IF 9.4 1区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Joseph L.-H. Tsui, Mengyan Zhang, Prathyush Sambaturu, Simon Busch-Moreno, Marc A. Suchard, Oliver G. Pybus, Seth Flaxman, Elizaveta Semenova, Moritz U. G. Kraemer
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引用次数: 0

摘要

跟踪新病原体的传播对于设计及时有效的公共卫生应对措施至关重要。政策制定者面临的挑战是如何分配有限的资源用于不同地点的检测和监控,目的是最大限度地获取有关流行和发病率潜在趋势的信息。我们将这一决策过程建模为无向、无权重图上的迭代节点分类问题,其中节点代表地点,边代表传染源在地点间的移动。首先,随机选择一个节点进行测试,并确定其为感染或未感染。然后,测试反馈被用来更新未观察到的节点被感染的概率估计值,并为下一次迭代中选择节点进行测试提供信息,直到某些测试预算耗尽为止。利用这一框架,我们对之前开发的节点选择主动学习策略(包括节点熵和贝叶斯分歧主动学习)的性能进行了评估和比较。我们利用合成网络和经验网络上的模拟疫情,探讨了这些策略在不同疫情爆发情况下的性能。此外,我们还提出了一种考虑每个候选节点邻居间感染预测的距离加权平均熵的策略。在测试预算较少的情况下,我们提出的策略在大多数疫情爆发场景下都优于现有策略,这突出表明在策略设计中需要考虑探索与开发之间的权衡。我们的研究结果可为设计针对新发病原体和流行性病原体的经济有效的监控策略提供参考,并减少在资源有限情况下与早期风险评估相关的不确定性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Toward optimal disease surveillance with graph-based active learning
Tracking the spread of emerging pathogens is critical to the design of timely and effective public health responses. Policymakers face the challenge of allocating finite resources for testing and surveillance across locations, with the goal of maximizing the information obtained about the underlying trends in prevalence and incidence. We model this decision-making process as an iterative node classification problem on an undirected and unweighted graph, in which nodes represent locations and edges represent movement of infectious agents among them. To begin, a single node is randomly selected for testing and determined to be either infected or uninfected. Test feedback is then used to update estimates of the probability of unobserved nodes being infected and to inform the selection of nodes for testing at the next iterations, until certain test budget is exhausted. Using this framework, we evaluate and compare the performance of previously developed active learning policies for node selection, including Node Entropy and Bayesian Active Learning by Disagreement. We explore the performance of these policies under different outbreak scenarios using simulated outbreaks on both synthetic and empirical networks. Further, we propose a policy that considers the distance-weighted average entropy of infection predictions among neighbors of each candidate node. Our proposed policy outperforms existing ones in most outbreak scenarios given small test budgets, highlighting the need to consider an exploration–exploitation trade-off in policy design. Our findings could inform the design of cost-effective surveillance strategies for emerging and endemic pathogens and reduce uncertainties associated with early risk assessments in resource-constrained situations.
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来源期刊
CiteScore
19.00
自引率
0.90%
发文量
3575
审稿时长
2.5 months
期刊介绍: The Proceedings of the National Academy of Sciences (PNAS), a peer-reviewed journal of the National Academy of Sciences (NAS), serves as an authoritative source for high-impact, original research across the biological, physical, and social sciences. With a global scope, the journal welcomes submissions from researchers worldwide, making it an inclusive platform for advancing scientific knowledge.
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