Modeling and Simulation to Support Antibiotic Stewardship and Epidemiological Decision-Making in Healthcare Settings

项目摘要 我们的提议召集了一批特殊的研究人员加入传染病建模， 医疗保健（MIND）网络。犹他州大学是我们项目的中心，主要节点在哈佛 公共卫生学院和牛津大学。我们的建议包括两个项目：第一个使用模型， 数据，以检查抗生素选择的耐药生物体，导致HAI，包括C。艰难，和 第二，创建使用本地数据的建模工具，以改善感染控制干预措施的实施。 我们的提案涵盖的主题领域包括抗生素耐药性、患者的连通性、监测， 经济建模、基因组学和流行病学研究的模拟。我们的项目高度 在数据和方法方面相互联系，利用我们研究团队的广泛专业知识， 全面的数据资源的可用性，以支持在医疗流行病学中使用模型。的 第一个项目旨在促进对抗生素耐药性驱动因素的科学理解， 加强模型的实际应用，以指导抗生素管理政策。我们将测试关于 哪种选择机制对给定的生物种类和抗性类型最有影响。我们将 根据旁观者选择的程度对抗生素治疗和微生物进行分类， 微生物接触抗生素。不同类别抗生素选择的影响 将比较广谱抗生素。这些分析的结果将支持下列参数化： 前向模拟模型，我们将使用它来评估减少抗生素使用的影响，特别是 通过缩短治疗时间。将在药物类别和微生物中检查结局。的 第二个项目将生成可应用于本地数据的共享工具，以帮助医疗保健 流行病学家和公共卫生人员就感染控制的实施作出决定 干预措施。我们为目标1所做的工作将为流行病学家提供一个统计数据包， 他们自己的携带和感染数据来估计相关的传播速率参数。这将使 确定接触预防措施和其他感染控制措施的有效性 机构。一个扩展将是增加基因组数据，以提高传播估计的准确性 树在目标2中，我们将使用“潜在预防病例指标”来评估支持监测的算法 导致医疗相关感染的病原体。这项工作的成果将是一个统计数据包 协助流行病学家决定何时有必要对可能爆发的疾病进行干预 要么开展调查，查明可能的来源，要么采取额外的控制措施。在 目标3，我们将在区域性的抗虫生物模型中纳入定制 根据当地患者流量和卫生经济数据评价替代干预措施。则输出将为 仿真和经济建模工具，以指导协调控制策略的实施。

PROJECT SUMMARY Our proposal assembles an exceptional group of researchers to join the Modeling Infectious Diseases in Healthcare (MIND) network. The University of Utah is the hub for our program, with major nodes at Harvard School of Public Health and Oxford University. Our proposal includes two projects: the first uses models and data to examine antibiotic selection for resistant organisms that cause HAI, including C. difficile, and the second creates modeling tools that use local data to improve implementation of infection control interventions. Thematic areas covered by our proposal include antibiotic resistance, connectedness of patients, surveillance, economic modeling, genomics, and simulations of epidemiologic studies. Our projects are highly interconnected with respect to data and methods, leveraging the broad expertise of our research teams and the availability of comprehensive data resources to support the use of models in healthcare epidemiology. The first project is intended to advance scientific understanding of the drivers of antibiotic resistance and to enhance the practical use of models to guide antibiotic stewardship policies. We will test hypotheses about which mechanisms of selection are most influential for a given organism class and type of resistance. We will categorize antibiotic treatments and organisms with respect to the magnitude of bystander selection, due to exposure of commensal organisms to antibiotics. The impact of antibiotic selection exerted by different classes of broad-spectrum antibiotics will be compared. The outputs of these analyses will support parameterization of forward simulation models, which we will use to evaluate the effects of reducing antibiotic use, particularly through decreasing treatment duration. Outcomes will be examined across drug class and organism. The second project will generate shareable tools that can be applied to local data to help healthcare epidemiologists and public health personnel make decisions regarding the implementation of infection control interventions. Our work for Aim 1 will give epidemiologists a statistical package to fit transmission models to their own carriage and infection data to estimate relevant transmission rate parameters. This will enable determination of the effectiveness of contact precautions and other infection control measures in their own institution. An extension will be to add genomic data to improve the accuracy of estimation of transmission trees. In Aim 2, we will use a “potentially prevented cases metric” to evaluate algorithms to support surveillance of pathogens that cause healthcare-associated infection. The product of this work will be a statistical package to assist epidemiologists in the decision about when it may be warranted to intervene on a possible outbreak either by launching an investigation to detect possible sources or by instituting additional control measures. In Aim 3, we will incorporate into regional models of antibiotic-resistant organisms the capacity to tailor the evaluation of alternative interventions to local patient flow and health economic data. The output will be a simulation and economic modeling tool to guide implementation of coordinated control strategies.