Co-Occurrence Analysis of COVID-19 Publications with an Emphasis on the Global Health Governance (GHG)

Background

Analyzing co-occurrence is an effective way to monitor the overview of topic spreading. The present study aimed to conduct a co-occurrence analysis of scientific publications related to COVID-19, emphasizing Global Health Governance (GHG).

Materials and Methods

This applied research with an analytical approach was carried out on all the scientific publications related to COVID-19, emphasizing GHG (51056 records), extracted from PubMed Central on 26/01/2022. The research population consisted of all the scientific publications related to COVID-19, emphasizing GHG (51056 records), extracted from PubMed Central on 26/01/2022. The data were analyzed using BibExcel, UCINET, Excel, and SPSS software, and Spearman's test was used to confirm correlations.

Results

The co-word network of the thematic area of COVID-19 includes 226 nodes and 7292 edges. COVID-19 and the pandemic formed the most co-word pairs with 2224 connections. The COVID-19* mental health and COVID-19* anxiety, with 1019 and 925 connections, are ranked next, respectively. The term COVID-19 is ranked first with a centrality index of 225. The keywords of pandemic and public health are ranked second and third with the centrality index of 217 and 206, respectively.

Conclusion

The global approach of studies related to COVID-19 is more inclined to the epidemiological and public health fields. Assuming the GHG, detailed and comprehensive planning should be performed to strengthen these studies and pave the way for international cooperation, determining research requisites, and developing applied research studies.

Assessing the impact of COVID-19 pandemic on the performance indicators of safety management using the analytic hierarchy process (AHP) in an electricity industry

Introduction

The importance of evaluating the performance of different management systems in industries necessitates examining the performance of the Health, Safety, and Environment (HSE) management system along with other management systems. Particularly during the COVID-19 pandemic, there is a greater need than ever to assess the impact of the COVID-19 spread on the performance of the HSE management system compared to before this pandemic. This research aims to investigate safety performance indicators (SPIs) on the performance of the HSE management system and the impact of the COVID-19 spread on these indicators.

Methods

This cross-sectional, descriptive-analytical study was conducted to evaluate the influence of the COVID-19 outbreak on the safety performance to revise the industry safety index in an electricity distribution company using the multi-criteria decision-making method before and after the disease epidemic in three stages. In the first part, the safety indicators were identified according to the comprehensive safety indicators available in the industries and experts' opinions. In the second part, safety indicators were ranked, weighted, and prioritized using the Analytic Hierarchy Process (AHP). In the third part, these indicators were calculated and compared in the periods before and after the outbreak of COVID-19.

Results

Two main criteria, namely the "effectiveness criterion" and the "applicability/calculation criterion", were identified for the evaluation and pairwise comparisons of performance indicators. Among these two criteria, the applicability/calculation criterion had higher priority and importance for the evaluation of indicators. Pairwise comparisons of the indicators indicated that the "accident frequency rate" and "safe T-factor" indicators (with weights of 0.238 and 0.023 respectively) had the highest and lowest priorities, respectively, for the assessment of organizational safety performance among the SPIs.

Conclusion

Based on the calculations of the indicators and their analyses before and after the outbreak of COVID-19, the current status of the safety performance of the HSE unit was not significantly affected by this pandemic. However, the investigations carried out while collecting the data needed to calculate the indicators and evaluate the performance of this unit demonstrated that some indicators were not considered sufficiently in the studied electricity industry. Since conducting regular performance evaluations greatly impacts the achievement of continuous improvement, more attention should be paid to compiled indicators, which should be periodically assessed in the organization to achieve continuous improvement.

An analysis of COVID-19 and WHO global research roadmap: knowledge mapping and future research agenda

In this study, we review the publications on the COVID-19 pandemic indexed in the Scopus database from 1st January to 15th October 2020, using bibliometric analysis. In essence, we evaluate the publications against the global research roadmap outlined by the World Health Organization (WHO). We find that academic publications are contributed by researchers worldwide, with the USA and China being the top contributors in several parameters. We show that most publications are clustered around the issues related to the outbreak, diagnosis, candidate medicine, and disease treatment by analyzing publication keywords. Besides, considerable importance is given to unravel the connection between the present viral disease with its previous strains and the anxiety and stress-related problems that arise from the COVID-19. We further visualize the knowledge structure of the publication with thematic mapping and illustrate that mental health-related research denotes a specialized research theme besides public health issues being the basic theme. Our findings aid to comprehend how the scholarly publications have developed to address the prioritized research agendas by the WHO. Thus, our study can guide researchers to explore the less studied areas. Consequently, it will assist policymakers and governments in developing integrated and effective policies to fight the pandemic.

ParsBERT topic modeling of Persian scientific articles about COVID-19

Purpose

The COVID-19 pandemic has indisputably impacted every aspect of human life, and a host of studies have investigated its different aspects. This paper models the contents of Persian literature on COVID-19.

Method

This is a descriptive-exploratory study in which 815 articles were collected from the Magiran database. The articles were published before March 2022. The abstracts and titles were used in the modeling. The modeling was performed by combining the latent Dirichlet allocation (LDA) algorithm with ParsBERT.

Findings

Topic modeling indicated ten major topics, including medicine, psychology, humanities, politics, management, biology, economics, culture, engineering, and religion. The articles under the category of medicine had the largest cluster (42.3%), while engineering and religion had the smallest clusters (1.1% each).

Conclusion

The found topics in the created clusters have structural relationships. The COVID-19 effect on physical and mental health (medical and psychological topics) is the most crucial factor. These clusters provide evidence that COVID-19 affects all facets of human society at three levels: the individual, family, and society. Aside from the ten critical clusters in the humanities field, the utmost disorder is related to teaching and learning. For the first time, this research has presented a model of scientific communication in the field of COVID-19 based on the data collected from a Persian database - Magiran.

Predictive factors of Quality of Life in older adults during the COVID-19 pandemic

BACKGROUND

Given the vulnerability of older people to COVID-19, it is important to consider their physical and mental wellbeing and quality of life (QoL) in the COVID-19 pandemic. Therefore, the present study was aimed to identify the QoL and its predictive factors among a sample of Iranian older adults during the COVID-19 pandemic.

METHODS

This descriptive and cross-sectional study was conducted on 500 older people residing in Qazvin, Iran, from May 22th to November 21rd, 2021. Multistage cluster sampling method was used for selecting the eligible older adults. Data were collected using the demographic checklist, fear of COVID-19 scale, and Elderly Quality of Life Questionnaire (LIPAD). The multivariate regression model was used for determining the predictive factors of QoL in older people.

RESULTS

The mean age of older participants was 69.17 ± 6.75 years old. The results of multivariate regression model showed that fear of COVID-19, age, marital status, level of education, living arrangement, and economic situation were the significant predictors of QoL in the older adults during the COVID-19 pandemic.

CONCLUSIONS

It is recommended to pay close attention to divorced, lonely, and illiterate older people and those with low economic situation during the COVID-19 pandemic.

Synergistic networks of COVID-19’s top papers

Purpose

Synergy indicators and social network analysis (SNA), as practical tools, provide the possibility of explaining the pattern of scientific collaboration and visualization of network relations. Recognition of scientific capacities is the basis of synergy. The present study aims to measure and discover the synergistic networks of COVID-19’s top papers at the level of co-authorship, countries, journals, bibliographic couples and titles.

Design/methodology/approach

The synergy indicator, co-authorship co-citation network analysis methods were applied. The research population comprises COVID-19’s top papers indexed in Essential Science Indicator and Web of Science Core Collection 2020 and 2021. Excel 2016, UCINET 6.528.0.0 2017, NetDraw, Ravar Matrix, VOSviewer version 1.6.14 and Python 3.9.5 were applied to analyze the data and visualize the networks.

Findings

The findings indicate that considering the three possible possibilities for authors, countries and journals, more redundancy and information are created and potential for further cooperation is observed. The synergy of scientific collaboration has revealed that “Wang, Y,” “USA” and “Science of the Total Environment” have the most effective capabilities and results. “Guan (2020b)” and “Zhou (2020)” are bibliographic couplings that have received the most citations. The keywords “CORONAVIRUS DISEASE 2019 (COVID-19)” were the most frequent in article titles.

Originality/value

In a circumstance that the world is suffering from a COVID-19 pandemic and all scientists are conducting various researches to discover vaccines, medicines and new treatment methods, scientometric studies, and analysis of social networks of COVID-19 publications to be able to specify the synergy rate and the scientific collaboration networks, are not only innovative and original but also of great importance and priority; SNA tools along with the synergy indicator is capable of visualizing the complicated and multifaceted pattern of scientific collaboration in COVID-19. As a result, analyses can help identify existing capacities and define a new space for using COVID-19 researchers’ capabilities.

A bibliometric analysis of pandemic and epidemic studies in economics: future agenda for COVID-19 research

With the rapid global spread of the COVID-19 pandemic, researchers from diverse fields of study have contributed markedly in different research aspects. Considering the substantial economic significance of the pandemic at the micro and macro level throughout the world, we review the scientific publications in the discipline of Economics. To draw a broad inference, we analyze a total of 1,636 scientific publications starting from 1974, which covers the period of earlier pandemics or epidemics that have a close association with COVID-19 using bibliometric analysis. Our analysis and mapping reveal key information related to the contributors at different levels, including author, institution, country, and publication sources. Besides, we identify the historical concentration of research using scientific clustering and illustrate transformations at different times. Moreover, recognizing the underlying inadequacy of economics research, we propose several areas of future research. Our findings and suggestions are expected to act as a roadmap to potential research opportunities and notable implications for business and policymakers.

Retrospective and prospective approaches of coronavirus publications in the last half-century: a Latent Dirichlet allocation analysis

Purpose

The present article's primary purpose is the topic modeling of the global coronavirus publications in the last 50 years.

Design/methodology/approach

The present study is applied research that has been conducted using text mining. The statistical population is the coronavirus publications that have been collected from the Web of Science Core Collection (1970–2020). The main keywords were extracted from the Medical Subject Heading browser to design the search strategy. Latent Dirichlet allocation and Python programming language were applied to analyze the data and implement the text mining algorithms of topic modeling.

Findings

The findings indicated that the SARS, science, protein, MERS, veterinary, cell, human, RNA, medicine and virology are the most important keywords in the global coronavirus publications. Also, eight important topics were identified in the global coronavirus publications by implementing the topic modeling algorithm. The highest number of publications were respectively on the following topics: “structure and proteomics,” “Cell signaling and immune response,” “clinical presentation and detection,” “Gene sequence and genomics,” “Diagnosis tests,” “vaccine and immune response and outbreak,” “Epidemiology and Transmission” and “gastrointestinal tissue.”

Originality/value

The originality of this article can be considered in three ways. First, text mining and Latent Dirichlet allocation were applied to analyzing coronavirus literature for the first time. Second, coronavirus is mentioned as a hot topic of research. Finally, in addition to the retrospective approaches to 50 years of data collection and analysis, the results can be exploited with prospective approaches to strategic planning and macro-policymaking.

Medical negligence in healthcare organizations and its impact on patient safety and public health: a bibliometric study

Background: Medical negligence is an increasing public health concern among healthcare providers worldwide as it affects patient safety. It poses a significant risk of patient injury, disease, disability, or death. The WHO has recognized deficiencies in patient safety as a global healthcare issue to be addressed. This study aimed to analyze various components of medical negligence research literature. Methods: Bibliographic data visualizations tools like Biblioshiny (RStudio) and VOSviewer were used besides MS Excel to examine the types of documents, annual scientific production, top contributing authors and their impact, authorship patterns and collaboration, top contributing countries and organizations, most significant sources of publication, most cited documents, and most frequently used keywords. Bibliometric methods were used to analyze the bibliographic records of research output on medical negligence downloaded from the Web of Science Core Collection. Results: The annual productivity of medical negligence research was increasing gradually. The most productive period for medical negligence research was 2011-2020. Bird contributed the highest number of publications to medical negligence literature while Brennan emerged as the highly cited author. Single-authored publications on medical negligence were not highly cited. The United States was the highest contributing country and the University of South Florida was the highest contributing organization while Harvard University was a highly cited organization. Nine out of the top ten contributing organizations were academic institutions and most of them belonged to the United States. The most important sources of publication on this topic were The Lancet and British Medical Journal. Localio et al. was the most important research article on medical negligence research. Conclusion: Due to increasing attention on this topic, there was a sharp increase in the research output on medical negligence. This is of significance as the WHO set in motion a patient safety program almost two decades ago.

A SARS-CoV-2 cytopathicity dataset generated by high-content screening of a large drug repurposing collection

SARS-CoV-2 is a novel coronavirus responsible for the COVID-19 pandemic, in which acute respiratory infections are associated with high socio-economic burden. We applied high-content screening to a well-defined collection of 5632 compounds including 3488 that have undergone previous clinical investigations across 600 indications. The compounds were screened by microscopy for their ability to inhibit SARS-CoV-2 cytopathicity in the human epithelial colorectal adenocarcinoma cell line, Caco-2. The primary screen identified 258 hits that inhibited cytopathicity by more than 75%, most of which were not previously known to be active against SARS-CoV-2 in vitro. These compounds were tested in an eight-point dose response screen using the same image-based cytopathicity readout. For the 67 most active molecules, cytotoxicity data were generated to confirm activity against SARS-CoV-2. We verified the ability of known inhibitors camostat, nafamostat, lopinavir, mefloquine, papaverine and cetylpyridinium to reduce the cytopathic effects of SARS-CoV-2, providing confidence in the validity of the assay. The high-content screening data are suitable for reanalysis across numerous drug classes and indications and may yield additional insights into SARS-CoV-2 mechanisms and potential therapeutic strategies.

Coronavirus research before 2020 is more relevant than ever, especially when interpreted for COVID-19

The speed with which biomedical specialists were able to identify and characterize COVID-19 was partly due to prior research with other coronaviruses. Early epidemiological comparisons with Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS), also made it easier to predict COVID-19’s likely spread and lethality. This article assesses whether academic interest in prior coronavirus research has translated into interest in the primary source material, using Mendeley reader counts for early academic impact evidence. The results confirm that SARS and MERS research in 2008–2017 experienced anomalously high increases in Mendeley readers in April–May 2020. Nevertheless, studies learning COVID-19 lessons from SARS and MERS or using them as a benchmark for COVID-19 have generated much more academic interest than primary studies of SARS or MERS. Thus, research that interprets prior relevant research for new diseases when they are discovered seems to be particularly important to help researchers to understand its implications in the new context.