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Outbreak dynamics of COVID-19 in China and the United States

On March 11, 2020, the World Health Organization declared the coronavirus disease 2019, COVID-19, a global pandemic. In an unprecedented collective effort, massive amounts of data are now being collected worldwide to estimate the immediate and long-term impact of this pandemic on the health system a...

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Autores principales: Peirlinck, Mathias, Linka, Kevin, Sahli Costabal, Francisco, Kuhl, Ellen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7185268/
https://www.ncbi.nlm.nih.gov/pubmed/32342242
http://dx.doi.org/10.1007/s10237-020-01332-5
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author Peirlinck, Mathias
Linka, Kevin
Sahli Costabal, Francisco
Kuhl, Ellen
author_facet Peirlinck, Mathias
Linka, Kevin
Sahli Costabal, Francisco
Kuhl, Ellen
author_sort Peirlinck, Mathias
collection PubMed
description On March 11, 2020, the World Health Organization declared the coronavirus disease 2019, COVID-19, a global pandemic. In an unprecedented collective effort, massive amounts of data are now being collected worldwide to estimate the immediate and long-term impact of this pandemic on the health system and the global economy. However, the precise timeline of the disease, its transmissibility, and the effect of mitigation strategies remain incompletely understood. Here we integrate a global network model with a local epidemic SEIR model to quantify the outbreak dynamics of COVID-19 in China and the United States. For the outbreak in China, in [Formula: see text] provinces, we found a latent period of 2.56 ± 0.72 days, a contact period of 1.47 ± 0.32 days, and an infectious period of 17.82 ± 2.95 days. We postulate that the latent and infectious periods are disease-specific, whereas the contact period is behavior-specific and can vary between different provinces, states, or countries. For the early stages of the outbreak in the United States, in [Formula: see text] states, we adopted the disease-specific values from China and found a contact period of 3.38 ± 0.69 days. Our network model predicts that—without the massive political mitigation strategies that are in place today—the United States would have faced a basic reproduction number of 5.30 ± 0.95 and a nationwide peak of the outbreak on May 10, 2020 with 3 million infections. Our results demonstrate how mathematical modeling can help estimate outbreak dynamics and provide decision guidelines for successful outbreak control. We anticipate that our model will become a valuable tool to estimate the potential of vaccination and quantify the effect of relaxing political measures including total lockdown, shelter in place, and travel restrictions for low-risk subgroups of the population or for the population as a whole.
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spelling pubmed-71852682020-04-28 Outbreak dynamics of COVID-19 in China and the United States Peirlinck, Mathias Linka, Kevin Sahli Costabal, Francisco Kuhl, Ellen Biomech Model Mechanobiol Original Paper On March 11, 2020, the World Health Organization declared the coronavirus disease 2019, COVID-19, a global pandemic. In an unprecedented collective effort, massive amounts of data are now being collected worldwide to estimate the immediate and long-term impact of this pandemic on the health system and the global economy. However, the precise timeline of the disease, its transmissibility, and the effect of mitigation strategies remain incompletely understood. Here we integrate a global network model with a local epidemic SEIR model to quantify the outbreak dynamics of COVID-19 in China and the United States. For the outbreak in China, in [Formula: see text] provinces, we found a latent period of 2.56 ± 0.72 days, a contact period of 1.47 ± 0.32 days, and an infectious period of 17.82 ± 2.95 days. We postulate that the latent and infectious periods are disease-specific, whereas the contact period is behavior-specific and can vary between different provinces, states, or countries. For the early stages of the outbreak in the United States, in [Formula: see text] states, we adopted the disease-specific values from China and found a contact period of 3.38 ± 0.69 days. Our network model predicts that—without the massive political mitigation strategies that are in place today—the United States would have faced a basic reproduction number of 5.30 ± 0.95 and a nationwide peak of the outbreak on May 10, 2020 with 3 million infections. Our results demonstrate how mathematical modeling can help estimate outbreak dynamics and provide decision guidelines for successful outbreak control. We anticipate that our model will become a valuable tool to estimate the potential of vaccination and quantify the effect of relaxing political measures including total lockdown, shelter in place, and travel restrictions for low-risk subgroups of the population or for the population as a whole. Springer Berlin Heidelberg 2020-04-27 2020 /pmc/articles/PMC7185268/ /pubmed/32342242 http://dx.doi.org/10.1007/s10237-020-01332-5 Text en © Springer-Verlag GmbH Germany, part of Springer Nature 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Original Paper
Peirlinck, Mathias
Linka, Kevin
Sahli Costabal, Francisco
Kuhl, Ellen
Outbreak dynamics of COVID-19 in China and the United States
title Outbreak dynamics of COVID-19 in China and the United States
title_full Outbreak dynamics of COVID-19 in China and the United States
title_fullStr Outbreak dynamics of COVID-19 in China and the United States
title_full_unstemmed Outbreak dynamics of COVID-19 in China and the United States
title_short Outbreak dynamics of COVID-19 in China and the United States
title_sort outbreak dynamics of covid-19 in china and the united states
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7185268/
https://www.ncbi.nlm.nih.gov/pubmed/32342242
http://dx.doi.org/10.1007/s10237-020-01332-5
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