III: Medium: Collaborative Research: Detecting and Controlling Network-based Spread of Hospital Acquired Infections

医院获得性感染（HAI）正在成为全球卫生系统的主要挑战。HAI的检测和控制是具有挑战性的和资源密集型的，因为患者治疗和医院设施消毒的成本高，使其成为基本的公共卫生问题。尽管它对医院非常重要，临床和流行病学研究人员也很感兴趣，但这些问题仍然知之甚少。该项目旨在开发一种新的基于网络的方法，使用模型和数据科学来改善医院感染控制。该提案汇集了一个高度多学科的研究人员团队，并将导致计算机科学（数据挖掘，机器学习，图形挖掘，社交网络和优化），网络科学（数学模型和动力系统）和计算流行病学（传染病和医院流行病学）的不同领域的基本贡献。计划中的工作对公共卫生有直接影响，例如，它可以导致新的设计政策和医院感染控制指南。研究结果将被纳入研究生课程，教程，竞赛和研讨会，使计算生物学家和数据挖掘者聚集在一起。在研究HAI爆发方面存在若干挑战，主要是因为HAI传播的动力学比其他疾病（如流感）复杂得多，因为涉及更多的因素和途径。为了克服这些问题，项目团队将使用一类新的双模式级联模型，这种模型与标准模型具有非常不同的动态特性，并且尚未在数据挖掘中进行过研究。将调查以下主题：（1）监测，HAI爆发的早期检测，（2）设计干预措施以控制HAI的传播，以及（3）模拟和估计HAI的暴露风险。一组统一的问题将被考虑，包括建模，检测，控制和推理失踪的感染。这些都是网络上具有挑战性的随机优化问题，项目团队将使用数据挖掘，机器学习和组合优化工具发明严格且可扩展的方法。他们的研究将使用一家公立医院独特的细粒度大规模手术数据集，并辅以其他医院的数据。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Hospital Acquired Infections (HAIs) are becoming a major challenge in health systems worldwide. Detection and control of HAIs are challenging and resource intensive, because of the high costs of patient treatment and disinfection of hospital facilities, making them fundamental public health problems. Despite its huge importance for hospitals, and the interest from both clinical and epidemiological researchers, these problems remain poorly understood. This project seeks to develop a novel network-based approach to improve hospital infection control using models and data science. This proposal brings together a highly multi-disciplinary team of researchers, and will lead to fundamental contributions in different areas of computer science (data mining, machine learning, graph mining, social networks, and optimization), network science (mathematical models and dynamical systems) and computational epidemiology (infectious diseases, and hospital epidemiology). The planned work has immediate implications for public health e.g. it can lead to new design policies and guidance for hospital infection control. Research findings will be incorporated into graduate level classes, tutorials, contests and workshops to bring computational biologists and data miners together. There are several challenges in studying HAI outbreaks primarily because the dynamics of HAI spread are much more complex than other diseases, such as influenza, due to many more factors and pathways involved. To overcome these issues, the project team will use a new class of two-mode cascade models, which have very different dynamics than the standard models, and have not been studied in data mining. The will investigate the following topics: (1) Surveillance, early detection of HAI outbreaks, (2) Designing interventions to control the spread of HAIs, and (3) Modeling and estimating exposure risk for HAIs. A unified set of problems will be considered, including modeling, detection, control and inference of missing infections. These are challenging stochastic optimization problems on networks, and the project team will invent rigorous and scalable methods using tools from data mining, machine learning and combinatorial optimization. Their research will use a unique fine-grained, large-scale data set of operations from a public hospital, supplemented with data from other hospitals. The results will be validated with the help of domain experts including epidemiologists and clinicians involved in hospital infection control.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.