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The SIR model towards the data: One year of Covid-19 pandemic in Italy case study and plausible “real” numbers

In this work, the SIR epidemiological model is reformulated so to highlight the important effective reproduction number, as well as to account for the generation time, the inverse of the incidence rate, and the infectious period (or removal period), the inverse of the removal rate. The aim is to che...

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Autor principal: Lazzizzera, Ignazio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8336674/
https://www.ncbi.nlm.nih.gov/pubmed/34377623
http://dx.doi.org/10.1140/epjp/s13360-021-01797-y
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author Lazzizzera, Ignazio
author_facet Lazzizzera, Ignazio
author_sort Lazzizzera, Ignazio
collection PubMed
description In this work, the SIR epidemiological model is reformulated so to highlight the important effective reproduction number, as well as to account for the generation time, the inverse of the incidence rate, and the infectious period (or removal period), the inverse of the removal rate. The aim is to check whether the relationships the model poses among the various observables are actually found in the data. The study case of the second through the third wave of the Covid-19 pandemic in Italy is taken. Given its scale invariance, initially the model is tested with reference to the curve of swab-confirmed infectious individuals only. It is found to match the data, if the curve of the removed (that is healed or deceased) individuals is assumed underestimated by a factor of about 3 together with other related curves. Contextually, the generation time and the removal period, as well as the effective reproduction number, are obtained fitting the SIR equations to the data; the outcomes prove to be in good agreement with those of other works. Then, using knowledge of the proportion of Covid-19 transmissions likely occurring from individuals who didn’t develop symptoms, thus mainly undetected, an estimate of the real numbers of the epidemic is obtained, looking also in good agreement with results from other, completely different works. The line of this work is new, and the procedures, computationally really inexpensive, can be applied to any other national or regional case besides Italy’s study case here.
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spelling pubmed-83366742021-08-06 The SIR model towards the data: One year of Covid-19 pandemic in Italy case study and plausible “real” numbers Lazzizzera, Ignazio Eur Phys J Plus Regular Article In this work, the SIR epidemiological model is reformulated so to highlight the important effective reproduction number, as well as to account for the generation time, the inverse of the incidence rate, and the infectious period (or removal period), the inverse of the removal rate. The aim is to check whether the relationships the model poses among the various observables are actually found in the data. The study case of the second through the third wave of the Covid-19 pandemic in Italy is taken. Given its scale invariance, initially the model is tested with reference to the curve of swab-confirmed infectious individuals only. It is found to match the data, if the curve of the removed (that is healed or deceased) individuals is assumed underestimated by a factor of about 3 together with other related curves. Contextually, the generation time and the removal period, as well as the effective reproduction number, are obtained fitting the SIR equations to the data; the outcomes prove to be in good agreement with those of other works. Then, using knowledge of the proportion of Covid-19 transmissions likely occurring from individuals who didn’t develop symptoms, thus mainly undetected, an estimate of the real numbers of the epidemic is obtained, looking also in good agreement with results from other, completely different works. The line of this work is new, and the procedures, computationally really inexpensive, can be applied to any other national or regional case besides Italy’s study case here. Springer Berlin Heidelberg 2021-08-04 2021 /pmc/articles/PMC8336674/ /pubmed/34377623 http://dx.doi.org/10.1140/epjp/s13360-021-01797-y Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Regular Article
Lazzizzera, Ignazio
The SIR model towards the data: One year of Covid-19 pandemic in Italy case study and plausible “real” numbers
title The SIR model towards the data: One year of Covid-19 pandemic in Italy case study and plausible “real” numbers
title_full The SIR model towards the data: One year of Covid-19 pandemic in Italy case study and plausible “real” numbers
title_fullStr The SIR model towards the data: One year of Covid-19 pandemic in Italy case study and plausible “real” numbers
title_full_unstemmed The SIR model towards the data: One year of Covid-19 pandemic in Italy case study and plausible “real” numbers
title_short The SIR model towards the data: One year of Covid-19 pandemic in Italy case study and plausible “real” numbers
title_sort sir model towards the data: one year of covid-19 pandemic in italy case study and plausible “real” numbers
topic Regular Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8336674/
https://www.ncbi.nlm.nih.gov/pubmed/34377623
http://dx.doi.org/10.1140/epjp/s13360-021-01797-y
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