Infectious diseases are part of human history, shaping societies, economies, and daily life. From seasonal influenza to global pandemics such as COVID‑19, predicting how diseases spread and how interventions work is critical. Mathematical tools, particularly differential equations, allow us to observe the underlying dynamics of epidemics: the flow of people between susceptible, infected, and recovered states. This paper explores the application of differential equations in epidemic modeling, discusses classical models like SIR and SEIR, examines real-world examples, and highlights how these models guide public health strategies. The paper also addresses challenges and outlines directions for future research, emphasizing the importance of mathematical modeling in understanding and managing epidemics.
Lavkush Pandey (Wed,) studied this question.