Data-Driven Comparative Analysis of SI, SIS, and SIR Models for Infectious Disease Transmission Dynamics
Abstract: This paper presents a comparative study of three classical epidemic compartmental models SI (Susceptible-Infected), SIS (Susceptible-Infected-Susceptible), and SIRS (Susceptible-Infected-Recovered-Susceptible) augmented with a data-driven framework for adaptive parameter estimation. The central argument is that traditional epidemic models, which assume fixed transmission and recovery rates, are inadequate for capturing real-world disease dynamics, and that incorporating data-driven based dynamic parameter estimation yields smoother, more realistic epidemic forecasting. The study covers mathematical formulation, analytical solutions, and numerical simulation using Euler's method, equilibrium/stability analysis, and graphical comparison of classical and data-driven variants of each mod. Comparative analysis demonstrates that data-driven approaches support outbreak forecasting, parameter estimation, and epidemic transmission analysis.
Keywords: Transmission Dynamics, Infectious Diseases, Data-Driven Prediction and Epidemic Models
How to Cite?: Rajeev Kishore, Kamlesh Kumar Sharma, "Data-Driven Comparative Analysis of SI, SIS, and SIR Models for Infectious Disease Transmission Dynamics", Volume 15 Issue 9, September 2026, International Journal of Science and Research (IJSR), Pages: 1782-1791, https://www.ijsr.net/getabstract.php?paperid=SR26917211405, DOI: https://dx.doi.org/10.21275/SR26917211405