Uncertainty quantification in mechanistic epidemic models via cross-entropy approximate Bayesian computation
Description
This paper proposes a data-driven approximate Bayesian computation framework for parameter estimation and uncertainty quantification of epidemic models, which incorporates two novelties: (i) the identification of the initial conditions by using
