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The computation of case fatality rate for novel coronavirus (COVID-19) based on Bayes theorem: An observational study
BACKGROUND: When a new disease such starts to spread, the commonly asked questions are how deadly is it? and how many people are likely to die of this outbreak? The World Health Organization (WHO) announced in a press conference on January 29, 2020 that the death rate of COVID-19 was 2% on the case...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Wolters Kluwer Health
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7249957/ https://www.ncbi.nlm.nih.gov/pubmed/32481256 http://dx.doi.org/10.1097/MD.0000000000019925 |
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author | Chang, Chi-Sheng Yeh, Yu-Tsen Chien, Tsair-Wei Lin, Jui-Chung John Cheng, Bor-Wen Kuo, Shu-Chun |
author_facet | Chang, Chi-Sheng Yeh, Yu-Tsen Chien, Tsair-Wei Lin, Jui-Chung John Cheng, Bor-Wen Kuo, Shu-Chun |
author_sort | Chang, Chi-Sheng |
collection | PubMed |
description | BACKGROUND: When a new disease such starts to spread, the commonly asked questions are how deadly is it? and how many people are likely to die of this outbreak? The World Health Organization (WHO) announced in a press conference on January 29, 2020 that the death rate of COVID-19 was 2% on the case fatality rate (CFR). It was underestimated assuming no lag days from symptom onset to deaths while many CFR formulas have been proposed, the estimation on Bays theorem is worthy of interpretation. Hence, it is hypothesized that the over-loaded burdens of treating patients and capacities to contain the outbreak (LSBHRS) may increase the CFR. METHODS: We downloaded COVID-19 outbreak numbers from January 21 to February 14, 2020, in countries/regions on a daily basis from Github that contains information on confirmed cases in >30 Chinese locations and other countries/regions. The pros and cons were compared among the 5 formula of CFR, including [A] deaths/confirmed; [B] deaths/(deaths + recovered); [C] deaths/(cases x days ago); [D] Bayes estimation based on [A] and the outbreak (LSBHRS) in each country/region; and [E] Bayes estimation based on [C] deaths/(cases x days ago). The coefficients of variance (CV = the ratio of the standard deviation to the mean) were applied to measure the relative variability for each CFR. A dashboard was developed for daily display of the CFR across each region. RESULTS: The Bayes based on (A)[D] has the lowest CV (=0.10) followed by the deaths/confirmed (=0.11) [A], deaths/(deaths + recoveries) (=0.42) [B], Bayes based on (C) (=0.49) [E], and deaths/(cases x days ago) (=0.59) [C]. All final CFRs will be equal using the formula (from, A to E). A dashboard was developed for the daily reporting of the CFR. The CFR (3.7%) greater than the prior CFR of 2.2% was evident in LSBHRS, increasing the CFR. A dashboard was created to present the CFRs on COVID-19. CONCLUSION: We suggest examining both trends of the Bayes based on both deaths/(cases 7 days ago) and deaths/confirmed cases as a reference to the final CFR. An app developed for displaying the provisional CFR with the 2 CFR trends can improve the underestimated CFR reported by WHO and media. |
format | Online Article Text |
id | pubmed-7249957 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Wolters Kluwer Health |
record_format | MEDLINE/PubMed |
spelling | pubmed-72499572020-06-15 The computation of case fatality rate for novel coronavirus (COVID-19) based on Bayes theorem: An observational study Chang, Chi-Sheng Yeh, Yu-Tsen Chien, Tsair-Wei Lin, Jui-Chung John Cheng, Bor-Wen Kuo, Shu-Chun Medicine (Baltimore) 4400 BACKGROUND: When a new disease such starts to spread, the commonly asked questions are how deadly is it? and how many people are likely to die of this outbreak? The World Health Organization (WHO) announced in a press conference on January 29, 2020 that the death rate of COVID-19 was 2% on the case fatality rate (CFR). It was underestimated assuming no lag days from symptom onset to deaths while many CFR formulas have been proposed, the estimation on Bays theorem is worthy of interpretation. Hence, it is hypothesized that the over-loaded burdens of treating patients and capacities to contain the outbreak (LSBHRS) may increase the CFR. METHODS: We downloaded COVID-19 outbreak numbers from January 21 to February 14, 2020, in countries/regions on a daily basis from Github that contains information on confirmed cases in >30 Chinese locations and other countries/regions. The pros and cons were compared among the 5 formula of CFR, including [A] deaths/confirmed; [B] deaths/(deaths + recovered); [C] deaths/(cases x days ago); [D] Bayes estimation based on [A] and the outbreak (LSBHRS) in each country/region; and [E] Bayes estimation based on [C] deaths/(cases x days ago). The coefficients of variance (CV = the ratio of the standard deviation to the mean) were applied to measure the relative variability for each CFR. A dashboard was developed for daily display of the CFR across each region. RESULTS: The Bayes based on (A)[D] has the lowest CV (=0.10) followed by the deaths/confirmed (=0.11) [A], deaths/(deaths + recoveries) (=0.42) [B], Bayes based on (C) (=0.49) [E], and deaths/(cases x days ago) (=0.59) [C]. All final CFRs will be equal using the formula (from, A to E). A dashboard was developed for the daily reporting of the CFR. The CFR (3.7%) greater than the prior CFR of 2.2% was evident in LSBHRS, increasing the CFR. A dashboard was created to present the CFRs on COVID-19. CONCLUSION: We suggest examining both trends of the Bayes based on both deaths/(cases 7 days ago) and deaths/confirmed cases as a reference to the final CFR. An app developed for displaying the provisional CFR with the 2 CFR trends can improve the underestimated CFR reported by WHO and media. Wolters Kluwer Health 2020-05-22 /pmc/articles/PMC7249957/ /pubmed/32481256 http://dx.doi.org/10.1097/MD.0000000000019925 Text en Copyright © 2020 the Author(s). Published by Wolters Kluwer Health, Inc. http://creativecommons.org/licenses/by-nc/4.0 This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc/4.0 This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections. |
spellingShingle | 4400 Chang, Chi-Sheng Yeh, Yu-Tsen Chien, Tsair-Wei Lin, Jui-Chung John Cheng, Bor-Wen Kuo, Shu-Chun The computation of case fatality rate for novel coronavirus (COVID-19) based on Bayes theorem: An observational study |
title | The computation of case fatality rate for novel coronavirus (COVID-19) based on Bayes theorem: An observational study |
title_full | The computation of case fatality rate for novel coronavirus (COVID-19) based on Bayes theorem: An observational study |
title_fullStr | The computation of case fatality rate for novel coronavirus (COVID-19) based on Bayes theorem: An observational study |
title_full_unstemmed | The computation of case fatality rate for novel coronavirus (COVID-19) based on Bayes theorem: An observational study |
title_short | The computation of case fatality rate for novel coronavirus (COVID-19) based on Bayes theorem: An observational study |
title_sort | computation of case fatality rate for novel coronavirus (covid-19) based on bayes theorem: an observational study |
topic | 4400 |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7249957/ https://www.ncbi.nlm.nih.gov/pubmed/32481256 http://dx.doi.org/10.1097/MD.0000000000019925 |
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