Effective control of an epidemic relies on the rapid discovery and isolation of infected individuals. Because many infectious diseases spread through interaction, contact tracing is widely used to facilitate case discovery and control. However, what determines the efficacy of contact tracing has not been fully understood. 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. We argue that, even if the directionality of infection is unknown, it is possible to perform backward-aiming contact tracing. Using simulations on both synthetic and high-resolution empirical contact datasets, we show that strategically executed contact tracing can prevent a substantial fraction of transmissions with a higher efficiency—in terms of prevented cases per isolation—than case isolation alone. Our results call for a revision of current contact-tracing strategies so that they leverage all forms of bias. It is particularly crucial that we incorporate backward and deep tracing in a digital context while adhering to the privacy-preserving requirements of these new platforms.

有效控制流行病依赖于快速发现和隔离受感染的个体。由于许多传染病通过相互作用传播，因此接触者追踪被广泛用于促进病例发现和控制。然而，决定接触者追踪效果的因素尚未完全清楚。在这里，我们揭示，与“前向”追踪（追踪疾病传播的对象）相比，“后向”追踪（追踪疾病传播的对象）更为有效。反向追踪的有效性是由于接触异质性引起的简单但被忽视的偏见。我们认为，即使感染的方向未知，也可以进行向后瞄准的接触者追踪。使用合成和高分辨率经验接触数据集的模拟，我们表明，战略性地执行接触者追踪可以比单独的病例隔离更有效地阻止大部分传播——就每次隔离预防的病例而言。我们的结果要求修订当前的接触者追踪策略，以便它们利用所有形式的偏见。在遵守这些新平台的隐私保护要求的同时，我们在数字环境中结合向后和深度追踪尤为重要。