Using climate & mobility data to model SARS-CoV-2


This fall, SARS-CoV-2 hospitalizations in the United States have been growing rapidly, and in Colorado, where our team has been modeling the course of SARS-CoV-2 for state policy-makers, hospitalizations have now greatly exceeded the “first wave” of hospitalizations in the state. It is unclear to what extent this growth is due to changes in policy, behavior, risk perception and/or seasonal forcings. The effect that seasonal weather change has on the transmission potential of SARS-CoV-2 and how this is linked to weather-related human behaviors remains to be fully characterized. This is an important gap because accounting for theoretical seasonality in models of SARS-CoV-2 leads to projections of the future course of the pandemic that are markedly different from models that ignore seasonal forcing. In other words, if certain climatic conditions favor the transmission of SARS-CoV-2, we may need more aggressive policies to control the virus during peak transmission season. Being able to accurately characterize seasonal forcings in mathematical transmission models of SARS-CoV-2 can improve our ability to identify high-risk transmission periods and inform interventions and resource allocation plans that anticipate seasonal changes in transmission. Here, we propose two specific aims to investigate the role of seasonal changes in weather on the transmission of SARSCoV-2. We focus on untangling the mechanisms by which climatic conditions may alter both environmental conditions that favor/impede SARS-CoV-2 transmission and human behavior, as well as using the findings to refine mechanistic models of SARS-CoV-2.

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