Spatial patterns and environmental and socio-economic risk factors of dengue fever have been studied widely on a coarse scale; however, there are few such quantitative studies on a fine scale. There is a need to investigate these factors on a fine scale for dengue fever. In this study, a dataset of dengue fever cases and environmental and socio-economic factors was constructed at 1-km spatial resolution, in particular 'land types' (LT), obtained from the first high resolution remote sensing satellite launched from China (GF-1 satellite), and 'land surface temperature', obtained from moderate resolution imaging spectroradiometer (MODIS) images. Spatial analysis methods, including point density, average nearest neighbor, spatial autocorrelation, and hot spot analysis, were used to analyze spatial patterns of dengue fever. Spearman rank correlation and ordinary least squares (OLS) were used to explore associated environmental and socio-economic risk factors of dengue fever in five districts of Guangzhou City, China in 2014. A total of 30553 dengue fever cases were reported in the districts of Baiyun, Haizhu, Yuexiu, Liwan, and Tianhe of Guangzhou, China in 2014. Dengue fever cases showed strong seasonal variation. The cases from August to October accounted for 96.3% of the total cases in 2014. The top three districts for dengue fever morbidity were Baiyun (1.32%), Liwan (0.62%), and Haizhu (0.60%). Strong spatial clusters of dengue fever cases were observed. Areas of high density for dengue fever were located at the district junctions. The dengue fever outbreak was significantly correlated with LT, normalized difference water index (NDWI), land surface temperature of daytime (LSTD), land surface temperature of nighttime (LSTN), population density (PD), and gross domestic product (GDP) (correlation coefficients of 0.483, 0.456, 0.612, 0.699, 0.705, and 0.205, respectively). The OLS equation was built with dengue fever cases as the dependent variable and LT, LSTN, and PD as explanatory variables. The residuals were not spatially autocorrelated. The adjusted R-squared was 0.320. The findings of spatio-temporal patterns and risk factors of dengue fever can provide scientific information for public health practitioners to formulate targeted, strategic plans and implement effective public health prevention and control measures.

The mosquito Aedes albopitus is a competent vector for the transmission of many blood-borne pathogens. An important factor that affects the mosquitoes’ development and spreading is climate, such as temperature, precipitation and photoperiod. Existing climate-driven mechanistic models overlook the seasonal pattern of diapause, referred to as the survival strategy of mosquito eggs being dormant and unable to hatch under extreme weather. With respect to diapause, several issues remain unaddressed, including identifying the time when diapause eggs are laid and hatched under different climatic conditions, demarcating the thresholds of diapause and non-diapause periods, and considering the mortality rate of diapause eggs. Here we propose a generic climate-driven mechanistic population model of Ae. albopitus applicable to most Ae. albopictus-colonized areas. The new model is an improvement over the previous work by incorporating the diapause behaviors with many modifications to the stage-specific mechanism of the mosquitoes’ life-cycle. monthly Container Index (CI) of Ae. albopitus collected in two Chinese cities, Guangzhou and Shanghai is used for model validation. The simulation results by the proposed model is validated with entomological field data by the Pearson correlation coefficient r 2 in Guangzhou (r 2 = 0.84) and in Shanghai (r 2 = 0.90). In addition, by consolidating the effect of diapause-related adjustments and temperature-related parameters in the model, the improvement is significant over the basic model. The model highlights the importance of considering diapause in simulating Ae. albopitus population. It also corroborates that temperature and photoperiod are significant in affecting the population dynamics of the mosquito. By refining the relationship between Ae. albopitus population and climatic factors, the model serves to establish a mechanistic relation to the growth and decline of the species. Understanding this relationship in a better way will benefit studying the transmission and the spatiotemporal distribution of mosquito-borne epidemics and eventually facilitating the early warning and control of the diseases.

Global warming has a marked influence on the life cycle of epidemic vectors as well as their interactions with human beings. The Aedes albopictus mosquito as the vector of dengue fever surged exponentially in the last decade, raising ecological and epistemological concerns of how climate change altered its growth rate and population dynamics. As the global warming pattern is considerably uneven across four seasons, with a confirmed stronger effect in winter, an emerging need arises as to exploring how the seasonal warming effects influence the annual development of Ae. albopictus. The model consolidates a 35-year climate dataset and designs fifteen warming patterns that increase the temperature of selected seasons. Based on a recently developed mechanistic population model of Ae. albopictus, the model simulates the thermal reaction of blood-fed adults by systematically increasing the temperature from 0.5 to 5 °C at an interval of 0.5 °C in each warming pattern. The results show the warming effects are different across seasons. The warming effects in spring and winter facilitate the development of the species by shortening the diapause period. The warming effect in summer is primarily negative by inhibiting mosquito development. The warming effect in autumn is considerably mixed. However, these warming effects cannot carry over to the following year, possibly due to the fact that under the extreme weather in winter the mosquito fully ceases from development and survives in terms of diapause eggs. As the historical pattern of global warming manifests seasonal fluctuations, this study provides corroborating and previously ignored evidence of how such seasonality affects the mosquito development. Understanding this short-term temperature-driven mechanism as one chain of the transmission events is critical to refining the thermal reaction norms of the epidemic vector under global warming as well as developing effective mosquito prevention and control strategies.

Hemorrhagic fever with renal syndrome (HFRS) is an important public health problem in China. The identification of the spatiotemporal pattern of HFRS will provide a foundation for the effective control of the disease. Based on the incidence of HFRS, as well as environmental factors, and social-economic factors of China from 2005–2012, this paper identified the spatiotemporal characteristics of HFRS distribution and the factors that impact this distribution. The results indicate that the spatial distribution of HFRS had a significant, positive spatial correlation. The spatiotemporal heterogeneity was affected by the temperature, precipitation, humidity, NDVI of January, NDVI of August for the previous year, land use, and elevation in 2005–2009. However, these factors did not explain the spatiotemporal heterogeneity of HFRS incidences in 2010–2012. Spatiotemporal heterogeneity of provincial HFRS incidences and its relation to environmental factors would provide valuable information for hygiene authorities to design and implement effective measures for the prevention and control of HFRS in China.

Dengue fever (DF) is a common mosquito-borne viral infectious disease in the world, and increasingly severe DF epidemics in China have seriously affected people’s health in recent years. Thus, investigating spatiotemporal patterns and potential influencing factors of DF epidemics in typical regions is critical to consolidate effective prevention and control measures for these regional epidemics. A generalized additive model (GAM) was used to identify potential contributing factors that influence spatiotemporal epidemic patterns in typical DF epidemic regions of China (e.g., the Pearl River Delta [PRD] and the Border of Yunnan and Myanmar [BYM]). In terms of influencing factors, environmental factors including the normalized difference vegetation index (NDVI), temperature, precipitation, and humidity, in conjunction with socioeconomic factors, such as population density (Pop), road density, land-use, and gross domestic product, were employed. DF epidemics in the PRD and BYM exhibit prominent spatial variations at 4 km and 3 km grid scales, characterized by significant spatial clustering over the Guangzhou-Foshan, Dehong, and Xishuangbanna areas. The GAM that integrated the Pop-urban land ratio (ULR)-NDVI-humidity-temperature factors for the PRD and the ULR-Road density-NDVI-temperature-water land ratio-precipitation factors for the BYM performed well in terms of overall accuracy, with Akaike Information Criterion values of 61 859.89 and 826.65, explaining a total variance of 83.4 and 97.3%, respectively. As indicated, socioeconomic factors have a stronger influence on DF epidemics than environmental factors in the study area. Among these factors, Pop (PRD) and ULR (BYM) were the socioeconomic factors explaining the largest variance in regional epidemics, whereas NDVI was the environmental factor explaining the largest variance in both regions. In addition, the common factors (ULR, NDVI, and temperature) in these two regions exhibited different effects on regional epidemics. The spatiotemporal patterns of DF in the PRD and BYM are influenced by environmental and socioeconomic factors, the socioeconomic factors may play a significant role in DF epidemics in cases where environmental factors are suitable and differ only slightly throughout an area. Thus, prevention and control resources should be fully allocated by referring to the spatial patterns of primary influencing factors to better consolidate the prevention and control measures for DF epidemics.

Dengue fever (DF) is a common and rapidly spreading vector-borne viral disease in tropical and subtropical regions. In recent years, this imported disease has posed an increasing threat to public health in China, especially in many southern cities. Although the severity of DF outbreaks in these cities is generally associated with known risk factors at various administrative levels, spatial heterogeneities of these associations remain little understood on a finer scale. In this study, the neighboring Guangzhou and Foshan (GF) cities were considered as a joint area for characterizing the spatial variations in the 2014 DF epidemic at various grid levels from 1 × 1 km² to 6 × 6 km². On an appropriate scale, geographically weighted regression (GWR) models were employed to interpret the influences of socioeconomic and environmental factors on this epidemic across the GF area. DF transmissions in Guangzhou and Foshan cities presented synchronous temporal changes and spatial expansions during the main epidemic months. Across the GF area, this epidemic was obviously spatially featured at various grid levels, especially on the 2 × 2 km² scale. Its spatial variations were relatively sufficiently explained by population size, road density, and economic status integrated in the GWR model with the lowest Akaike Information Criterion (AICc = 5227.97) and highest adjusted R square (0.732) values. These results indicated that these three socioeconomic factors acted as geographical determinants of spatial variability of the 2014 DF epidemic across the joint GF area, although some other potential factors should be added to improve the explaining the spatial variations in the central zones. This work improves our understanding of the effects of socioeconomic conditions on the spatial variations in this epidemic and helps local hygienic authorities to make targeted joint interventions for preventing and controlling this epidemic across the GF area.