Analysis, interpretation and evaluation of COVID-19 using mathematical modeling

This paper presents a summary of mathematical experiments conducted to evaluate factors that impact the spread and containment of infectious diseases. While the research was inspired by the SARS-CoV-2 virus which causes COVID-19, the results are applicable for modeling all infectious diseases. A previously published modified SEIR (Susceptible Exposed Infected Recovered) model is used in the study. The impact of social distancing, governance during the outbreak, contact tracing and testing, as well as vaccinations have been considered. A base case to model epidemics is developed, loosely tied to publically available data for COVID-19. Key factors that impact the spread of the pandemic are varied in the model to study the impact of policy, social behaviors, and vaccine development. Results show that while social distancing and lockdowns can “flatten the curve,” or reduce the number of peak infections, quick development, distribution and intake of vaccinations can result in infectious disease containment.