Application and Significance of SIRVB Model in Analyzing COVID-19 Dynamics

Area of Science:

• Physical Chemistry
• Epidemiology
• Mathematical Modeling

Background:

• COVID-19 cases surged globally in summer 2024, necessitating advanced epidemiological models.
• Herd immunity and vaccination have reduced the pandemic's lethality.
• Existing models like SIR and SIRV do not fully capture disease dynamics.

Purpose of the Study:

• To introduce an updated kinetic model, SIRVB, incorporating virus breakthrough.
• To provide students with practical experience in epidemiological modeling using real-world COVID-19 data.
• To develop a simplified yet comprehensive model for explaining global COVID-19 kinetics.

Main Methods:

• Students utilized World Health Organization (WHO) COVID-19 case and vaccination data.
• Implementation and practice of Susceptible, Infected, Recovered (SIR) and SIRV models.
• Development and application of the novel Susceptible, Infectious, Recovered, Vaccinated, Breakthrough (SIRVB) model.

Main Results:

• The SIRVB model effectively explains COVID-19 kinetics across diverse countries over four years.
• Parameters derived from the models correlate with various national indices.
• The study demonstrates the utility of kinetic models in understanding disease spread.

Conclusions:

• The SIRVB model offers a simplified yet powerful tool for analyzing infectious disease dynamics.
• Model parameters provide valuable insights correlating with country-specific data.
• These models are crucial for researchers and policymakers in predicting future epidemic trajectories.