Optimization of stochastic virus detection in contact networks

We develop network models to represent the dynamics of a virus spreading in a contact network. Based on the resulting dynamics governing the spread, we present optimization models to rapidly detect the virus. We consider two goals, maximizing the probability of detecting a virus by a time threshold and minimizing the expected time to detection. We establish submodularity results for these objective functions and, using data from a mobile service provider, we show that a greedy heuristic performs surprisingly well.