  The vast majority of strategies aimed at controlling contagion processes on
networks considers the connectivity pattern of the system as either quenched or
annealed. However, in the real world many networks are highly dynamical and
evolve in time concurrently to the contagion process. Here, we derive an
analytical framework for the study of control strategies specifically devised
for time-varying networks. We consider the removal/immunization of individual
nodes according the their activity in the network and develop a block variable
mean-field approach that allows the derivation of the equations describing the
evolution of the contagion process concurrently to the network dynamic. We
derive the critical immunization threshold and assess the effectiveness of the
control strategies. Finally, we validate the theoretical picture by simulating
numerically the information spreading process and control strategies in both
synthetic networks and a large-scale, real-world mobile telephone call dataset
