A Decision Analytic Approach for Social Distancing Policies During COVID-19 Pandemic

The COVID-19 pandemic has become a crucial public health issue in many countries including the United States. In the absence of the right vaccine strain and sufficient antiviral stockpiles on hand, non-pharmaceutical interventions have become valuable public health tools at the early stages of the pandemic and they are employed by many countries across the globe. These interventions are designed to increase social distancing between individuals to reduce the transmission of the virus and eventually dampen the burden on the healthcare system. The virus transmissibility is a function of the average number of contacts individuals have in their communities and it is highly dependent on population density and daily mobility patterns, along with other social factors. These show significant variation across the United States. In this article, we study the effectiveness of social distancing measures in communities with different population density. Specifically, first we show how the empirical estimation of reproduction number differs for two completely different states, thus the experience of the COVID-19 outbreak is drastically different, suggesting different outbreak growth rates in practice. Second, we develop an age-structured compartmental model for simulating the disease spread in order to demonstrate the variation in the observed outbreak characteristics. We find that early trigger and late trigger options present a trade-off between the peak magnitude and the overall death toll of the outbreak which may also vary across different populations.