Multi-scale modeling and phylodynamics for healthcare associated infections

卫生保健相关感染（HAI）是可预防的发病率和死亡率的重要来源。 HAI的传播模型是了解病原体传播和评估 控制干预。模型特别有助于解决传播阻断干预问题， 用于阐明设施之间的连接性及其对控制HAI的影响。机制 潜在的抗生素耐药性，如共同选择，在传播模型中受到的关注较少。 此外，关键指标-如耐药细菌的群体水平适合度和耐药性状的影响 关于健康的信息往往是未知的。这限制了我们对抗菌药物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 压力该提案的目标是开发更明确地解决抗性性状的模型， 支持识别HAI传播源和途径的建模工具。我们将使用 模型，以进一步识别HAI来源并评估和优化干预措施。特别是，我们将解决 以下专题领域：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）。我们组建了一个跨学科的研究小组， 传染病建模、HAI医院流行病学和临床、应用数学和基因组学 位于北卡罗来纳州州立大学、华盛顿大学（WU）和田纳西大学。我们计划 建立在我们以前和目前的合作，在这个团队：开发建模方法， 解决HAI传播问题;扩展抗生素动力学方法;建立抗生素耐药性动力学模型。 位于密苏里州圣路易斯的WU和巴恩斯犹太医院的CDC-Epi中心将是主要的数据来源。 此外，我们将使用国家一级的公开数据源。我们将实现以下目标：1） 制定改进的方法，以推断感染HAI的途径并优化HAI监测， 控制：我们将开发考虑HAI主要路径的病房级和医院级网络模型 采集和患者连接。我们将应用最优化方法来确定环境采样 协议和具有成本效益的控制策略。2)系统动力学用于评估抗菌素耐药性的适合度 病原体：我们将应用和改进多类型的出生-死亡模型，以探索大型 病原体致病性的抗微生物药物抗性性状的数量，并加快方法来量化适合 大量菌株，3）多重耐药微生物的多尺度模型：广谱 产β-内酰胺酶（ESBL）的肠杆菌科作为案例研究：我们将开发两种药物， 方程为基础的模型，占多尺度动态阻力传输。这将大大 扩大模型的应用，以评估干预措施，如抗菌药物管理和快速 试验.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.