Optimal containment of epidemics over temporal activity-driven networks

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 epidemic dynamics using Markov processes involves the eigenvalues of a transition probability matrix whose size grows exponentially with the number of nodes. To overcome this computational challenge, we derive an upper-bound on the decay ratio of the number of infected nodes in terms of the eigenvalues of a 2 × 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 validate our theoretical results via numerical simulations.