Immunization strategies in networks with missing data
Immunization strategies in networks with missing data
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DOI：
10.1371/journal.pcbi.1007897
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发表时间：
2020-05
影响因子：
4.3
通讯作者：
Samuel F. Rosenblatt;Jeffrey A. Smith;Robin Gauthier;Laurent Hébert-Dufresne
中科院分区：
文献类型：
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作者：
Samuel F. Rosenblatt;Jeffrey A. Smith;Robin Gauthier;Laurent Hébert-Dufresne
基于网络的干预策略可以成为减少各种环境中有害传染病的有效且具有成本效益的方法。根据研究，这些策略通常不切实际，因为它们通常假设完全了解网络结构，这在实践中是不常见的。在本文中，我们研究了不同的免疫策略在现实条件下的表现，其中策略由部分观察到的网络数据提供信息。我们的结果表明，全球免疫策略（例如程度免疫）在大多数情况下是最佳的；例外情况是在缺失数据水平非常高的情况下，熟人免疫等随机策略在最大限度地减少疫情方面开始超过它们。在某些情况下，随机策略更加稳健，因为它们可能会以不同的方式受到缺失数据的影响。事实上，我们提出的熟人免疫的一种变体利用逻辑上现实的持续调查干预过程作为有针对性的数据恢复的一种形式，以随着丢失数据水平的增加而改进。这些结果支持了靶向免疫作为一般做法的有效性。他们还强调了将网络视为理想化数学对象的风险：高估网络数据的准确性并放弃额外探究的回报。
Network-based intervention strategies can be effective and cost-efficient approaches to curtailing harmful contagions in myriad settings. As studied, these strategies are often impractical to implement, as they typically assume complete knowledge of the network structure, which is unusual in practice. In this paper, we investigate how different immunization strategies perform under realistic conditions—where the strategies are informed by partially-observed network data. Our results suggest that global immunization strategies, like degree immunization, are optimal in most cases; the exception is at very high levels of missing data, where stochastic strategies, like acquaintance immunization, begin to outstrip them in minimizing outbreaks. Stochastic strategies are more robust in some cases due to the different ways in which they can be affected by missing data. In fact, one of our proposed variants of acquaintance immunization leverages a logistically-realistic ongoing survey-intervention process as a form of targeted data-recovery to improve with increasing levels of missing data. These results support the effectiveness of targeted immunization as a general practice. They also highlight the risks of considering networks as idealized mathematical objects: overestimating the accuracy of network data and foregoing the rewards of additional inquiry.