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

TL;DR

This paper introduces a novel method for real-time monitoring of COVID-19 pandemic parameters using a SEIRD model combined with MEWMA control charts, demonstrated on Qatar's data to aid decision-making.

Contribution

It develops an integrated approach combining SEIRD modeling with MEWMA monitoring to track pandemic parameters dynamically, enhancing decision support during COVID-19.

Findings

Effective detection of changes in transmission and recovery rates.

Application to Qatar's COVID-19 data demonstrates practical utility.

Improved real-time monitoring of pandemic dynamics.

Abstract

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.