Multivariate Techniques for Monitoring Susceptible, Exposed, Infected, Recovered, Death, and Vaccination Model...

Area of Science:

• Epidemiology and Public Health
• Statistical Process Control
• Mathematical Modeling

Background:

• Effective pandemic response requires simultaneous monitoring of transmission, infection, recovery, and fatality rates.
• The Susceptible, Exposed, Infected, Recovered, Death, and Vaccination (SEIRDV) model is crucial for understanding disease dynamics.
• Existing statistical methods need enhancement for real-time pandemic parameter monitoring.

Purpose of the Study:

• To introduce and apply Multivariate Exponentially Weighted Moving Average (MEWMA) and Multivariate Cumulative Sum (MCUSUM) control charts for SEIRDV model parameter monitoring.
• To assess the effectiveness of public health interventions and track emerging variants using real-time data.
• To provide a robust framework for decision-making during health crises like the COVID-19 pandemic.

Main Methods:

• Application of MEWMA and MCUSUM control charts to monitor SEIRDV model parameters.
• Utilizing COVID-19 data from the State of Qatar for methodological validation.
• Employing an augmented particle Markov chain Monte Carlo scheme for enhanced parameter estimation.

Main Results:

• MEWMA and MCUSUM charts effectively monitor SEIRDV model parameters.
• The augmented particle Markov chain Monte Carlo scheme provides improved accuracy and robustness in real-time monitoring.
• The methodology offers practical utility for assessing intervention effectiveness and pandemic dynamics.

Conclusions:

• MEWMA and MCUSUM control charts are valuable tools for real-time pandemic surveillance.
• The enhanced monitoring approach supports informed decision-making during public health emergencies.
• This methodology can be applied to future pandemics for improved response strategies.