Browsing by Author "Pinto, Luan Crisostomo"
Now showing 1 - 2 of 2
Results Per Page
Sort Options
- Parametric study of a stochastic SEIR model for a COVID-19 post-pandemic scenarioPublication . Balsa, Carlos; Padua, Everaldo Junior Borges Garcia de; Pinto, Luan Crisostomo; Rufino, JoséDespite 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.
- Towards a stochastic SEIR model for the COVID-19 post-pandemic scenarioPublication . Balsa, Carlos; Padua, Everaldo Junior Borges Garcia de; Pinto, Luan Crisostomo; Rufino, JoséWith the current recession of the global COVID-19 pandemic, the corresponding epidemic models need to be adapted to reflect this new reality and continue assisting public health authorities in the definition of policies and decision making. With that aim, this paper presents a SEIR epidemic model for the representation of the COVID-19 pos-pandemic scenario. The model considers the effect of countermeasures such as vaccination and quarantine, and the consequences of the progressive loss of immunity. A deterministic formulation and a first stochastic version of the model are presented, and their implementation in MATLAB is evaluated and compared. To cope with the computational demands of the application of the Monte Carlo method, the implementation of the stochastic version follows a parallel approach that proved to be highly scalable and efficient in a multi-core computational system. The preliminary evaluation results, with fixed parameters, point to a cyclic evolution of the pandemic and a tendency for stabilization in the future.