Model Selection and Parameter Estimation for Fractional SIR Model Based on the Combination of Reinforcement Learning and ABC-SMC

Abstract

A novel algorithm is proposed to increase the effectiveness of model selection and parameter estimation for the fractional susceptible-infected-recovered model. It combines reinforcement learning (RL) and approximate Bayesian computation sequential Monte Carlo (ABC-SMC) instead of ABC to improve the process of model selection and parameter estimation, where RL is used for model selection and ABC-SMC is exploited for parameter estimation. Numerical simulations illustrate that the combined algorithm (RL-ABC-SMC) significantly outperforms the ABC-SMC algorithm in terms of model selection. Finally, we consider the application of the proposed methodology.