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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...
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Published in: | International journal of environmental research and public health 2021-01, Vol.18 (3), p.1090 |
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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 |
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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 & 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. 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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> |
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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 |
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