Analyzing the influence of differential equations in mathematical modeling approaches for Covid-19: A bibliometric perspective

Abstract

Objective. This study aims to provide a comprehensive analysis of global publications on differential equations in mathematical modeling approaches for understanding and combating the COVID-19 pandemic. Design/Methodology/Approach. The study analyses a dataset of 964 documents from 353 sources from 2020 – 2023. Various parameters, such as publication growth rate, citation impact, collaboration patterns, document types, and distribution of citations, are examined. The analysis utilizes tables and figures to present the findings effectively. Results/Discussion. The analysis reveals a decline in publication output over the years, indicated by a negative annual growth rate. However, the dataset remains comprehensive and contributes valuable insights to the field. The publications have made significant contributions, evidenced by the average citations per document and the extensive reference list. Collaboration among authors is prevalent, with a substantial portion of co-authorships being international. The study identifies prominent papers with high citation counts, emphasizing their influence and recognition within the academic community. Conclusion. The findings highlight the need for continued research efforts and advancements in differential equations in mathematical modeling approaches for COVID-19. The study emphasizes the importance of maintaining a robust scientific impact and contributing effectively to the ongoing fight against the pandemic. It underscores the significance of collaboration and highlights countries and institutions with notable productivity and impact in the field. Originality/Value. This study provides a comprehensive analysis of global publications on the role of differential equations in mathematical modeling for COVID-19. It presents novel insights into publication trends, citation impact, collaboration patterns, and distribution of citations. The findings contribute to understanding the research landscape and offer valuable information for researchers and practitioners seeking to advance the field and combat the COVID-19 pandemic effectively.