  In this paper, we study the dynamics of epidemic processes taking place in
temporal and adaptive networks. Building on the activity-driven network model,
we propose an adaptive model of epidemic processes, where the network topology
dynamically changes due to both exogenous factors independent of the epidemic
dynamics as well as endogenous preventive measures adopted by individuals in
response to the state of the infection. A direct analysis of the model using
Markov processes involves the spectral analysis of a transition probability
matrix whose size grows exponentially with the number of nodes. To overcome
this limitation, we derive an upper-bound on the decay rate of the number of
infected nodes in terms of the eigenvalues of a $2 \times 2$ matrix. Using this
upper bound, we propose an efficient algorithm to tune the parameters
describing the endogenous preventive measures in order to contain epidemics
over time. We confirm our theoretical results via numerical simulations.
