An online time-to-event dashboard comparing the effective control of COVID-19 among continents using the inflection point on an ogive curve

Abstract

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.

Comparison of prediction accuracies between mathematical models to make projections of confirmed cases during the COVID-19 pandamic by country/region

“…COVID-19 has required analyses of data regarding NCICs reported worldwide and by country/region using an online dashboard. [ 6 , 35 , 36 ] Some COVID-19 dashboards developed by prestigious organizations, such as the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU), [ 4 ] the WHO, [ 37 ] Leszkiewicz personal dashboard, [ 38 ] HealthMap, [ 39 ] and the dashboard created by Schiffmann who is an 18-year-old high school senior from Washington State in the United States, [ 40 ] are full of interest and charm with visualizations for reporting the current state of the COVID-19. Nevertheless, these dashboards lack important information and analysis on making a projection about the evolution of CNICs during the COVID-19 epidemic.…”

Predicting Medical Fees for Hospitalized Inpatients and the Determination of Inflection Point

“…The proliferation of COVID-19 drew great attention from all over the world, particularly with more than 201,441 pieces of COVID-19-related research papers found on PubMed Central (PMC) [4]. Among them, the focus of public attention toward this disease was the turning point (or inflection point, IP) [5][6][7][8][9], which (1) geometrically means an IP between concavity and convexity of a continuous ogive curve and (2) medically means a confidence indicator of successful infection control for countries.…”

“…When formula 11 is applied, the IP calculated by the authors [15] is 19.4(=ln (1929.18)/0.39) ≈ 20 in year (i.e., 2016 − 1997 + 1). [15] This paper applies (1) the Microsoft Excel add-in of Solver [8,9] to parameter estimations of the model (Formula 7) with a bulk of data (2,2,2,7,4,8,12,15,21,38,34,35,60,87,109,178,267,410,648,765) from prior research targeting years 1997 to 2016 [15], and (2) uses Formula 11 to compute the inflection point. An example of IP determination using time-series data is displayed on the website once an integer number is an input in the box [31].…”

Using the Absolute Advantage Coefficient(AAC) to Measure the Strength of Damage Hit by COVID-19 in India on a Growth-Share Matrix

“…However, those dashboards merely displayed basic information on the numbers of death and con rmed cases of COVID-19 on a world map [14]. None of those dashboards were equipped with more sophisticated knowledge to ful ll the public's interest and need of the COVID-19 situation, such as using (i) the growth/share matrix(GSM) coined by the Boston Consulting Group (BCG) in 1970 to classify countries/regions in feature (i.e., growth on the Y-axis and share on the X-axis) [15][16][17], and (ii) the absolute advantage coe cient(AAC) [18][19][20][21][22] to report the strength of damage hit by COIVD-19 when compared to the next two countries/regions(e.g., what are the situations in India using the GSM and the AAC when the deadly second wave of Covid-19 spreads from cities to small towns [23]. The GSM is the most famous and simple portfolio planning matrix suggested to organizations to achieve a balance between the four categories of products a company produces [24,25], and was established in1970 by Bruce Doolin Henderson for the BCG in Boston, Massachusetts, the USA.…”