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Geographic risk assessment of COVID-19 transmission using recent data: An observational study

BACKGROUND: The US Centers for Disease Control and Prevention (CDC) regularly issues “travel health notices” that address disease outbreaks of novel coronavirus disease (COVID)-19 in destinations worldwide. The notices are classified into 3 levels based on the risk posed by the outbreak and what pre...

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Autores principales: Jen, Tung-Hui, Chien, Tsair-Wei, Yeh, Yu-Tsen, Lin, Jui-Chung John, Kuo, Shu-Chun, Chou, Willy
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Wolters Kluwer Health 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7302653/
https://www.ncbi.nlm.nih.gov/pubmed/32541529
http://dx.doi.org/10.1097/MD.0000000000020774
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author Jen, Tung-Hui
Chien, Tsair-Wei
Yeh, Yu-Tsen
Lin, Jui-Chung John
Kuo, Shu-Chun
Chou, Willy
author_facet Jen, Tung-Hui
Chien, Tsair-Wei
Yeh, Yu-Tsen
Lin, Jui-Chung John
Kuo, Shu-Chun
Chou, Willy
author_sort Jen, Tung-Hui
collection PubMed
description BACKGROUND: The US Centers for Disease Control and Prevention (CDC) regularly issues “travel health notices” that address disease outbreaks of novel coronavirus disease (COVID)-19 in destinations worldwide. The notices are classified into 3 levels based on the risk posed by the outbreak and what precautions should be in place to prevent spreading. What objectively observed criteria of these COVID-19 situations are required for classification and visualization? This study aimed to visualize the epidemic outbreak and the provisional case fatality rate (CFR) using the Rasch model and Bayes's theorem and developed an algorithm that classifies countries/regions into categories that are then shown on Google Maps. METHODS: We downloaded daily COVID-19 outbreak numbers for countries/regions from the GitHub website, which contains information on confirmed cases in more than 30 Chinese locations and other countries/regions. The Rasch model was used to estimate the epidemic outbreak for each country/region using data from recent days. All responses were transformed by using the logarithm function. The Bayes's base CFRs were computed for each region. The geographic risk of transmission of the COVID-19 epidemic was thus determined using both magnitudes (i.e., Rasch scores and CFRs) for each country. RESULTS: The top 7 countries were Iran, South Korea, Italy, Germany, Spain, China (Hubei), and France, with values of {4.53, 3.47, 3.18, 1.65, 1.34 1.13, 1.06} and {13.69%, 0.91%, 47.71%, 0.23%, 24.44%, 3.56%, and 16.22%} for the outbreak magnitudes and CFRs, respectively. The results were consistent with the US CDC travel advisories of warning level 3 in China, Iran, and most European countries and of level 2 in South Korea on March 16, 2020. CONCLUSION: We created an online algorithm that used the CFRs to display the geographic risks to understand COVID-19 transmission. The app was developed to display which countries had higher travel risks and aid with the understanding of the outbreak situation.
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spelling pubmed-73026532020-06-29 Geographic risk assessment of COVID-19 transmission using recent data: An observational study Jen, Tung-Hui Chien, Tsair-Wei Yeh, Yu-Tsen Lin, Jui-Chung John Kuo, Shu-Chun Chou, Willy Medicine (Baltimore) 4400 BACKGROUND: The US Centers for Disease Control and Prevention (CDC) regularly issues “travel health notices” that address disease outbreaks of novel coronavirus disease (COVID)-19 in destinations worldwide. The notices are classified into 3 levels based on the risk posed by the outbreak and what precautions should be in place to prevent spreading. What objectively observed criteria of these COVID-19 situations are required for classification and visualization? This study aimed to visualize the epidemic outbreak and the provisional case fatality rate (CFR) using the Rasch model and Bayes's theorem and developed an algorithm that classifies countries/regions into categories that are then shown on Google Maps. METHODS: We downloaded daily COVID-19 outbreak numbers for countries/regions from the GitHub website, which contains information on confirmed cases in more than 30 Chinese locations and other countries/regions. The Rasch model was used to estimate the epidemic outbreak for each country/region using data from recent days. All responses were transformed by using the logarithm function. The Bayes's base CFRs were computed for each region. The geographic risk of transmission of the COVID-19 epidemic was thus determined using both magnitudes (i.e., Rasch scores and CFRs) for each country. RESULTS: The top 7 countries were Iran, South Korea, Italy, Germany, Spain, China (Hubei), and France, with values of {4.53, 3.47, 3.18, 1.65, 1.34 1.13, 1.06} and {13.69%, 0.91%, 47.71%, 0.23%, 24.44%, 3.56%, and 16.22%} for the outbreak magnitudes and CFRs, respectively. The results were consistent with the US CDC travel advisories of warning level 3 in China, Iran, and most European countries and of level 2 in South Korea on March 16, 2020. CONCLUSION: We created an online algorithm that used the CFRs to display the geographic risks to understand COVID-19 transmission. The app was developed to display which countries had higher travel risks and aid with the understanding of the outbreak situation. Wolters Kluwer Health 2020-06-12 /pmc/articles/PMC7302653/ /pubmed/32541529 http://dx.doi.org/10.1097/MD.0000000000020774 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
spellingShingle 4400
Jen, Tung-Hui
Chien, Tsair-Wei
Yeh, Yu-Tsen
Lin, Jui-Chung John
Kuo, Shu-Chun
Chou, Willy
Geographic risk assessment of COVID-19 transmission using recent data: An observational study
title Geographic risk assessment of COVID-19 transmission using recent data: An observational study
title_full Geographic risk assessment of COVID-19 transmission using recent data: An observational study
title_fullStr Geographic risk assessment of COVID-19 transmission using recent data: An observational study
title_full_unstemmed Geographic risk assessment of COVID-19 transmission using recent data: An observational study
title_short Geographic risk assessment of COVID-19 transmission using recent data: An observational study
title_sort geographic risk assessment of covid-19 transmission using recent data: an observational study
topic 4400
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7302653/
https://www.ncbi.nlm.nih.gov/pubmed/32541529
http://dx.doi.org/10.1097/MD.0000000000020774
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