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Epidemiological model for the inhomogeneous spatial spreading of COVID-19 and other diseases
We suggest a novel mathematical framework for the in-homogeneous spatial spreading of an infectious disease in human population, with particular attention to COVID-19. Common epidemiological models, e.g., the well-known susceptible-exposed-infectious-recovered (SEIR) model, implicitly assume uniform...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7894958/ https://www.ncbi.nlm.nih.gov/pubmed/33606684 http://dx.doi.org/10.1371/journal.pone.0246056 |
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author | Tsori, Yoav Granek, Rony |
author_facet | Tsori, Yoav Granek, Rony |
author_sort | Tsori, Yoav |
collection | PubMed |
description | We suggest a novel mathematical framework for the in-homogeneous spatial spreading of an infectious disease in human population, with particular attention to COVID-19. Common epidemiological models, e.g., the well-known susceptible-exposed-infectious-recovered (SEIR) model, implicitly assume uniform (random) encounters between the infectious and susceptible sub-populations, resulting in homogeneous spatial distributions. However, in human population, especially under different levels of mobility restrictions, this assumption is likely to fail. Splitting the geographic region under study into areal nodes, and assuming infection kinetics within nodes and between nearest-neighbor nodes, we arrive into a continuous, “reaction-diffusion”, spatial model. To account for COVID-19, the model includes five different sub-populations, in which the infectious sub-population is split into pre-symptomatic and symptomatic. Our model accounts for the spreading evolution of infectious population domains from initial epicenters, leading to different regimes of sub-exponential (e.g., power-law) growth. Importantly, we also account for the variable geographic density of the population, that can strongly enhance or suppress infection spreading. For instance, we show how weakly infected regions surrounding a densely populated area can cause rapid migration of the infection towards the populated area. Predicted infection “heat-maps” show remarkable similarity to publicly available heat-maps, e.g., from South Carolina. We further demonstrate how localized lockdown/quarantine conditions can slow down the spreading of disease from epicenters. Application of our model in different countries can provide a useful predictive tool for the authorities, in particular, for planning strong lockdown measures in localized areas—such as those underway in a few countries. |
format | Online Article Text |
id | pubmed-7894958 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-78949582021-03-01 Epidemiological model for the inhomogeneous spatial spreading of COVID-19 and other diseases Tsori, Yoav Granek, Rony PLoS One Research Article We suggest a novel mathematical framework for the in-homogeneous spatial spreading of an infectious disease in human population, with particular attention to COVID-19. Common epidemiological models, e.g., the well-known susceptible-exposed-infectious-recovered (SEIR) model, implicitly assume uniform (random) encounters between the infectious and susceptible sub-populations, resulting in homogeneous spatial distributions. However, in human population, especially under different levels of mobility restrictions, this assumption is likely to fail. Splitting the geographic region under study into areal nodes, and assuming infection kinetics within nodes and between nearest-neighbor nodes, we arrive into a continuous, “reaction-diffusion”, spatial model. To account for COVID-19, the model includes five different sub-populations, in which the infectious sub-population is split into pre-symptomatic and symptomatic. Our model accounts for the spreading evolution of infectious population domains from initial epicenters, leading to different regimes of sub-exponential (e.g., power-law) growth. Importantly, we also account for the variable geographic density of the population, that can strongly enhance or suppress infection spreading. For instance, we show how weakly infected regions surrounding a densely populated area can cause rapid migration of the infection towards the populated area. Predicted infection “heat-maps” show remarkable similarity to publicly available heat-maps, e.g., from South Carolina. We further demonstrate how localized lockdown/quarantine conditions can slow down the spreading of disease from epicenters. Application of our model in different countries can provide a useful predictive tool for the authorities, in particular, for planning strong lockdown measures in localized areas—such as those underway in a few countries. Public Library of Science 2021-02-19 /pmc/articles/PMC7894958/ /pubmed/33606684 http://dx.doi.org/10.1371/journal.pone.0246056 Text en © 2021 Tsori, Granek http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://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 Epidemiological model for the inhomogeneous spatial spreading of COVID-19 and other diseases |
title | Epidemiological model for the inhomogeneous spatial spreading of COVID-19 and other diseases |
title_full | Epidemiological model for the inhomogeneous spatial spreading of COVID-19 and other diseases |
title_fullStr | Epidemiological model for the inhomogeneous spatial spreading of COVID-19 and other diseases |
title_full_unstemmed | Epidemiological model for the inhomogeneous spatial spreading of COVID-19 and other diseases |
title_short | Epidemiological model for the inhomogeneous spatial spreading of COVID-19 and other diseases |
title_sort | epidemiological model for the inhomogeneous spatial spreading of covid-19 and other diseases |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7894958/ https://www.ncbi.nlm.nih.gov/pubmed/33606684 http://dx.doi.org/10.1371/journal.pone.0246056 |
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