Global research trend in the treatment of the new Coronavirus diseases (COVID-19) : bibliometric analysis.
Mbogning Fonkou, M. D.; YACOUBA, A.
Show abstract
The Coronavirus 2019 (COVID-19) pandemic has caused worldwide concern and has become a major medical problem. Vaccines and therapeutics are important interventions for the management of this outbreak. This study aims to used bibliometric methods to identify research trends in the domain of therapeutics and vaccines to cure patients with COVID-19 since the beginning of the pandemic. The Web of Science Core Collection database was retrieved for articles on therapeutic approaches to coronavirus disease management published between January 1, 2020 and May 20, 2020. Identified and analyzed the data included title, corresponding author, language, publication time, publication type, research focus. A total of 1569 articles on coronavirus therapeutic means from 84 countries were published in 620 journals. We note the remarkable progressive increase in the number of publications related to research on therapies and vaccines for COVID-19. The United States provided the largest number of articles (405), followed by China (364). Journal of Medical Virology published most of them (n = 40). 1005 (64.05%) were articles, 286 (18.23%) were letters, 230 (14.66%) were reviews. The terms "COVID- 19" or "SARS-CoV-2" or "Coronavirus" or "hydroxychloroquine" or "chloroquine" or "2019-nCOV" or "ACE2" or "treatment" or "remdesivir" or "pneumonia" were most frequently used, as shown in the density visualization map. A network analysis based on keyword co-occurrence revealed five distinct types of studies: clinical, biological, epidemiological, pandemic management, and therapeutics (vaccines and treatments). COVID-19 is a major disease that has had an impact on international public health at the global level. Several avenues for treatment and vaccines have been explored. Most of them focus on older drugs used to treat other diseases that have been effective for other types of coronaviruses. There is a discrepancy in the results obtained from the studies of the drugs included in this study. Randomized clinical trials are needed to evaluate older drugs and develop new treatment options.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Clinical Laboratory Parameters Associated with Severe or Critical Novel Coronavirus Disease 2019 (COVID-19): A Systematic Review and Meta-analysis 94%
- Factors Associated with Disease Severity and Mortality among Patients with Coronavirus Disease 2019: A Systematic Review and Meta-Analysis 94%
- Survival analysis of hospital length of stay of novel coronavirus (COVID-19) pneumonia patients in Sichuan, China 93%
Similar papers in this journal
- Incubation Period and Other Epidemiological Characteristics of 2019 Novel Coronavirus Infections with Right Truncation: A Statistical Analysis of Publicly Available Case Data 92%
- Early Outpatient Treatment Of COVID-19: A Retrospective Analysis Of 392 Cases In Italy 92%
- A Call For Better Methodological Quality Of Reviews On Using Artificial Intelligence For COVID-19 Detection In Medical Imaging – An Umbrella Systematic Review 91%
Similar papers in this journal
- COVIEdb: A database for potential immune epitopes of coronaviruses 93%
- Echinacea purpurea for the Long-term Prevention of Viral Respiratory Tract Infections during COVID-19 Pandemic: A Randomized, Open, Controlled, Exploratory Clinical Study 91%
- Evaluation of the current therapeutic approaches for COVID-19: a meta-analysis 90%
Similar papers in this journal
- Google Trends as a predictive tool for COVID-19 vaccinations in Italy: a retrospective infodemiological analysis 91%
- Emergence of first strains of SARS-CoV-2 lineage B.1.1.7 in Romania 89%
- The Influence of Covid-19 Vaccine on Daily Cases, Hospitalization, and Death Rate in Tennessee: A Case Study in the United States 89%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.