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A modified Susceptible-Infected-Recovered model for observed under-reported incidence data
Fitting Susceptible-Infected-Recovered (SIR) models to incidence data is problematic when not all infected individuals are reported. Assuming an underlying SIR model with general but known distribution for the time to recovery, this paper derives the implied differential-integral equations for obser...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8827465/ https://www.ncbi.nlm.nih.gov/pubmed/35139110 http://dx.doi.org/10.1371/journal.pone.0263047 |
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author | Trejo, Imelda Hengartner, Nicolas W. |
author_facet | Trejo, Imelda Hengartner, Nicolas W. |
author_sort | Trejo, Imelda |
collection | PubMed |
description | Fitting Susceptible-Infected-Recovered (SIR) models to incidence data is problematic when not all infected individuals are reported. Assuming an underlying SIR model with general but known distribution for the time to recovery, this paper derives the implied differential-integral equations for observed incidence data when a fixed fraction of newly infected individuals are not observed. The parameters of the resulting system of differential equations are identifiable. Using these differential equations, we develop a stochastic model for the conditional distribution of current disease incidence given the entire past history of reported cases. We estimate the model parameters using Bayesian Markov Chain Monte-Carlo sampling of the posterior distribution. We use our model to estimate the transmission rate and fraction of asymptomatic individuals for the current Coronavirus 2019 outbreak in eight American Countries: the United States of America, Brazil, Mexico, Argentina, Chile, Colombia, Peru, and Panama, from January 2020 to May 2021. Our analysis reveals that the fraction of reported cases varies across all countries. For example, the reported incidence fraction for the United States of America varies from 0.3 to 0.6, while for Brazil it varies from 0.2 to 0.4. |
format | Online Article Text |
id | pubmed-8827465 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-88274652022-02-10 A modified Susceptible-Infected-Recovered model for observed under-reported incidence data Trejo, Imelda Hengartner, Nicolas W. PLoS One Research Article Fitting Susceptible-Infected-Recovered (SIR) models to incidence data is problematic when not all infected individuals are reported. Assuming an underlying SIR model with general but known distribution for the time to recovery, this paper derives the implied differential-integral equations for observed incidence data when a fixed fraction of newly infected individuals are not observed. The parameters of the resulting system of differential equations are identifiable. Using these differential equations, we develop a stochastic model for the conditional distribution of current disease incidence given the entire past history of reported cases. We estimate the model parameters using Bayesian Markov Chain Monte-Carlo sampling of the posterior distribution. We use our model to estimate the transmission rate and fraction of asymptomatic individuals for the current Coronavirus 2019 outbreak in eight American Countries: the United States of America, Brazil, Mexico, Argentina, Chile, Colombia, Peru, and Panama, from January 2020 to May 2021. Our analysis reveals that the fraction of reported cases varies across all countries. For example, the reported incidence fraction for the United States of America varies from 0.3 to 0.6, while for Brazil it varies from 0.2 to 0.4. Public Library of Science 2022-02-09 /pmc/articles/PMC8827465/ /pubmed/35139110 http://dx.doi.org/10.1371/journal.pone.0263047 Text en https://creativecommons.org/publicdomain/zero/1.0/This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 (https://creativecommons.org/publicdomain/zero/1.0/) public domain dedication. |
spellingShingle | Research Article Trejo, Imelda Hengartner, Nicolas W. A modified Susceptible-Infected-Recovered model for observed under-reported incidence data |
title | A modified Susceptible-Infected-Recovered model for observed under-reported incidence data |
title_full | A modified Susceptible-Infected-Recovered model for observed under-reported incidence data |
title_fullStr | A modified Susceptible-Infected-Recovered model for observed under-reported incidence data |
title_full_unstemmed | A modified Susceptible-Infected-Recovered model for observed under-reported incidence data |
title_short | A modified Susceptible-Infected-Recovered model for observed under-reported incidence data |
title_sort | modified susceptible-infected-recovered model for observed under-reported incidence data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8827465/ https://www.ncbi.nlm.nih.gov/pubmed/35139110 http://dx.doi.org/10.1371/journal.pone.0263047 |
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