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State-specific projection of COVID-19 infection in the United States and evaluation of three major control measures
Most models of the COVID-19 pandemic in the United States do not consider geographic variation and spatial interaction. In this research, we developed a travel-network-based susceptible-exposed-infectious-removed (SEIR) mathematical compartmental model system that characterizes infections by state a...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7773742/ https://www.ncbi.nlm.nih.gov/pubmed/33380729 http://dx.doi.org/10.1038/s41598-020-80044-3 |
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author | Chen, Shi Li, Qin Gao, Song Kang, Yuhao Shi, Xun |
author_facet | Chen, Shi Li, Qin Gao, Song Kang, Yuhao Shi, Xun |
author_sort | Chen, Shi |
collection | PubMed |
description | Most models of the COVID-19 pandemic in the United States do not consider geographic variation and spatial interaction. In this research, we developed a travel-network-based susceptible-exposed-infectious-removed (SEIR) mathematical compartmental model system that characterizes infections by state and incorporates inflows and outflows of interstate travelers. Modeling reveals that curbing interstate travel when the disease is already widespread will make little difference. Meanwhile, increased testing capacity (facilitating early identification of infected people and quick isolation) and strict social-distancing and self-quarantine rules are most effective in abating the outbreak. The modeling has also produced state-specific information. For example, for New York and Michigan, isolation of persons exposed to the virus needs to be imposed within 2 days to prevent a broad outbreak, whereas for other states this period can be 3.6 days. This model could be used to determine resources needed before safely lifting state policies on social distancing. |
format | Online Article Text |
id | pubmed-7773742 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-77737422021-01-07 State-specific projection of COVID-19 infection in the United States and evaluation of three major control measures Chen, Shi Li, Qin Gao, Song Kang, Yuhao Shi, Xun Sci Rep Article Most models of the COVID-19 pandemic in the United States do not consider geographic variation and spatial interaction. In this research, we developed a travel-network-based susceptible-exposed-infectious-removed (SEIR) mathematical compartmental model system that characterizes infections by state and incorporates inflows and outflows of interstate travelers. Modeling reveals that curbing interstate travel when the disease is already widespread will make little difference. Meanwhile, increased testing capacity (facilitating early identification of infected people and quick isolation) and strict social-distancing and self-quarantine rules are most effective in abating the outbreak. The modeling has also produced state-specific information. For example, for New York and Michigan, isolation of persons exposed to the virus needs to be imposed within 2 days to prevent a broad outbreak, whereas for other states this period can be 3.6 days. This model could be used to determine resources needed before safely lifting state policies on social distancing. Nature Publishing Group UK 2020-12-30 /pmc/articles/PMC7773742/ /pubmed/33380729 http://dx.doi.org/10.1038/s41598-020-80044-3 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Chen, Shi Li, Qin Gao, Song Kang, Yuhao Shi, Xun State-specific projection of COVID-19 infection in the United States and evaluation of three major control measures |
title | State-specific projection of COVID-19 infection in the United States and evaluation of three major control measures |
title_full | State-specific projection of COVID-19 infection in the United States and evaluation of three major control measures |
title_fullStr | State-specific projection of COVID-19 infection in the United States and evaluation of three major control measures |
title_full_unstemmed | State-specific projection of COVID-19 infection in the United States and evaluation of three major control measures |
title_short | State-specific projection of COVID-19 infection in the United States and evaluation of three major control measures |
title_sort | state-specific projection of covid-19 infection in the united states and evaluation of three major control measures |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7773742/ https://www.ncbi.nlm.nih.gov/pubmed/33380729 http://dx.doi.org/10.1038/s41598-020-80044-3 |
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