A Sensitive Analysis of Pandemic COVID-19 under SIR, SEIR, and SIS Models with Incorporation of Real-World Factors
Abstract
Accurate modelling of infectious disease spread is vital for informing timely and
effective public health responses. This study presents an enhanced epidemic modelling
framework using the classical SIR, SEIR, and SIS models, extended to incorporate real
world dynamics such as time-varying transmission rates, public health interventions,
and spatial heterogeneity. All models were developed and simulated in MATLAB,
integrating ordinary and partial differential equations to capture both temporal and
regional variations in disease transmission. Simulations demonstrated how changes in
policy—such as lockdown timing, mobility restrictions, and vaccination levels—
significantly influence epidemic trajectories. The models were validated against actual
COVID-19 and influenza data using nonlinear optimization and error analysis. Results
highlighted the importance of adaptive, data-driven strategies in epidemic control. This
research establishes MATLAB as a comprehensive platform for epidemic modelling
and provides a flexible, validated framework that can support both academic inquiry
and public health planning.
How to Cite
Rachna, Menaka , "A Sensitive Analysis of Pandemic COVID-19 under SIR, SEIR, and SIS Models with Incorporation of Real-World Factors", Vol. 3, Issue 3, 30-06-2025, pp. 1-12.
