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An online time-to-event dashboard comparing the effective control of COVID-19 among continents using the inflection point on an ogive curve: Observational study
BACKGROUND: During the COVID-19 pandemic, one of the frequently asked questions is which countries (or continents) are severely hit. Aside from using the number of confirmed cases and the fatality to measure the impact caused by COVID-19, few adopted the inflection point (IP) to represent the contro...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Lippincott Williams & Wilkins
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7969250/ https://www.ncbi.nlm.nih.gov/pubmed/33725830 http://dx.doi.org/10.1097/MD.0000000000024749 |
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author | Lee, Keng-Wei Chien, Tsair-Wei Yeh, Yu-Tsen Chou, Willy Wang, Hsien-Yi |
author_facet | Lee, Keng-Wei Chien, Tsair-Wei Yeh, Yu-Tsen Chou, Willy Wang, Hsien-Yi |
author_sort | Lee, Keng-Wei |
collection | PubMed |
description | BACKGROUND: During the COVID-19 pandemic, one of the frequently asked questions is which countries (or continents) are severely hit. Aside from using the number of confirmed cases and the fatality to measure the impact caused by COVID-19, few adopted the inflection point (IP) to represent the control capability of COVID-19. How to determine the IP days related to the capability is still unclear. This study aims to (i) build a predictive model based on item response theory (IRT) to determine the IP for countries, and (ii) compare which countries (or continents) are hit most. METHODS: We downloaded COVID-19 outbreak data of the number of confirmed cases in all countries as of October 19, 2020. The IRT-based predictive model was built to determine the pandemic IP for each country. A model building scheme was demonstrated to fit the number of cumulative infected cases. Model parameters were estimated using the Solver add-in tool in Microsoft Excel. The absolute advantage coefficient (AAC) was computed to track the IP at the minimum of incremental points on a given ogive curve. The time-to-event analysis (a.k.a. survival analysis) was performed to compare the difference in IPs among continents using the area under the curve (AUC) and the respective 95% confidence intervals (CIs). An online comparative dashboard was created on Google Maps to present the epidemic prediction for each country. RESULTS: The top 3 countries that were hit severely by COVID-19 were France, Malaysia, and Nepal, with IP days at 263, 262, and 262, respectively. The top 3 continents that were hit most based on IP days were Europe, South America, and North America, with their AUCs and 95% CIs at 0.73 (0.61–0.86), 0.58 (0.31–0.84), and 0.54 (0.44–0.64), respectively. An online time–event result was demonstrated and shown on Google Maps, comparing the IP probabilities across continents. CONCLUSION: An IRT modeling scheme fitting the epidemic data was used to predict the length of IP days. Europe, particularly France, was hit seriously by COVID-19 based on the IP days. The IRT model incorporated with AAC is recommended to determine the pandemic IP. |
format | Online Article Text |
id | pubmed-7969250 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Lippincott Williams & Wilkins |
record_format | MEDLINE/PubMed |
spelling | pubmed-79692502021-03-18 An online time-to-event dashboard comparing the effective control of COVID-19 among continents using the inflection point on an ogive curve: Observational study Lee, Keng-Wei Chien, Tsair-Wei Yeh, Yu-Tsen Chou, Willy Wang, Hsien-Yi Medicine (Baltimore) 4400 BACKGROUND: During the COVID-19 pandemic, one of the frequently asked questions is which countries (or continents) are severely hit. Aside from using the number of confirmed cases and the fatality to measure the impact caused by COVID-19, few adopted the inflection point (IP) to represent the control capability of COVID-19. How to determine the IP days related to the capability is still unclear. This study aims to (i) build a predictive model based on item response theory (IRT) to determine the IP for countries, and (ii) compare which countries (or continents) are hit most. METHODS: We downloaded COVID-19 outbreak data of the number of confirmed cases in all countries as of October 19, 2020. The IRT-based predictive model was built to determine the pandemic IP for each country. A model building scheme was demonstrated to fit the number of cumulative infected cases. Model parameters were estimated using the Solver add-in tool in Microsoft Excel. The absolute advantage coefficient (AAC) was computed to track the IP at the minimum of incremental points on a given ogive curve. The time-to-event analysis (a.k.a. survival analysis) was performed to compare the difference in IPs among continents using the area under the curve (AUC) and the respective 95% confidence intervals (CIs). An online comparative dashboard was created on Google Maps to present the epidemic prediction for each country. RESULTS: The top 3 countries that were hit severely by COVID-19 were France, Malaysia, and Nepal, with IP days at 263, 262, and 262, respectively. The top 3 continents that were hit most based on IP days were Europe, South America, and North America, with their AUCs and 95% CIs at 0.73 (0.61–0.86), 0.58 (0.31–0.84), and 0.54 (0.44–0.64), respectively. An online time–event result was demonstrated and shown on Google Maps, comparing the IP probabilities across continents. CONCLUSION: An IRT modeling scheme fitting the epidemic data was used to predict the length of IP days. Europe, particularly France, was hit seriously by COVID-19 based on the IP days. The IRT model incorporated with AAC is recommended to determine the pandemic IP. Lippincott Williams & Wilkins 2021-03-12 /pmc/articles/PMC7969250/ /pubmed/33725830 http://dx.doi.org/10.1097/MD.0000000000024749 Text en Copyright © 2021 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 Lee, Keng-Wei Chien, Tsair-Wei Yeh, Yu-Tsen Chou, Willy Wang, Hsien-Yi An online time-to-event dashboard comparing the effective control of COVID-19 among continents using the inflection point on an ogive curve: Observational study |
title | An online time-to-event dashboard comparing the effective control of COVID-19 among continents using the inflection point on an ogive curve: Observational study |
title_full | An online time-to-event dashboard comparing the effective control of COVID-19 among continents using the inflection point on an ogive curve: Observational study |
title_fullStr | An online time-to-event dashboard comparing the effective control of COVID-19 among continents using the inflection point on an ogive curve: Observational study |
title_full_unstemmed | An online time-to-event dashboard comparing the effective control of COVID-19 among continents using the inflection point on an ogive curve: Observational study |
title_short | An online time-to-event dashboard comparing the effective control of COVID-19 among continents using the inflection point on an ogive curve: Observational study |
title_sort | online time-to-event dashboard comparing the effective control of covid-19 among continents using the inflection point on an ogive curve: observational study |
topic | 4400 |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7969250/ https://www.ncbi.nlm.nih.gov/pubmed/33725830 http://dx.doi.org/10.1097/MD.0000000000024749 |
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