Exploiting deterministic algorithms to perform global sensitivity analysis for continuous-time Markov chain compartmental models with application to epidemiology

In this paper, we develop an approach of global sensitivity analysis for compartmental models based on continuous-time Markov chains. We propose to measure the sensitivity of quantities of interest by representing the Markov chain as a deterministic function of the uncertain parameters and a random variable with known distribution modeling intrinsic randomness. This representation is exact and does not rely on meta-modeling. An application to a SARS-CoV-2 epidemic model is included to illustrate the practical impact of our approach.