Elizabeth B Amona

Conference 2023 Live Talk

Talk title

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

Authors and Affiliations

Elizabeth B Amona1, Ryad A Ghanam2, Edward L Boone1, Indranil Sahoo1, Laith J Abu-Raddad3

1. Department of Statistical Sciences and Operations Research, Virginia Commonwealth University, Richmond, Virginia, 23284, USA
2. Department of Liberal Arts and Sciences, Virginia Commonwealth University in Qatar, Education City, Doha, Qatar
3. Weill Cornell Medicine, Qatar

Abstract

Background

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.

Methods

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.

Results

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.

Conclusions

This work provides a novel extension of the Susceptible, Exposed, Infected, Recovered, Death (SEIRD) model to the SEIRDV model by adding a Vaccination compartment and incorporating interventions to help understand the impact of government policies on disease transmission rates. Additionally, the impact of vaccination is also studied using time varying effective reproduction number. All inferences are made under the Bayesian framework. The model is able to treat the Susceptible and Exposed compartments as latent variables, since no data is observed about them other than approximate initial values. The model is implemented on COVID-19 dataset for the State of Qatar.