Parametric Study of a Stochastic SEIR Model for a COVID-19 Post-Pandemic Scenario

Despite the end of the COVID-19 pandemic was decreed by the WHO, this disease has not disappeared and continues to claim victims. Thus, it remains important to follow up, monitor, and project its evolution in the short term. To that end, mathematical models are a precious tool. Based on its results, it is possible to take preventive measures that minimize the spread of this contagious disease. This study focuses on the stochastic SEIR epidemic model adapted to a post-pandemic scenario. The main factors that influence the spread and containment of the disease are considered, namely, the rates of transmission, vaccination, and quarantine. The results obtained point to a probability of nearly 12% of the appearance of a major epidemic outbreak that could affect a large part of the population. Without vaccination, it is expected that an epidemic outbreak will infect 75% of the population. Therefore, the maintenance of adequate vaccination rates is an essential measure to overcome the loss of immunity from the vaccinated or recovered individuals.