通过有针对性地采用接触者追踪应用程序来优化传染病传播的缓解
Optimising the mitigation of epidemic spreading through targeted adoption of contact tracing apps
摘要：
持续的COVID-19大流行是人类历史上首次在全球范围内部署数字接触追踪的流行病。追踪和排除病毒检测呈阳性的个人的所有接触者可以帮助减缓流行病，但接触者追踪的影响受到人口中接触者追踪应用程序普遍采用率低的严重限制。我们在这里推导出一个分析表达式的有效性的联系人跟踪应用程序安装策略在SIR模型上给定的联系人图。我们提出了一个分散的启发式，以提高固定采用率下的接触跟踪的有效性，其目标是一组个人安装接触跟踪应用程序，并可以很容易地实现。大量的现实世界的接触网络上的模拟证实，这种启发式代表了一个可行的替代目前的最先进的。
英文摘要：
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.
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