Modeling COVID-19 infection in high-risk settings and low-risk settings

Abstract

In this research paper we present a mathematical model for COVID-19 in high-risk settings and low-risk settings which might be infection dynamics between hotspots and less risky communities. The main idea was to couple the SIR model with alternating risk levels from the two different settings high and low-risk settings. Therefore, building from this model we partition the infected class into two categories, the symptomatic and the asymptomatic. Using this approach we simulated COVID-19 dynamics in low and high-risk settings with auto-switching risk settings. Again, the model was analyzed using both analytic methods and numerical methods. The results of this study suggest that switching risk levels in different settings plays a pivotal role in COVID-19 progression dynamics. Hence, population reaction time to adhere to preventative measures and interventions ought to be implemented with flash speed targeting first the high-risk setting while containing the dynamics in low-risk settings.