ArticleInfectious diseases of poverty2019
Spatiotemporal characteristics and primary influencing factors of typical dengue fever epidemics in China.
Article in Infectious diseases of poverty, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 3 of them syntheses that pooled it.
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Who cites it
23 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Climatic, Socioecological and Environmental Determinants of Aedes spp. Dynamics at the Community Interface: A Systematic Review With Reflections From a One Health Perspective.Tropical medicine & international health : TM & IH · 2026Pooled it
- Land use and land cover change and its impacts on dengue dynamics in China: A systematic review.PLoS neglected tropical diseases · 2021Pooled it
- The role of urbanisation in the spread of Aedes mosquitoes and the diseases they transmit-A systematic review.PLoS neglected tropical diseases · 2021Pooled it
- Street-view vegetation, human mobility, and dengue fever: context-dependent risk in urban green spaces.BMC public health · 2026Article
- An explainable covariate compartmental model for predicting the spatio-temporal patterns of dengue in Sri Lanka.PLoS computational biology · 2025Article
- Impact of urban heat island effect on dengue incidence: a remote sensing approach using thermal and high-resolution optical imagery.BMC public health · 2025Article
- Spatiotemporal distribution of sustained dengue hotspots associated with climate and urbanisation in Singapore.Scientific reports · 2025Article
- Forecasting and mapping dengue fever epidemics in China: a spatiotemporal analysis.Infectious diseases of poverty · 2024Article
- Spatial model of Dengue Hemorrhagic Fever (DHF) risk: scoping review.BMC public health · 2023Article
- Spatio-Temporal Determinants of Dengue Epidemics in the Central Region of Burkina Faso.Tropical medicine and infectious disease · 2023Article
- Socio-economic factors and its influence on the association between temperature and dengue incidence in 61 Provinces of the Philippines, 2010-2019.PLoS neglected tropical diseases · 2023Observational
- Sociodemographic and environmental factors associated with dengue, Zika, and chikungunya among adolescents from two Brazilian capitals.PLoS neglected tropical diseases · 2023Article
- Influences of Differentiated Residence and Workplace Location on the Identification of Spatiotemporal Patterns of Dengue Epidemics: A Case Study in Guangzhou, China.International journal of environmental research and public health · 2022Article
- Climate factors and dengue fever in Burkina Faso from 2017 to 2019.Journal of public health in Africa · 2022Article
- Specific urban units identified in tuberculosis epidemic using a geographical detector in Guangzhou, China.Infectious diseases of poverty · 2022Article
- Spatiotemporal analysis of dengue fever in Burkina Faso from 2016 to 2019.BMC public health · 2022Article
- Diseases spectrum in the field of spatiotemporal patterns mining of infectious diseases epidemics: A bibliometric and content analysis.Frontiers in public health · 2022Review
- Increasingly expanded future risk of dengue fever in the Pearl River Delta, China.PLoS neglected tropical diseases · 2021Article
- The Seroprevalence of Dengue Virus Infection and Its Association With Iron (Fe) Level in Pregnant Women in Guangzhou, China.Frontiers in medicine · 2021Article
- Semi-Supervised Text Classification Framework: An Overview of Dengue Landscape Factors and Satellite Earth Observation.International journal of environmental research and public health · 2020Review
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Abstract
backgroundDengue 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.
methodsA 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.
resultsDF 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.
conclusionsThe 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.
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