ABSTRACT
Background: When the COVID-19 outbreak spreads to the world, many articles related to it have been published in academics. Since the largest quantity of confirmed cases was reported in China till April 14, 2020, whether the number of Chinese articles of research associated with the COVID-19 topped globally is required to be examined. Thus, an objective measure determining the dominant role in a group should be defined. This study aims to propose an index (strength coefficient, SC) to evaluate the most influential research affiliations in publications of COVID-19. Methods: We simulated data to verify the separation index that can be viable in use for determining the dominant one that has the absolute advantage in a group. We selected 4,369 articles as of April 14, 2020, with abstracts from the Pubmed Central (PMC) based on the keywords COVID-19 or 2019-nCOV. An author-weighted scheme (AWS) was applied to quantify coauthor credits in the article byline. Social network analysis incorporated with SC(from 0 to 1.0 and cutoff=0/70) was applied to display the influential (1) article types, (2)countries, (3)medical subject headings(MeSH terms), and (4) research affiliations. Visual dashboards were created for displaying the results on Google Maps. Results: We observed that the top one(SC) in each topic consists of (1) journal article(0.81), (2) China(0.61),(3) Acad Radol, (4) betacoronavirus (0.66), and (5) Hazhong University of Science and Technology(0.77) in article types, countries, journals, MeSH terms, and research affiliations, respectively. Conclusion: We applied the SC to identify the strength of the top one over the next two. The SC was useful and viable in verifying the dominant role in a group. The implementation and application are worthy of further studies in the future.
Subject(s)
COVID-19 , Vision DisordersABSTRACT
Background The country of Italy was placed on lockdown when the number of cases of COVID-19 increased exponentially. It is of great concern which countries/regions would have the most similar patterns to Italy and the comparison of patterns, assuming homogeneity of variance within datasets, among regions worldwide, became necessary. There were no results in past literature regarding the identification of COVID-19 patterns among countries/regions. We were therefore motivated to propose an appropriate mathematic method, using a specific country/region (e.g., Italy), for example, in detecting similar patterns at the respective peak of the outbreak. A visual display highlighting COVID-19 patterns was proposed in this study using vector mathematics.Methods We downloaded COVID-19 outbreak numbers with confirmed cases in countries/regions on a daily basis from the Github website. The top peak point was identified for each country/region. Next, thirteen-time points were assigned before and after the inflection point. COVID-19 patterns were assessed by inspecting both similarity and distance using angle(or cosine theta in trigonometric function) and Chi-square statistics. Two sets of data on confirmed cases, one based on cases per 100,000 population and the other does not, were compared using rankings as well as four quadrants divided by similarity and distance. An app was developed to display regions with similar COVID-19 patterns to the selected country.Results The top four countries presenting with the most similar COVID-19 patterns to Italy were Switzerland, Norway, Iceland, and Luxembourg with Chi-square statistics per one freedom degree at 0.12, 0.37, 0.58, and 0.80, respectively, based on a population of 100,000. Visualizations with four quadrants and world map were shown on Google Maps. Rank correlation in rankings was − 0.03 between two sets of confirmed cases with and without using the basis of cases per population of 100,000.Conclusion We proposed and demonstrated a method using both criteria of similarity and distance from vectors in mathematics to identify countries that are most similar to Italy in terms of the pattern at the peak of the outbreak. The app was developed to display countries/regions with the most similar COVID-19 patterns to the targeted country/region using a dashboard laid on Google Maps. This method can be generalized and applied to study other countries/regions for the current pandemic and other past infectious disease in history.
Subject(s)
COVID-19 , Communicable DiseasesABSTRACT
Background: One of the key factors to understand the spread of COVID-19 is the case fatality rate(CFR) rates for counties/regions. However, many merely reported those CFRs on the basis of total confirmed cases(TCC) instead of per population of 100, 000. As such, the disparate definitions of CFR yield inconsistent results in COVID-19 situations. Whether both incident rate and the CFR can be indicators to identify a country/region hit by COVID-19 under(or out of) control is still unclear. This study aims to (1) develop a diagram to disperse both TCC and CFR on a population base of 100,000(namely TCC100 and CFR100, respectively) using the Kano model, (2)discuss selected countries/regions with success on preventative measures to keep COVID-19 under control(i.e., relatively lower TCC100 and CFR100 or CFR), and (3) design an app to display both TCC100 and CFR100 or CFR for all infected countries/regions. Methods: We downloaded the COVID-19 data of confirmed cases and deaths in countries/regions on a daily basis from the Github website. Three values, TCC100, CFR100, and CFR, were computed for each country/region and displayed on the Kano model. The lower TCC100 and CFR values were deemed as having the COVID-19 situation more under control. An app was developed to display both TCC100 and CFR100 on Google Maps. Results: The correlation coefficient(CC) between TCC100 and CFR100 is 0.92(t=37.87) based on 285 count data (i.e., 12, 225, and 48 dispersed on the Kano diagram) obtained in comparison to the pair of TCC100 and CFR with the CC=-0.02(t=-0.25) and counts(i.e., 56, 145, and 84 on the Kano diagram). The state of Washington, USA, was found having the highest TCC100(=3498.15), CFR100 (=16548.96) and CFR(=5%) in comparison to (1)Taiwan[TCC100(=1.65), CFR100 (=2.17), CFR(=1%)] and (2)South Korea[TCC100(=20.34), CFR100 (=39.8), CFR(=2%)] on April 12,2020. Conclusion: We illustrated both TCC100 and CFR along with CFR100 to compare countries/regions on a Kano diagram to understand the COVID-19 situations better. A larger TCC100 value was strongly associated with a larger CFR100 value that needs further verifications in the future. An app was developed to display both TCC100 and CFR for countries/regions on a dashboard laid over Google Maps.