Modeling of the Long-Term Epidemic Dynamics of COVID-19 in the United States
Coronavirus 2019 (COVID-19) is causing a severe pandemic that has resulted in millions of confirmed cases and deaths around the world. In the absence of effective drugs for treatment, non-pharmaceutical interventions are the most effective approaches to control the disease. Although some countries h...
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description | Coronavirus 2019 (COVID-19) is causing a severe pandemic that has resulted in millions of confirmed cases and deaths around the world. In the absence of effective drugs for treatment, non-pharmaceutical interventions are the most effective approaches to control the disease. Although some countries have the pandemic under control, all countries around the world, including the United States (US), are still in the process of controlling COVID-19, which calls for an effective epidemic model to describe the transmission dynamics of COVID-19. Meeting this need, we have extensively investigated the transmission dynamics of COVID-19 from 22 January 2020 to 14 February 2021 for the 50 states of the United States, which revealed the general principles underlying the spread of the virus in terms of intervention measures and demographic properties. We further proposed a time-dependent epidemic model, named T-SIR, to model the long-term transmission dynamics of COVID-19 in the US. It was shown in this paper that our T-SIR model could effectively model the epidemic dynamics of COVID-19 for all 50 states, which provided insights into the transmission dynamics of COVID-19 in the US. The present study will be valuable to help understand the epidemic dynamics of COVID-19 and thus help governments determine and implement effective intervention measures or vaccine prioritization to control the pandemic. |
doi_str_mv | 10.3390/ijerph18147594 |
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In the absence of effective drugs for treatment, non-pharmaceutical interventions are the most effective approaches to control the disease. Although some countries have the pandemic under control, all countries around the world, including the United States (US), are still in the process of controlling COVID-19, which calls for an effective epidemic model to describe the transmission dynamics of COVID-19. Meeting this need, we have extensively investigated the transmission dynamics of COVID-19 from 22 January 2020 to 14 February 2021 for the 50 states of the United States, which revealed the general principles underlying the spread of the virus in terms of intervention measures and demographic properties. We further proposed a time-dependent epidemic model, named T-SIR, to model the long-term transmission dynamics of COVID-19 in the US. It was shown in this paper that our T-SIR model could effectively model the epidemic dynamics of COVID-19 for all 50 states, which provided insights into the transmission dynamics of COVID-19 in the US. The present study will be valuable to help understand the epidemic dynamics of COVID-19 and thus help governments determine and implement effective intervention measures or vaccine prioritization to control the pandemic.</description><identifier>ISSN: 1660-4601</identifier><identifier>ISSN: 1661-7827</identifier><identifier>EISSN: 1660-4601</identifier><identifier>DOI: 10.3390/ijerph18147594</identifier><identifier>PMID: 34300045</identifier><language>eng</language><publisher>BASEL: Mdpi</publisher><subject>Coronaviruses ; COVID-19 ; COVID-19 vaccines ; Demography ; Disease control ; Disease prevention ; Disease transmission ; Environmental Sciences ; Environmental Sciences & Ecology ; Epidemic models ; Epidemics ; Epidemiology ; Infectious diseases ; Intervention ; Life Sciences & Biomedicine ; Mathematical models ; Pandemics ; Pharmaceuticals ; Population ; Public health ; Public, Environmental & Occupational Health ; Science & Technology ; Social distancing ; Trends ; Vaccines ; Viruses</subject><ispartof>International journal of environmental research and public health, 2021-07, Vol.18 (14), p.7594, Article 7594</ispartof><rights>2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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In the absence of effective drugs for treatment, non-pharmaceutical interventions are the most effective approaches to control the disease. Although some countries have the pandemic under control, all countries around the world, including the United States (US), are still in the process of controlling COVID-19, which calls for an effective epidemic model to describe the transmission dynamics of COVID-19. Meeting this need, we have extensively investigated the transmission dynamics of COVID-19 from 22 January 2020 to 14 February 2021 for the 50 states of the United States, which revealed the general principles underlying the spread of the virus in terms of intervention measures and demographic properties. We further proposed a time-dependent epidemic model, named T-SIR, to model the long-term transmission dynamics of COVID-19 in the US. It was shown in this paper that our T-SIR model could effectively model the epidemic dynamics of COVID-19 for all 50 states, which provided insights into the transmission dynamics of COVID-19 in the US. The present study will be valuable to help understand the epidemic dynamics of COVID-19 and thus help governments determine and implement effective intervention measures or vaccine prioritization to control the pandemic.</description><subject>Coronaviruses</subject><subject>COVID-19</subject><subject>COVID-19 vaccines</subject><subject>Demography</subject><subject>Disease control</subject><subject>Disease prevention</subject><subject>Disease transmission</subject><subject>Environmental Sciences</subject><subject>Environmental Sciences & Ecology</subject><subject>Epidemic models</subject><subject>Epidemics</subject><subject>Epidemiology</subject><subject>Infectious diseases</subject><subject>Intervention</subject><subject>Life Sciences & Biomedicine</subject><subject>Mathematical models</subject><subject>Pandemics</subject><subject>Pharmaceuticals</subject><subject>Population</subject><subject>Public health</subject><subject>Public, Environmental & Occupational Health</subject><subject>Science & Technology</subject><subject>Social distancing</subject><subject>Trends</subject><subject>Vaccines</subject><subject>Viruses</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>GIZIO</sourceid><sourceid>HGBXW</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNqNkc1P3DAQxa2qCCjl2nOkXipVAU_8EedSqQq0RdqKQ6FXy-tMdr1K7K2dtOK_r5dFCDhxmtH499549Aj5APSMsYaeuw3G7RoU8Fo0_A05BilpySWFt0_6I_IupQ2lTHHZHJIjxhmllItjsvgZOhycXxWhL6Y1FovgV-UNxrG43LoOR2eLiztvck07pL3-fXVRQlM4f4_fejdhV_yazITpPTnozZDw9KGekNtvlzftj3Jx_f2q_booLWvEVC571lmpjJAClDRNI2ohe8gTK60AibySqgKztNSaWnWqBuh7Kow0FRNYsxPyZe-7nZcjdhb9FM2gt9GNJt7pYJx-_uLdWq_CX60YFRJoNvj0YBDDnxnTpEeXLA6D8RjmpCshBFDZwG7XxxfoJszR5_N2FBccauCZOttTNoaUIvaPnwGqd0Hp50Flwee94B8uQ5-sQ2_xUZTDkbWsOKtyRyHT6vV063IYLvg2zH5i_wH0HaQ_</recordid><startdate>20210716</startdate><enddate>20210716</enddate><creator>Huang, Derek</creator><creator>Tao, Huanyu</creator><creator>Wu, Qilong</creator><creator>Huang, Sheng-You</creator><creator>Xiao, Yi</creator><general>Mdpi</general><general>MDPI AG</general><general>MDPI</general><scope>17B</scope><scope>BLEPL</scope><scope>DTL</scope><scope>DVR</scope><scope>EGQ</scope><scope>GIZIO</scope><scope>HGBXW</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/0000-0002-4209-4565</orcidid><orcidid>https://orcid.org/0000-0002-5023-3166</orcidid></search><sort><creationdate>20210716</creationdate><title>Modeling of the Long-Term Epidemic Dynamics of COVID-19 in the United States</title><author>Huang, Derek ; 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subjects | Coronaviruses COVID-19 COVID-19 vaccines Demography Disease control Disease prevention Disease transmission Environmental Sciences Environmental Sciences & Ecology Epidemic models Epidemics Epidemiology Infectious diseases Intervention Life Sciences & Biomedicine Mathematical models Pandemics Pharmaceuticals Population Public health Public, Environmental & Occupational Health Science & Technology Social distancing Trends Vaccines Viruses |
title | Modeling of the Long-Term Epidemic Dynamics of COVID-19 in the United States |
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