Previously submitted to: JMIR mHealth and uHealth (no longer under consideration since Feb 03, 2021)
Date Submitted: Jul 16, 2020
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
An App for Comparing the Epidemic Impacts in Continents and Trends of the Confirmed Cases on COVID-19 using the Online Rasch Model: An Observational Study
ABSTRACT
Background:
When a novel coronavirus (e.g., COVID-19) starts to spread, two of the most frequently asked questions are about (1) the overall trend of daily confirmed cases increasing or decreasing during the on-going outbreak epidemic and (2) the worst-hit continents for COVID-19 in the recent weeks. Finding the trend of the outbreak spread and the epidemic impacts on continents amid COVID-19 is continuously an urgent concern.
Objective:
This study aims to (1) inspect the epidemic trend over days, (2) develop an online algorithm to draw the epidemic impacts for COVID-19 among continents, and (3) design an app for a better understanding of the outbreak situation on Google Maps.
Methods:
We downloaded the COVID-19 outbreak numbers from Jun 24 to July 13, 2020, from Github that contains the number of confirmed cases in countries/regions. Three methods were used to compare differences in COVID-19-struck measures, including (1)the traditional summation score, (2) the Rasch logit score, and (3) the weighted score(i.e., adjusted by the estimated variance). Rasch model was applied to estimate the overall item (i.e., day) difficulties and the COVID-19-struck measures for all countries/areas. The epidemic trend was assessed by the correlation coefficient (CC) computed by the item difficulties over the observed days. An online algorithm based on the Rasch model was built for displaying the outbreak trend and the epidemic effects in comparison for continents using the forest tree plot and the analysis of variance(ANOVA). An app was developed to understand the daily epidemic trends on Google Maps.
Results:
The three methods used for comparing differences in COVID-19-struck measures were displayed somewhat different. A line chart was drawn online to present the trend measured by item(i.e., day) difficulties approaching stability with CC=-0.07. Differences in COVID-19-struck impacts were observed among continents using ANOVA(p<0.001= Chidist(160.31, 5)) and the forest tree plot. A dashboard was created to present the COVID-19 situation on Google Maps.
Conclusions:
The three methods used for comparing differences in COVID-19-struck measures were displayed somewhat different. A line chart was drawn online to present the trend measured by item(i.e., day) difficulties approaching stability with CC=-0.07. Differences in COVID-19-struck impacts were observed among continents using ANOVA(p<0.001= Chidist(160.31, 5)) and the forest tree plot. A dashboard was created to present the COVID-19 situation on Google Maps. Clinical Trial: Nil
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.