Analytical pipelines for data and model integration: finding informed pathways for antimicrobial resistance control

数据和模型集成的分析管道：寻找抗菌药物耐药性控制的知情途径

• 批准号: 10546441
• 负责人: Cristina Lanzas
• 金额: $ 42.8万
• 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: 2020-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10546441
• 关键词: Address; Antibiotics; Antimicrobial Resistance; Biomedical Research; Clostridium difficile; Collection; Complex; Computer Models; Data; Diagnostic; Discipline; Effectiveness of Interventions; Epidemiology; Genotype; Heterogeneity; Infection; Infectious Diseases Research; Intervention; Joints; Laboratories; Medical Records; Metadata; Methods; Modeling; Molecular Biology; Multi-Drug Resistance; Pathway interactions; Pattern; Pharmaceutical Preparations; Phenotype; Public Health; Research Activity; Resistance; Risk Factors; Source; System; Time; Vaccination; Visualization; antimicrobial resistant pathogen; data pipeline; data streams; design; health care settings; open source; pathogen; phenotypic data; surveillance data; tool; transmission process; web site

Routinely-collected data generated in surveillance and diagnostic laboratories and medical records contain information valuable for understanding the transmission of antimicrobial resistant pathogens. This information can be harnessed to inform transmission models and to design and evaluate mitigation strategies. However, current modeling approaches often focus on addressing one resistance on a pathogen at a time which leads to key features of resistant pathogen dynamics such as multidrug resistance and pathogen interactions to not be commonly addressed. In order to capitalize on the data being generated via surveillance and diagnostic activity, we propose to carry out the following research activities: 1) we will develop graphical models to integrate multiple data streams (phenotypic, genotypic resistances and metadata) to support analysis and visualization of complex resistant patterns and joint distribution of resistances, 2) we will apply and evaluate the analytical pipeline to data collected in national-level surveillance systems, 3) we will develop and evaluate agent-based models for pathogen transmission in health-care settings that incorporate multidrug resistance features and pathogen interactions and apply graphical modeling approaches to analyze and validate the models, and 4) we will disseminate the tools by creating open source packages and through website implementation. With the developed tools we will provide a path to quantify changes on complex resistance patterns over time or across sources, identify drugs that can lead to further selection of first choice drugs, identify cluster of risk factor for resistance and evaluate vaccination, antibiotic stewardship and heterogeneity interventions in the presence of pathogen and resistance interactions. The challenges of dealing with multiple streams of data and complex models are not unique to infectious disease research. The developed workflows will be applicable to a broad spectrum of biomedical research questions, particularly those that involve the collection of mixed data and simultaneous genotypic and phenotypic data. Similarly, agent-based models are increasingly used across all biomedical disciplines, from molecular biology to epidemiology, and therefore advances in their analyses can have a broader positive impact in multiple biomedical disciplines.

在监测和诊断实验室以及医疗记录中产生的自愿收集的数据包括 这些信息对了解耐药性病原体的传播有价值。这些信息 可以用来为传输模型提供信息，并设计和评估缓解策略。然而，在这方面， 当前的建模方法通常集中于一次解决病原体上的一种抗性，这导致 耐药病原体动力学的关键特征，如多药耐药性和病原体相互作用， 一般处理。为了利用通过监测和诊断产生的数据， 活动，我们建议开展以下研究活动：1）我们将开发图形模型， 整合多个数据流（表型、基因型抗性和元数据）以支持分析， 可视化复杂的阻力模式和阻力的联合分布，2）我们将应用和评估 国家一级监测系统收集的数据的分析管道，3）我们将开发和评估 在卫生保健环境中采用基于病原体的模型，其中包括多药耐药性 特征和病原体相互作用，并应用图形建模方法来分析和验证 模型，以及4）我们将通过创建开源软件包和通过网站传播这些工具 实施.通过开发的工具，我们将提供一条量化复杂阻力变化的途径 随着时间的推移或跨来源的模式，确定药物，可以导致进一步选择首选药物， 确定耐药风险因素集群，并评估疫苗接种、抗生素管理和异质性 在存在病原体和抗性相互作用的情况下进行干预。 处理多个数据流和复杂模型的挑战并不是传染病所独有的 疾病研究。开发的工作流程将适用于广泛的生物医学研究 问题，特别是那些涉及收集混合数据和同时基因型和 表型数据同样，基于代理的模型越来越多地用于所有生物医学学科，从 从分子生物学到流行病学，因此他们的分析进展可以有更广泛的积极意义。 影响多个生物医学学科。