An explainable covariate compartmental model for predicting the spatio-temporal patterns of dengue in Sri Lanka.

ABSTRACT: A majority of all infectious diseases manifest some climate-sensitivity. However, many of those sensitivities are not well understood as meteorological drivers of infectious diseases co-occur with other drivers exhibiting complex non-linear influences and feedback. This makes it hard to dissect their individual contributions. Here we apply a novel deep learning Explainable AI (XAI) compartment model with covariate drivers and dynamic feedback to predict and explain the dengue incidence across Sri Lanka. We compare the compartmental Susceptible-Exposed-Infected-Recovered (SEIR) model to a deep learning model without a compartmental structure. We find that the covariate compartmental hybrid model performs better and can describe drivers of the dengue spatiotemporal incidence over time. The s

SUBMITTER: Liu Y 

PROVIDER: S-EPMC12500091 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature