Published Research on COVID-19 in the Eastern Mediterranean Region: Bibliometric Analysis
Bibliographic record
Abstract
BACKGROUND: The challenges presented by the COVID-19 pandemic have led to unprecedented global research activity. The Eastern Mediterranean Region (EMR) continues to contribute to COVID-19 research driven by the unique challenges of the region, including the protracted conflicts, already stressed health systems, and serious health and social inequalities. OBJECTIVE: This study aims to provide an overview of the publication activities and trends in COVID-19 research in the EMR from the onset of the disease to early 2022 using bibliometric methods. METHODS: A literature search using Scopus was conducted from December 1, 2019, to January 31, 2022, using keywords relevant to COVID-19 and the World Health Organization (WHO) EMR country list. Data were exported and analyzed using Microsoft Excel and the Citation Overview function on Scopus. The quality of journals was determined using SCImago Journal Rank and CiteScore. VOSviewer software was used to visualize the relationships between authors, countries, and key terms used in the retrieved documents. RESULTS: A total of 6880 documents were retrieved, of which 1805 (26.24%) were from the Kingdom of Saudi Arabia (KSA) and 1782 (25.90%) from Iran, followed by Pakistan, Egypt, and Jordan. Most published documents were affiliated with EMR universities, primarily the Tehran University of Medical Sciences in Iran and King Saud University in KSA (396/6880, 5.76%, and 370/6880, 5.4%, respectively), while only 407 (5.92%) of 6880 documents were associated with universities outside the EMR. For most of the identified publications (5020/6880, 72.97%), no funding source was reported, while King Saud University contributed the largest share (282/1860, 15.16%) of funded publications. Retrieved documents were cited 53,516 times, with an average of 7.78 (SD 34.30). Iran was the EMR country with the most links to other countries (77 links and total link strength of 1279). The 5 authors with the most publications were from KSA, Qatar, and Jordan. There were 290 high-frequency keywords that occurred ≥10 times and were linked in 7 different clusters. The cluster with the most linked keywords was related to epidemiology and mortality. Recent topics included vaccines, vaccination, machine learning, and online learning. CONCLUSIONS: This is the first study to show trends in and project future developments of COVID-19 research activity in the EMR. Authors and institutions who led research on COVID-19 in the region were from Iran and KSA. There were multiple regional collaborative efforts; however, international collaboration was limited. Recently, interest has been shifting toward topics related to vaccination, machine learning, and online learning. Understanding the current state of research is instrumental to future research production, and our study will inform regional research initiatives on emerging concepts, as well as opportunities for collaboration and funding.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.574 | 0.688 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.875 | 0.943 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.021 | 0.004 |
| Research integrity | 0.000 | 0.014 |
| Insufficient payload (model declined to judge) | 0.018 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".