tetano
Editor, Senior Moderator
Public Health
. 2020 Apr 28;185:27-29.
doi: 10.1016/j.puhe.2020.04.016. Online ahead of print.
Examining the Effect of Social Distancing on the Compound Growth Rate of COVID-19 at the County Level (United States) Using Statistical Analyses and a Random Forest Machine Learning Model
J S Cobb[SUP] 1 [/SUP], M A Seale[SUP] 2 [/SUP]
Affiliations
Abstract
Objectives: The goal of the present work is to investigate trends among US counties and coronavirus disease 2019 (COVID-19) growth rates in relation to the existence of shelter-in-place (SIP) orders in that county.
Study design: This is a 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 3139 US counties. Compound growth was chosen as it gives a single number that can be used in machine learning to represent the 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 SIP 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% compared with the counties that did not implement 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: SIP 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 SIP order.
Keywords: COVID-19; Machine learning; SARS-CoV-2; Shelter-in-place; Social distancing; Statistics.
. 2020 Apr 28;185:27-29.
doi: 10.1016/j.puhe.2020.04.016. Online ahead of print.
Examining the Effect of Social Distancing on the Compound Growth Rate of COVID-19 at the County Level (United States) Using Statistical Analyses and a Random Forest Machine Learning Model
J S Cobb[SUP] 1 [/SUP], M A Seale[SUP] 2 [/SUP]
Affiliations
- PMID: 32526559
- DOI: 10.1016/j.puhe.2020.04.016
Abstract
Objectives: The goal of the present work is to investigate trends among US counties and coronavirus disease 2019 (COVID-19) growth rates in relation to the existence of shelter-in-place (SIP) orders in that county.
Study design: This is a 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 3139 US counties. Compound growth was chosen as it gives a single number that can be used in machine learning to represent the 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 SIP 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% compared with the counties that did not implement 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: SIP 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 SIP order.
Keywords: COVID-19; Machine learning; SARS-CoV-2; Shelter-in-place; Social distancing; Statistics.