Mathematical Model of COVID-19 Pandemic with Double Dose Vaccination

Area of Science:

• Epidemiology
• Mathematical Modeling
• Public Health

Background:

• COVID-19 remains a significant global health challenge.
• Vaccination is a key strategy in controlling the pandemic.
• Understanding transmission dynamics is crucial for effective interventions.

Purpose of the Study:

• To formulate and analyze a mathematical model for COVID-19 transmission.
• To incorporate the impact of first and second vaccine doses.
• To identify key parameters influencing disease spread and control.

Main Methods:

• Developed an eight-dimensional system of ordinary differential equations.
• Calculated the control reproduction number to assess disease spread.
• Calibrated the model using Malaysian COVID-19 data (Feb 2021-Feb 2022).
• Performed global sensitivity analysis using Partial Rank Correlation Coefficient (PRCC).

Main Results:

• The COVID-free equilibrium is stable if the control reproduction number is less than unity.
• Effective transmission rate, first and second dose vaccination rates, and recovery rate post-second dose are most influential parameters.
• Numerical simulations demonstrate that increased vaccination rates reduce infected individuals.

Conclusions:

• Adherence to preventive measures, particularly vaccination, is critical for reducing COVID-19 spread.
• Higher first and second dose vaccination rates significantly decrease the disease burden.
• Mathematical modeling provides valuable insights for public health strategies against COVID-19.