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Spatio-temporal spread of COVID-19: Comparison of the inhomogeneous SEPIR model and data from South Carolina

During the COVID-19 pandemic authorities have been striving to obtain reliable predictions for the spreading dynamics of the disease. We recently developed a multi-“sub-populations” (multi-compartments: susceptible, exposed, pre-symptomatic, infectious, recovered) model, that accounts for the spatia...

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Autores principales: Tsori, Yoav, Granek, Rony
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9182687/
https://www.ncbi.nlm.nih.gov/pubmed/35679238
http://dx.doi.org/10.1371/journal.pone.0268995
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author Tsori, Yoav
Granek, Rony
author_facet Tsori, Yoav
Granek, Rony
author_sort Tsori, Yoav
collection PubMed
description During the COVID-19 pandemic authorities have been striving to obtain reliable predictions for the spreading dynamics of the disease. We recently developed a multi-“sub-populations” (multi-compartments: susceptible, exposed, pre-symptomatic, infectious, recovered) model, that accounts for the spatial in-homogeneous spreading of the infection and shown, for a variety of examples, how the epidemic curves are highly sensitive to location of epicenters, non-uniform population density, and local restrictions. In the present work we test our model against real-life data from South Carolina during the period May 22 to July 22 (2020). During this period, minimal restrictions have been employed, which allowed us to assume that the local basic reproduction number is constant in time. We account for the non-uniform population density in South Carolina using data from NASA’s Socioeconomic Data and Applications Center (SEDAC), and predict the evolution of infection heat-maps during the studied period. Comparing the predicted heat-maps with those observed, we find high qualitative resemblance. Moreover, the Pearson’s correlation coefficient is relatively high thus validating our model against real-world data. We conclude that the model accounts for the major effects controlling spatial in-homogeneous spreading of the disease. Inclusion of additional sub-populations (compartments), in the spirit of several recently developed models for COVID-19, can be easily performed within our mathematical framework.
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spelling pubmed-91826872022-06-10 Spatio-temporal spread of COVID-19: Comparison of the inhomogeneous SEPIR model and data from South Carolina Tsori, Yoav Granek, Rony PLoS One Research Article During the COVID-19 pandemic authorities have been striving to obtain reliable predictions for the spreading dynamics of the disease. We recently developed a multi-“sub-populations” (multi-compartments: susceptible, exposed, pre-symptomatic, infectious, recovered) model, that accounts for the spatial in-homogeneous spreading of the infection and shown, for a variety of examples, how the epidemic curves are highly sensitive to location of epicenters, non-uniform population density, and local restrictions. In the present work we test our model against real-life data from South Carolina during the period May 22 to July 22 (2020). During this period, minimal restrictions have been employed, which allowed us to assume that the local basic reproduction number is constant in time. We account for the non-uniform population density in South Carolina using data from NASA’s Socioeconomic Data and Applications Center (SEDAC), and predict the evolution of infection heat-maps during the studied period. Comparing the predicted heat-maps with those observed, we find high qualitative resemblance. Moreover, the Pearson’s correlation coefficient is relatively high thus validating our model against real-world data. We conclude that the model accounts for the major effects controlling spatial in-homogeneous spreading of the disease. Inclusion of additional sub-populations (compartments), in the spirit of several recently developed models for COVID-19, can be easily performed within our mathematical framework. Public Library of Science 2022-06-09 /pmc/articles/PMC9182687/ /pubmed/35679238 http://dx.doi.org/10.1371/journal.pone.0268995 Text en © 2022 Tsori, Granek https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Tsori, Yoav
Granek, Rony
Spatio-temporal spread of COVID-19: Comparison of the inhomogeneous SEPIR model and data from South Carolina
title Spatio-temporal spread of COVID-19: Comparison of the inhomogeneous SEPIR model and data from South Carolina
title_full Spatio-temporal spread of COVID-19: Comparison of the inhomogeneous SEPIR model and data from South Carolina
title_fullStr Spatio-temporal spread of COVID-19: Comparison of the inhomogeneous SEPIR model and data from South Carolina
title_full_unstemmed Spatio-temporal spread of COVID-19: Comparison of the inhomogeneous SEPIR model and data from South Carolina
title_short Spatio-temporal spread of COVID-19: Comparison of the inhomogeneous SEPIR model and data from South Carolina
title_sort spatio-temporal spread of covid-19: comparison of the inhomogeneous sepir model and data from south carolina
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9182687/
https://www.ncbi.nlm.nih.gov/pubmed/35679238
http://dx.doi.org/10.1371/journal.pone.0268995
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