ArticleGlobal epidemiology2025
Reflections on explainable artificial intelligence for predicting dengue outbreaks in Bangladesh.
Article in Global epidemiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
The trial behind it
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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Who cites it
1 citing paper in PubMed.
- District-Level Dengue Early Warning Prediction System in Bangladesh Using Hybrid Explainable AI and Bayesian Deep Learning.Tropical medicine and infectious disease · 2026Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Explainable artificial intelligence for predicting dengue outbreaks in Bangladesh using eco-climatic triggers," presents an advance method on artificial intelligence (AI) in the integration of data science and epidemiology combining with machine learning (ML) techniques. We reflect here on XAI's vast implication in public health research. Its potential to inform climate sensitive diseases prediction in low level income settings. Dengue is one of the fastest growing public health challenge in densely populated and climate-sensitive regions like Bangladesh. We developed a novel framework on this study that combines predictive accuracy with interpretability and expandability against this fast growing health problem through XAI. It enables the actionable insights for early warning systems (EWS) and disease prevention.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.