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

Abstract

Objectives: The goal of the present work is to investigate trends among US counties and coronavirus disease 2019 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.

Spatio-temporal estimation of the daily cases of COVID-19 in worldwide using random forest machine learning algorithm

“…Epidemiological, statistical and mathematical models have also been introduced to predict the distribution, to observe the changes depending on meteorological conditions, and to examine the structure of this epidemic which affects all countries globally [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] . Besides, the performance of machine learning approaches for the diagnosis and treatment of the disease was also studied [22] , [23] , [24] , [25] , [26] , [27] , [28] . All these studies reveal the general structure of such an epidemic and disease that humanity has not encountered before and its effects on society.…”

Underreporting of Death by COVID-19 in Brazil's Second Most Populous State

“…Coronavirus 2 (CoV-2) is a new beta-coronavirus related to severe acute respiratory syndrome (SARS) that emerged in December 2019 in China and became a pandemic in March 2020 due to its high infection and mortality rates (1)(2)(3). COVID-19 was the official name given to the disease caused by the new coronavirus of 2019 (SARS-CoV-2) by the World Health Organization (WHO) (1).…”

Prediction of COVID-19 pandemic measuring criteria using support vector machine, prophet and linear regression models in Indian scenario

“…They have used statistical analysis and random forest approach to analyze the cumulative growth trend of COVID-19. They have concluded that the growth trend of COVID-19 is effectively reduced after SIP order of US government and countries with higher density getting more benefit from the SIP order [3].…”