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

Edward L Boone; Abdel-Salam G Abdel-Salam; Indranil Sahoo; Ryad Ghanam; Xi Chen; Aiman Hanif

doi:10.1080/02664763.2021.1985091

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

J Appl Stat. 2021 Oct 8;50(2):231-246. doi: 10.1080/02664763.2021.1985091. eCollection 2023.

Authors

Edward L Boone¹, Abdel-Salam G Abdel-Salam², Indranil Sahoo¹, Ryad Ghanam³, Xi Chen⁴, Aiman Hanif¹

Affiliations

¹ Department of Statistical Sciences and Operations Research, Virginia Commonwealth University, Richmond, VA, USA.
² Department of Mathematics, Statistics and Physics, College of Arts and Sciences, Qatar University, Doha.
³ Department of Liberal Arts and Science, Virginia Commonwealth University in Qatar, Doha, Qatar.
⁴ Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, USA.

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.

Keywords: COVID-19; Epidemiology; Multivariate exponentially weighted moving average; augmented particle Markov chain Monte Carlo; process monitoring.