ArticleBMC infectious diseases2024
Improving dengue fever predictions in Taiwan based on feature selection and random forests.
Article in BMC infectious diseases, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Advances in mosquito-borne disease surveillance using machine learning.New microbes and new infections · 2026Article
- DengueGNN: Graph-based deep learning for modeling disease spread dynamics and prediction.Scientific reports · 2026Article
- Managing and controlling diseases transmitted by Aedes mosquitoes: a review on best practices.Tropical medicine and health · 2026Review
- Spatiotemporal patterns and meteorological associations of mosquito density: insights from mosquito surveillance in Chengdu, China.Frontiers in cellular and infection microbiology · 2026Article
- The Impact of Extreme Weather on Dengue Fever in Guangzhou, China: A Zero-Inflated Negative Binomial Spatial Lag Analysis.GeoHealth · 2025Article
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3 authors.
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Abstract
backgroundDengue fever is a well-studied vector-borne disease in tropical and subtropical areas of the world. Several methods for predicting the occurrence of dengue fever in Taiwan have been proposed. However, to the best of our knowledge, no study has investigated the relationship between air quality indices (AQIs) and dengue fever in Taiwan.
resultsThis study aimed to develop a dengue fever prediction model in which meteorological factors, a vector index, and AQIs were incorporated into different machine learning algorithms. A total of 805 meteorological records from 2013 to 2015 were collected from government open-source data after preprocessing. In addition to well-known dengue-related factors, we investigated the effects of novel variables, including particulate matter with an aerodynamic diameter < 10 µm (PM
conclusionsOur study is the first to demonstrate that AQI negatively affects dengue fever occurrence in Taiwan. The proposed prediction model can be used as an early warning system for public health to prevent dengue fever outbreaks.
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