Based on the above ideas, we proposed a targeted method to effectively contain infectious disease epidemics. This strategy involves estimating the infection network of a severe disease using incidences data from common infections sharing the same infection network, and fragmenting the network into disconnected pieces using a graph partitioning method. To test the proposed strategy in principle, we first generate artificial social networks of different sizes but with roughly the same clustering characteristics to serve as our infection networks. Then we simulate multiple SIR epidemics on the networks to play the role of common infections circulating in society. We apply censorships on the incidence data collected to emulate the low reporting rates of common infections, and use the censored incidences to construct estimates of the original infection networks. To mitigate new epidemics, we fragment the estimated infection networks. To do this, we apply a graph partitioning method on the estimated networks to identify the smallest sets of nodes that, when removed, will efficiently break the networks up into isolated pieces. Finally, we evaluate the effectiveness of this targeted strategy by comparing against traditional untargeted methods. While each of the problems has been studied independently (see for example [8, 18] for network reconstruction and [19] [20] [21] [22] for graph partitioning), to the best of our knowledge no work has been done applying both methods to control epidemics.