Department of Cardiology.
Department of Medical Research, Chi-Mei Medical Center, Tainan, Taiwan.
Medicine (Baltimore). 2021 Mar 12;100(10):e24749. doi: 10.1097/MD.0000000000024749.
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.
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.
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.
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.
在 COVID-19 大流行期间，人们经常询问的一个问题是哪些国家（或大洲）受到了严重打击。除了使用确诊病例和死亡率来衡量 COVID-19 造成的影响外，很少有国家采用拐点（IP）来表示 COVID-19 的控制能力。如何确定与能力相关的 IP 天数仍不清楚。本研究旨在：（i）建立基于项目反应理论（IRT）的预测模型来确定国家的 IP；（ii）比较受打击最严重的国家（或大洲）。
我们下载了截至 2020 年 10 月 19 日所有国家 COVID-19 确诊病例数的暴发数据。建立了基于 IRT 的预测模型来确定每个国家的大流行 IP。展示了一个模型构建方案来拟合累积感染病例数。使用 Microsoft Excel 中的 Solver 加载项估计模型参数。使用给定的概化曲线的最小增量点计算绝对优势系数（AAC）来跟踪 IP。使用曲线下面积（AUC）和各自的 95%置信区间（CI）对时间事件分析（又名生存分析）进行了比较，以比较不同大陆的 IP 差异。在 Google 地图上创建了一个在线比较仪表板，以展示每个国家的疫情预测。
COVID-19 严重打击的前 3 个国家是法国、马来西亚和尼泊尔，其 IP 天数分别为 263、262 和 262。根据 IP 天数，受打击最严重的前 3 个大洲是欧洲、南美洲和北美洲，其 AUC 和 95%CI 分别为 0.73（0.61-0.86）、0.58（0.31-0.84）和 0.54（0.44-0.64）。演示并显示了一个在线时间事件结果，比较了各大洲的 IP 概率。
使用拟合流行数据的 IRT 建模方案来预测 IP 天数的长度。根据 IP 天数，欧洲，特别是法国，受到 COVID-19 的严重打击。建议使用 IRT 模型和 AAC 来确定大流行的 IP。
