Towards a Stochastic SEIR Model for the COVID-19 Post-Pandemic Scenario

With 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.