Examining the Effect of Social Distancing on the Compound Growth Rate of SARS-CoV-2 at the County Level (United States) Using Statistical Analyses and a Random Forest Machine Learning Model

Cobb, J. S.; Seale, M. A.

Abstract Objectives The goal of the present work is to investigate trends among US counties and COVID-19 growth rate in relation to the existence of shelter in place (SIP) orders in that county. Study Design Prospective cohort study. Methods Compound growth rates were calculated using cumulative confirmed COVID-19 cases from January 21, 2020, to March 31, 2020 in all 3,139 US counties. Compound growth was chosen as it gives a single number that can be used in machine learning to represent speed of virus spread during defined time intervals. Statistical analyses and a random forest machine learning model were used to analyze the data for differences in counties with and without shelter in place orders. Results Statistical analyses revealed that the March 16 presidential recommendation (limiting gatherings to < 10 people) lowered the compound growth rate of COVID-19 for all counties in the US by 6.6%, and the counties that implemented SIP after March 16 had a further reduction of 7.8% over the counties with no SIP after March 16. A random forest machine learning model was built to predict compound growth rate after a SIP order and was found to have an accuracy of 92.3%. The random forest found that population, longitude, and population per square mile were the most important features when predicting the effect of SIP. Conclusions Shelter in place orders were found to be effective at reducing the growth rate of COVID-19 cases in the US. Counties with a large population or a high population density were found to benefit the most from a shelter in place order.