Using the Kano model to display the most cited authors and affiliated countries in schizophrenia research

Circle packing charts generated by ChatGPT to identify the characteristics of articles by anesthesiology authors in 2022: Bibliometric analysis

BACKGROUND

The ChatGPT (Open AI, San Francisco, CA), denoted by the Chat Generative Pretrained Transformer, has been a hot topic for discussion over the past few months. A verification of whether the code for drawing circle packing charts (CPCs) with R can be generated by ChatGPT and used to identify characteristics of articles by anesthesiology authors is needed. This study aimed to provide insights into article characteristics in the field of anesthesiology and to highlight the potential of ChatGPT for data visualization techniques (e.g., CPCs) in bibliometric analysis.

METHODS

A total of 23,012 articles were indexed in PubMed in 2022 by authors in the field of anesthesiology. The code for drawing CPCs with R was generated by ChatGPT and then modified by the authors to identify the characteristics of articles in 2 forms: 23,012 and 100 top-impact factors in journals (T100IF). Using CPCs and 3 other visualizations-network charts, impact beam plots, and Sankey diagrams-we were able to display article features commonly used in bibliometric analysis. The author-weighted scheme and absolute advantage coefficient were used to assess dominant entities, such as countries, institutes, authors, and themes (defined by PubMed and MeSH terms).

RESULTS

Our findings indicate that: further modifications should be made to the code generated by ChatGPT for drawing CPCs in R; publications in the field of anesthesiology are dominated by China, followed by the United States and Japan; Capital Medical University (China) and Showa University Hospital (Japan) dominate research institutes in terms of publications and IF, respectively; and COVID-19 is the most frequently reported theme in T100IF, accounting for 29%.

CONCLUSIONS

No such articles with CPCs regarding bibliometrics have ever been found in PubMed. The code for drawing CPCs with R can be generated by ChatGPT, but further modification is required for implementation in bibliometrics. CPCs should be used in future studies to identify the characteristics of articles in other areas of research rather than limiting them to anesthesiology, as we did in this study.

Classification and citation analysis of the 100 top-cited articles on nurse resilience using chord diagrams: A bibliometric analysis

BACKGROUND

Studies of most-cited articles have been frequently conducted on various topics and in various medical fields. To date, no study has examined the characteristics of articles associated with theme classifications and research achievements of article entities related to nursing resilience. This study aims to graphically depict the characteristics of the 100 top-cited articles addressing nurse resilience (T100NurseR), diagram the relationship between articles and author collaborations according to themes extracted from article keywords, and examine whether article keywords are correlated with article citations.

METHODS

T100NurseR publications were retrieved from the Web of Science (WoS) core collection on October 13, 2022. Themes associated with articles were explored using coword analysis in WoS keywords plus. The document category, journal ranking based on impact factor, authorship, and L-index and Y-index were used to analyze the dominant entities. To report the themes of T100NurseR and their research achievements in comparison to article entities and verify the hypothesis that keyword mean citation can be used to predict article citations, 5 visualizations were applied, including network diagrams, chord diagrams, dot plots, Kano diagrams, and radar plots.

RESULTS

Citations per article averaged 61.96 (range, 25-514). There were 5 themes identified in T100NurseR, including Parses theory, nurse resilience, conflict management, nursing identity, and emotional intelligence. For countries, institutes, departments, and authors in comparison of category, journal impact factor, authorship, and L-index scores, Australia (129.80), the University of Western Sydney (23.12), Nursing (87.17), and Kim Foster (23.76) are the dominant entities. The weighted number of citations according to Keywords Plus in WoS is significantly correlated with article citations (Pearson R = 0.94; P = .001).

CONCLUSION

We present diagrams to guide evidence-based clinical decision-making in nurse resilience based on the characteristics of the T100NurseR articles. Article citations can be predicted using weighted keywords. Future bibliographical studies may apply the 5 visualizations to relevant studies, not being solely restricted to T100NurseR.

Thematic analysis of articles on artificial intelligence with spine trauma, vertebral metastasis, and osteoporosis using chord diagrams: A systematic review and meta-analysis

BACKGROUND

Spine trauma, vertebral metastases, and osteoporosis (SVO) can result in serious health problems. If the diagnosis of SVO is delayed, the prognosis may be deteriorated. The use of artificial intelligence (AI) is an essential method for minimizing the diagnostic errors associated with SVO. research achievements (RAs) of SVO on AI are required as a result of the greatest number of studies on AI solutions reported. The study aimed to: classify article themes using visualizations, illustrate the characteristics of SVO on AI recently, compare RAs of SVO on AI between entities (e.g., countries, institutes, departments, and authors), and determine whether the mean citations of keywords can be used to predict article citations.

METHODS

A total of 31 articles from SVO on AI (denoted by T31SVOAI) have been found in Web of Science since 2018. The dominant entities were analyzed using the CJAL score and the Y-index. Five visualizations were applied to report: the themes of T31SVOAI and their RAs in comparison for article entities and verification of the hypothesis that the mean citations of keywords can predict article citations, including: network diagrams, chord diagrams, dot plots, a Kano diagram, and radar plots.

RESULTS

There were five themes classified (osteoporosis, personalized medicine, fracture, deformity, and cervical spine) by a chord diagram. The dominant entities with the highest CJAL scores were the United States (22.05), the University of Pennsylvania (5.72), Radiology (6.12), and Nithin Kolanu (Australia) (9.88). The majority of articles were published in Bone, J. Bone Miner. Res., and Arch. Osteoporos., with an equal count (=3). There was a significant correlation between the number of article citations and the number of weighted keywords (F = 392.05; P < .0001).

CONCLUSION

A breakthrough was achieved by displaying the characteristics of T31SVOAI using the CJAL score, the Y-index, and the chord diagram. Weighted keywords can be used to predict article citations. The five visualizations employed in this study may be used in future bibliographical studies.

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

BACKGROUND

A consensus exists that the first author and corresponding author make the most contribution to the publication of an article. The Y-index has been proposed to assess the scientific achievements of authors, institutions, and countries/regions (AIC/R for short) based on the number of first-author publications (FPs) and corresponding-author publications (RPs). Nonetheless, the Y-index is defined in terms of count and radian (represented by j and h) instead of using the relative radius and angle degree to simplify understanding. In the literature, a method for drawing radar diagrams online with the Y-index is also lacking. This study was conducted to enhance the Y-index with an additional relative radius denoted by k and the angle degree represented by h* (named Yk-index), include easy-to-use features (e.g., copying and pasting) for the delivery of the online Radar-Yk, and identify which one of AIC/R contributed the most to a scientific journal.

METHODS

From the Web of Science (WoS) database, we downloaded 9498 abstracts of articles published in the journal of Medicine (Baltimore) in 2020 and 2021. Three visual representations were used, including a Sankey diagram, a choropleth map, and a radar diagram, to identify the characteristics of contributions by AIC/R to Medicine (Baltimore) using the Yk-index (j, k, h*). A demonstration of Rada-Yk with easy-to-use features was given using the copy-and-paste technique.

RESULTS

We found that Qiu Chen (China), Sichuan University (China), China, and South Korea (based on regions, e.g., provinces/metropolitan areas in China) were the most productive AIC/R, with their Yk equal to 27,715, 12415.1, and 2045, respectively; a total of 85.6% of the published articles in Medicine (Baltimore) came from the 3 countries (China, South Korea, and Japan); and this method of drawing the Radar-Yk online was provided and successfully demonstrated.

CONCLUSION

A breakthrough was achieved by developing the online Radar-Yk to show the most contributions to Medicine (Baltimore). Visualization of Radar-Yk could be replicated for future academic research and applications on other topics in future bibliographical studies.

A comparison of 3 productive authors' research domains based on sources from articles, cited references and citing articles using social network analysis

BACKGROUND

An individual's research domain (RD) can be determined from objective publication data (e.g., medical subject headings and Medical Subject Headings (MeSH) terms) by performing social network analysis. Bibliographic coupling (such as cocitation) is a similarity metric that relies on citation analysis to determine the similarity in RD between 2 articles. This study compared RD consistency between articles as well as their cited references and citing articles (ARCs).

METHODS

A total of 1388 abstracts were downloaded from PubMed and authored by 3 productive authors. Based on the top 3 clusters in social network analysis, similarity in RD was observed by comparing their consistency using the major MeSH terms in author articles, cited references and citing articles (ARC). Impact beam plots with La indices were drawn and compared for each of the 3 authors.

RESULTS

Sung-Ho Jang (South Korea), Chia-Hung Kao (Taiwan), and Chin-Hsiao Tseng (Taiwan) published 445, 780, and 163 articles, respectively. Dr Jang's RD is physiology, and Dr Kao and Dr Tseng's RDs are epidemiology. We confirmed the consistency of the RD terms by comparing the major MeSH terms in the ARC. Their La indexes were 5, 5, and 6, where a higher value indicates more extraordinary research achievement.

CONCLUSION

RD consistency was confirmed by comparing the main MeSH terms in ARC. The 3 approaches of RD determination (based on author articles, the La index, and the impact beam plots) were recommended for bibliographical studies in the future.

Demand analysis of telenursing among empty-nest elderly individuals with chronic diseases based on the Kano model

Aim

The increase in empty-nest elderly individuals with chronic diseases poses a major challenge to the provision of public health services in China. Telenursing can effectively relieve the pressure of public health services to a certain extent. This study aims to explore the telenursing needs of empty-nest elderly individuals with chronic diseases based on the Kano model to provide references for improving the quality of telenursing.

Methods

Participants were selected from five rural communities and five urban communities in Yangzhou and Nantong, Jiangsu Province, China. A total of 348 empty-nest elderly individuals with chronic diseases were included. The participants received a sociodemographic characteristics questionnaire, and their telenursing needs were surveyed and analyzed based on the Kano model.

Results

Of the 15 quality attributes evaluated by the participants, 3 telenursing services were categorized as "must-be quality", 5 were categorized as "one-dimensional quality", 5 were categorized as "attractive quality", and 2 were categorized as "indifferent quality". The proportion of individuals who desired telenursing services ranged from 47.41 to 83.62%, the better values (satisfaction) ranged from 35.29-83.98%, and the worse values (dissatisfaction) ranged from 10.91 to 63.27%. There were no significant differences in any items of telenursing needs for between participants in Yangzhou and Nantong (all P > 0.05), and there were also no significant differences in all items between rural and urban communities (all P > 0.05).

Conclusion

Based on the Kano model, it was found that empty-nest elderly individuals with chronic diseases had a positive attitude toward telenursing and that they had different levels of need for different telenursing services. These findings provided a theoretical basis for medical decision-makers to formulate medical policies and provided a scientific foundation for nursing managers to improve telenursing services to meet the needs of the empty-nest elderly individuals with chronic diseases.

Using the Sankey diagram to visualize article features on the topics of whole-exome sequencing (WES) and whole-genome sequencing (WGS) since 2012: Bibliometric analysis

BACKGROUND

Sequencing technologies, such as whole-exome sequencing (WES) and whole-genome sequencing (WGS), have been increasingly applied to medical research in recent years. Which countries, journals, and institutes (called entities) contributed most to the fields (WES/WGS) remains unknown. Temporal bar graphs (TBGs) are frequently used in trend analysis of publications. However, how to draw the TBG on the Sankey diagram is not well understood in bibliometrics. We thus aimed to investigate the evolution of article entities in the WES/WGS fields using publication-based TBGs and compare the individual research achievements (IRAs) among entities.

METHODS

A total of 3599 abstracts downloaded from icite analysis were matched to entities, including article identity numbers, citations, publication years, journals, affiliated countries/regions of origin, and medical subject headings (MeSH terms) in PubMed on March 12, 2022. The relative citation ratio (RCR) was extracted from icite analysis to compute the hT index (denoting the IRA, taking both publications and citations into account) for each entity in the years between 2012 and 2021. Three types of visualizations were applied to display the trends of publications (e.g., choropleth maps and the enhanced TBGs) and IRAs (e.g., the flowchart on the Sankey diagram) for article entities in WES/WGS.

RESULTS

We observed that the 3 countries (the US, China, and the UK) occupied most articles in the WES/WGS fields since 2012, the 3 entities (i.e., top 5 journals, research institutes, and MeSH terms) were demonstrated on the enhanced TBGs, the top 2 MeSH terms were genetics and methods in WES and WGS, and the IRAs of 6 article entities with their hT-indices were succinctly and simultaneously displayed on a single Sankey diagram that was never launched in bibliographical studies.

CONCLUSION

The number of WES/WGS-related articles has dramatically increased since 2017. TBGs, particularly with hTs on the Sankey, are recommended for research on a topic (or in a discipline) to compare trends of publications and IRAs for entities in future bibliographical studies.

The Author Impact Factor as a Metric to Evaluate the Impact of Neurosurgical Researchers

OBJECTIVE

The objective of this study was to assess the Author Impact Factor (AIF) as a useful metric and as a complement to the h-index among neurosurgical researchers.

METHODS

The 5-year AIF and h-index were compared among 3 groups of researchers: 1) the 100 most prolific of all time within general neurosurgical journals ("Experienced"), 2) the 100 most prolific during the 2015-2019 period within general neurosurgical journals ("Trending Group"), and 3) the 100 postgraduation year 7 neurosurgical residents with the highest h-index ("Amateur").

RESULTS

The Amateur group had a lower median h-index than the Experienced (6 vs. 55; P < 0.001) and Trending (6 vs. 43; P < 0.001) groups. The highest h-index of the Amateur group (24) was lower than the first quartile of the Experienced (46.25) and Trending (26.00) groups. The Amateur group had a lower median 5-year AIF than the Experienced (2.15 vs. 3.17; P < 0.001) and Trending (2.15 vs. 2.85; P = 0.02) groups. Unlike the h-index, the gap between the 5-year AIF distribution of the Amateur group and other groups was not profound. Although there was a positive correlation between the metrics in the 3 groups, they did not proxy for each other. For instance, while the h-index of some experienced authors that have not published recently was high, their AIFs were zero. Also, some Amateur authors published very impactful articles and had a high 5-year AIF. However, since their number of publications is inevitably low, their h-index were low.

CONCLUSIONS

The AIF provides intuitive and complementary information to the h-index regarding the research output of neurosurgical authors.

Vaccination associated with gross domestic product and fewer deaths in countries and regions: A verification study

Background:

Vaccination can have a substantial impact on mitigating COVID-19 outbreaks. However, the vaccine rollout rates associated with the gross domestic product (GDP) and few deaths are required for verification. Three hypotheses were made:

Methods:

The corresponding CNCCs and deaths were downloaded from the GitHub website. Four variables, including IP days on CNCCs and deaths, GDP per capita, and vaccine doses administered per 100 people (VD100) in countries/regions, were collected. Correlation coefficients (CCs) between variables were computed to verify the association with vaccination rates. Four tasks were achieved:

Results:

We observed that

Conclusion:

Our results indicate that vaccination has a significant effect on mitigating COVID-19 outbreaks, even with limited protection against infection. Continued compliance with nonpharmaceutical interventions is essential to the fight against COVID-19 in the future.

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

BACKGROUND

The COVID-19 pandemic caused >0.228 billion infected cases as of September 18, 2021, implying an exponential growth for infection worldwide. Many mathematical models have been proposed to predict the future cumulative number of infected cases (CNICs). Nevertheless, none compared their prediction accuracies in models. In this work, we compared mathematical models recently published in scholarly journals and designed online dashboards that present actual information about COVID-19.

METHODS

All CNICs were downloaded from GitHub. Comparison of model R2 was made in 3 models based on quadratic equation (QE), modified QE (OE-m), and item response theory (IRT) using paired-t test and analysis of variance (ANOVA). The Kano diagram was applied to display the association and the difference in model R2 on a dashboard.

RESULTS

We observed that the correlation coefficient was 0.48 (t = 9.87, n = 265) between QE and IRT models based on R2 when modeling CNICs in a short run (dated from January 1 to February 16, 2021). A significant difference in R2 was found (P < .001, F = 53.32) in mean R2 of 0.98, 0.92, and 0.84 for IRT, OE-mm, and QE, respectively. The IRT-based COVID-19 model is superior to the counterparts of QE-m and QE in model R2 particularly in a longer period of infected days (i.e., in the entire year in 2020).

CONCLUSION

An online dashboard was demonstrated to display the association and difference in prediction accuracy among predictive models. The IRT mathematical model was recommended to make projections about the evolution of CNICs for each county/region in future applications, not just limited to the COVID-19 epidemic.

Predicting the number of article citations on the topic of pemphigus vulgaris with the 100 top-cited articles since 2011: A protocol for systematic review and meta-analysis

BACKGROUND

Pemphigus vulgaris (PV) is a rare autoimmune blistering disease characterized by intraepithelial and mucocutaneous blister formation and erosion. Numerous articles related to PV have been published. However, which articles have a tremendous influence is still unknown, and factors affecting article citation numbers remain unclear. We aimed to visualize the prominent entities using the top 100 most-cited articles on the topic of PV (T100PV), and investigate whether medical subject headings (i.e., MeSH terms) can be used to predict article citations.

METHODS

By searching the PubMed Central (PMC) database, the T100PV abstracts since 2011 were downloaded. Citation analysis was performed to compare the dominant entities in article topics, authors, and research institutes using social network analysis (SNA) and Kano diagrams. We examined the MeSH prediction power against article citations using correlation coefficients (CCs).

RESULTS

The most cited article (125 times) was authored by Ellebrecht from the University of Pennsylvania in the US. The most productive countries were Germany (28%) and the US (25%). Most articles were published in J Invest Dermatol (16%) and Br J Dermatol (10%). Kasperkiewicz (Germany) and the Normandie University (France) were the most cited authors and research institutes, respectively. The most frequently occurred MeSH terms were administration and dosage, immunology, and metabolism. MeSH terms were evident in the prediction power on the number of article citations (F = 19.77; P < .001).

CONCLUSION

A breakthrough was achieved by developing dashboards to display the T100PV. MeSH terms can be used to predict the T100PV citations. These T100PV visualizations can be applied in future studies.

Suitability of h- and x-indices for evaluating authors' individual research achievements in a given short period of years: A bibliometric analysis

BACKGROUND

The h-index of a researcher refers to the maximum number h of his/her publications that has at least h citations via the concept of the square area. The x-index is determined by the maximum area of a rectangle under the curve to interpret authors' individual research achievements (IRAs). However, the properties of both metrics have not been compared and discussed before. This study aimed to investigate whether both metrics of h- and x-index are suitable for evaluating IRAs in a short period of years.

METHODS

By searching the PubMed database (Pubmed.com), we used the keyword "PLoS One" (journal) and downloaded 50,000 articles published in 2015 and 2016. A total of 146,346 citations were listed in PubMed Central and 27,035 authors(with h-index ≥1) were divided into 3 parts. Correlation coefficients among metrics (ie, AIF, h, g, Ag, and x-index) were examined. The bootstrapping method used for estimating 95% confidence intervals was applied to compare differences in metrics among author groups. The most cited authors and topic burst were visualized by social network analysis. The most prominent countries/areas were highlighted by the x-index and displayed via choropleth maps.

RESULTS

Results demonstrated that, first, the h-index had the least relation to other metrics and failed to differentiate authors' IRAs among groups, particularly in a short time period. Second, the top 3 highest x-index for countries were the United States, China, and the UK but with the productivity-oriented feature. Third, the most cited medical subject headings (ie, MeSH terms) were genome, metabolome, and microbiology, and the most cited author was Lori Newman (whose x-index = 13.52, and h = 2) from Switzerland with the article (PMID = 26646541) cited 291 times. The need for the x-index combined with a visual map for displaying authors' IRAs was verified and recommended.

CONCLUSIONS

We verified that the h-index failed to differentiate authors' IRAs among author groups in a short time period. The x-index combined with the Kano map is recommended in research for a better understanding of the authors' IRAs in other journals or disciplines, not just limited to the journal of PloS One as we did in this study.

Using Social Network Analysis to Identify Spatiotemporal Spread Patterns of COVID-19 around the World: Online Dashboard Development

The COVID-19 pandemic has spread widely around the world. Many mathematical models have been proposed to investigate the inflection point (IP) and the spread pattern of COVID-19. However, no researchers have applied social network analysis (SNA) to cluster their characteristics. We aimed to illustrate the use of SNA to identify the spread clusters of COVID-19. Cumulative numbers of infected cases (CNICs) in countries/regions were downloaded from GitHub. The CNIC patterns were extracted from SNA based on CNICs between countries/regions. The item response model (IRT) was applied to create a general predictive model for each country/region. The IP days were obtained from the IRT model. The location parameters in continents, China, and the United States were compared. The results showed that (1) three clusters (255, n = 51, 130, and 74 in patterns from Eastern Asia and Europe to America) were separated using SNA, (2) China had a shorter mean IP and smaller mean location parameter than other counterparts, and (3) an online dashboard was used to display the clusters along with IP days for each country/region. Spatiotemporal spread patterns can be clustered using SNA and correlation coefficients (CCs). A dashboard with spread clusters and IP days is recommended to epidemiologists and researchers and is not limited to the COVID-19 pandemic.

The Use of hx-index to compare research achievements for ophthalmology authors in Mainland China, Hong Kong, and Taiwan since 2010

BACKGROUND:

Ophthalmology authors in mainland China, Hong Kong, or Taiwan were interested in knowing their individual research achievements (IRAs). This study was to evaluate the most cited authors, institutes, and regions in the mainland, Hong Kong, and Taiwan in the field of ophthalmology in the recent 10 years using the hx-index and to display the result with visual representations.

METHODS:

Using the PubMed search engine to download data, we conducted an observational study of citation analyses in affiliated research institutes and regions (provinces/areas) of all ophthalmology authors since 2010. A total of 19,364 published articles from 22,393 authors in the mainland, Hong Kong, and Taiwan were analyzed. The x-index and the Kano model were complemental to the hx-index in identifying IRAs. A pyramid plot was used to illustrate the importance of the author-weighted scheme (AWS) used in evaluating IRAs in academics. The hx-index combining both advantages of the h and x-index was proposed to assess individual IRAs. Furthermore, we drew: 1. a choropleth map on Google Maps to visualize the differences of IRA among regions and 2. a visual display to present individual hx-indexes.

RESULTS:

There is a significant rise over time in the number of publications. The top-ranking regions in hx-index were Shanghai (26.82), Guangdong (25.82), and Beijing (25.81). We demonstrated that Dr Wu from Taiwan published 144 articles in PMC and used the example to explain the importance of AWS when IRAs were assessed.

CONCLUSIONS:

With an overall increase in publications in the field of ophthalmology, contributions assessed by hx-indexes and the AWS should be encouraged and promoted more in the future.

Using the IPcase Index with Inflection Points and the Corresponding Case Numbers to Identify the Impact Hit by COVID-19 in China: An Observation Study

Coronavirus disease 2019 (COVID-19) occurred in Wuhan and rapidly spread around the world. Assessing the impact of COVID-19 is the first and foremost concern. The inflection point (IP) and the corresponding cumulative number of infected cases (CNICs) are the two viewpoints that should be jointly considered to differentiate the impact of struggling to fight against COVID-19 (SACOVID). The CNIC data were downloaded from the GitHub website on 23 November 2020. The item response theory model (IRT) was proposed to draw the ogive curve for every province/metropolitan city/area in China. The ipcase-index was determined by multiplying the IP days with the corresponding CNICs. The IRT model was parameterized, and the IP days were determined using the absolute advantage coefficient (AAC). The difference in SACOVID was compared using a forest plot. In the observation study, the top three regions hit severely by COVID-19 were Hong Kong, Shanghai, and Hubei, with IPcase indices of 1744, 723, and 698, respectively, and the top three areas with the most aberrant patterns were Yunnan, Sichuan, and Tianjin, with IP days of 5, 51, and 119, respectively. The difference in IP days was determined (χ2 = 5065666, df = 32, p < 0.001) among areas in China. The IRT model with the AAC is recommended to determine the IP days during the COVID-19 pandemic.

Geographic risk assessment of COVID-19 transmission using recent data: An observational study

BACKGROUND

The US Centers for Disease Control and Prevention (CDC) regularly issues "travel health notices" that address disease outbreaks of novel coronavirus disease (COVID)-19 in destinations worldwide. The notices are classified into 3 levels based on the risk posed by the outbreak and what precautions should be in place to prevent spreading. What objectively observed criteria of these COVID-19 situations are required for classification and visualization? This study aimed to visualize the epidemic outbreak and the provisional case fatality rate (CFR) using the Rasch model and Bayes's theorem and developed an algorithm that classifies countries/regions into categories that are then shown on Google Maps.

METHODS

We downloaded daily COVID-19 outbreak numbers for countries/regions from the GitHub website, which contains information on confirmed cases in more than 30 Chinese locations and other countries/regions. The Rasch model was used to estimate the epidemic outbreak for each country/region using data from recent days. All responses were transformed by using the logarithm function. The Bayes's base CFRs were computed for each region. The geographic risk of transmission of the COVID-19 epidemic was thus determined using both magnitudes (i.e., Rasch scores and CFRs) for each country.

RESULTS

The top 7 countries were Iran, South Korea, Italy, Germany, Spain, China (Hubei), and France, with values of {4.53, 3.47, 3.18, 1.65, 1.34 1.13, 1.06} and {13.69%, 0.91%, 47.71%, 0.23%, 24.44%, 3.56%, and 16.22%} for the outbreak magnitudes and CFRs, respectively. The results were consistent with the US CDC travel advisories of warning level 3 in China, Iran, and most European countries and of level 2 in South Korea on March 16, 2020.

CONCLUSION

We created an online algorithm that used the CFRs to display the geographic risks to understand COVID-19 transmission. The app was developed to display which countries had higher travel risks and aid with the understanding of the outbreak situation.

The Most-Cited Authors Who Published Papers in JMIR mHealth and uHealth Using the Authorship-Weighted Scheme: Bibliometric Analysis

Background

Many previous papers have investigated most-cited articles or most productive authors in academics, but few have studied most-cited authors. Two challenges are faced in doing so, one of which is that some different authors will have the same name in the bibliometric data, and the second is that coauthors’ contributions are different in the article byline. No study has dealt with the matter of duplicate names in bibliometric data. Although betweenness centrality (BC) is one of the most popular degrees of density in social network analysis (SNA), few have applied the BC algorithm to interpret a network’s characteristics. A quantitative scheme must be used for calculating weighted author credits and then applying the metrics in comparison.

Objective

This study aimed to apply the BC algorithm to examine possible identical names in a network and report the most-cited authors for a journal related to international mobile health (mHealth) research.

Methods

We obtained 676 abstracts from Medline based on the keywords “JMIR mHealth and uHealth” (Journal) on June 30, 2018. The author names, countries/areas, and author-defined keywords were recorded. The BCs were then calculated for the following: (1) the most-cited authors displayed on Google Maps; (2) the geographical distribution of countries/areas for the first author; and (3) the keywords dispersed by BC and related to article topics in comparison on citation indices. Pajek software was used to yield the BC for each entity (or node). Bibliometric indices, including h-, g-, and x-indexes, the mean of core articles on g(Ag)=sum (citations on g-core/publications on g-core), and author impact factor (AIF), were applied.

Results

We found that the most-cited author was Sherif M Badawy (from the United States), who had published six articles on JMIR mHealth and uHealth with high bibliometric indices (h=3; AIF=8.47; x=4.68; Ag=5.26). We also found that the two countries with the highest BC were the United States and the United Kingdom and that the two keyword clusters of mHealth and telemedicine earned the highest indices in comparison to other counterparts. All visual representations were successfully displayed on Google Maps.

Conclusions

The most cited authors were selected using the authorship-weighted scheme (AWS), and the keywords of mHealth and telemedicine were more highly cited than other counterparts. The results on Google Maps are novel and unique as knowledge concept maps for understanding the feature of a journal. The research approaches used in this study (ie, BC and AWS) can be applied to other bibliometric analyses in the future.