  We present a method aimed to compute the communicability (broadcast and
receive) of nodes through causal paths in temporal networks. The method
considers all possible combinations of chronologically ordered products of
adjacency matrices of the network snapshots and by means of a damping procedure
favors the paths that have high communication efficiency. We apply the method
to four real-world networks of face-to-face human contacts and identify the
nodes with high communicability. The accuracy of the method is proved by
studying the spread of an epidemic in the networks using the
susceptible-infected-recovered model. We show that if a node with high
broadcast is chosen as the origin of the outbreak of infection then the
epidemic spreads early while it is delayed and inhibited if the origin of
infection is a node with low broadcast. Receiving nodes can be treated as
broadcasters if the arrow of time is reversed.
