The ongoing COVID-19 pandemic is the first epidemic in human history in which digital contact-tracing has been deployed at a global scale. Tracking and quarantining all the contacts of individuals who test positive to a virus can help slowing-down an epidemic, but the impact of contact-tracing is severely limited by the generally low adoption of contact-tracing apps in the population. We derive here an analytical expression for the effectiveness of contact-tracing app installation strategies in a SIR model on a given contact graph. We propose a decentralised heuristic to improve the effectiveness of contact tracing under fixed adoption rates, which targets a set of individuals to install contact-tracing apps, and can be easily implemented. Simulations on a large number of real-world contact networks confirm that this heuristic represents a feasible alternative to the current state of the art.

中文翻译：

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通过有针对性地采用联系人跟踪应用程序来优化缓解流行病的方法

正在进行中的COVID-19大流行是人类历史上的第一种流行病，其数字接触追踪已在全球范围内部署。跟踪和隔离对病毒测试呈阳性的个人的所有联系人可以帮助减缓流行病的发生，但是由于人群中接触追踪应用程序的普遍使用率较低，接触追踪的影响受到严重限制。我们在这里导出一个解析表达式，用于在给定的联系图上的SIR模型中联系跟踪应用程序安装策略的有效性。我们提出了一种分散式启发式方法，以提高在固定采用率下的联系人跟踪的有效性，该方法针对的是一组要安装联系人跟踪应用程序的个人，并且可以轻松实现。