Mathematical Modeling of Epidemics: A Computational Approach

Abstract

The spread of infectious diseases poses significant challenges to public health systems worldwide. Mathematical modeling plays a crucial role in understanding the dynamics of epidemic transmission and evaluating effective control strategies. This study focuses on the mathematical modeling of epidemics using computational approaches to analyze disease transmission patterns within a population. Classical epidemic models such as the Susceptible–Infected–Recovered (SIR) framework are used to represent the interaction between individuals in different disease states. By incorporating computational simulations, the model can analyze epidemic growth, predict outbreak behavior, and evaluate the impact of intervention strategies such as vaccination, quarantine, and social distancing. The study highlights the importance of mathematical and computational tools in predicting disease spread and assisting policymakers in making informed public health decisions.