Efficient sentinel surveillance strategies for preventing epidemics on networks
Efficient sentinel surveillance strategies for preventing epidemics on networks
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DOI：
10.1371/journal.pcbi.1007517
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发表时间：
2019-11-01
影响因子：
4.3
通讯作者：
Gershenson, Carlos
中科院分区：
文献类型：
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作者：
Colman, Ewan;Holme, Petter;Gershenson, Carlos
作者摘要在易感染某些传染病的个体网络中，为了在大多数损害发生之前检测到感染，最佳的监测位置是什么？在本文中，我们通过考虑各种用于哨点放置的启发式策略来解决这个问题，这些策略可以在现实世界中实现，而不需要过多的计算量，甚至不需要关于网络结构的完美数据。我们发现，尝试在网络的不同区域分布哨兵的策略在高度模块化或空间嵌入的网络中执行得最好，而针对连接最好的个人的策略在个人之间存在相当大的联系异质性时效果最好。我们的结果可以作为指导方针，帮助决定何时应该或不应该实施某些策略。监测在防止新出现的传染病成为流行病方面发挥着至关重要的作用。在有可能监测某些人、交通枢纽或医院的感染状况的情况下，及早发现疾病可以在大多数损害发生之前实施干预措施，或者至少可以减轻其影响。本文讨论了在易患某种传染病的个体的网络中应该选择哪些节点的问题，以使伤亡人数最小化。通过在一组经验网络和合成网络上模拟疾病暴发，我们表明最佳策略取决于网络的拓扑特征。对于高度模块化或空间嵌入的网络，最好将哨点放置在分布在不同区域的节点上。但是，如果异构度很高，则首选以网络中心为目标的策略。我们进一步考虑了网络样本不完整的后果，并证明了新信息的价值随着收集的数据的增加而减少。最后，利用图论中已知的结果，利用稀疏网络数据的碎片化结构，我们发现了两种启发式算法的进一步边际改进。
Author summary In a network of individuals susceptible to some infectious disease, what are the best locations to monitor in order to detect the infection before most damage can be done? In this paper we address this question by considering various heuristic strategies for sentinel placement that can potentially be implemented in real-world situations without requiring excessive amounts of computation, or even having perfect data about the structure of the network. We find that strategies that attempt to distribute sentinels over different regions of the network perform best in highly modular or spatially embedded networks, whereas the strategy of targeting the most well connected individuals works best when there is a considerable amount of contact heterogeneity between individuals. Our results may be used as a guideline to help decide when certain strategies should, or should not, be implemented.Surveillance plays a crucial role in preventing emerging infectious diseases from becoming epidemic. In circumstances where it is possible to monitor the infection status of certain people, transport hubs, or hospitals, early detection of the disease allows interventions to be implemented before most of the damage can occur, or at least its impact can be mitigated. This paper addresses the question of which nodes we should select in a network of individuals susceptible to some infectious disease in order to minimize the number of casualties. By simulating disease outbreaks on a collection of empirical and synthetic networks we show that the best strategy depends on topological characteristics of the network. For highly modular or spatially embedded networks it is better to place the sentinels on nodes distributed across different regions. However, if the degree heterogeneity is high, then a strategy that targets network hubs is preferred. We further consider the consequences of having an incomplete sample of the network and demonstrate that the value of new information diminishes as more data is collected. Finally we find further marginal improvements using two heuristics informed by known results in graph theory that exploit the fragmented structure of sparse network data.