  Epidemic spreading can be suppressed by the introduction of containment
measures such as social distancing and lock downs. Yet, when such measures are
relaxed, new epidemic waves and infection cycles may occur. Here we explore
this issue in compartmentalized epidemic models on graphs in presence of a
feedback between the infection state of the population and the structure of its
social network for the case of discontinuous control. We show that in random
graphs the effect of containment measures is simply captured by a
renormalization of the effective infection rate that accounts for the change in
the branching ratio of the network. In our simple setting, a piece-wise
mean-field approximations can be used to derive analytical formulae for the
number of epidemic waves and their length. A variant of the model with
imperfect information is used to model data of the recent covid-19 epidemics in
the Basque Country and Lombardy, where we estimate the extent of social network
disruption during lock downs and characterize the dynamical attractors.
