Scalable Distributed Reproduction Numbers of Network Epidemics with Differential Privacy.

Reproduction numbers are widely used for the estimation and prediction of epidemic spreading processes over networks. However, conventional reproduction numbers of an overall network do not indicate where an epidemic is spreading. Therefore, we propose a novel notion of local distributed reproduction numbers to capture the spreading behaviors of each node in a network. We first show how to compute them and then use them to derive new conditions under which an outbreak can occur. These conditions are then used to derive new conditions for the existence, uniqueness, and stability of equilibrium states of the underlying epidemic model. Building upon these local distributed reproduction numbers, we define cluster distributed reproduction numbers to model the spread between clusters composed of nodes. Furthermore, we demonstrate that the local distributed reproduction numbers can be aggregated into cluster distributed reproduction numbers at different scales. However, both local and cluster distributed reproduction numbers can reveal the frequency of interactions between nodes in a network, which raises privacy concerns. Thus, we next develop a privacy framework that implements a differential privacy mechanism to provably protect the frequency of interactions between nodes when computing distributed reproduction numbers. Numerical experiments show that, even under differential privacy, the distributed reproduction numbers provide accurate estimates of the epidemic spread while also providing more insights than conventional reproduction numbers.