Multi-scale modeling and phylodynamics for healthcare associated infections

医疗保健相关感染(HAI)是可预防的发病率和死亡率的重要来源。 HAI传播模型是了解病原体传播和评估的基石方法 控制干预措施。模型在解决传播阻断干预方面特别有帮助， 以阐明设施之间的连通性，以及它们对控制HAI的影响。机制 潜在的抗菌素耐药性，如共同选择，在传播模型中受到的关注较少。 此外，关键指标--如耐药细菌的种群水平适合度和耐药特性的影响 在健康方面--往往是未知的。这限制了我们对抗菌剂之间复杂关系的理解 药物使用和耐药性以及旨在改变药物选择的干预措施的有效性 压力。这项提议的目标是开发更明确地解决抗性特征和 支持HAI传播源和传播途径识别的建模工具。我们将使用 进一步确定HAI来源并评估和优化干预措施的模型。特别是，我们将解决 下列主题领域：抗菌素耐药性(A)、监测(A)、基因组学(B)和模拟 流行病学研究(B)。我们已经组建了一个跨学科的研究小组，他们在 传染病建模、HAI医院流行病学和临床、应用数学和基因组学 位于北卡罗来纳州立大学、华盛顿大学(吴)和田纳西大学。我们计划 在本团队以前和当前合作的基础上：为以下项目开发建模方法 解决HAI传播问题；扩展系统动力学方法；以及建立抗菌素耐药性动态模型。 密苏里州圣路易斯的吴和巴恩斯犹太医院的疾控中心-埃皮中心将是主要的数据来源。 此外，我们将使用国家级公开可用的数据源。我们将实现以下目标：1) 开发改进的方法来推断HAI的获取途径，并优化HAI监测和 控制：我们将开发考虑HAI主要途径的病房和医院级网络模型 获取和患者连接。我们将应用最优化方法来识别环境采样 协议和具有成本效益的控制策略。2)系统动力学评价抗菌药耐药性适合度 病原体：我们将应用和提炼多种类型的生死模型，以探索大 病原体系统发育上的抗菌素耐药特征的数量，并加快量化方法以适应 大量菌株，以及3)多药耐药生物的多尺度模型：扩展谱 产β-内酰胺酶(ESBL)肠杆菌科的案例研究：我们将开发试剂和 基于方程的模型，解释了阻力传播的多尺度动力学。这将极大地 扩展模型在评估干预措施方面的应用，如抗菌药物管理和快速 测试。我们的模型和工具将向更广泛的社区提供。

Healthcare-associated infections (HAI) are a significant source of preventable morbidity and mortality. Transmission models for HAI are a cornerstone method to both understand pathogen spread and evaluate control interventions. Models have been particularly helpful in addressing transmission-blocking interventions, for elucidating the connectivity among facilities, and their implications for controlling HAI. Mechanisms underlying antimicrobial resistance, such as co-selection, have received less attention in transmission models. In addition, key metrics—such as population-level fitness of resistant bacteria and the effect of resistant traits on fitness—are often unknown. This limits our understanding of the complex relationship between antimicrobial drug use and resistance, as well as the effectiveness of interventions aimed at changing drug selection pressure. The objective of this proposal is to develop models that more explicitly address resistance traits and modeling tools that support the identification of transmission sources and pathways for HAI. We will use the models to further identify HAI sources and evaluate and optimize interventions. In particular, we will address the following thematic areas: antimicrobial resistance (A), surveillance (A), genomics (B), and simulation of epidemiological studies (B). We have assembled an interdisciplinary group of researchers with expertise in infectious disease modeling, HAI hospital epidemiology and clinics, applied mathematics, and genomics located at North Carolina State University, Washington University (WU) and University of Tennessee. We plan to build on our previous and current collaborations among this team to: develop modeling approaches for addressing HAI transmission; extend phylodynamics methods; and model antimicrobial resistance dynamics. The CDC-Epi Center at WU and Barnes-Jewish Hospital in St. Louis, Missouri, will be the main source of data. Additionally, we will use nation-level publicly available data sources. We will carry out the following aims: 1) Develop improved approaches for inferring routes of acquisition of HAI and optimizing HAI surveillance and control: We will develop ward- and hospital- level network models that take into account the main routes of HAI acquisition and patient connectivity. We will apply optimization methods to identify environmental sampling protocols and cost-effective control strategies. 2) Phylodynamics to estimate fitness of antimicrobial resistance pathogens: We will apply and refine multi-type birth-death models to explore the fitness effects of a large number of antimicrobial-resistant traits on pathogen phylogenies, and speed the methods to quantify fitness for large numbers of strains, and 3) Multi-scale models for multidrug-resistant organisms: extended-spectrum beta-lactamase (ESBL)- producing Enterobacteriaceae as case study: We will develop both agent- and equation-based models that account for multi-scale dynamics of resistance transmission. This will greatly expand the models’ applications for evaluating interventions such as antimicrobial stewardship and rapid testing. Our models and tools will be made available to the broader community.