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A framework for reconstructing SARS-CoV-2 transmission dynamics using excess mortality data
The transmission dynamics and burden of SARS-CoV-2 in many regions of the world is still largely unknown due to the scarcity of epidemiological analyses and lack of testing to assess the prevalence of disease. In this work, we develop a quantitative framework based on excess mortality data to recons...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9156676/ https://www.ncbi.nlm.nih.gov/pubmed/35641529 http://dx.doi.org/10.1038/s41467-022-30711-y |
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author | Ghafari, Mahan Watson, Oliver J. Karlinsky, Ariel Ferretti, Luca Katzourakis, Aris |
author_facet | Ghafari, Mahan Watson, Oliver J. Karlinsky, Ariel Ferretti, Luca Katzourakis, Aris |
author_sort | Ghafari, Mahan |
collection | PubMed |
description | The transmission dynamics and burden of SARS-CoV-2 in many regions of the world is still largely unknown due to the scarcity of epidemiological analyses and lack of testing to assess the prevalence of disease. In this work, we develop a quantitative framework based on excess mortality data to reconstruct SARS-CoV-2 transmission dynamics and assess the level of underreporting in infections and deaths. Using weekly all-cause mortality data from Iran, we are able to show a strong agreement between our attack rate estimates and seroprevalence measurements in each province and find significant heterogeneity in the level of exposure across the country with 11 provinces reaching near 100% attack rates. Despite having a young population, our analysis reveals that incorporating limited access to medical services in our model, coupled with undercounting of COVID-19-related deaths, leads to estimates of infection fatality rate in most provinces of Iran that are comparable to high-income countries. |
format | Online Article Text |
id | pubmed-9156676 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-91566762022-06-02 A framework for reconstructing SARS-CoV-2 transmission dynamics using excess mortality data Ghafari, Mahan Watson, Oliver J. Karlinsky, Ariel Ferretti, Luca Katzourakis, Aris Nat Commun Article The transmission dynamics and burden of SARS-CoV-2 in many regions of the world is still largely unknown due to the scarcity of epidemiological analyses and lack of testing to assess the prevalence of disease. In this work, we develop a quantitative framework based on excess mortality data to reconstruct SARS-CoV-2 transmission dynamics and assess the level of underreporting in infections and deaths. Using weekly all-cause mortality data from Iran, we are able to show a strong agreement between our attack rate estimates and seroprevalence measurements in each province and find significant heterogeneity in the level of exposure across the country with 11 provinces reaching near 100% attack rates. Despite having a young population, our analysis reveals that incorporating limited access to medical services in our model, coupled with undercounting of COVID-19-related deaths, leads to estimates of infection fatality rate in most provinces of Iran that are comparable to high-income countries. Nature Publishing Group UK 2022-05-31 /pmc/articles/PMC9156676/ /pubmed/35641529 http://dx.doi.org/10.1038/s41467-022-30711-y Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Ghafari, Mahan Watson, Oliver J. Karlinsky, Ariel Ferretti, Luca Katzourakis, Aris A framework for reconstructing SARS-CoV-2 transmission dynamics using excess mortality data |
title | A framework for reconstructing SARS-CoV-2 transmission dynamics using excess mortality data |
title_full | A framework for reconstructing SARS-CoV-2 transmission dynamics using excess mortality data |
title_fullStr | A framework for reconstructing SARS-CoV-2 transmission dynamics using excess mortality data |
title_full_unstemmed | A framework for reconstructing SARS-CoV-2 transmission dynamics using excess mortality data |
title_short | A framework for reconstructing SARS-CoV-2 transmission dynamics using excess mortality data |
title_sort | framework for reconstructing sars-cov-2 transmission dynamics using excess mortality data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9156676/ https://www.ncbi.nlm.nih.gov/pubmed/35641529 http://dx.doi.org/10.1038/s41467-022-30711-y |
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