ArticleGeoHealth2026
An Explainable Machine Learning Framework for Predicting West Nile Virus and Uncovering Key Transmission Drivers.
Article in GeoHealth, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
3 authors.
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Abstract
This study introduces Disease Vector Intelligence (DVI), an explainable Machine Learning (ML) framework developed within the EYWA (EarlY WArning system for mosquito-borne diseases) ecosystem. DVI integrates big Earth Observation (EO), socioeconomic, and estimated mosquito abundance data to predict the presence or absence of West Nile Virus (WNV) cases, expressed as a risk score at a high spatiotemporal resolution. The SHAP (SHapley Additive exPlanations) method is employed for a local-level feature importance analysis to uncover key drivers with a high impact on the prediction. The framework consists of two ML models: one intended to be used as an Early Warning System (EWS) predicting at a regional scale, and the other identifying areas susceptible to WNV transmission at a 2 × 2 km
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Registered trials
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