ArticleTropical medicine and infectious disease2023
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.
Article in Tropical medicine and infectious disease, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Innovative technologies to address neglected tropical diseases in African settings with persistent sociopolitical instability.Nature communications · 2024Pooled it
- Global Genomic Surveillance.Methods in molecular biology (Clifton, N.J.) · 2027Article
- Explainable Machine Learning for Predicting Dengue Recovery Duration: Insights from Multi-Center Clinical Data.Healthcare (Basel, Switzerland) · 2026Article
- Explainable artificial intelligence for predicting dengue outbreaks in Bangladesh using eco-climatic triggers.Global epidemiology · 2025Article
- Precision Prediction for Dengue Fever in Singapore: A Machine Learning Approach Incorporating Meteorological Data.Tropical medicine and infectious disease · 2024Article
- Possible role of artificial intelligence in diagnosis of cases with non-specific signs and symptoms of dengue: A comment.Clinics (Sao Paulo, Brazil) · 2024Article
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Authors and funding
3 authors.
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Abstract
Dengue fever is a prevalent mosquito-borne disease that burdens communities in subtropical and tropical regions. Dengue transmission is ecologically complex; several environmental conditions are critical for the spatial and temporal distribution of dengue. Interannual variability and spatial distribution of dengue transmission are well-studied; however, the effects of land cover and use are yet to be investigated. Therefore, we applied an explainable artificial intelligence (AI) approach to integrate the EXtreme Gradient Boosting and Shapley Additive Explanation (SHAP) methods to evaluate spatial patterns of the residences of reported dengue cases based on various fine-scale land-cover land-use types, Shannon's diversity index, and household density in Kaohsiung City, Taiwan, between 2014 and 2015. We found that the proportions of general roads and residential areas play essential roles in dengue case residences with nonlinear patterns. Agriculture-related features were negatively associated with dengue incidence. Additionally, Shannon's diversity index showed a U-shaped relationship with dengue infection, and SHAP dependence plots showed different relationships between various land-use types and dengue incidence. Finally, landscape-based prediction maps were generated from the best-fit model and highlighted high-risk zones within the metropolitan region. The explainable AI approach delineated precise associations between spatial patterns of the residences of dengue cases and diverse land-use characteristics. This information is beneficial for resource allocation and control strategy modification.
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Registered trials
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