ArticlePLoS neglected tropical diseases2018
Mapping dengue risk in Singapore using Random Forest.
Article in PLoS neglected tropical diseases, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 4 of them syntheses that pooled it.
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Who cites it
45 citing papers in PubMed, 4 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 of published literature on mosquito control action thresholds across the world.PLoS neglected tropical 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
- The role of urbanisation in the spread of Aedes mosquitoes and the diseases they transmit-A systematic review.PLoS neglected tropical diseases · 2021Pooled it
- Spatiotemporal dynamics of dengue: a town level study in Guangdong, China.BMC infectious diseases · 2026Article
- Bridging ensemble model and public health practice: an approach for refining understanding of seasonal dengue transmission patterns in Bangladesh.BMC infectious diseases · 2026Article
- Expanding mountain-type zoonotic visceral leishmaniasis in China: a multi-source data-driven investigation.The Lancet regional health. Western Pacific · 2026Article
- Leveraging geographic information system for dengue surveillance: a scoping review.Tropical medicine and health · 2025Review
- Spatiotemporal distribution of sustained dengue hotspots associated with climate and urbanisation in Singapore.Scientific reports · 2025Article
- Annual global dengue dynamics are related to multi-source factors revealed by a machine learning prediction analysis.PLoS neglected tropical diseases · 2025Article
- Do Random Forest-Driven Climate Envelope Models Require Variable Selection? A Case Study onInsects · 2025Article
- Suppression of Aedes aegypti may not affect sympatric Aedes albopictus populations: findings from two years of entomological surveillance in Singapore.Scientific reports · 2025Article
- Harnessing artificial intelligence for enhanced public health surveillance: a narrative review.Frontiers in public health · 2025Review
- Spatial model of Dengue Hemorrhagic Fever (DHF) risk: scoping review.BMC public health · 2023Article
- Singapore's 5 decades of dengue prevention and control-Implications for global dengue control.PLoS neglected tropical diseases · 2023Article
- The Potential of Surveillance Data for Dengue Risk Mapping: An Evaluation of Different Approaches in Cuba.Tropical medicine and infectious disease · 2023Article
- Artificial intelligence in public health: the potential of epidemic early warning systems.The Journal of international medical research · 2023Review
- Telehealth utilization in U.S. medicare beneficiaries aged 65 years and older during the COVID-19 pandemic.BMC public health · 2023Article
- Application of big data and artificial intelligence in epidemic surveillance and containment.Intelligent medicine · 2023Review
- Predicting the distribution of COVID-19 through CGAN-Taking Macau as an example.Frontiers in big data · 2023Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSingapore experiences endemic dengue, with 2013 being the largest outbreak year known to date, culminating in 22,170 cases. Given the limited resources available, and that vector control is the key approach for prevention in Singapore, it is important that public health professionals know where resources should be invested in. This study aims to stratify the spatial risk of dengue transmission in Singapore for effective deployment of resources. METHODOLOGY/PRINCIPAL
findingsRandom Forest was used to predict the risk rank of dengue transmission in 1km2 grids, with dengue, population, entomological and environmental data. The predicted risk ranks are categorized and mapped to four color-coded risk groups for easy operation application. The risk maps were evaluated with dengue case and cluster data. Risk maps produced by Random Forest have high accuracy. More than 80% of the observed risk ranks fell within the 80% prediction interval. The observed and predicted risk ranks were highly correlated ([Formula: see text]≥0.86, P <0.01). Furthermore, the predicted risk levels were in excellent agreement with case density, a weighted Kappa coefficient of more than 0.80 (P <0.01). Close to 90% of the dengue clusters occur in high risk areas, and the odds of cluster forming in high risk areas were higher than in low risk areas.
conclusionsThis study demonstrates the potential of Random Forest and its strong predictive capability in stratifying the spatial risk of dengue transmission in Singapore. Dengue risk map produced using Random Forest has high accuracy, and is a good surveillance tool to guide vector control operations.
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