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In this paper we apply a methodology introduced in Navarro Jimenez et al(2016) in the framework of chemical reaction networks to perform a globalsensitivity analysis on simulations of a continuous-time Markov chain modelmotivated by epidemiology. Our goal is to quantify not only the effects ofuncertain parameters such as epidemic parameters (transmission rate, meansojourn duration in compartments), but also those of intrinsic randomness andinteractions between epidemic parameters and intrinsic randomness. For thatpurpose, following what was proposed in Navarro Jimenez et al, we leveragethree exact simulation algorithms for continuous-time Markov chains from thestate of the art which we combine with common tools from variance-basedsensitivity analysis as introduced in Sobol (1993). Also, we discuss the impactof the choice of the simulation algorithm used for the simulations on theresults of sensitivity analysis. Such a discussion is new, at least to ourknowledge. In a numerical section, we implement and compare three sensitivityanalyses based on simulations obtained from different exact simulationalgorithms of a SARS-CoV-2 epidemic model.

Performing global sensitivity analysis on simulations of a continuous-time Markov chain model motivated by epidemiology