Case isolation and contact tracing is a widely-used intervention method for controlling epidemic outbreaks. 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  . We also show that, when the number of traced contacts is small, the number of prevented transmission per traced node is even higher---although most traced individuals are healthy---than that from case isolation without contact tracing . Our results also have important consequences for new implementations of digital contact tracing and we argue backward and deep tracing can be incorporated without the important sacrificing privacy-preserving requirements of these new platforms.
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.
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Case isolation and
Discovering and isolating infected individuals is a cornerstone of epidemic control. Because many infectious diseases spread through close contacts,
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widely-used intervention method for controlling epidemic outbreaks. Here, we argue that the effectiveness
key tool for case discovery and control. However, although contact tracing has been performed widely, the mathematical understanding
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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
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
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The effectiveness of backward tracing is due to simple but overlooked biases arising from the heterogeneity in contacts.
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networks with a power-law degree distribution
both synthetic and high-resolution empirical contact datasets
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this deep contact tracing can potentially prevent almost all further transmissions
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, strategically executed contact tracing can prevent a significant fraction of further transmissions
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that, when the number of traced contacts is small,
that---in terms of
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transmission per traced node is even higher---although most traced individuals are healthy---than that from case isolation without contact tracing .
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.
D
new implementations of
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and we argue
because it will be crucial to incorporate the capability for
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can be incorporated without the important sacrificing
while adhering to the