ARTIFICIAL INTELLIGENCE ORIENTED BIBLIOMETRIC META ANALYSIS OF COVID ANALYSIS IN INDIA

Abstract

Background: SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) is the source of the novel coronavirus disease, COVID-19, which remains a serious and immediate threat to global health. The virus that causes COVID-19 spreads when an infected person coughs or sneezes or when the infected person comes into contact with a healthy person. In many ways, science, technology, and artificial intelligence have been instrumental in overcoming this pandemic. Methods: A bibliometric, cross-sectional review of the literature on COVID-19-related papers from Web of Science journals was conducted with an emphasis on Indian research and trends. The current investigation employs bibliometric analysis, augmented by artificial intelligence approaches, to examine the extensive corpus of COVID-19-related publications from India. This study examined co-authorship maps, authors, institutions, nations, and keywords. Artificial intelligence, deep learning, and machine learning approaches have been used to analyze the COVID-19 mathematical model. The present investigation examines the publishing patterns of issues pertaining to the forecasting of COVID-19 numbers in India from 2016 to January 25, 2025, using the Web of Science (WOS) and Scopus databases. The AI approaches like random walk, clustering using Walktrap algorithm,Fruchterman-Reingold algorithm, networking distance approach has been utilized. Findings: The most often cited authors, their connected institutions, trends in COVID-19 publications and citations, and network visualizations of author co-authorship, co-occurrences, and co-citation of referenced sources are examined using bibliometric analysis. Conclusion: The findings provide valuable insights into predicting virus propagation using machine learning techniques, presenting additional opportunities for further investigation. The current study identifies key trends, notable studies, and emerging gaps in this field.  The bibliometric meta-analysis yields new insights for future studies and encourages the exploration of the socio-economic impacts of the pandemic, utilizing the current results in a broader context.