A topic trend analysis on COVID-19 literature

Area of Science:

• Bibliometrics
• Infectious Disease Epidemiology
• Public Health

Background:

• The COVID-19 pandemic spurred exponential growth in scientific publications.
• Existing text mining efforts for SARS-CoV-2 and COVID-19 literature are limited in scope and temporal coverage.
• A comprehensive analysis of COVID-19 research trends is crucial for understanding pandemic response and future preparedness.

Purpose of the Study:

• To identify major research topics within the COVID-19 literature.
• To analyze the temporal trends of these topics from November 2019 to December 2021.
• To compare the study's corpus with the COVID-19 Open Research Dataset (CORD-19).

Main Methods:

• Extensive literature search across PubMed, Scopus, and Web of Science (WoS).
• Structural topic modeling applied to retrieved abstracts.
• Temporal trend analysis of identified topics and comparison with CORD-19.

Main Results:

• 10 distinct research topics were identified from 269,186 publications.
• Clinical manifestations of COVID-19 were the most consistently popular topic.
• Early pandemic focus on Telemedicine, Vaccine development, and Epidemiology shifted towards COVID-19's impact on mental health, Forecasting, and Molecular Biology.

Conclusions:

• The study provides a classification of 10 topics for COVID-19 research publications with nonlinear temporal trends.
• Multiple citation databases are necessary for comprehensive literature retrieval.
• The findings offer a focused approach for systematic and rapid reviews during the specified period (Nov 2019-Dec 2021).