The recent COVID-19 pandemic underscores the significance of early-stage non-pharmacological intervention strategies. The widespread use of masks and the systematic implementation of contact tracing strategies provide a potentially equally effective and socially less impactful alternative to more conventional approaches, such as large-scale mobility restrictions. However, manual contact tracing faces strong limitations in accessing the network of contacts, and the scalability of currently implemented protocols for smartphone-based digital contact tracing becomes impractical during the rapid expansion phases of the outbreaks, due to the surge in exposure notifications and associated tests. A substantial improvement in digital contact tracing can be obtained through the integration of probabilistic techniques for risk assessment that can more effectively guide the allocation of new diagnostic tests. In this study, we first quantitatively analyze the diagnostic and social costs associated with these containment measures based on contact tracing, employing three state-of-the-art models of SARS-CoV-2 spreading. Our results suggest that probabilistic techniques allow for more effective mitigation at a lower cost. Secondly, our findings reveal a remarkable efficacy of probabilistic contact-tracing techniques in performing backward and multi-step tracing and capturing super-spreading events.

翻译：近期COVID-19大流行凸显了早期非药物干预策略的重要性。口罩的广泛使用与接触追踪策略的系统性实施，为大规模流动限制等传统方法提供了一种潜在等效且社会影响更小的替代方案。然而，人工接触追踪在获取接触网络方面存在明显局限，而当前基于智能手机的数字接触追踪协议在疫情快速扩散阶段因暴露通知及相关检测数量激增而难以扩展。通过整合风险评估的概率技术来更有效地指导新诊断检测的分配，可显著改进数字接触追踪效果。本研究首先基于三种先进的SARS-CoV-2传播模型，定量分析了接触追踪相关防控措施的诊断成本与社会成本。结果表明概率技术能以更低成本实现更有效的疫情缓解。其次，我们的发现揭示了概率接触追踪技术在实施逆向追踪、多步追踪及捕获超级传播事件方面具有显著效能。