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The COVID-19 pandemic has highlighted the crucial role of health sector decision-makers in establishing and evaluating effective treatment and prevention policies. To inform sound decisions, it is essential to simultaneously monitor multiple pandemic characteristics, including transmission rates, infection rates, recovery rates (which indicate treatment efficacy), and fatality rates. This study introduces an innovative application of existing methodologies: the Multivariate Exponentially Weighted Moving Average (MEWMA) and Multivariate Cumulative Sum (MCUSUM) control charts (CCs), used for monitoring the parameters of the Susceptible, Exposed, Infected, Recovered, Death, and Vaccination (SEIRDV) model. The methodology is applied to COVID-19 data from the State of Qatar, offering new insights into the pandemic’s dynamics. By monitoring changes in the model parameters, this study aims to assess the effectiveness of interventions and track the impact of emerging variants. The results underscore the practical utility of these methodologies for decision-making during similar pandemics. Additionally, this study employs an augmented particle Markov chain Monte Carlo scheme that enables real-time monitoring of SEIRDV model parameters, offering improved estimation accuracy and robustness compared to traditional approaches. The results demonstrate that MEWMA and MCUSUM charts are effective tools for monitoring SEIRDV model parameters and can support decision-making in any similar pandemic. Full article