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

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。犹他州大学将与哈佛公共卫生学院一起作为中心 作为一个主要节点。我们提出了一个研究计划，将使用模型和数据， 为预防和控制与卫生保健有关的疾病的证据作出重大贡献 由于抗药性病原体引起的感染。在CDC的2019年抗生素耐药性威胁报告中， 估计耐药感染的负担，包括艰难梭菌，超过300万例， 一年，约48，000人死亡。CDC威胁报告中列出的两个紧急威胁- 碳青霉烯类耐药肠杆菌科和碳青霉烯类耐药不动杆菌-和四个严重的 威胁-产超广谱β-内酰胺酶肠杆菌科，耐甲氧西林 金黄色葡萄球菌、万古霉素耐药肠球菌和多重耐药铜绿假单胞菌 - 我们的提案中直接提到了这一点。 我们的建议包括两个紧密交织和互补的项目，以解决关键的争议， 感染控制和抗生素管理，强调动态模型在医疗保健中的作用 流行病学项目1使用横向和纵向感染控制策略之间的区别， 一个系统评估预防传播干预措施的框架，从医院一级到 房间到区域水平，有多个连接的设施。项目1有力地利用了模型、数据和 CDC资助的“颗粒建模-模拟医疗保健的传播- 医院相关感染和控制策略”项目。对于目标1.1，我们将使用基于代理的 模型，其中有详细的代表性的传输组件，以模拟试验的水平 干预措施。对于目标1.2，我们将使用数学理论和数据分析的混合来评估该设施- 垂直控制策略在一系列监视和集群场景中的水平影响。目标1.3 我们将使用多设施框架来模拟水平和垂直组合的影响， 对多种病原体进行干预，并进行成本效益分析， 人口水平的影响，由于传输减少。项目2利用了巨大的数据资源 与VA卫生系统的电子健康记录相关联，以检查抗生素选择的效果 对一系列高优先级病原体的抗生素耐药性造成压力。我们将测试假设 来自进化模型的影响，共同选择和时间关系， 抗生素使用的变化和抗生素耐药性之间的关系。我们还将应用多变量时间序列 方法和经验动态建模来预测和解释抗性趋势。我们的目标是改善 了解为什么某些形式的细菌耐药性正在减少，其他的正在增加，而其他的正在减少。 相对平衡。

Our proposal assembles an exceptional group of researchers to join the Modeling Infectious Diseases in Healthcare (MInD) network. The University of Utah will serve as the hub, with Harvard School of Public Health operating as a major node. We propose a program of research that will use models and data to make significant contributions to the body of evidence that informs prevention and control of healthcare-associated infections due to antibiotic-resistant pathogens. In the CDC’s 2019 Antibiotic Resistance Threat Report, it was estimated that the burden of resistant infections, including Clostridioides difficile, exceeded 3 million cases per year, with approximately 48,000 deaths. Two of the urgent threats listed in CDC’s Threat Report – carbapenem-resistant Enterobacteriaceae and carbapenem resistant Acinetobacter – and four of the serious threats – extended spectrum beta lactamase-producing Enterobacteriaceae, methicillin-resistant Staphylococcus aureus, vancomycin resistant enterococci, and multi-drug resistant Pseudomonas aeruginosa – are directly addressed in our proposal. Our proposal includes two tightly interwoven and complementary projects that tackle key controversies in infection control and antibiotic stewardship, highlighting the role that dynamic models can play in healthcare epidemiology. Project 1 uses the distinction between horizontal and vertical infection control strategies as a framework to systematically evaluate interventions to prevent transmission, ranging from the level of a hospital room to the region level, with multiple connected facilities. Project 1 powerfully leverages the model, data, and results generated by the CDC-funded "Granular Modeling – Simulating the Transmission of Healthcare- Associated Infections in Hospitals and Control Strategies" project. For Aim 1.1, we will use our agent based model, which has detailed representation of transmission components, to simulate trials of horizontal interventions. For Aim 1.2 we will use a blend of mathematical theory and data analysis to assess the facility- level impact of vertical control strategies across a range of surveillance and cohorting scenarios. For Aim 1.3 we will use a multi-facility framework to model the effects of combinations of horizontal and vertical interventions across multiple pathogens and perform cost-effectiveness analyses that account for indirect population-level effects due to transmission reduction. Project 2 leverages the immense data resources associated with the VA health system’s electronic health records to examine the effect of antibiotic selection pressure on antibiotic resistance profiles for a broad set of high priority pathogens. We will test hypotheses derived from evolutionary models about the impact of co-selection and about the temporal relationships between changes in antibiotic use and antibiotic resistance. We will also apply multivariate time series methods and empirical dynamic modeling to forecast and explain trends in resistance. Our goal is to improve understanding of why some forms of bacterial resistance are decreasing, others are increasing, and others are at relative equilibrium.