A combined epidemiologic and genomic approach to identify and control transmission events of hospital associated infections

项目摘要/摘要 这是为林赛·基根博士申请K01奖的申请书，他是华盛顿大学的传染病流行病学家。 犹他大学。基根博士正在确立自己在传染病动力学领域的年轻研究员的地位。 解决与公共卫生有关的传染病传播和控制问题。本K01大奖 将提供必要的支持，以实现以下发展目标：(1)基因组方面的专业知识 与流行病学有关的方法；(2)生物统计学方面的专门知识，特别是概率论、网络 模型和贝叶斯统计；(3)与病原体传播有关的医疗流行病学专业知识 在医疗保健设施(HCF)内；以及(4)加强研究领导和管理技能。至 为了实现这些目标，基根博士组建了一个指导团队，成员包括：马修·萨莫尔博士 (初级导师)，医疗保健流行病学家和医疗保健数学建模专家 传染病；马克·利普西奇博士(共同导师)，流行病学家和公认的数学领袖 传染病建模，包括医疗保健相关感染建模，迈克尔·鲁宾博士 (共同导师)，传染病内科医生和支持抗菌药的翻译科学专家 管理、感染预防和领导。基根博士还组建了一个由四名顾问组成的团队 拥有生物统计学、基因组流行病学、数学建模和医院流行病学方面的专业知识。 抗生素抗药性细菌对公众健康构成重大威胁，导致超过280万人感染 在美国，每年有3.5万人死亡。这些感染的负担集中在母婴传播中心；以及 如何控制这些病原体仍然是相当大的争论的来源。基于从 在CDC之前的研究中，基根博士的中心假设是与医疗保健相关的病原体正在传播 主要是通过病人脱落和来源不足而间接地在病人和环境表面之间 控制力。通过追求以下具体目标，基根博士将检验她的假设，并开发方法来 适用于未来的数据集(适用于K01期间的未来拟议的R01应用)。在具体目标1中，Dr。 基根将检验这一假设，即病原体之间的聚集性将得到系统发育的支持 从环境中隔离的和从患者身上分离的。在具体目标2中，她将构建和 验证将接触网络数据与病原体基因组数据相结合的模型，以概率地推断 HCF内传播事件的方向。在具体目标3中，她将检验以下假设：大多数 病人之间的传播事件是由环境表面介导的。 这项拟议的研究意义重大，因为它解决了改善感染控制的一个关键障碍： 如果不量化不同来源在病原体传播中的作用，感染控制实践就不能 有意针对关键传输步骤。提出的研究是创新的，因为一种方法 推断细菌的传播事件目前并不存在，尽管它在感染控制中至关重要。

PROJECT SUMMARY/ABSTRACT This is an application for a K01 award for Dr. Lindsay Keegan, an infectious disease epidemiologist at the University of Utah. Dr. Keegan is establishing herself as a young investigator in infectious disease dynamics addressing public health relevant questions on the spread and control of infectious diseases. This K01 award will provide the support necessary to accomplish the following goals to develop: (1) expertise in genomic methods as it relates to epidemiology; (2) expertise in biostatistics particularly probability theory, network models, and Bayesian statistics; (3) expertise in healthcare epidemiology as it relates to pathogen transmission within healthcare facilities (HCF); and (4) and strengthen research leadership and management skills. To achieve these goals, Dr. Keegan has assembled a mentoring team comprised of: Dr. Matthew Samore (primary mentor), a healthcare epidemiologist and expert in mathematical modeling of healthcare associated infections; and Dr. Marc Lipsitch (co-mentor), an epidemiologist and a recognized leader in mathematical modeling of infectious diseases including modeling healthcare associated infections, and Dr. Michael Rubin (co-mentor), and infectious disease physician and an expert on translational science to support antimicrobial stewardship and infection prevention and leadership. Dr. Keegan has also assembled a team of four advisors with expertise in biostatistics, genomic epidemiology, mathematical modeling, and hospital epidemiology. Antibiotic resistant bacteria pose a significant public health threat, causing over 2.8 million infections and over 35,000 deaths each year in the United States. The burden of these infections is concentrated within HCFs; and how to control these pathogens remains the source of considerable debate. Based on data collected from a prior CDC study, Dr. Keegan’s central hypothesis is that healthcare associated pathogens are spreading primarily indirectly between patients and environmental surfaces via patient shedding and inadequate source control. By pursuing the following Specific Aims, Dr. Keegan will test her hypotheses and develop methods to apply to future data sets (for a future proposed R01 application during the K01 period). In Specific Aim 1, Dr. Keegan will test the hypothesis that there will be phylogenetic support for clustering between pathogens isolated from the environment and those isolated from patients. In Specific Aim 2 she will construct and validate a model that integrates contact network data with pathogen genomic data to probabilistically infer the direction of transmission events within a HCF. In Specific Aim 3, she will test the hypothesis that the majority of patient-to-patient transmission events are mediated by environmental surfaces. The proposed research is significant because it addresses a critical barrier to improving infection control: without quantifying the role of different sources for pathogen transmission, infection control practices cannot intentionally target the key transmission steps. The proposed research is innovative because a method to infer transmission events for bacteria does not currently exist despite its critical importance in infection control.