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Journal of Data Science


Incorporating Interventions to an Extended SEIRD Model with Vaccination: Application to COVID-19 in Qatar
Volume 22, Issue 1 (2024), pp. 97–115
Pub. online: 13 June 2023      Type: Data Science In Action      Open accessOpen Access
Received
28 October 2022
Accepted
14 May 2023
Published
13 June 2023

Abstract

The COVID-19 outbreak of 2020 has required many governments to develop and adopt mathematical-statistical models of the pandemic for policy and planning purposes. To this end, this work provides a tutorial on building a compartmental model using Susceptible, Exposed, Infected, Recovered, Deaths and Vaccinated (SEIRDV) status through time. The proposed model uses interventions to quantify the impact of various government attempts made to slow the spread of the virus. Furthermore, a vaccination parameter is also incorporated in the model, which is inactive until the time the vaccine is deployed. A Bayesian framework is utilized to perform both parameter estimation and prediction. Predictions are made to determine when the peak Active Infections occur. We provide inferential frameworks for assessing the effects of government interventions on the dynamic progression of the pandemic, including the impact of vaccination. The proposed model also allows for quantification of number of excess deaths averted over the study period due to vaccination.

Supplementary material

 Supplementary Material
The supplementary material contains the functional form of the posterior distribution of the model parameters, a discussion on the behavior of R e ( t ) over time, and some trace plots validating convergence of model parameters. The dataset and code used for this project can be found at https://github.com/elizabethamona/SEIRDV-model.

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2024 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.
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Keywords
Bayesian statistics compartmental model epidemiology intervention analysis reproduction number

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