Epidemic spreading is a widely studied process due to its importance and possibly grave consequences for society. While the classical context of epidemic spreading refers to pathogens transmitted among humans or animals, it is straightforward to apply similar ideas to the spread of information (e.g., a rumor) or the spread of computer viruses. This paper addresses the question of how to optimally select nodes for monitoring in a network of timestamped contact events between individuals. We consider three optimization objectives: the detection likelihood, the time until detection, and the population that is affected by an outbreak. The optimization approach we use is based on a simple greedy approach and has been proposed in a seminal paper focusing on information spreading and water contamination. We extend this work to the setting of disease spreading and present its application with two example networks: a timestamped network of sexual contacts and a network of animal transports between farms. We apply the optimization procedure to a large set of outbreak scenarios that we generate with a susceptible-infectious-recovered model. We find that simple heuristic methods that select nodes with high degree or many contacts compare well in terms of outbreak detection performance with the (greedily) optimal set of nodes. Furthermore, we observe that nodes optimized on past periods may not be optimal for outbreak detection in future periods. However, seasonal effects may help in determining which past period generalizes well to some future period. Finally, we demonstrate that the detection performance depends on the simulation settings. In general, if we force the simulator to generate larger outbreaks, the detection performance will improve, as larger outbreaks tend to occur in the more connected part of the network where the top monitoring nodes are typically located. A natural progression of this work is to analyze how a representative set of outbreak scenarios can be generated, possibly taking into account more realistic propagation models.

由于其重要性以及对社会的严重影响，流行病的传播是一个被广泛研究的过程。虽然流行病传播的经典背景是指在人或动物之间传播的病原体，但将类似的思想应用于信息传播（例如谣言）或计算机病毒的传播是很简单的。本文解决了如何在个人之间带有时间戳的联系事件网络中最佳选择节点进行监视的问题。我们考虑了三个优化目标：发现可能性，直到发现的时间以及受到疫情影响的种群。我们使用的优化方法基于简单的贪婪方法，并且已在开创性论文中提出，其重点是信息传播和水污染。我们将这项工作扩展到疾病传播的背景，并通过两个示例网络介绍其应用：带时间戳的性接触网络和农场之间的动物运输网络。我们将优化程序应用于我们通过使用易感感染恢复模型。我们发现，选择具有高度或许多联系的节点的简单启发式方法在爆发检测性能方面与（贪婪的）最佳节点集相比具有很好的对比。此外，我们观察到在过去的时间段上优化的节点对于未来的时间段爆发检测可能不是最佳的。但是，季节影响可能有助于确定哪个过去的时期可以很好地推广到将来的某个时期。最后，我们证明了检测性能取决于仿真设置。通常，如果我们迫使模拟器生成较大的爆发，则检测性能将得到改善，因为较大的爆发往往发生在通常位于顶部监视节点的网络连接程度更高的部分。