Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, United States.
Levich Institute and Physics Department, City College of New York, New York, United States.
Elife. 2018 Dec 18;7:e40977. doi: 10.7554/eLife.40977.
Methicillin-resistant (MRSA) is a continued threat to human health in both community and healthcare settings. In hospitals, control efforts would benefit from accurate estimation of asymptomatic colonization and infection importation rates from the community. However, developing such estimates remains challenging due to limited observation of colonization and complicated transmission dynamics within hospitals and the community. Here, we develop an inference framework that can estimate these key quantities by combining statistical filtering techniques, an agent-based model, and real-world patient-to-patient contact networks, and use this framework to infer nosocomial transmission and infection importation over an outbreak spanning 6 years in 66 Swedish hospitals. In particular, we identify a small number of patients with disproportionately high risk of colonization. In retrospective control experiments, interventions targeted to these individuals yield a substantial improvement over heuristic strategies informed by number of contacts, length of stay and contact tracing.
耐甲氧西林金黄色葡萄球菌（MRSA）是社区和医疗机构中对人类健康的持续威胁。在医院中，控制措施将受益于对无症状定植和社区感染输入率的准确估计。然而，由于对定植的观察有限，以及医院和社区内复杂的传播动态，因此开发此类估计仍然具有挑战性。在这里，我们通过结合统计过滤技术、基于代理的模型和真实的患者-患者接触网络，开发了一种推断框架，可以通过该框架推断在 66 家瑞典医院中持续 6 年的暴发中，医院内传播和感染输入的情况。特别是，我们确定了一小部分患者具有不成比例的高定植风险。在回顾性对照实验中，针对这些个体的干预措施比基于接触次数、住院时间和接触追踪的启发式策略有了显著的改善。
