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Parametric study of a stochastic SEIR model for a COVID-19 post-pandemic scenario
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Advisor(s)
Abstract(s)
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.
Description
Keywords
COVID-19 Post-pandemic scenario Parametric study Stochastic SEIR model
Pedagogical Context
Citation
Balsa, Carlos; Padua, Everaldo Junior Borges Garcia de; Pinto, Luan Crisostomo; Rufino, José (2024). Parametric study of a stochastic SEIR model for a COVID-19 post-pandemic scenario. In 3rd International Conference on Advanced Research in Technologies, Information, Innovation and Sustainability (ARTIIS). ISSN 1865-0929. 1937, p. 43-57
Publisher
Springer Nature