ArticleBMC infectious diseases2025
Multivariate forecasting of dengue infection in Bangladesh: evaluating the influence of data downscaling on machine learning predictive accuracy.
Article in BMC infectious diseases, 2025. 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.
- Recent advances in machine learning and Bayesian modeling for tropical disease prediction, diagnosis, and risk analysis: a scoping review.BMC infectious diseases · 2026Article
- Bridging ensemble model and public health practice: an approach for refining understanding of seasonal dengue transmission patterns in Bangladesh.BMC infectious diseases · 2026Article
- Modelling climatic and temporal dynamics of dengue transmission in Bangladesh using deep learning models.PLOS global public health · 2026Article
- Article
- A comparative evaluation of multiple machine learning approaches for forecasting dengue outbreaks in Bangladesh.Scientific reports · 2025Article
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1 author.
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Abstract
The increasing incidence of dengue virus (DENV) infections poses significant public health challenges in Bangladesh, demanding advanced forecasting methodologies to guide timely interventions. This study introduces a rigorous multivariate time series analysis, integrating meteorological factors with state-of-the-art machine learning (ML) models, to predict DENV case trends across different temporal scales. Leveraging a robust data pipeline, this research incorporates a strategic downscaling technique, applying the Stochastic Bayesian Downscaling (SBD) algorithm to convert monthly DENV case data to daily frequency. This approach addresses key issues in the handling of sparse datasets and missing data, offering novel insights into the potential accuracy benefits of data downscaling in time series forecasting. Among the models assessed, the decision tree demonstrated superior performance on the actual monthly data, achieving an accuracy of
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