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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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Autores principales: Huang, Derek, Tao, Huanyu, Wu, Qilong, Huang, Sheng-You, Xiao, Yi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8305610/
https://www.ncbi.nlm.nih.gov/pubmed/34300045
http://dx.doi.org/10.3390/ijerph18147594
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author Huang, Derek
Tao, Huanyu
Wu, Qilong
Huang, Sheng-You
Xiao, Yi
author_facet Huang, Derek
Tao, Huanyu
Wu, Qilong
Huang, Sheng-You
Xiao, Yi
author_sort Huang, Derek
collection PubMed
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.
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spelling pubmed-83056102021-07-25 Modeling of the Long-Term Epidemic Dynamics of COVID-19 in the United States Huang, Derek Tao, Huanyu Wu, Qilong Huang, Sheng-You Xiao, Yi Int J Environ Res Public Health Article 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. MDPI 2021-07-16 /pmc/articles/PMC8305610/ /pubmed/34300045 http://dx.doi.org/10.3390/ijerph18147594 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/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/).
spellingShingle Article
Huang, Derek
Tao, Huanyu
Wu, Qilong
Huang, Sheng-You
Xiao, Yi
Modeling of the Long-Term Epidemic Dynamics of COVID-19 in the United States
title Modeling of the Long-Term Epidemic Dynamics of COVID-19 in the United States
title_full Modeling of the Long-Term Epidemic Dynamics of COVID-19 in the United States
title_fullStr Modeling of the Long-Term Epidemic Dynamics of COVID-19 in the United States
title_full_unstemmed Modeling of the Long-Term Epidemic Dynamics of COVID-19 in the United States
title_short Modeling of the Long-Term Epidemic Dynamics of COVID-19 in the United States
title_sort modeling of the long-term epidemic dynamics of covid-19 in the united states
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8305610/
https://www.ncbi.nlm.nih.gov/pubmed/34300045
http://dx.doi.org/10.3390/ijerph18147594
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