Mathematical Population Studies

Mathematical and Artificial Intelligence Modeling for Population Health and Biomedical Systems

The integration of artificial intelligence (AI) with mathematical modeling has the potential to revolutionize population-level studies in health sciences. This special issue emphasizes rigorous mathematical approaches to understanding and analyzing biological populations, physiological processes, and health informatics with a focus on population dynamics.

We invite original contributions that develop, analyze, and apply mathematical frameworks and AI-driven models rooted in population-level phenomena. The scope includes generalized learning algorithms designed for large-scale genomic and omics data, providing insight into population genetics, disease risk stratification, and biomarker discovery. Dynamical systems and differential equations modeling the complex physiological processes and interactions within and across populations are also a core focus, enabling improved understanding of disease progression, biological regulation, and epidemiology. Additionally, graph theory and network analysis applied to population health informatics, including disease transmission, gene interaction networks, and healthcare connectivity, are of great interest.

Mathematical rigor, population relevance, and methodological innovation are key criteria. Studies that bridge AI methods with population health modeling, including theoretical development, computational techniques, and data-driven validation, are encouraged. The issue aims to showcase advances that enhance predictive accuracy, interpretability, and utility in population health research, laying a strong mathematical foundation for future AI applications in medicine.