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

III：媒介：合作研究：检测和控制医院获得性感染的网络传播

基本信息

批准号：
1955797
负责人：
Anil Kumar Vullikanti
金额：
$ 39.2万
依托单位：
University of Virginia Main Campus
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-06-15 至 2024-05-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1955797&HistoricalAwards=false
关键词：
III Medium Collaborative Research Detecting

项目摘要

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.

医院获得性感染 (HAI) 正在成为全球卫生系统面临的重大挑战。由于患者治疗和医院设施消毒的成本高昂，医院感染的检测和控制具有挑战性且需要大量资源，使其成为根本性的公共卫生问题。尽管它对医院非常重要，并且临床和流行病学研究人员都对此很感兴趣，但人们对这些问题仍然知之甚少。该项目旨在开发一种基于网络的新型方法，利用模型和数据科学来改善医院感染控制。该提案汇集了高度多学科的研究人员团队，并将在计算机科学（数据挖掘、机器学习、图挖掘、社交网络和优化）、网络科学（数学模型和动力系统）的不同领域做出基础性贡献）和计算流行病学（传染病和医院流行病学）。计划中的工作对公共卫生具有直接影响，例如它可以为医院感染控制制定新的设计政策和指南。研究成果将纳入研究生课程、教程、竞赛和研讨会，将计算生物学家和数据挖掘者聚集在一起。研究 HAI 爆发存在一些挑战，主要是因为 HAI 传播的动态比流感等其他疾病复杂得多，因为涉及的因素和途径更多。为了克服这些问题，项目团队将使用一类新型的两模式级联模型，该模型的动力学与标准模型有很大不同，并且尚未在数据挖掘中进行研究。他们将研究以下主题：(1) 监测、早期发现 HAI 爆发，(2) 设计干预措施以控制 HAI 的传播，以及 (3) 建模和估计 HAI 的暴露风险。将考虑一组统一的问题，包括缺失感染的建模、检测、控制和推断。这些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等工具发明严格且可扩展的方法。他们的研究将使用公立医院独特的细粒度、大规模的手术数据集，并辅以其他医院的数据。结果将在包括流行病学家和参与医院感染控制的临床医生在内的领域专家的帮助下进行验证。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。