Immunization using a heterogeneous geo-spatial population model: A qualitative perspective on COVID-19 vaccination strategies.

Epidemic modeling has been a key tool for understanding the impact of global viral outbreaks for over two decades. Recent developments of the COVID-19 pandemic have accelerated research using compartmental models, like SI, SIR, SEIR, with their appropriate modifications. However, there is a large body of recent research consolidated on homogeneous population mixing models, which are known to offer reduced tractability, and render conclusions hard to quantify. As such, based on our recent work, introducing the heterogeneous geo-spatial mobility population model (GPM), we adapt a modified SIR-V (susceptible-infected-recovered-vaccinated) epidemic model which embodies the idea of patient relapse from R back to S, vaccination of R and S patients (reducing their infectiousness), thus altering the infectiousness of V patients (from λn to λr). Simulation results spanning over a period of t = 2000 days (6 years, the period « 2020-2025) compare the impact of an epidemic outbreak with variable vaccination strategies, starting after 1 year (as is the case of COVID-19). The infected proportion in the remaining 5-year period is analyzed using vaccination rates from rv = 0 (no vaccination) to rv = 1. While rv < 0.4 is less effective during the earlier stages, all strategies with rv > 0.4 show a similar downward convergence reducing the number of infected by more than half, compared to no vaccination. Given the complexity of epidemic processes, we conclude that higher vaccination rates yield similar results, but a minimal rv = 0.4 (40% of population over five years) should be targeted.