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

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 strongest drivers in our model in order of importance are precipitation, socio-demographics, and normalized vegetation index. The novel method demonstrated can be used to leverage known infectious disease dynamics while accounting for the influence of other drivers and different population immunity contexts. While allowing for interpretation of the covariate driver influences, the approach bridges the gap between dynamical compartmental and data driven dynamical models.Author summary: This research presents a novel epidemiological model that integrates climate and socioeconomic factors to better understand the spread of mosquito-borne diseases. While climate sensitivity of these diseases is well established, interactions with other drivers remain poorly understood. We integrated a compartmental model with a long short-term memory structure to disentangle contributions of precipitation, demographics, and vegetation index to dengue cases in Sri Lanka. Our model outperforms purely data-driven approaches, capturing critical non-linear dynamics like lagged climate effects and population immunity. These findings highlight the key role of precipitation and demographic factors in disease transmission and offer valuable insights for improving disease control strategies.