We investigate the information-theoretical limits of inference tasks in epidemic spreading on graphs, in the large-size limit. The typical inference tasks consist in computing observables of the posterior distribution of the epidemic model given observations taken from a ground truth (sometimes called planted) random trajectory. We give theoretical predictions on the posterior probability distribution of the trajectory of each individual, conditioned to observations on the state of individuals at given times, focusing on the Susceptible Infectious (SI) model. The epidemic dynamics induces non-trivial long-range correlations among individuals' states, resulting in non-local correlated quenched disorder which unfortunately is typically hard to handle. To overcome this difficulty, we divide the dynamical process into two sets of variables: a set of stochastic independent variables (representing transmission delays), plus a set of correlated variables (the infection times) that depend deterministically on the first. Treating the former as quenched variables and the latter as dynamic ones, computing disorder average becomes feasible by means of the Replica Symmetric cavity method. In the Bayes-optimal condition, i.e. when true dynamic parameters are known, the inference task is expected to fall in the Replica Symmetric regime. We indeed provide predictions for the information theoretic limits of various inference tasks, in form of phase diagrams. We also identify a region, in the Bayes-Optimal setting, with strong hints of Replica Symmetry Breaking. When true parameters are unknown, we show how a maximum-likelihood procedure is able to recover them with mostly unaffected performance.