Global research trends in mathematical modeling for epidemiology: A bibliometric analysis

Abstract

Mathematical modeling has become an indispensable tool in epidemiology, particularly in infectious disease transmission dynamics and public health decisions. Over the past decades, modeling methods have evolved from traditional approaches to AI-driven methods to offer better predictive measures and capability to process large, complex data. The coronavirus 19 (COVID-19) pandemic made a pivotal turn in the field, where almost half of the papers published in the study were written since the beginning of the COVID-19 (2019–2025). The aim of this study was to explore mathematical modeling in epidemiology using bibliometric analysis. Metadata were retrieved from Scopus database and processed using VosViewer for network visualization analysis. A total of 11,032 papers were retrieved, where eight research clusters were found, covering topics from basic reproduction models, disease control covering tuberculosis & human immunodeficiency virus (HIV), vaccination, and most recently being the COVID-19 pandemic. Studies on mathematical modeling in epidemiology were most reported by authors from the United States (documents: 3689, citations: 181054), United Kingdom (documents: 1785, citations: 93842), and China (documents: 1089, citations: 29379). This study provides insight into current progress in epidemiological modeling and identifies less-explored topics that warrant further investigation to meet future global health challenges, including the development of robust, adaptive models that integrate artificial intelligence and deep learning for data-deficient settings.