  Discovering and isolating infected individuals is a cornerstone of epidemic
control. Because many infectious diseases spread through close contacts,
contact tracing is a key tool for case discovery and control. However, although
contact tracing has been performed widely, the mathematical understanding 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. The effectiveness of backward tracing is due to simple but
overlooked biases arising from the heterogeneity in contacts. Using simulations
on both synthetic and high-resolution empirical contact datasets, we show that
even at a small probability of detecting infected individuals, strategically
executed contact tracing can prevent a significant fraction of further
transmissions. We also show that---in terms of the number of prevented
transmissions per isolation---case isolation combined with a small amount of
contact tracing is more efficient than case isolation alone. By demonstrating
that backward contact tracing is highly effective at discovering
super-spreading events, we argue that the potential effectiveness of contact
tracing has been underestimated. Therefore, there is a critical need for
revisiting current contact tracing strategies so that they leverage all forms
of biases. Our results also have important consequences for digital contact
tracing because it will be crucial to incorporate the capability for backward
and deep tracing while adhering to the privacy-preserving requirements of these
new platforms.
