Quantifying Projected Impact of Social Distancing Policies on COVID-19 Outcomes in the US

Chaoqi Yang, Ruijie Wang, Fangwei Gao, Dachun Sun, Jiawei Tang, Tarek Abdelzaher

Current social distancing measures to impede COVID-19 (such as shelter-in-place) are economically unsustainable in the long term. Models are needed to understand the implications of possible relaxation options for these measures. We report such models, together with corresponding parameter estimation techniques and prediction outcomes, borrowing insights from another domain; namely, information cascades. Our models assume that the containment of the virus into isolated locales is no longer possible. Instead, we explore options that reduce the rate of spread. We predict COVID-19 contagion trends in different geographic regions under a "what if" scenario to understand the effects of potential policy decisions on regional trends. Our model allows experimentation with other policies that, we hope, can contribute to socially viable outcomes both in terms of health system capacity and economic impact. We apply our model to over 30 highly impacted states in the US and public the results at https://covid19predictions.csl.illinois.edu/

Knowledge Graph

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