An App for Classifying the Trend Magnitudes and the Multiple Infection Rates(MIR) of Novel Coronavirus (2019-nCoV) in Countries/Regions: A Data-Driven Analysis (Preprint)
Authors: Chien Tsair-Wei , Kan Wei-Chih , Yeh Yu-Tsen , Kuo Shu-Chun 
Published: February 2020

BACKGROUND

An outbreak of the novel coronavirus (2019-nCoV) pneumonia hits the city of Wuhan, China, in December 2019 and subsequently spread to other provinces/regions of China as well as foreign countries. An online dashboard regularly updating the worldwide status of the coronavirus outbreak would be beneficial to the public understanding of the almost-real-time 2019-nCoV situation. Some online dashboards were equipped with wow-features on a world map. However, only displaying the case numbers of the outbreak across countries/provinces/regions is insufficient to the public. The trends of the outbreak and variations of multiple infection rate (MIR) would be greatly informative in displaying on a dashboard in the form of an app.

OBJECTIVE

This study aims to (1) present the MIR in comparison for each counties/regions, (2) develop an algorithm that classifies entities into four clusters (e.g., ready to rise, increasing, slowing down, and ready to decrease with four steps and quadrants named 4SQ diagram for short) shown on Google Maps, and (3) design an app for better understanding the outbreak situation.

METHODS

We downloaded 2019-nCoV outbreak numbers in countries/regions on a daily basis from Google Sheet that contains information on confirmed cases in more than 30 Chinese locations and other countries/regions. Choropleth maps and Kano diagrams were drawn based on the 4SQ diagram. The Kano diagram was applied to present the classification feature for each country/region using a dashboard presenting on Google Maps. One novel presentation was used to identify the recent MIR changes across sectors. Four clusters of the 2019-nCoV outbreak were dynamically classified. The other four basic features were involved including (1) an overall visual display on case counts, (2) a choropleth map, (3) daily MIR trend changes, and (4)three-type trend charts. The Separation Index (SI) was applied to assess the role Hubei(China) played in the outbreak situation. An app aimed for public understandings based on a dashboard to classify and visualize with Google Maps was introduced.

RESULTS

We made improvements on the display of classification of the outbreak and the death rate for each region, for example, 2.01% and 2.87% for all cases and Hubei(China) only, respectively. Three-type trend-charts were automatically linked to choropleth maps and the Kano diagrams in near real-time. Importantly, the sequential trend for each region on a daily basis classifies outbreak attributes (e.g., Japan was increasing and Taiwan ready to rise on February 6, 2019). The SI for Hubei(China) reaches 0.96, extremely higher than the cutting point at 0.7. The highest MIR(=0.26) was British Columbia(Canada) on February 9, 2020.

CONCLUSIONS

The unique features for display the outbreak situation of the 2019-nCoV were proposed in this study. Visualizations using the 4SQ diagram, SI, and the MIR based on time series were present displaying dashboards on Google Maps. An app developed for visualizing the data is required for application in the future.