An interpretable 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, making it hard to dissect their individual contributions. Here we apply a novel deep learning XAI compartment model with covariate drivers and dynamic feedback to predict and explain the dengue incidence across Sri Lanka. We compare a compartmental SEIR model to a deep learning model without a compartmental structure. We find 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. The novel approach allows interpretations of the covariate influences and bridges the gap between dynamical compartmental and data driven dynamical models.