Using the absolute advantage coefficient (AAC) to measure the strength of damage hit by COVID-19 in India on a growth-share matrix

Abstract

Background The COVID-19 pandemic occurred and rapidly spread around the world. Some online dashboards have included essential features on a world map. However, only transforming data into visualizations for countries/regions is insufficient for the public need. This study aims to (1) develop an algorithm for classifying countries/regions into four quadrants inn GSM and (2) design an app for a better understanding of the COVID-19 situation. Methods We downloaded COVID-19 outbreak numbers daily from the Github website, including 189 countries/regions. A four-quadrant diagram was applied to present the classification of each country/region using Google Maps run on dashboards. A novel presentation scheme was used to identify the most struck entities by observing (1) the multiply infection rate (MIR) and (2) the growth trend in the recent 7 days. Four clusters of the COVID-19 outbreak were dynamically classified. An app based on a dashboard aimed at public understanding of the outbreak types and visualizing of the COVID-19 pandemic with Google Maps run on dashboards. The absolute advantage coefficient (AAC) was used to measure the damage hit by COVID-19 referred to the next two countries severely hit by COVID-19. Results We found that the two hypotheses were supported: India (i) is in the increasing status as of April 28, 2021; (ii) has a substantially higher ACC(= 0.81 > 0.70), and (iii) has a substantially higher ACC(= 0.66 < 0.70) as of May 17, 2021. Conclusion Four clusters of the COVID-19 outbreak were dynamically classified online on an app making the public understand the outbreak types of COVID-19 pandemic shown on dashboards. The app with GSM and AAC is recommended for researchers in other disease outbreaks, not just limited to COVID-19.

The use of radar plots with the Yk-index to identify which authors contributed the most to the journal of Medicine in 2020 and 2021: A bibliometric analysis

“…[66] As usual, we report the most productive AIC/R in bibliometrics. One noteworthy difference is that both China and the USA were divided into provinces/metropolitan cities and states in comparison with other countries with smaller populations for research, which is similar to previous studies [51,[67][68][69] illustrating the geographical distribution of indicators on a choropleth map for readers to easily comprehend the study results. Otherwise, the most productive countries would be located in either the United States or China.…”

Using Sankey diagrams to explore the trend of article citations in the field of bladder cancer: Research achievements in China higher than those in the United States

“…The STBG was proposed in this study, including HS, BS, and the trend. The trend has been proposed in the study [36] (e.g., with 4 scenarios denoted by colors in the last column (see Fig. 1D).…”

“…[32,33] We are thus motivated to illustrate the Sankey diagram [34,35] to enhance the traditional TBG (called Sankey TBG [STBG]) using the inflection point (IP) to express the hot spot (HS), the burst strength (BS) and the trend stages to highlight the development in the latest 4 time points. [36,37] The third research question was to demonstrate how the STBG can be applied to explore the trend of article citations in the BC field.…”

Using sentiment analysis to identify similarities and differences in research topics and medical subject headings (MeSH terms) between Medicine (Baltimore) and the Journal of the Formosan Medical Association (JFMA) in 2020

“…The AACs are 0.85 and 0.82 (>0.70) [19][20][21][22] ) for Medicine and JFMA, respectively, indicating mainland China and Taiwan have a strong dominance in the 2 journals.…”