ArticleInternational journal of environmental research and public health2017
Ecological Niche Modeling Identifies Fine-Scale Areas at High Risk of Dengue Fever in the Pearl River Delta, China.
Article in International journal of environmental research and public health, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 3 of them syntheses that pooled it.
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Who cites it
21 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- A systematic review of the data, methods and environmental covariates used to map Aedes-borne arbovirus transmission risk.BMC infectious diseases · 2023Pooled it
- A Systematic Review on Modeling Methods and Influential Factors for Mapping Dengue-Related Risk in Urban Settings.International journal of environmental research and public health · 2022Pooled it
- Land use and land cover change and its impacts on dengue dynamics in China: A systematic review.PLoS neglected tropical diseases · 2021Pooled it
- Determination of dengue high-risk areas in the Philippines: a kernel density estimation, inverse distance weighting, and ecological niche modeling.Parasites & vectors · 2025Article
- Fine-Scale Risk Mapping for Dengue Vector Using Spatial Downscaling in Intra-Urban Areas of Guangzhou, China.Insects · 2025Article
- Vulnerability characteristics and adaptation strategies of public health systems in different regions of China.Frontiers in public health · 2025Review
- Local environmental factors drive distributions of ecologically-contrasting mosquito species (Diptera: Culicidae).Scientific reports · 2024Article
- Article
- Mapping African Swine Fever and Highly Pathogenic Avian Influenza Outbreaks along the Demilitarized Zone in the Korean Peninsula.Transboundary and emerging diseases · 2024Article
- An Integrative Explainable Artificial Intelligence Approach to Analyze Fine-Scale Land-Cover and Land-Use Factors Associated with Spatial Distributions of Place of Residence of Reported Dengue Cases.Tropical medicine and infectious disease · 2023Article
- Climate Change and Vector-Borne Diseases in China: A Review of Evidence and Implications for Risk Management.Biology · 2022Review
- Increasingly expanded future risk of dengue fever in the Pearl River Delta, China.PLoS neglected tropical diseases · 2021Article
- The association between tropical cyclones and dengue fever in the Pearl River Delta, China during 2013-2018: A time-stratified case-crossover study.PLoS neglected tropical diseases · 2021Article
- Dengue risk assessment using multicriteria decision analysis: A case study of Bhutan.PLoS neglected tropical diseases · 2021Article
- Early forecasting of the potential risk zones of COVID-19 in China's megacities.The Science of the total environment · 2020Article
- The driver of dengue fever incidence in two high-risk areas of China: A comparative study.Scientific reports · 2019Article
- Urban villages as transfer stations for dengue fever epidemic: A case study in the Guangzhou, China.PLoS neglected tropical diseases · 2019Article
- Spatiotemporal characteristics and primary influencing factors of typical dengue fever epidemics in China.Infectious diseases of poverty · 2019Article
- Influence of Host and Environmental Factors on the Distribution of the Japanese Encephalitis VectorInternational journal of environmental research and public health · 2018Article
- Climate variability and dengue hemorrhagic fever in Southeast Sulawesi Province, Indonesia.Environmental science and pollution research international · 2018Article
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Authors and funding
8 authors.
Funding
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Abstract
Dengue fever (DF) is one of the most common and rapidly spreading mosquito-borne viral diseases in tropical and subtropical regions. In recent years, this imported disease has posed a serious threat to public health in China, especially in the Pearl River Delta (PRD). Although the severity of DF outbreaks in the PRD is generally associated with known risk factors, fine scale assessments of areas at high risk for DF outbreaks are limited. We built five ecological niche models to identify such areas including a variety of climatic, environmental, and socioeconomic variables, as well as, in some models, extracted principal components. All the models we tested accurately identified the risk of DF, the area under the receiver operating characteristic curve (AUC) were greater than 0.8, but the model using all original variables was the most accurate (AUC = 0.906). Socioeconomic variables had a greater impact on this model (total contribution 55.27%) than climatic and environmental variables (total contribution 44.93%). We found the highest risk of DF outbreaks on the border of Guangzhou and Foshan (in the central PRD), and in northern Zhongshan (in the southern PRD). Our fine-scale results may help health agencies to focus epidemic monitoring tightly on the areas at highest risk of DF outbreaks.
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