  Mosquito-borne diseases cause significant public health burden, mostly in
tropical and sub-tropical regions, and are widely emerging or re-emerging in
areas where previously absent. Understanding, predicting, and mitigating the
spread of mosquito-borne disease in diverse populations and geographies are
ongoing modeling challenges. We propose a hybrid network-patch model for the
spread of mosquito-borne pathogens that accounts for the movement of
individuals through mosquito habitats and responds to environmental factors
such as rainfall and temperature. Our approach extends the capabilities of
existing agent-based models for individual movement developed to predict the
spread of directly transmitted pathogens in populations. To extend to
mosquito-borne disease, agent-based models are coupled with differential
equations representing `clouds' of mosquitoes in geographic patches that
account for mosquito ecology, including heterogeneity in mosquito density,
emergence rates, and extrinsic incubation period. We illustrate the method by
adapting an agent-based model for human movement across a network to
mosquito-borne disease. We investigated the importance of heterogeneity in
mosquito population dynamics and host movement on pathogen transmission,
motivating the utility of detailed models of individual behavior. We observed
that the total number of infected people is greater in heterogeneous patch
models with one high risk patch and high or medium human movement than it would
be in a random mixing homogeneous model. Our hybrid agent-based/differential
equation model is able to quantify the importance of the heterogeneity in
predicting the spread and invasion of mosquito-borne pathogens. Mitigation
strategies can be more effective when guided by realistic models created by
extending the capabilities of existing agent-based models to include
vector-borne diseases.
