Using Social Networks For Modeling And Optimization In A Healthcare Setting

Our results show that, in general, the quality of solutions produced by these approximations is highly dependent on the dynamics of disease spread. However, experiments show that simple policies, like vaccinating the most well-connected or most mobile individuals, perform much better than a random vaccination policy. And finally we consider the problem of finding a set of individuals to act as indicators for important healthcare related events on a social network for infectious disease experts. We model this problem as a generalization of the budgeted maximum coverage problem studied previously and show that in fact our problem is much more difficult to solve in general. But by exposing a property of this network, we provide analysis showing that a simple greedy approach for picking indicators provides a near-optimal (constant-factor) approximation.