Speaker
Description
Pronounced demographic heterogeneity in immunity is a defining feature of malaria; slowly acquired through repeated and sustained exposure, yet vanishing when this ceases. While compartmental models offer computational efficiency and may capture population-level trends, they often simplify the complex, cumulative nature of acquired immunity against the disease. Furthermore, varying levels of acquired immunity may differentially modify the sensitivity of the disease to climate, thus creating systematic spatially-dependent biases unaccounted for by population-average modelling approaches. We present VECTRI-ABM, a novel grid-based mechanistic framework where the climate-sensitive vector ecology model, VECTRI, is integrated with an agent-based model (ABM) of human health, replacing its original compartmental SEIR module to explicitly resolve individual-level demographic and immunity traits.
We applied this framework to Senegal and The Gambia - a region characterized by a pronounced north-south rainfall gradient, with diverse transmission intensity regimes. Through counterfactual experiments, we quantify the systematic biases in modelled incidence that arise when individual exposure history is neglected. We show how these biases shift as a function of the rainfall regime and host age, identifying the demographic and climatic thresholds where population-level averages fail to capture malaria dynamics. Finally, we discuss how identifying these systematic biases supports the design of more effective, targeted interventions for malaria prevention and elimination programs.
