Loading…

Estimating Unreported COVID-19 Cases with a Time-Varying SIR Regression Model

Potential unreported infection might impair and mislead policymaking for COVID-19, and the contemporary spread of COVID-19 varies in different counties of the United States. It is necessary to estimate the cases that might be underestimated based on county-level data, to take better countermeasures...

Full description

Saved in:
Bibliographic Details
Published in:International journal of environmental research and public health 2021-01, Vol.18 (3), p.1090
Main Authors: Peng, Zhenghong, Ao, Siya, Liu, Lingbo, Bao, Shuming, Hu, Tao, Wu, Hao, Wang, Ru
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c418t-1ccb9028a5020430f4679f0c56f08610d2db37678a1ba79254b26a0428ce5d723
cites cdi_FETCH-LOGICAL-c418t-1ccb9028a5020430f4679f0c56f08610d2db37678a1ba79254b26a0428ce5d723
container_end_page
container_issue 3
container_start_page 1090
container_title International journal of environmental research and public health
container_volume 18
creator Peng, Zhenghong
Ao, Siya
Liu, Lingbo
Bao, Shuming
Hu, Tao
Wu, Hao
Wang, Ru
description Potential unreported infection might impair and mislead policymaking for COVID-19, and the contemporary spread of COVID-19 varies in different counties of the United States. It is necessary to estimate the cases that might be underestimated based on county-level data, to take better countermeasures against COVID-19. We suggested taking time-varying Susceptible-Infected-Recovered (SIR) models with unreported infection rates (UIR) to estimate factual COVID-19 cases in the United States. Both the SIR model integrated with unreported infection rates (SIRu) of fixed-time effect and SIRu with time-varying parameters (tvSIRu) were applied to estimate and compare the values of transmission rate (TR), UIR, and infection fatality rate (IFR) based on US county-level COVID-19 data. Based on the US county-level COVID-19 data from 22 January (T ) to 20 August (T ) in 2020, SIRu was first tested and verified by Ordinary Least Squares (OLS) regression. Further regression of SIRu at the county-level showed that the average values of TR, UIR, and IFR were 0.034%, 19.5%, and 0.51% respectively. The ranges of TR, UIR, and IFR for all states ranged from 0.007-0.157 (mean = 0.048), 7.31-185.6 (mean = 38.89), and 0.04-2.22% (mean = 0.22%). Among the time-varying TR equations, the power function showed better fitness, which indicated a decline in TR decreasing from 227.58 (T ) to 0.022 (T ). The general equation of tvSIRu showed that both the UIR and IFR were gradually increasing, wherein, the estimated value of UIR was 9.1 (95%CI 5.7-14.0) and IFR was 0.70% (95%CI 0.52-0.95%) at T . Despite the declining trend in TR and IFR, the UIR of COVID-19 in the United States is still on the rise, which, it was assumed would decrease with sufficient tests or improved countersues. The US medical system might be largely affected by severe cases amidst a rapid spread of COVID-19.
doi_str_mv 10.3390/ijerph18031090
format article
fullrecord <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_7908085</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2486154231</sourcerecordid><originalsourceid>FETCH-LOGICAL-c418t-1ccb9028a5020430f4679f0c56f08610d2db37678a1ba79254b26a0428ce5d723</originalsourceid><addsrcrecordid>eNpdkc1Lw0AQxRdRtFavHiXgxUvq7Ec2m4sgsWqhpVDbXpdNsmm3pNm6myr-96aopXqagfnN4z0eQlcYepQmcGdW2m2WWADFkMAR6mDOIWQc8PHBfobOvV8BUMF4corOKI0oRJx20KjvG7NWjakXwax2emNdo4sgHc8HjyFOglR57YMP0ywDFUzNWodz5T539OtgEkz0wmnvja2DkS10dYFOSlV5ffkzu2j21J-mL-Fw_DxIH4ZhzrBoQpznWQJEqAgIMAol43FSQh7xEgTHUJAiozGPhcKZihMSsYxwBYyIXEdFTGgX3X_rbrbZWhe5rhunKrlxbRT3Ka0y8u-lNku5sO8yTkCAiFqB2x8BZ9-22jdybXyuq0rV2m69JKz1ETFCcYve_ENXduvqNt6OYpjErdOW6n1TubPeO13uzWCQu6bk36bah-vDCHv8txr6BZL5jbE</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2484127767</pqid></control><display><type>article</type><title>Estimating Unreported COVID-19 Cases with a Time-Varying SIR Regression Model</title><source>NCBI_PubMed Central(免费)</source><source>Publicly Available Content Database</source><source>Free Full-Text Journals in Chemistry</source><source>Coronavirus Research Database</source><creator>Peng, Zhenghong ; Ao, Siya ; Liu, Lingbo ; Bao, Shuming ; Hu, Tao ; Wu, Hao ; Wang, Ru</creator><creatorcontrib>Peng, Zhenghong ; Ao, Siya ; Liu, Lingbo ; Bao, Shuming ; Hu, Tao ; Wu, Hao ; Wang, Ru</creatorcontrib><description>Potential unreported infection might impair and mislead policymaking for COVID-19, and the contemporary spread of COVID-19 varies in different counties of the United States. It is necessary to estimate the cases that might be underestimated based on county-level data, to take better countermeasures against COVID-19. We suggested taking time-varying Susceptible-Infected-Recovered (SIR) models with unreported infection rates (UIR) to estimate factual COVID-19 cases in the United States. Both the SIR model integrated with unreported infection rates (SIRu) of fixed-time effect and SIRu with time-varying parameters (tvSIRu) were applied to estimate and compare the values of transmission rate (TR), UIR, and infection fatality rate (IFR) based on US county-level COVID-19 data. Based on the US county-level COVID-19 data from 22 January (T ) to 20 August (T ) in 2020, SIRu was first tested and verified by Ordinary Least Squares (OLS) regression. Further regression of SIRu at the county-level showed that the average values of TR, UIR, and IFR were 0.034%, 19.5%, and 0.51% respectively. The ranges of TR, UIR, and IFR for all states ranged from 0.007-0.157 (mean = 0.048), 7.31-185.6 (mean = 38.89), and 0.04-2.22% (mean = 0.22%). Among the time-varying TR equations, the power function showed better fitness, which indicated a decline in TR decreasing from 227.58 (T ) to 0.022 (T ). The general equation of tvSIRu showed that both the UIR and IFR were gradually increasing, wherein, the estimated value of UIR was 9.1 (95%CI 5.7-14.0) and IFR was 0.70% (95%CI 0.52-0.95%) at T . Despite the declining trend in TR and IFR, the UIR of COVID-19 in the United States is still on the rise, which, it was assumed would decrease with sufficient tests or improved countersues. The US medical system might be largely affected by severe cases amidst a rapid spread of COVID-19.</description><identifier>ISSN: 1660-4601</identifier><identifier>ISSN: 1661-7827</identifier><identifier>EISSN: 1660-4601</identifier><identifier>DOI: 10.3390/ijerph18031090</identifier><identifier>PMID: 33530563</identifier><language>eng</language><publisher>Switzerland: MDPI AG</publisher><subject>Asymptomatic ; Coronaviruses ; COVID-19 ; COVID-19 - epidemiology ; Disease Notification - statistics &amp; numerical data ; Disease transmission ; Epidemic models ; Fatalities ; Humans ; Infections ; Models, Statistical ; Pandemics ; Parameter estimation ; Population ; Regression Analysis ; United States - epidemiology</subject><ispartof>International journal of environmental research and public health, 2021-01, Vol.18 (3), p.1090</ispartof><rights>2021. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2021 by the authors. 2021</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c418t-1ccb9028a5020430f4679f0c56f08610d2db37678a1ba79254b26a0428ce5d723</citedby><cites>FETCH-LOGICAL-c418t-1ccb9028a5020430f4679f0c56f08610d2db37678a1ba79254b26a0428ce5d723</cites><orcidid>0000-0002-9876-8506 ; 0000-0001-7107-8081</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2484127767/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2484127767?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25751,27922,27923,37010,37011,38514,43893,44588,53789,53791,74182,74896</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33530563$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Peng, Zhenghong</creatorcontrib><creatorcontrib>Ao, Siya</creatorcontrib><creatorcontrib>Liu, Lingbo</creatorcontrib><creatorcontrib>Bao, Shuming</creatorcontrib><creatorcontrib>Hu, Tao</creatorcontrib><creatorcontrib>Wu, Hao</creatorcontrib><creatorcontrib>Wang, Ru</creatorcontrib><title>Estimating Unreported COVID-19 Cases with a Time-Varying SIR Regression Model</title><title>International journal of environmental research and public health</title><addtitle>Int J Environ Res Public Health</addtitle><description>Potential unreported infection might impair and mislead policymaking for COVID-19, and the contemporary spread of COVID-19 varies in different counties of the United States. It is necessary to estimate the cases that might be underestimated based on county-level data, to take better countermeasures against COVID-19. We suggested taking time-varying Susceptible-Infected-Recovered (SIR) models with unreported infection rates (UIR) to estimate factual COVID-19 cases in the United States. Both the SIR model integrated with unreported infection rates (SIRu) of fixed-time effect and SIRu with time-varying parameters (tvSIRu) were applied to estimate and compare the values of transmission rate (TR), UIR, and infection fatality rate (IFR) based on US county-level COVID-19 data. Based on the US county-level COVID-19 data from 22 January (T ) to 20 August (T ) in 2020, SIRu was first tested and verified by Ordinary Least Squares (OLS) regression. Further regression of SIRu at the county-level showed that the average values of TR, UIR, and IFR were 0.034%, 19.5%, and 0.51% respectively. The ranges of TR, UIR, and IFR for all states ranged from 0.007-0.157 (mean = 0.048), 7.31-185.6 (mean = 38.89), and 0.04-2.22% (mean = 0.22%). Among the time-varying TR equations, the power function showed better fitness, which indicated a decline in TR decreasing from 227.58 (T ) to 0.022 (T ). The general equation of tvSIRu showed that both the UIR and IFR were gradually increasing, wherein, the estimated value of UIR was 9.1 (95%CI 5.7-14.0) and IFR was 0.70% (95%CI 0.52-0.95%) at T . Despite the declining trend in TR and IFR, the UIR of COVID-19 in the United States is still on the rise, which, it was assumed would decrease with sufficient tests or improved countersues. The US medical system might be largely affected by severe cases amidst a rapid spread of COVID-19.</description><subject>Asymptomatic</subject><subject>Coronaviruses</subject><subject>COVID-19</subject><subject>COVID-19 - epidemiology</subject><subject>Disease Notification - statistics &amp; numerical data</subject><subject>Disease transmission</subject><subject>Epidemic models</subject><subject>Fatalities</subject><subject>Humans</subject><subject>Infections</subject><subject>Models, Statistical</subject><subject>Pandemics</subject><subject>Parameter estimation</subject><subject>Population</subject><subject>Regression Analysis</subject><subject>United States - epidemiology</subject><issn>1660-4601</issn><issn>1661-7827</issn><issn>1660-4601</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>COVID</sourceid><sourceid>PIMPY</sourceid><recordid>eNpdkc1Lw0AQxRdRtFavHiXgxUvq7Ec2m4sgsWqhpVDbXpdNsmm3pNm6myr-96aopXqagfnN4z0eQlcYepQmcGdW2m2WWADFkMAR6mDOIWQc8PHBfobOvV8BUMF4corOKI0oRJx20KjvG7NWjakXwax2emNdo4sgHc8HjyFOglR57YMP0ywDFUzNWodz5T539OtgEkz0wmnvja2DkS10dYFOSlV5ffkzu2j21J-mL-Fw_DxIH4ZhzrBoQpznWQJEqAgIMAol43FSQh7xEgTHUJAiozGPhcKZihMSsYxwBYyIXEdFTGgX3X_rbrbZWhe5rhunKrlxbRT3Ka0y8u-lNku5sO8yTkCAiFqB2x8BZ9-22jdybXyuq0rV2m69JKz1ETFCcYve_ENXduvqNt6OYpjErdOW6n1TubPeO13uzWCQu6bk36bah-vDCHv8txr6BZL5jbE</recordid><startdate>20210126</startdate><enddate>20210126</enddate><creator>Peng, Zhenghong</creator><creator>Ao, Siya</creator><creator>Liu, Lingbo</creator><creator>Bao, Shuming</creator><creator>Hu, Tao</creator><creator>Wu, Hao</creator><creator>Wang, Ru</creator><general>MDPI AG</general><general>MDPI</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8C1</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>COVID</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>M0S</scope><scope>M1P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-9876-8506</orcidid><orcidid>https://orcid.org/0000-0001-7107-8081</orcidid></search><sort><creationdate>20210126</creationdate><title>Estimating Unreported COVID-19 Cases with a Time-Varying SIR Regression Model</title><author>Peng, Zhenghong ; Ao, Siya ; Liu, Lingbo ; Bao, Shuming ; Hu, Tao ; Wu, Hao ; Wang, Ru</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c418t-1ccb9028a5020430f4679f0c56f08610d2db37678a1ba79254b26a0428ce5d723</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Asymptomatic</topic><topic>Coronaviruses</topic><topic>COVID-19</topic><topic>COVID-19 - epidemiology</topic><topic>Disease Notification - statistics &amp; numerical data</topic><topic>Disease transmission</topic><topic>Epidemic models</topic><topic>Fatalities</topic><topic>Humans</topic><topic>Infections</topic><topic>Models, Statistical</topic><topic>Pandemics</topic><topic>Parameter estimation</topic><topic>Population</topic><topic>Regression Analysis</topic><topic>United States - epidemiology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Peng, Zhenghong</creatorcontrib><creatorcontrib>Ao, Siya</creatorcontrib><creatorcontrib>Liu, Lingbo</creatorcontrib><creatorcontrib>Bao, Shuming</creatorcontrib><creatorcontrib>Hu, Tao</creatorcontrib><creatorcontrib>Wu, Hao</creatorcontrib><creatorcontrib>Wang, Ru</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>ProQuest_Health &amp; Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Public Health Database</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>Coronavirus Research Database</collection><collection>ProQuest Central</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health &amp; Medical Complete (Alumni)</collection><collection>Health &amp; Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>International journal of environmental research and public health</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Peng, Zhenghong</au><au>Ao, Siya</au><au>Liu, Lingbo</au><au>Bao, Shuming</au><au>Hu, Tao</au><au>Wu, Hao</au><au>Wang, Ru</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Estimating Unreported COVID-19 Cases with a Time-Varying SIR Regression Model</atitle><jtitle>International journal of environmental research and public health</jtitle><addtitle>Int J Environ Res Public Health</addtitle><date>2021-01-26</date><risdate>2021</risdate><volume>18</volume><issue>3</issue><spage>1090</spage><pages>1090-</pages><issn>1660-4601</issn><issn>1661-7827</issn><eissn>1660-4601</eissn><abstract>Potential unreported infection might impair and mislead policymaking for COVID-19, and the contemporary spread of COVID-19 varies in different counties of the United States. It is necessary to estimate the cases that might be underestimated based on county-level data, to take better countermeasures against COVID-19. We suggested taking time-varying Susceptible-Infected-Recovered (SIR) models with unreported infection rates (UIR) to estimate factual COVID-19 cases in the United States. Both the SIR model integrated with unreported infection rates (SIRu) of fixed-time effect and SIRu with time-varying parameters (tvSIRu) were applied to estimate and compare the values of transmission rate (TR), UIR, and infection fatality rate (IFR) based on US county-level COVID-19 data. Based on the US county-level COVID-19 data from 22 January (T ) to 20 August (T ) in 2020, SIRu was first tested and verified by Ordinary Least Squares (OLS) regression. Further regression of SIRu at the county-level showed that the average values of TR, UIR, and IFR were 0.034%, 19.5%, and 0.51% respectively. The ranges of TR, UIR, and IFR for all states ranged from 0.007-0.157 (mean = 0.048), 7.31-185.6 (mean = 38.89), and 0.04-2.22% (mean = 0.22%). Among the time-varying TR equations, the power function showed better fitness, which indicated a decline in TR decreasing from 227.58 (T ) to 0.022 (T ). The general equation of tvSIRu showed that both the UIR and IFR were gradually increasing, wherein, the estimated value of UIR was 9.1 (95%CI 5.7-14.0) and IFR was 0.70% (95%CI 0.52-0.95%) at T . Despite the declining trend in TR and IFR, the UIR of COVID-19 in the United States is still on the rise, which, it was assumed would decrease with sufficient tests or improved countersues. The US medical system might be largely affected by severe cases amidst a rapid spread of COVID-19.</abstract><cop>Switzerland</cop><pub>MDPI AG</pub><pmid>33530563</pmid><doi>10.3390/ijerph18031090</doi><orcidid>https://orcid.org/0000-0002-9876-8506</orcidid><orcidid>https://orcid.org/0000-0001-7107-8081</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1660-4601
ispartof International journal of environmental research and public health, 2021-01, Vol.18 (3), p.1090
issn 1660-4601
1661-7827
1660-4601
language eng
recordid cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_7908085
source NCBI_PubMed Central(免费); Publicly Available Content Database; Free Full-Text Journals in Chemistry; Coronavirus Research Database
subjects Asymptomatic
Coronaviruses
COVID-19
COVID-19 - epidemiology
Disease Notification - statistics & numerical data
Disease transmission
Epidemic models
Fatalities
Humans
Infections
Models, Statistical
Pandemics
Parameter estimation
Population
Regression Analysis
United States - epidemiology
title Estimating Unreported COVID-19 Cases with a Time-Varying SIR Regression Model
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-14T10%3A32%3A08IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Estimating%20Unreported%20COVID-19%20Cases%20with%20a%20Time-Varying%20SIR%20Regression%20Model&rft.jtitle=International%20journal%20of%20environmental%20research%20and%20public%20health&rft.au=Peng,%20Zhenghong&rft.date=2021-01-26&rft.volume=18&rft.issue=3&rft.spage=1090&rft.pages=1090-&rft.issn=1660-4601&rft.eissn=1660-4601&rft_id=info:doi/10.3390/ijerph18031090&rft_dat=%3Cproquest_pubme%3E2486154231%3C/proquest_pubme%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c418t-1ccb9028a5020430f4679f0c56f08610d2db37678a1ba79254b26a0428ce5d723%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2484127767&rft_id=info:pmid/33530563&rfr_iscdi=true