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Using the Kano model to associate the number of confirmed cases of COVID-19 in a population of 100,000 with case fatality rates: An observational study
An important factor in understanding the spread of COVID-19 is the case fatality rate (CFR) for each county. However, many of research reported CFRs on total confirmed cases (TCCs) rather than per 100,000 people. The disparate definitions of CFR in COVID-19 result in inconsistent results. It remains...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Lippincott Williams & Wilkins
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9477709/ https://www.ncbi.nlm.nih.gov/pubmed/36123944 http://dx.doi.org/10.1097/MD.0000000000030648 |
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author | Hsu, Sheng-Yao Chien, Tsair-Wei Yeh, Yu-Tsen Chou, Willy |
author_facet | Hsu, Sheng-Yao Chien, Tsair-Wei Yeh, Yu-Tsen Chou, Willy |
author_sort | Hsu, Sheng-Yao |
collection | PubMed |
description | An important factor in understanding the spread of COVID-19 is the case fatality rate (CFR) for each county. However, many of research reported CFRs on total confirmed cases (TCCs) rather than per 100,000 people. The disparate definitions of CFR in COVID-19 result in inconsistent results. It remains uncertain whether the incident rate and CFR can be compared to identify countries affected by COVID-19 that are under (or out of) control. This study aims to develop a diagram for dispersing TCC and CFR on a population of 100,000 (namely, TCC100 and CFR100) using the Kano model, to examine selected countries/regions that have successfully implemented preventative measures to keep COVID-19 under control, and to design an app displaying TCC100 and CFR100 for all infected countries/regions. METHODS: Data regarding confirmed cases and deaths of COVID-19 in countries/regions were downloaded daily from the GitHub website. For each country/region, 3 values (TCC100, CFR100, and CFR) were calculated and displayed on the Kano diagram. The lower TCC100 and CFR values indicated that the COVID-19 situation was more under control. The app was developed to display both CFR100/CFR against TCC100 on Google Maps. RESULTS: Based on 286 countries/regions, the correlation coefficient (CC) between TCC100 and CFR100 was 0.51 (t = 9.76) in comparison to TCC100 and CFR with CC = 0.02 (t = 0.3). As a result of the traditional scatter plot using CFR and TCC100, Andorra was found to have the highest CFR100 (=6.62%), TCC100 (=935.74), and CFR (=5.1%), but lower CFR than New York (CFR = 7.4%) and the UK (CFR = 13.5%). There were 3 representative countries/regions that were compared: Taiwan [TCC100 (=1.65), CFR100 (=2.17), CFR (=1%)], South Korea [TCC100 (=20.34), CFR100 (=39.8), CFR (=2%), and Vietnam [TCC100 (=0.26), CFR100 (=0), CFR (=0%)]. CONCLUSION: A Kano diagram was drawn to compare TCC100 against CFT (or CFR100) to gain a better understanding of COVID-19. There is a strong association between a higher TCC100 value and a higher CFR100 value. A dashboard was developed to display both CFR100/CFR against TCC100 for countries/regions. |
format | Online Article Text |
id | pubmed-9477709 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Lippincott Williams & Wilkins |
record_format | MEDLINE/PubMed |
spelling | pubmed-94777092022-09-16 Using the Kano model to associate the number of confirmed cases of COVID-19 in a population of 100,000 with case fatality rates: An observational study Hsu, Sheng-Yao Chien, Tsair-Wei Yeh, Yu-Tsen Chou, Willy Medicine (Baltimore) Research Article An important factor in understanding the spread of COVID-19 is the case fatality rate (CFR) for each county. However, many of research reported CFRs on total confirmed cases (TCCs) rather than per 100,000 people. The disparate definitions of CFR in COVID-19 result in inconsistent results. It remains uncertain whether the incident rate and CFR can be compared to identify countries affected by COVID-19 that are under (or out of) control. This study aims to develop a diagram for dispersing TCC and CFR on a population of 100,000 (namely, TCC100 and CFR100) using the Kano model, to examine selected countries/regions that have successfully implemented preventative measures to keep COVID-19 under control, and to design an app displaying TCC100 and CFR100 for all infected countries/regions. METHODS: Data regarding confirmed cases and deaths of COVID-19 in countries/regions were downloaded daily from the GitHub website. For each country/region, 3 values (TCC100, CFR100, and CFR) were calculated and displayed on the Kano diagram. The lower TCC100 and CFR values indicated that the COVID-19 situation was more under control. The app was developed to display both CFR100/CFR against TCC100 on Google Maps. RESULTS: Based on 286 countries/regions, the correlation coefficient (CC) between TCC100 and CFR100 was 0.51 (t = 9.76) in comparison to TCC100 and CFR with CC = 0.02 (t = 0.3). As a result of the traditional scatter plot using CFR and TCC100, Andorra was found to have the highest CFR100 (=6.62%), TCC100 (=935.74), and CFR (=5.1%), but lower CFR than New York (CFR = 7.4%) and the UK (CFR = 13.5%). There were 3 representative countries/regions that were compared: Taiwan [TCC100 (=1.65), CFR100 (=2.17), CFR (=1%)], South Korea [TCC100 (=20.34), CFR100 (=39.8), CFR (=2%), and Vietnam [TCC100 (=0.26), CFR100 (=0), CFR (=0%)]. CONCLUSION: A Kano diagram was drawn to compare TCC100 against CFT (or CFR100) to gain a better understanding of COVID-19. There is a strong association between a higher TCC100 value and a higher CFR100 value. A dashboard was developed to display both CFR100/CFR against TCC100 for countries/regions. Lippincott Williams & Wilkins 2022-09-16 /pmc/articles/PMC9477709/ /pubmed/36123944 http://dx.doi.org/10.1097/MD.0000000000030648 Text en Copyright © 2022 the Author(s). Published by Wolters Kluwer Health, Inc. https://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) (https://creativecommons.org/licenses/by-nc/4.0/) , 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. |
spellingShingle | Research Article Hsu, Sheng-Yao Chien, Tsair-Wei Yeh, Yu-Tsen Chou, Willy Using the Kano model to associate the number of confirmed cases of COVID-19 in a population of 100,000 with case fatality rates: An observational study |
title | Using the Kano model to associate the number of confirmed cases of COVID-19 in a population of 100,000 with case fatality rates: An observational study |
title_full | Using the Kano model to associate the number of confirmed cases of COVID-19 in a population of 100,000 with case fatality rates: An observational study |
title_fullStr | Using the Kano model to associate the number of confirmed cases of COVID-19 in a population of 100,000 with case fatality rates: An observational study |
title_full_unstemmed | Using the Kano model to associate the number of confirmed cases of COVID-19 in a population of 100,000 with case fatality rates: An observational study |
title_short | Using the Kano model to associate the number of confirmed cases of COVID-19 in a population of 100,000 with case fatality rates: An observational study |
title_sort | using the kano model to associate the number of confirmed cases of covid-19 in a population of 100,000 with case fatality rates: an observational study |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9477709/ https://www.ncbi.nlm.nih.gov/pubmed/36123944 http://dx.doi.org/10.1097/MD.0000000000030648 |
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