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
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. This is a prospective cohort study. Compound growth rates were calculated using cumulative confir...
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| Veröffentlicht in: | Public health (London) 2020-08, Vol.185, p.27-29 |
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| description | 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.
This is a prospective cohort study.
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.
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.
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.
•The March 16 guidelines for coronavirus disease 2019 [COVID-19] reduced the compound growth rate of confirmed cases by 6.6%.•Counties that issued a shelter-in-place order saw a further reduction in the compound growth rate of COVID-19 cases by 7.8%.•Random forest showed that population and pop./sq. mile were key metrics for determining the contribution of shelter-in-place. |
| doi_str_mv | 10.1016/j.puhe.2020.04.016 |
| format | Article |
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This is a prospective cohort study.
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.
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.
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.
•The March 16 guidelines for coronavirus disease 2019 [COVID-19] reduced the compound growth rate of confirmed cases by 6.6%.•Counties that issued a shelter-in-place order saw a further reduction in the compound growth rate of COVID-19 cases by 7.8%.•Random forest showed that population and pop./sq. mile were key metrics for determining the contribution of shelter-in-place.</description><identifier>ISSN: 0033-3506</identifier><identifier>ISSN: 1476-5616</identifier><identifier>EISSN: 1476-5616</identifier><identifier>DOI: 10.1016/j.puhe.2020.04.016</identifier><identifier>PMID: 32526559</identifier><language>eng</language><publisher>Netherlands: Elsevier Ltd</publisher><subject>Cohort analysis ; Coronavirus Infections - epidemiology ; Coronavirus Infections - prevention & control ; Coronaviruses ; COVID-19 ; COVID-19 - epidemiology ; COVID-19 - prevention & control ; Data Interpretation, Statistical ; Disease control ; Growth rate ; Health status ; Humans ; Learning algorithms ; Machine Learning ; Models, Theoretical ; Pandemics - prevention & control ; Physical Distancing ; Pneumonia, Viral - epidemiology ; Pneumonia, Viral - prevention & control ; Population ; Population Density ; Prospective Studies ; Public Health - legislation & jurisprudence ; SARS-CoV-2 ; Shelter-in-place ; Social distancing ; Statistical analysis ; Statistics ; United States - epidemiology ; Viral diseases ; Viruses</subject><ispartof>Public health (London), 2020-08, Vol.185, p.27-29</ispartof><rights>2020 The Royal Society for Public Health</rights><rights>Copyright © 2020 The Royal Society for Public Health. All rights reserved.</rights><rights>Copyright Elsevier Science Ltd. Aug 2020</rights><rights>2020 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved. 2020 The Royal Society for Public Health</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c483t-52e990fff2239468a221b6b058dba95b4fd0bb47a237bd175192ee12329633283</citedby><cites>FETCH-LOGICAL-c483t-52e990fff2239468a221b6b058dba95b4fd0bb47a237bd175192ee12329633283</cites><orcidid>0000-0002-3081-5556</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.puhe.2020.04.016$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>230,314,780,784,885,3548,27922,27923,30997,45993</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32526559$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Cobb, J.S.</creatorcontrib><creatorcontrib>Seale, M.A.</creatorcontrib><title>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</title><title>Public health (London)</title><addtitle>Public Health</addtitle><description>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.
This is a prospective cohort study.
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.
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.
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.
•The March 16 guidelines for coronavirus disease 2019 [COVID-19] reduced the compound growth rate of confirmed cases by 6.6%.•Counties that issued a shelter-in-place order saw a further reduction in the compound growth rate of COVID-19 cases by 7.8%.•Random forest showed that population and pop./sq. mile were key metrics for determining the contribution of shelter-in-place.</description><subject>Cohort analysis</subject><subject>Coronavirus Infections - epidemiology</subject><subject>Coronavirus Infections - prevention & control</subject><subject>Coronaviruses</subject><subject>COVID-19</subject><subject>COVID-19 - epidemiology</subject><subject>COVID-19 - prevention & control</subject><subject>Data Interpretation, Statistical</subject><subject>Disease control</subject><subject>Growth rate</subject><subject>Health status</subject><subject>Humans</subject><subject>Learning algorithms</subject><subject>Machine Learning</subject><subject>Models, Theoretical</subject><subject>Pandemics - prevention & control</subject><subject>Physical Distancing</subject><subject>Pneumonia, Viral - epidemiology</subject><subject>Pneumonia, Viral - prevention & control</subject><subject>Population</subject><subject>Population Density</subject><subject>Prospective Studies</subject><subject>Public Health - legislation & jurisprudence</subject><subject>SARS-CoV-2</subject><subject>Shelter-in-place</subject><subject>Social distancing</subject><subject>Statistical analysis</subject><subject>Statistics</subject><subject>United States - epidemiology</subject><subject>Viral diseases</subject><subject>Viruses</subject><issn>0033-3506</issn><issn>1476-5616</issn><issn>1476-5616</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>7QJ</sourceid><recordid>eNp9kstuEzEUhkcIREPhBVggS2zKYoJvc7GEkFAoUKlSF1C2lsc-kziasYPtCeTZeDk8TaiABStbPt_5fGz9RfGc4CXBpH69Xe6mDSwppniJ-TIfPSgWhDd1WdWkflgsMGasZBWuz4onMW4xxrRh1ePijNGK1lUlFsXPyx9qtM66NUobQND3oBPyPYpeWzUgY2NSTs917-4Q7cedn5xB6-C_pw0KKsHcsLr5evW-JAKpdOImlw5ogD0M6OLW2QQGfU6Zjq_QFGdjVqfstzpfpJwaDhFi3hikstUZP6LeB4gJjUpvrIMsU-Fu1tEbGJ4Wj3o1RHh2Ws-L2w-XX1afyuubj1erd9el5i1LZUVBCNz3PaVM8LpVlJKu7nDVmk6JquO9wV3HG0VZ0xnSVERQAEIZFTVjtGXnxdujdzd1IxgNLgU1yF2wowoH6ZWVf1ec3ci138uGtDUlJAsuToLgv035QXK0UcMwKAd-ipJyQkUrGt5k9OU_6NZPIf_NTHFBG04IzhQ9Ujr4GAP098MQLOdsyK2csyHnbEjMZT7KTS_-fMZ9y-8wZODNEYD8mXsLQUZtwWkwNuRUSOPt__y_AMS9zVc</recordid><startdate>20200801</startdate><enddate>20200801</enddate><creator>Cobb, J.S.</creator><creator>Seale, M.A.</creator><general>Elsevier Ltd</general><general>Elsevier Science Ltd</general><general>The Royal Society for Public Health. 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This is a prospective cohort study.
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.
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.
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.
•The March 16 guidelines for coronavirus disease 2019 [COVID-19] reduced the compound growth rate of confirmed cases by 6.6%.•Counties that issued a shelter-in-place order saw a further reduction in the compound growth rate of COVID-19 cases by 7.8%.•Random forest showed that population and pop./sq. mile were key metrics for determining the contribution of shelter-in-place.</abstract><cop>Netherlands</cop><pub>Elsevier Ltd</pub><pmid>32526559</pmid><doi>10.1016/j.puhe.2020.04.016</doi><tpages>3</tpages><orcidid>https://orcid.org/0000-0002-3081-5556</orcidid><oa>free_for_read</oa></addata></record> |
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| source | MEDLINE; Applied Social Sciences Index & Abstracts (ASSIA); ScienceDirect Journals (5 years ago - present) |
| subjects | Cohort analysis Coronavirus Infections - epidemiology Coronavirus Infections - prevention & control Coronaviruses COVID-19 COVID-19 - epidemiology COVID-19 - prevention & control Data Interpretation, Statistical Disease control Growth rate Health status Humans Learning algorithms Machine Learning Models, Theoretical Pandemics - prevention & control Physical Distancing Pneumonia, Viral - epidemiology Pneumonia, Viral - prevention & control Population Population Density Prospective Studies Public Health - legislation & jurisprudence SARS-CoV-2 Shelter-in-place Social distancing Statistical analysis Statistics United States - epidemiology Viral diseases Viruses |
| title | 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 |
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