Incorporating Interventions to an Extended SEIRD Model with Vaccination: Application to COVID-19 in Qatar

TL;DR

This paper develops an extended SEIRDV model incorporating interventions and vaccination, using Bayesian methods to estimate parameters and predict COVID-19 dynamics in Qatar, assessing intervention impacts and vaccination benefits.

Contribution

It introduces a novel compartmental model with intervention and vaccination effects, employing Bayesian inference for parameter estimation and pandemic prediction.

Findings

Model accurately predicts peak active infections.

Quantifies excess deaths averted by vaccination.

Assesses impact of government interventions on pandemic progression.

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.