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Why case fatality ratios can be misleading: individual- and population-based mortality estimates and factors influencing them
Different ways of calculating mortality during epidemics have yielded very different results, particularly during the current COVID-19 pandemic. For example, the “CFR” has been interchangeably called the case fatality ratio, case fatality rate, and case fatality risk, often without standard mathemat...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8849595/ https://www.ncbi.nlm.nih.gov/pubmed/32554901 http://dx.doi.org/10.1088/1478-3975/ab9e59 |
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author | Böttcher, Lucas Xia, Mingtao Chou, Tom |
author_facet | Böttcher, Lucas Xia, Mingtao Chou, Tom |
author_sort | Böttcher, Lucas |
collection | PubMed |
description | Different ways of calculating mortality during epidemics have yielded very different results, particularly during the current COVID-19 pandemic. For example, the “CFR” has been interchangeably called the case fatality ratio, case fatality rate, and case fatality risk, often without standard mathematical definitions. The most commonly used CFR is the case fatality ratio, typically constructed using the estimated number of deaths to date divided by the estimated total number of confirmed infected cases to date. How does this CFR relate to an infected individual’s probability of death? To explore such issues, we formulate both a survival probability model and an associated infection duration-dependent SIR model to define individual- and population-based estimates of dynamic mortality measures to show that neither of these are directly represented by the case fatality ratio. The key parameters that affect the dynamics of different mortality estimates are the incubation period and the time individuals were infected before confirmation of infection. Using data on the recent SARS-CoV-2 outbreaks, we estimate and compare the different dynamic mortality estimates and highlight their differences. Informed by our modeling, we propose more systematic methods to determine mortality during epidemic outbreaks and discuss sensitivity to confounding effects and uncertainties in the data arising from, e.g., undertesting and heterogeneous populations. |
format | Online Article Text |
id | pubmed-8849595 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
record_format | MEDLINE/PubMed |
spelling | pubmed-88495952022-02-16 Why case fatality ratios can be misleading: individual- and population-based mortality estimates and factors influencing them Böttcher, Lucas Xia, Mingtao Chou, Tom Phys Biol Article Different ways of calculating mortality during epidemics have yielded very different results, particularly during the current COVID-19 pandemic. For example, the “CFR” has been interchangeably called the case fatality ratio, case fatality rate, and case fatality risk, often without standard mathematical definitions. The most commonly used CFR is the case fatality ratio, typically constructed using the estimated number of deaths to date divided by the estimated total number of confirmed infected cases to date. How does this CFR relate to an infected individual’s probability of death? To explore such issues, we formulate both a survival probability model and an associated infection duration-dependent SIR model to define individual- and population-based estimates of dynamic mortality measures to show that neither of these are directly represented by the case fatality ratio. The key parameters that affect the dynamics of different mortality estimates are the incubation period and the time individuals were infected before confirmation of infection. Using data on the recent SARS-CoV-2 outbreaks, we estimate and compare the different dynamic mortality estimates and highlight their differences. Informed by our modeling, we propose more systematic methods to determine mortality during epidemic outbreaks and discuss sensitivity to confounding effects and uncertainties in the data arising from, e.g., undertesting and heterogeneous populations. 2020-09-23 /pmc/articles/PMC8849595/ /pubmed/32554901 http://dx.doi.org/10.1088/1478-3975/ab9e59 Text en https://creativecommons.org/licenses/by-nc-nd/3.0/During the embargo period (the 12 month period from the publication of the Version of Record of this article), the Accepted Manuscript is fully protected by copyright and cannot be reused or reposted elsewhere. As the Version of Record of this article is going to be / has been published on a subscription basis, this Accepted Manuscript is available for reuse under a CC BY-NC-ND 3.0 licence after the 12 month embargo period. After the embargo period, everyone is permitted to use copy and redistribute this article for non-commercial purposes only, provided that they adhere to all the terms of the licence https://creativecommons.org/licences/by-nc-nd/3.0 (https://creativecommons.org/licenses/by-nc-nd/3.0/) |
spellingShingle | Article Böttcher, Lucas Xia, Mingtao Chou, Tom Why case fatality ratios can be misleading: individual- and population-based mortality estimates and factors influencing them |
title | Why case fatality ratios can be misleading: individual- and population-based mortality estimates and factors influencing them |
title_full | Why case fatality ratios can be misleading: individual- and population-based mortality estimates and factors influencing them |
title_fullStr | Why case fatality ratios can be misleading: individual- and population-based mortality estimates and factors influencing them |
title_full_unstemmed | Why case fatality ratios can be misleading: individual- and population-based mortality estimates and factors influencing them |
title_short | Why case fatality ratios can be misleading: individual- and population-based mortality estimates and factors influencing them |
title_sort | why case fatality ratios can be misleading: individual- and population-based mortality estimates and factors influencing them |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8849595/ https://www.ncbi.nlm.nih.gov/pubmed/32554901 http://dx.doi.org/10.1088/1478-3975/ab9e59 |
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