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Spreading of infections on random graphs: A percolation-type model for COVID-19

We introduce an epidemic spreading model on a network using concepts from percolation theory. The model is motivated by discussing the standard SIR model, with extensions to describe effects of lockdowns within a population. The underlying ideas and behaviour of the lattice model, implemented using...

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Detalles Bibliográficos
Autores principales: Croccolo, Fabrizio, Roman, H. Eduardo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Ltd. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7332959/
https://www.ncbi.nlm.nih.gov/pubmed/32834619
http://dx.doi.org/10.1016/j.chaos.2020.110077
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author Croccolo, Fabrizio
Roman, H. Eduardo
author_facet Croccolo, Fabrizio
Roman, H. Eduardo
author_sort Croccolo, Fabrizio
collection PubMed
description We introduce an epidemic spreading model on a network using concepts from percolation theory. The model is motivated by discussing the standard SIR model, with extensions to describe effects of lockdowns within a population. The underlying ideas and behaviour of the lattice model, implemented using the same lockdown scheme as for the SIR scheme, are discussed in detail and illustrated with extensive simulations. A comparison between both models is presented for the case of COVID-19 data from the USA. Both fits to the empirical data are very good, but some differences emerge between the two approaches which indicate the usefulness of having an alternative approach to the widespread SIR model.
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spelling pubmed-73329592020-07-06 Spreading of infections on random graphs: A percolation-type model for COVID-19 Croccolo, Fabrizio Roman, H. Eduardo Chaos Solitons Fractals Frontiers We introduce an epidemic spreading model on a network using concepts from percolation theory. The model is motivated by discussing the standard SIR model, with extensions to describe effects of lockdowns within a population. The underlying ideas and behaviour of the lattice model, implemented using the same lockdown scheme as for the SIR scheme, are discussed in detail and illustrated with extensive simulations. A comparison between both models is presented for the case of COVID-19 data from the USA. Both fits to the empirical data are very good, but some differences emerge between the two approaches which indicate the usefulness of having an alternative approach to the widespread SIR model. Elsevier Ltd. 2020-10 2020-07-03 /pmc/articles/PMC7332959/ /pubmed/32834619 http://dx.doi.org/10.1016/j.chaos.2020.110077 Text en © 2020 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Frontiers
Croccolo, Fabrizio
Roman, H. Eduardo
Spreading of infections on random graphs: A percolation-type model for COVID-19
title Spreading of infections on random graphs: A percolation-type model for COVID-19
title_full Spreading of infections on random graphs: A percolation-type model for COVID-19
title_fullStr Spreading of infections on random graphs: A percolation-type model for COVID-19
title_full_unstemmed Spreading of infections on random graphs: A percolation-type model for COVID-19
title_short Spreading of infections on random graphs: A percolation-type model for COVID-19
title_sort spreading of infections on random graphs: a percolation-type model for covid-19
topic Frontiers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7332959/
https://www.ncbi.nlm.nih.gov/pubmed/32834619
http://dx.doi.org/10.1016/j.chaos.2020.110077
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