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

doi:10.1097/MD.0000000000020774

中山醫學大學機構典藏 CSMUIR > 中山醫學大學研究成果 > 其他文獻 > Item 310902500/25047

Please use this identifier to cite or link to this item: https://ir.csmu.edu.tw:8080/ir/handle/310902500/25047

Title:	Geographic risk assessment of COVID-19 transmission using recent data An observational study
Authors:	Jen, TH;Chien, TW;Yeh, YT;Lin, JCJ;Kuo, SC;Chou, W
Keywords:	case fatality rate;geographic risk;novel coronavirus-19;outbreak magnitudes;Rasch model
Date:	2020
Issue Date:	2022-08-09T09:27:34Z (UTC)
Publisher:	LIPPINCOTT WILLIAMS & WILKINS
ISSN:	0025-7974
Abstract:	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.
URI:	http://dx.doi.org/10.1097/MD.0000000000020774 https://www.webofscience.com/wos/woscc/full-record/WOS:000549881200084 https://ir.csmu.edu.tw:8080/handle/310902500/25047
Relation:	MEDICINE ,2020 ,v99 ,issue 24
Appears in Collections:	[中山醫學大學研究成果] 其他文獻

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