Simulating the Mitigating Effects of Social Distancing and Vaccination during a Pandemic​

Abstract

Understanding the spread of a virus through a large population has always been important for mitigation, treatment, and prevention purposes. In the unprecedented wake of the COVID-19 (SARS-CoV-2) pandemic, techniques to understand and test preventative measures—like vaccination and quarantining—have become increasingly necessary. The developed compartmental model simulates the interactions between (N) people, with variable infection rates for vaccinated (alpha = 0.05) and unvaccinated (Beta = 0.25) people, as well as a (t = 15) day recovery from infection. Both the range of their infectivity, controlled by the quarantine variable (q), and the vaccination rate (f) of the population were varied. The main tests were on the variables of vaccine distribution and effectiveness, as well as quarantine range and the combination between the two. The results suggest that vaccination has a negative linear effect on infection cases over the course of the simulation, while quarantine has a minimal effect until higher amounts (over 80% quarantine), with the inverse being true for duration (increasing with stricter measures). Vaccination and quarantining also have a negative linear and exponential effect respectively on peak case count, which would be helpful in managing the patient flow into hospitals. Reproduction number was also found to be limited below 1.0 by vaccination measures, bringing the outbreak to an end. From these findings, it would be reasonable to suggest that vaccination is nearly ubiquitously helpful, while much stricter quarantine restrictions must be put in place for substantial effect, with the goal to flatten peak cases and decrease reproduction number.