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Modeling the transmission of COVID-19 in the US – A case study

We propose a mathematical model to investigate the transmission dynamics of COVID-19. The model incorporates both human-to-human and environment-to-human transmission pathways, and employs different transmission rates to represent the epidemiological characteristics at different time periods. Using...

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Detalles Bibliográficos
Autores principales: Yang, Chayu, Wang, Jin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: KeAi Publishing 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7809398/
https://www.ncbi.nlm.nih.gov/pubmed/33506152
http://dx.doi.org/10.1016/j.idm.2020.12.006
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author Yang, Chayu
Wang, Jin
author_facet Yang, Chayu
Wang, Jin
author_sort Yang, Chayu
collection PubMed
description We propose a mathematical model to investigate the transmission dynamics of COVID-19. The model incorporates both human-to-human and environment-to-human transmission pathways, and employs different transmission rates to represent the epidemiological characteristics at different time periods. Using this model and publicly reported data, we perform a case study for Hamilton County, the fourth-most populous county in the state of Tennessee and a region that could represent the typical situation of COVID-19 in the United States (US). Our data fitting and simulation results show that the environment may play an important role in the transmission and spread of the coronavirus. In addition, we numerically simulate a range of epidemic scenarios and make near-term forecasts on the development and trend of COVID-19 in Hamilton County.
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spelling pubmed-78093982021-01-26 Modeling the transmission of COVID-19 in the US – A case study Yang, Chayu Wang, Jin Infect Dis Model Special issue on Modelling and Forecasting the 2019 Novel Coronavirus (2019-nCoV) Transmission; Edited by Prof. Carlos Castillo-Chavez, Prof. Gerardo Chowell-Puente, Prof. Ping Yan, Prof. Jianhong Wu We propose a mathematical model to investigate the transmission dynamics of COVID-19. The model incorporates both human-to-human and environment-to-human transmission pathways, and employs different transmission rates to represent the epidemiological characteristics at different time periods. Using this model and publicly reported data, we perform a case study for Hamilton County, the fourth-most populous county in the state of Tennessee and a region that could represent the typical situation of COVID-19 in the United States (US). Our data fitting and simulation results show that the environment may play an important role in the transmission and spread of the coronavirus. In addition, we numerically simulate a range of epidemic scenarios and make near-term forecasts on the development and trend of COVID-19 in Hamilton County. KeAi Publishing 2020-12-30 /pmc/articles/PMC7809398/ /pubmed/33506152 http://dx.doi.org/10.1016/j.idm.2020.12.006 Text en © 2020 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Special issue on Modelling and Forecasting the 2019 Novel Coronavirus (2019-nCoV) Transmission; Edited by Prof. Carlos Castillo-Chavez, Prof. Gerardo Chowell-Puente, Prof. Ping Yan, Prof. Jianhong Wu
Yang, Chayu
Wang, Jin
Modeling the transmission of COVID-19 in the US – A case study
title Modeling the transmission of COVID-19 in the US – A case study
title_full Modeling the transmission of COVID-19 in the US – A case study
title_fullStr Modeling the transmission of COVID-19 in the US – A case study
title_full_unstemmed Modeling the transmission of COVID-19 in the US – A case study
title_short Modeling the transmission of COVID-19 in the US – A case study
title_sort modeling the transmission of covid-19 in the us – a case study
topic Special issue on Modelling and Forecasting the 2019 Novel Coronavirus (2019-nCoV) Transmission; Edited by Prof. Carlos Castillo-Chavez, Prof. Gerardo Chowell-Puente, Prof. Ping Yan, Prof. Jianhong Wu
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7809398/
https://www.ncbi.nlm.nih.gov/pubmed/33506152
http://dx.doi.org/10.1016/j.idm.2020.12.006
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