Non-Markovian SIR epidemic spreading model of COVID-19

We introduce non-Markovian SIR epidemic spreading model inspired by the characteristics of the COVID-19, by considering discrete- and continuous-time versions. The distributions of infection intensity and recovery period may take an arbitrary form. By taking corresponding choice of these functions,...

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Autores principales: Basnarkov, Lasko, Tomovski, Igor, Sandev, Trifce, Kocarev, Ljupco
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Ltd. 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9170541/
https://www.ncbi.nlm.nih.gov/pubmed/35694643
http://dx.doi.org/10.1016/j.chaos.2022.112286
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author Basnarkov, Lasko
Tomovski, Igor
Sandev, Trifce
Kocarev, Ljupco
author_facet Basnarkov, Lasko
Tomovski, Igor
Sandev, Trifce
Kocarev, Ljupco
author_sort Basnarkov, Lasko
collection PubMed
description We introduce non-Markovian SIR epidemic spreading model inspired by the characteristics of the COVID-19, by considering discrete- and continuous-time versions. The distributions of infection intensity and recovery period may take an arbitrary form. By taking corresponding choice of these functions, it is shown that the model reduces to the classical Markovian case. The epidemic threshold is analytically determined for arbitrary functions of infectivity and recovery and verified numerically. The relevance of the model is shown by modeling the first wave of the epidemic in Italy, Spain and the UK, in the spring, 2020.
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spelling pubmed-91705412022-06-07 Non-Markovian SIR epidemic spreading model of COVID-19 Basnarkov, Lasko Tomovski, Igor Sandev, Trifce Kocarev, Ljupco Chaos Solitons Fractals Article We introduce non-Markovian SIR epidemic spreading model inspired by the characteristics of the COVID-19, by considering discrete- and continuous-time versions. The distributions of infection intensity and recovery period may take an arbitrary form. By taking corresponding choice of these functions, it is shown that the model reduces to the classical Markovian case. The epidemic threshold is analytically determined for arbitrary functions of infectivity and recovery and verified numerically. The relevance of the model is shown by modeling the first wave of the epidemic in Italy, Spain and the UK, in the spring, 2020. Elsevier Ltd. 2022-07 2022-06-07 /pmc/articles/PMC9170541/ /pubmed/35694643 http://dx.doi.org/10.1016/j.chaos.2022.112286 Text en © 2022 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 Article
Basnarkov, Lasko
Tomovski, Igor
Sandev, Trifce
Kocarev, Ljupco
Non-Markovian SIR epidemic spreading model of COVID-19
title Non-Markovian SIR epidemic spreading model of COVID-19
title_full Non-Markovian SIR epidemic spreading model of COVID-19
title_fullStr Non-Markovian SIR epidemic spreading model of COVID-19
title_full_unstemmed Non-Markovian SIR epidemic spreading model of COVID-19
title_short Non-Markovian SIR epidemic spreading model of COVID-19
title_sort non-markovian sir epidemic spreading model of covid-19
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9170541/
https://www.ncbi.nlm.nih.gov/pubmed/35694643
http://dx.doi.org/10.1016/j.chaos.2022.112286
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