Journal of Data Science

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.

Abstract: It is important to estimate transmissibility of influenza virus during its growing phase for understanding the propagation of the virus. The estimation procedures of the transmissibility are usually based on the data generated in flu seasons. The data-generating process of the outbreak of influenza has many features. The data is generated by not only a biological process but also control measures such as flu vaccination. The estimation is discussed by considering the aspects of the data-generating process and using the model to capture the essential characteristics of flu transmission during the growing phase of a flu season.