We simulated S = 100 epidemics and censored 70% of the incidence data before estimating the infection network. The estimated network is shown in Figure 2 , superimposed onto the original network. The reconstruction is highly accurate in this test Figure 1 . An SIR epidemic spreading through a small network of N = 14 nodes. The epidemic starts on node 9 at t = 0. Thereafter, nodes 5 and 12 are infected at t = 1 by node 9, which recovers and become immune to subsequent infection. At t = 2, nodes 4 and 6 are infected by node 5 (which recovers), while nodes 10, 11, and 13 are infected by node 12 (which recovers). Following this, at t = 3, node 3 is infected by node 4 (which recovers), while node 8 is infected by node 6 (which recovers). Nodes 10, 11, and 13 recover without infecting any other nodes. Finally, at t = 4 (not shown), nodes 3 and 8 recover and the epidemic ends. In this figure, susceptible nodes are white, infected nodes are red, recovered nodes are green, and links responsible for the transmission are colored red for the time step they are activated. doi:10.1371/journal.pone.0022124.g001 case. From the total 1,584 links in the original network, the algorithm estimated 738 links correctly and made only 196 wrong estimates, resulting in 79% estimation accuracy. More importantly, the estimation found mostly links between highly connected 'hubs' that form the 'backbones' of the network. These backbones play an important role in disease transmissions as they link one highly-connected cluster (community) to another throughout the network.