Detecting Sources of Healthcare Associated Infections
Detecting Sources of Healthcare Associated Infections
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DOI：
10.1609/aaai.v37i4.25554
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发表时间：
2023-06
期刊：
影响因子：
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通讯作者：
Hankyu Jang;Andrew Fu;Jiaming Cui;M. Kamruzzaman;B. Prakash;A. Vullikanti;B. Adhikari;Sriram V. Pemmaraju
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文献类型：
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作者：
Hankyu Jang;Andrew Fu;Jiaming Cui;M. Kamruzzaman;B. Prakash;A. Vullikanti;B. Adhikari;Sriram V. Pemmaraju
医疗保健获得性感染(HAI)(例如耐甲氧西林金黄色葡萄球菌感染)具有复杂的传播途径，不仅通过人与人之间的直接接触传播，还通过受污染的表面传播。数学流行病学之前的工作已经导致了一类模型--我们称之为负载分担模型--提供了HAI在时间接触网络上传播的离散时间、随机形式化。本文的重点是负荷分担模型的源检测问题。SEIR型模型中的源检测问题已经得到了广泛的研究，但这一前人的工作并不适用于负载分担模型。我们证明了负载分担模型的源检测问题的自然形式在计算上是困难的，甚至是难以近似的。然后，我们提出了两种更容易处理的替代配方。我们的问题的可处理性关键取决于作为源集合的函数的预期感染数量的子模块。以前用于显示子模块的技术，如“活动图”技术，不适用于负载共享模型，我们的主要技术贡献是使用更复杂的“耦合”技术来显示子模块的结果。我们通过扩展子模优化的现有算法结果，并将其与负载共享模型的期望传播启发式相结合，为我们的两个问题提出了算法，从而获得数量级的加速比。我们给出了基于来自三家不同医院的细粒度电子病历数据的时间接触网络的实验结果。我们对这些网络上的合成疫情的结果表明，我们的算法比基线高出5.97倍。此外，基于医院爆发的艰难梭状芽胞杆菌感染的案例研究表明，我们的算法识别出具有临床意义的来源。
Healthcare acquired infections (HAIs) (e.g., Methicillin-resistant Staphylococcus aureus infection) have complex transmission pathways, spreading not just via direct person-to-person contacts, but also via contaminated surfaces. Prior work in mathematical epidemiology has led to a class of models – which we call load sharing models – that provide a discrete-time, stochastic formalization of HAI-spread on temporal contact networks. The focus of this paper is the source detection problem for the load sharing model. The source detection problem has been studied extensively in SEIR type models, but this prior work does not apply to load sharing models. We show that a natural formulation of the source detection problem for the load sharing model is computationally hard, even to approximate. We then present two alternate formulations that are much more tractable. The tractability of our problems depends crucially on the submodularity of the expected number of infections as a function of the source set. Prior techniques for showing submodularity, such as the "live graph" technique are not applicable for the load sharing model and our key technical contribution is to use a more sophisticated "coupling" technique to show the submodularity result. We propose algorithms for our two problem formulations by extending existing algorithmic results from submodular optimization and combining these with an expectation propagation heuristic for the load sharing model that leads to orders-of-magnitude speedup. We present experimental results on temporal contact networks based on fine-grained EMR data from three different hospitals. Our results on synthetic outbreaks on these networks show that our algorithms outperform baselines by up to 5.97 times. Furthermore, case studies based on hospital outbreaks of Clostridioides difficile infection show that our algorithms identify clinically meaningful sources.