Reconstructing an Epidemic Over Time

Abstract

We consider the problem of reconstructing an epidemic over time, or, more general, reconstructing the propagation of an activity in a network. Our input consists of a temporal network, which contains information about when two nodes interacted, and a sample of nodes that have been reported as infected. The goal is to recover the flow of the spread, including discovering the starting nodes, and identifying other likely-infected nodes that are not reported. The problem we consider has multiple applications, from public health to social media and viral marketing purposes.