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

在监测和诊断实验室产生的常规收集的数据和医疗记录包含 对了解抗菌素耐药病原体传播有价值的信息。此信息 可以被利用来为传播模型提供信息，并设计和评估缓解策略。然而， 目前的建模方法通常集中于一次处理病原菌上的一种抗性，这导致 耐药病原菌动态的关键特征，如多药耐药性和病原菌相互作用不是 通常是指。为了充分利用通过监测和诊断产生的数据 活动，我们计划开展以下研究活动：1)开发图形模型以 集成多个数据流(表型、遗传型抗性和元数据)以支持分析和 显示复杂的阻力模式和阻力的联合分布，2)我们将应用和评估 在国家级监测系统中收集的数据的分析管道，3)我们将开发和评估 包含多药耐药性的卫生保健环境中病原体传播的基于试剂的模型 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)我们将通过创建开放源码包和网站来传播这些工具 实施。有了开发的工具，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，确定可能导致进一步选择首选药物的药物， 确定耐药性风险因素群，评估疫苗接种、抗生素管理和异质性 存在病原体和抗药性交互作用的干预措施。 处理多个数据流和复杂模型的挑战并不是传染性的独有 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，特别是涉及收集混合数据和同时进行基因分型和 表型数据。同样，基于代理的模型越来越多地应用于所有生物医学学科，从 分子生物学对流行病学的影响，因此它们在分析方面的进步可以有更广泛的积极意义 对多个生物医学学科的影响。

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.