Acute infectious diseases, particularly lots of neglected tropical diseases (NTDs), pose significant public health challenges, especially in resource-limited settings where diagnostic and surveillance capacities are often inadequate. This scoping review systematically explores methodologies for estimating the burden of acute infectious NTDs, focusing on metrics such as incidence, mortality, and disability-adjusted life years (DALYs). We identified 60 studies, predominantly on malaria and dengue, with a growing emphasis on advanced computational approaches like machine learning and Bayesian geospatial modeling. Key findings highlight the evolution from traditional surveillance-based methods to integrated frameworks incorporating environmental, demographic, and health system covariates. However, challenges persist, including data sparsity, underreporting, and methodological uncertainties. The review underscores the need for improved data integration, standardized frameworks, and interdisciplinary collaboration to enhance the accuracy and utility of burden estimates.
Wang et al. (Mon,) studied this question.