Role of Mathematical Models in Simulating Disease Dynamics and Guiding Public Health Policy

This study presents a mathematical modeling approach to simulate the transmission dynamics of COVID-19 in India using the SEIR (Susceptible–Exposed–Infectious–Recovered) compartmental framework. Leveraging time-series epidemiological data from the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, the model was calibrated to reflect India’s initial outbreak conditions during the period from March 2020 to December 2021. The simulation aimed to reproduce the progression of the pandemic under a baseline, non-intervention scenario by solving a system of ordinary differential equations representing disease transitions. Model accuracy was assessed by comparing predicted infectious counts to actual active case data, yielding an R² score of −0.2339, RMSE of 91.54, and MAE of 39.85. These results indicate a significant deviation from real-world trends, largely due to fixed parameter assumptions and the exclusion of policy-driven dynamics. While the SEIR model provides foundational insight into disease progression, its baseline configuration demonstrates limited predictive capacity in highly dynamic and heterogeneous contexts. The findings emphasize the need for time-varying parameters, policy inputs, and data-driven adaptations in future epidemiological modeling efforts.