ArticleTransboundary and emerging diseases2025
Nowcasting Vector Mosquito Abundance and Determining Its Association With Malaria Epidemics in South Korea.
Article in Transboundary and emerging diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Developing machine learning models to forecast mosquito abundance for climate change impacts and early warning system.One health (Amsterdam, Netherlands) · 2026Article
- Suitable ecological niches of invasive malaria vector under present and projected climatic conditions in South of Iran.PLoS neglected tropical diseases · 2026Article
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Authors and funding
9 authors.
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Abstract
Since a resurgence occurred in 1993, malaria has remained an endemic disease in the Republic of Korea (ROK). A major challenge is the inaccessibility of current vector mosquito abundance data due to a 2-week reporting delay, which limits timely implementation of control measures. We aimed to nowcast mosquito abundance and assess its utility by evaluating the predictive value of mosquito abundance for malaria epidemic peaks. We used machine learning models to nowcast mosquito abundance, employing gradient boosting models (GBMs), extreme gradient boosting (XGB), and an ensemble model combining both. Various meteorological factors served as predictors. The models were trained with data from mosquito collection sites between 2009 and 2021 and tested with data from 2022. To evaluate the utility of nowcasting, we calculated the effective reproduction number (
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