Modeling and Simulation of the Dynamics of COVID-19 in Enclosed Spaces

Abstract

The COVID-19 pandemic has caused more than 16 million cases and 600,000 deaths worldwide in little over half a year. Most epidemic models studying the virus have focused on the macro scale, evaluating the progression of infected cases across large regions. However, the significant airborne infectivity of the virus has led to important public policy questions about safety measures in enclosed spaces like schools, aircraft, and hospitals. Alarmingly, there is a severe lack of coronavirus-specific literature that models the medium to long term progression of infections in these small spaces. In this work, we introduce a novel framework that combines the Wells-Riley airborne infection model, the SEIR epidemiological model, and an infectious concentration transport model. This flexible framework allows us to study the dynamics and progression of airborne diseases in enclosed spaces. While previous models of this type only studied timescales shorter than the incubation rate of the disease, our coupled model removes that limitation. We then apply this model to a benchmark application with parameters proposed by the CDC for COVID-19 and simulate scenarios that mimic the effects of control measures like wearing face-masks and changes in ventilation. We also build a graphical user interface to allow user-friendly scenario testing.