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    Please use this identifier to cite or link to this item: https://ir.cnu.edu.tw/handle/310902800/34451


    標題: 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
    作者: Lee, Keng-Wei
    Chien, Tsair-Wei
    Yeh, Yu-Tsen
    Chou, Willy
    Wang, Hsien-Yi
    貢獻者: Chi Mei Med Ctr, Dept Cardiol
    Chi Mei Med Ctr, Dept Med Res
    St Georges Univ London, Med Sch
    Chiali Chi Mei Hosp, Dept Phys Med & Rehabil
    Chia Nan Univ Pharm & Sci, Dept Sport Management
    Chi Mei Med Ctr, Nephrol Dept
    關鍵字: absolute advantage coefficient
    area under the curve
    COVID-19
    infection point
    item response theory
    ogive curve
    日期: 2021
    上傳時間: 2023-11-11 11:53:33 (UTC+8)
    出版者: LIPPINCOTT WILLIAMS & WILKINS
    摘要: 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.
    關聯: MEDICINE, v.100, n.10, e24749
    Appears in Collections:[運動管理系] 期刊論文

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