Before revision:  Here, we argue that the effectiveness of contact tracing and isolation is likely underestimated by existing studies because they do not take into account the different forms of heterogeneity and sampling biases from the network structure. Specifically, we show thatcontact tracing can be even more effectivethan acquaintance sampling at locating hubs. Our results call for the need for contact tracing to go both backward and forward, in multiple steps, to leverage all forms of positive biases .  Using simulations on networks with a power-law degree distribution , we show that this deep contact tracing can potentially prevent almost all further transmissions even at a small probability of detecting infected individuals  .
After revision:  Here, we argue that the effectiveness of contact tracing has not been fully established and it has not been clearly understood what determines the efficacy of contact tracing. Here, we reveal that, compared with "forward" tracing---tracing to whom disease spreads, "backward" tracing---tracing from whom disease spreads---is profoundly more effective . Using simulations on networks with a power-law degree distribution , we show that this deep contact tracing can potentially prevent almost all further transmissions even at a small probability of detecting infected individuals .