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.

中文翻译：

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通过基于图的主动学习实现最佳疾病监测


追踪新出现的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分配有限的资源进行跨地点的检测和监测，目的是最大限度地收集有关患病率和发病率潜在趋势的信息。我们将这个决策过程建模为无向和未加权图上的迭代节点分类问题，其中节点代表位置，边代表传染源在它们之间的移动。首先，随机选择一个节点进行测试，并确定是受感染还是未受感染。然后，测试反馈用于更新未观察到的节点被感染的概率的估计值，并通知在下一次迭代中选择要测试的节点，直到用完某些测试预算。使用这个框架，我们评估和比较了以前开发的用于节点选择的主动学习策略的性能，包括节点熵和通过不一致进行贝叶斯主动学习。我们使用合成和经验网络上的模拟爆发来探索这些策略在不同爆发情景下的性能。此外，我们提出了一个策略，该策略考虑了每个候选节点的邻居之间感染预测的距离加权平均熵。鉴于测试预算较小，我们提出的政策在大多数疫情情景中都优于现有政策，这凸显了在政策设计中考虑探索-利用权衡的必要性。我们的研究结果可以为针对新出现和地方性病原体的具有成本效益的监测策略的设计提供信息，并减少与资源受限情况下的早期风险评估相关的不确定性。