• Kang-Ting Tsai, Tsair-Wei Chien, Ju-Kuo Lin, Yu-Tsen Yeh, and Willy Chou.
• Center for Integrative Medicine, ChiMei Medical Center, Tainan, Taiwan.
• Medicine (Baltimore). 2021 Dec 17; 100 (50): e28134e28134.

BackgroundThe COVID-19 pandemic caused >0.228 billion infected cases as of September 18, 2021, implying an exponential growth for infection worldwide. Many mathematical models have been proposed to predict the future cumulative number of infected cases (CNICs). Nevertheless, none compared their prediction accuracies in models. In this work, we compared mathematical models recently published in scholarly journals and designed online dashboards that present actual information about COVID-19.MethodsAll CNICs were downloaded from GitHub. Comparison of model R2 was made in 3 models based on quadratic equation (QE), modified QE (OE-m), and item response theory (IRT) using paired-t test and analysis of variance (ANOVA). The Kano diagram was applied to display the association and the difference in model R2 on a dashboard.ResultsWe observed that the correlation coefficient was 0.48 (t = 9.87, n = 265) between QE and IRT models based on R2 when modeling CNICs in a short run (dated from January 1 to February 16, 2021). A significant difference in R2 was found (P < .001, F = 53.32) in mean R2 of 0.98, 0.92, and 0.84 for IRT, OE-mm, and QE, respectively. The IRT-based COVID-19 model is superior to the counterparts of QE-m and QE in model R2 particularly in a longer period of infected days (i.e., in the entire year in 2020).ConclusionAn online dashboard was demonstrated to display the association and difference in prediction accuracy among predictive models. The IRT mathematical model was recommended to make projections about the evolution of CNICs for each county/region in future applications, not just limited to the COVID-19 epidemic.Copyright © 2021 the Author(s). Published by Wolters Kluwer Health, Inc.

13 keywords...

hide…