Multivariate forecasting of dengue infection in Bangladesh: evaluating the influence of data downscaling on machine learning predictive accuracy
摘要
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