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Forecasting the spread of COVID-19 under different reopening strategies
We combine COVID-19 case data with mobility data to estimate a modified susceptible-infected-recovered (SIR) model in the United States. In contrast to a standard SIR model, we find that the incidence of COVID-19 spread is concave in the number of infectious individuals, as would be expected if peop...
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/PMC7683602/ https://www.ncbi.nlm.nih.gov/pubmed/33230234 http://dx.doi.org/10.1038/s41598-020-77292-8 |
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author | Liu, Meng Thomadsen, Raphael Yao, Song |
author_facet | Liu, Meng Thomadsen, Raphael Yao, Song |
author_sort | Liu, Meng |
collection | PubMed |
description | We combine COVID-19 case data with mobility data to estimate a modified susceptible-infected-recovered (SIR) model in the United States. In contrast to a standard SIR model, we find that the incidence of COVID-19 spread is concave in the number of infectious individuals, as would be expected if people have inter-related social networks. This concave shape has a significant impact on forecasted COVID-19 cases. In particular, our model forecasts that the number of COVID-19 cases would only have an exponential growth for a brief period at the beginning of the contagion event or right after a reopening, but would quickly settle into a prolonged period of time with stable, slightly declining levels of disease spread. This pattern is consistent with observed levels of COVID-19 cases in the US, but inconsistent with standard SIR modeling. We forecast rates of new cases for COVID-19 under different social distancing norms and find that if social distancing is eliminated there will be a massive increase in the cases of COVID-19. |
format | Online Article Text |
id | pubmed-7683602 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-76836022020-11-24 Forecasting the spread of COVID-19 under different reopening strategies Liu, Meng Thomadsen, Raphael Yao, Song Sci Rep Article We combine COVID-19 case data with mobility data to estimate a modified susceptible-infected-recovered (SIR) model in the United States. In contrast to a standard SIR model, we find that the incidence of COVID-19 spread is concave in the number of infectious individuals, as would be expected if people have inter-related social networks. This concave shape has a significant impact on forecasted COVID-19 cases. In particular, our model forecasts that the number of COVID-19 cases would only have an exponential growth for a brief period at the beginning of the contagion event or right after a reopening, but would quickly settle into a prolonged period of time with stable, slightly declining levels of disease spread. This pattern is consistent with observed levels of COVID-19 cases in the US, but inconsistent with standard SIR modeling. We forecast rates of new cases for COVID-19 under different social distancing norms and find that if social distancing is eliminated there will be a massive increase in the cases of COVID-19. Nature Publishing Group UK 2020-11-23 /pmc/articles/PMC7683602/ /pubmed/33230234 http://dx.doi.org/10.1038/s41598-020-77292-8 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 Liu, Meng Thomadsen, Raphael Yao, Song Forecasting the spread of COVID-19 under different reopening strategies |
title | Forecasting the spread of COVID-19 under different reopening strategies |
title_full | Forecasting the spread of COVID-19 under different reopening strategies |
title_fullStr | Forecasting the spread of COVID-19 under different reopening strategies |
title_full_unstemmed | Forecasting the spread of COVID-19 under different reopening strategies |
title_short | Forecasting the spread of COVID-19 under different reopening strategies |
title_sort | forecasting the spread of covid-19 under different reopening strategies |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7683602/ https://www.ncbi.nlm.nih.gov/pubmed/33230234 http://dx.doi.org/10.1038/s41598-020-77292-8 |
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