Hybrid agent-based modeling of Zika in the United States

Vector-borne infectious diseases present computational modelers with a unique challenge: finding the right balance between model fidelity and simulation costs. In this work, we introduce a hybrid agent-based model that achieves a balance that readily scales to problem sizes of millions of human agents and mosquitoes. Macroscopically, our model results agree with those from a low-cost compartmental model; microscopically, like agent-based models, it provides details at the individual level. We apply this model to a synthetic human population of 1.2 million individuals from Miami, Florida in the United States to model the Zika outbreak in the Fall of 2016. We identify two high-risk locations within this region, a detail which cannot be revealed by traditional compartmental models. The principles-based mathematical derivation of the hybrid agent-based model can be adapted to other scenarios facing similar tradeoffs.