Monitoring SEIRD model parameters using MEWMA for the COVID-19 pandemic with application to the State of Qatar

29 Jan 2021  ·  Edward L. Boone, Abdel-Salam G. Abdel-Salam, Indranil Sahoo, Ryad Ghanam, Xi Chen, Aiman Hanif ·

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.

PDF Abstract
No code implementations yet. Submit your code now


Methodology Applications


  Add Datasets introduced or used in this paper