Multi-scale modeling and phylodynamics for healthcare associated infections

医疗保健相关感染的多尺度建模和系统动力学

• 批准号: 10800779
• 负责人: Cristina Lanzas
• 金额: $ 46.5万
• 依托单位: NORTH CAROLINA STATE UNIVERSITY RALEIGH
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10800779
• 关键词: Multi; scale; modeling; phylodynamics; healthcare

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.

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。模型对于解决传播阻断干预措施特别有帮助， 阐明设施之间的连通性及其对控制 HAI 的影响。机制 潜在的抗菌药物耐药性，例如共同选择，在传播模型中受到的关注较少。 此外，关键指标——例如耐药细菌的种群水平适应性和耐药性状的影响 关于健身——通常是未知的。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特征和 支持识别 HAI 传播源和路径的建模工具。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）。我们组建了一个跨学科研究小组，他们拥有以下方面的专业知识： 传染病模型、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 (WU) 和田纳西大学。我们计划 以我们之前和当前团队之间的合作为基础：开发建模方法 解决 HAI 传播问题；扩展系统动力学方法；并建立抗菌药物耐药性动态模型。 西弗吉尼亚大学的 CDC-Epi 中心和密苏里州圣路易斯的 Barnes-Jewish 医院将成为主要数据来源。 此外，我们将使用国家级公开数据源。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：我们将开发考虑医院感染主要路线的病房和医院级网络模型 采集和患者连接。我们将应用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) 系统动力学评估抗菌药物耐药性的适应性 病原体：我们将应用和完善多种类型的生死模型，探索大群体的适应度效应 病原体系统发育中抗菌素耐药性特征的数量，并加快量化适合度的方法 大量菌株，以及3）多重耐药生物体的多尺度模型：扩展谱 产β-内酰胺酶（ESBL）的肠杆菌科细菌作为案例研究：我们将开发药剂和 基于方程的模型可以解释阻力传递的多尺度动态。这将大大 扩大模型在评估干预措施方面的应用，例如抗菌药物管理和快速 测试。我们的模型和工具将提供给更广泛的社区。