I commend the authors for their insightful work, “Interdisciplinary Dynamics in COVID-19 Research: Examining the Role of Computer Science and Collaboration Patterns.” This study addresses a critical topic, offering valuable insights into interdisciplinary collaboration during the COVID-19 pandemic. The use of Social Network Analysis (SNA), modularity analysis, and eigenvector centrality is innovative, and the visualizations provide a compelling depiction of collaboration patterns. However, the methodology raises some concerns, particularly regarding data selection and transparency. The reliance on Scopus as the primary database, while practical, may limit the study’s comprehensiveness, as other relevant databases like PubMed are excluded. Similarly, the rationale for excluding preprints and arXiv articles is unclear, potentially introducing biases. Moreover, while the Louvain method for modularity analysis is appropriate, its limitations, including non-deterministic outputs and sensitivity to network structure, are not acknowledged. A deeper discussion of these methodological constraints would enhance the study’s robustness.
The discussion of the role of computer science in interdisciplinary research is insightful but lacks empirical grounding. While the study suggests limited integration of computer science with medicine, this claim would benefit from concrete examples or comparative case studies to illustrate successes and challenges. Additionally, the categorization of disciplines into three broad groups—medicine, natural sciences, and social sciences—may oversimplify nuanced interdisciplinary interactions, potentially overlooking critical subfield connections. The cited literature, while extensive, misses foundational works on interdisciplinarity and recent studies on computer science applications in public health. Addressing these gaps, along with a more concise and structured presentation of the methodology and findings, would significantly enhance the manuscript’s clarity, accessibility, and impact. These refinements could strengthen the paper’s contribution to understanding interdisciplinary collaboration in global health research.