Mathematical Modeling in Modern Epidemiology and Public Health
Year: 2026
Type: book-chapter
Abstract: Mathematical modeling has become indispensable for understanding disease transmission dynamics, forecasting outbreak trajectories, and evaluating public health interventions. Traditional compartmental models provide intuitive frameworks but often assume homogeneous mixing, fixed parameters, and deterministic dynamics, limiting their applicability to complex, heterogeneous populations. This chapter reviews existing epidemiological modeling paradigms, identifies critical gaps in real-time parameter estimation, spatial heterogeneity, and intervention optimization, and introduces a hybrid compartmental-network framework with time-varying transmission rates, Bayesian state estimation, and optimal control for dynamic policy scheduling. Using simulated and empirical surveillance data, we demonstrate how the proposed method improves peak prevalence forecasting, reduces cumulative intervention costs, and enhances policy robustness compared to classical SEIR and agent-based baselines. The chapter concludes with recommendations for transparent, interoperable, and policy-ready epidemic modeling architectures.
Authors: I. Paulraj Jayasimman, M. Ariyanatchi
Institution: AMET University
Language: English
Subfield: Applied Mathematics
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7204145732