Computational study on the transmission of COVID-19 in Virginia using SIQR Model

Abstract

The worldwide outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) shed light on the importance of refining compartmental models in epidemiology. As people across the world have taken preventative measures such as quarantining and vaccination, modeling the spread of SARS-CoV-2 has become more complex. The study developed a variation on an already applicable computational model known as the susceptible-infectious-removed (SIR) model, by incorporating vaccination and quarantining over a 2-week period. The new SIQR model simulates the trajectory of infection based on Virginia SARS-CoV-2 data into the future using differential equations. The role of herd immunity, vaccination, and quarantining becomes more evident.

The R curve of the SIQR model shows a smooth logistic curve that takes longer to reach a horizontal slope, with a difference of ~100 days. The I curve flattens faster as members of the infected population are removed due to quarantining and recovery. In the long term, the recovered population is highest due to herd immunity as susceptible populations are either infected or vaccinated, and the vaccination rate hampers the infection rate.