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COVID-19 dynamics in an Ohio prison

Incarcerated individuals are a highly vulnerable population for infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Understanding the transmission of respiratory infections within prisons and between prisons and surrounding communities is a crucial component of pandemic prep...

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Autores principales: KhudaBukhsh, Wasiur R., Khalsa, Sat Kartar, Kenah, Eben, Rempała, Gregorz A., Tien, Joseph H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10098107/
https://www.ncbi.nlm.nih.gov/pubmed/37064663
http://dx.doi.org/10.3389/fpubh.2023.1087698
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author KhudaBukhsh, Wasiur R.
Khalsa, Sat Kartar
Kenah, Eben
Rempała, Gregorz A.
Tien, Joseph H.
author_facet KhudaBukhsh, Wasiur R.
Khalsa, Sat Kartar
Kenah, Eben
Rempała, Gregorz A.
Tien, Joseph H.
author_sort KhudaBukhsh, Wasiur R.
collection PubMed
description Incarcerated individuals are a highly vulnerable population for infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Understanding the transmission of respiratory infections within prisons and between prisons and surrounding communities is a crucial component of pandemic preparedness and response. Here, we use mathematical and statistical models to analyze publicly available data on the spread of SARS-CoV-2 reported by the Ohio Department of Rehabilitation and Corrections (ODRC). Results from mass testing conducted on April 16, 2020 were analyzed together with time of first reported SARS-CoV-2 infection among Marion Correctional Institution (MCI) inmates. Extremely rapid, widespread infection of MCI inmates was reported, with nearly 80% of inmates infected within 3 weeks of the first reported inmate case. The dynamical survival analysis (DSA) framework that we use allows the derivation of explicit likelihoods based on mathematical models of transmission. We find that these data are consistent with three non-exclusive possibilities: (i) a basic reproduction number >14 with a single initially infected inmate, (ii) an initial superspreading event resulting in several hundred initially infected inmates with a reproduction number of approximately three, or (iii) earlier undetected circulation of virus among inmates prior to April. All three scenarios attest to the vulnerabilities of prisoners to COVID-19, and the inability to distinguish among these possibilities highlights the need for improved infection surveillance and reporting in prisons.
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spelling pubmed-100981072023-04-14 COVID-19 dynamics in an Ohio prison KhudaBukhsh, Wasiur R. Khalsa, Sat Kartar Kenah, Eben Rempała, Gregorz A. Tien, Joseph H. Front Public Health Public Health Incarcerated individuals are a highly vulnerable population for infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Understanding the transmission of respiratory infections within prisons and between prisons and surrounding communities is a crucial component of pandemic preparedness and response. Here, we use mathematical and statistical models to analyze publicly available data on the spread of SARS-CoV-2 reported by the Ohio Department of Rehabilitation and Corrections (ODRC). Results from mass testing conducted on April 16, 2020 were analyzed together with time of first reported SARS-CoV-2 infection among Marion Correctional Institution (MCI) inmates. Extremely rapid, widespread infection of MCI inmates was reported, with nearly 80% of inmates infected within 3 weeks of the first reported inmate case. The dynamical survival analysis (DSA) framework that we use allows the derivation of explicit likelihoods based on mathematical models of transmission. We find that these data are consistent with three non-exclusive possibilities: (i) a basic reproduction number >14 with a single initially infected inmate, (ii) an initial superspreading event resulting in several hundred initially infected inmates with a reproduction number of approximately three, or (iii) earlier undetected circulation of virus among inmates prior to April. All three scenarios attest to the vulnerabilities of prisoners to COVID-19, and the inability to distinguish among these possibilities highlights the need for improved infection surveillance and reporting in prisons. Frontiers Media S.A. 2023-03-30 /pmc/articles/PMC10098107/ /pubmed/37064663 http://dx.doi.org/10.3389/fpubh.2023.1087698 Text en Copyright © 2023 KhudaBukhsh, Khalsa, Kenah, Rempała and Tien. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Public Health
KhudaBukhsh, Wasiur R.
Khalsa, Sat Kartar
Kenah, Eben
Rempała, Gregorz A.
Tien, Joseph H.
COVID-19 dynamics in an Ohio prison
title COVID-19 dynamics in an Ohio prison
title_full COVID-19 dynamics in an Ohio prison
title_fullStr COVID-19 dynamics in an Ohio prison
title_full_unstemmed COVID-19 dynamics in an Ohio prison
title_short COVID-19 dynamics in an Ohio prison
title_sort covid-19 dynamics in an ohio prison
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10098107/
https://www.ncbi.nlm.nih.gov/pubmed/37064663
http://dx.doi.org/10.3389/fpubh.2023.1087698
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