  During the current COVID-19 pandemic, decision makers are tasked with
implementing and evaluating strategies for both treatment and disease
prevention. In order to make effective decisions, they need to simultaneously
monitor various attributes of the pandemic such as transmission rate and
infection rate for disease prevention, recovery rate which indicates treatment
effectiveness as well as the mortality rate and others. This work presents a
technique for monitoring the pandemic by employing an Susceptible, Exposed,
Infected, Recovered Death model regularly estimated by an augmented particle
Markov chain Monte Carlo scheme in which the posterior distribution samples are
monitored via Multivariate Exponentially Weighted Average process monitoring.
This is illustrated on the COVID-19 data for the State of Qatar.
