Spatial and temporal prediction of <i>Aedes aegypti</i> populations with atmospheric and urban forms dependence.

Abstract

Accurately predicting mosquito population dynamics in cities requires models that couple climatic sensitivity with urban spatial heterogeneity. We developed a spatially explicit, climate-driven framework that integrates satellite imagery, field observations, and biology to simulate <i>Aedes aegypti</i> dynamics across heterogeneous urban landscapes. A decomposition technique was introduced to disentangle entomological observations from mixed urban sites into landscape-specific time series for houses, streets, and parks. We provide a robust parameter estimation through a constrained inverse problem, revealing distinct temperature responses and biological processes across environments. Model validation against both egg and adult mosquito data from five Brazilian cities yielded strong correlations with the majority falling between <i>ρ</i> = 0.4 and 0.8, confirming the model's ability to reproduce observed spatiotemporal patterns. This integration of climate dependence, landscape quantification, and empirical validation provides a potential tool for anticipating mosquito abundance across space and time. By identifying periods and locations of elevated risk, the framework supports targeted, cost-effective interventions against dengue and other vector-borne diseases in a rapidly urbanizing and warming world.