Mosquito-borne diseases cause significant public health burden, mostly intropical and sub-tropical regions, and are widely emerging or re-emerging inareas where previously absent. Understanding, predicting, and mitigating thespread of mosquito-borne disease in diverse populations and geographies areongoing modeling challenges. We propose a hybrid network-patch model for thespread of mosquito-borne pathogens that accounts for the movement ofindividuals through mosquito habitats and responds to environmental factorssuch as rainfall and temperature. Our approach extends the capabilities ofexisting agent-based models for individual movement developed to predict thespread of directly transmitted pathogens in populations. To extend tomosquito-borne disease, agent-based models are coupled with differentialequations representing `clouds' of mosquitoes in geographic patches thataccount for mosquito ecology, including heterogeneity in mosquito density,emergence rates, and extrinsic incubation period. We illustrate the method byadapting an agent-based model for human movement across a network tomosquito-borne disease. We investigated the importance of heterogeneity inmosquito population dynamics and host movement on pathogen transmission,motivating the utility of detailed models of individual behavior. We observedthat the total number of infected people is greater in heterogeneous patchmodels with one high risk patch and high or medium human movement than it wouldbe in a random mixing homogeneous model. Our hybrid agent-based/differentialequation model is able to quantify the importance of the heterogeneity inpredicting the spread and invasion of mosquito-borne pathogens. Mitigationstrategies can be more effective when guided by realistic models created byextending the capabilities of existing agent-based models to includevector-borne diseases.
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