The accuracy of the proposed risk assessment analysis is stable across variations of the temporal correlations of the system, whereas its predictive power depends on the degree of memory kept in the time evolution.

Ph, P, probability of a high(low) risk node to be infected a)?“ predictive power (fraction of infected nodes for which it is possible to compute the epidemic risk)

The observed differences in the predictive power of the approach are expected to be induced by the different temporal behavior of the two systems, resulting in a different amount of memory in preserving links (Fig.

In order to systematically explore the role of these temporal features on the accuracy and predictive power of our approach, we introduce a generic model for the generation of synthetic temporal networks.

In networks characterized by higher memory, the distribution of the predictive power (0 has a well defined peak, whereas for lower memory it is roughly uniform in the range (0 E [0, 0.4] (Fig.

One other important aspect to characterize is the predictive power of our risk assessment analysis.

We can then quantify the predictive power (0 as the fraction of infected nodes for which we could provide the epidemic risk, i.e.

4C-D display the distributions P(w) obtained for the two case studies, showing that a higher predictive power is obtained in the cattle trade network (peak at w 2 60%) with respect to the sexual contact network (peak at w 2 40%).