjournal.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/idop/default.aspx","title":"Infectious Diseases of Poverty","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d962c097-e662-4af0-b369-c320f94a5502","owner":[],"postedDate":"January 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-06-21T03:30:38+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-08 03:47:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3810038","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3810038","identity":"rs-3810038","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}