Mathematical Models for COVID-19 Pandemic: A Comparative Analysis - Narendra J. Padole; Vinod Ramteke; Manish L. Jivtode (Recent Trends in Mathematics, 2025)
The COVID-19 pandemic has really underlined the importance of mathematical modeling in understanding disease-spread dynamics and especially informing public health interventions. The paper aims to provide a comprehensive comparative analysis of various mathematical models used for COVID-19 studies, with a focus on assumptions underlying those models, strengths, and also the limitations in their applications as well as special focus is given to compartmental models, agent-based models, machine learning-enhanced models, and hybrid approaches. The insights developed from this analysis can inform future pandemic modeling and policy-making efforts.
Keywords: Hybrid Models; Machine Learning; Modeling for COVID-19; agent-based models; compartmental models; mathematical models; policy applications