Application of Gaussian process regression as a surrogate modeling method to assess the dynamics of COVID-19 propagation

Abstract

In this research, we aimed to assess the possibility of using surrogate modeling methods to replace time-consuming calculations related to modeling of COVID-19 dynamics. The Gaussian process regression (GPR) was used as a surrogate to replace detailed simulations by a COVID-19 multiagent model. Experiments were conducted with various kernels, as a result, in accordance with the quality metrics of the models, kernels were identified in which the surrogate gives the most accurate result (Rational Quadratic kernel and Additive kernel). It was demonstrated that by smoothing the dynamics of COVID-19 propagation, it is possible to achieve greater accuracy in GPR training. The obtained results prove the potential possibility of using surrogate modeling methods to conduct an uncertainty quantification of the multiagent model of COVID-19 propagation.