Integrative modeling of study design and transmission dynamics to infer epidemic drivers and inform decision-making: Applications to HIV and other emerging pathogens

项目摘要/摘要 候选人。这笔拟议的为期5年的NIAID K01赠款将用于支持研究和职业发展 史蒂文·贝兰博士，计算生物学和生物信息学中心(CCBB)博士后研究员 在德克萨斯大学奥斯汀分校(UT)。贝兰博士的长期职业目标是成为使用 识别和解决传染病问题的高性能计算。贝兰博士的研究有助于制定 并通过创新模拟解释研究设计驱动的偏差来解释流行病学研究 将实证研究明确建模为叠加在传输上的观察过程的方法 流程。他在流行病学、统计学、数学和疾病生态学方面的背景使贝兰博士 唯一有资格对传染病流行病学做出重大贡献的机构 传播模型和流行病学研究设计。他的研究已经带来了对艾滋病毒的关键见解 并帮助疾控中心计划了最近的埃博拉疫苗试验。贝兰博士的短期目标是 奖项是与新的导师和合作者建立关系，出版科学手稿以促进 他已经很好的出版记录，并在四个新领域开展培训：(1)在 计算统计学；(2)临床试验伦理；(3)决策支持工具开发；(4)艾滋病毒政策。他 将通过指导自学、德克萨斯大学的课程、哈佛大学和乔治敦大学的在线课程、 以及在华盛顿大学举办的暑期研讨会。贝兰博士的发展成为一名成功的 独立调查员将由一个具有传播专业知识的不同指导委员会指导 建模、研究设计、计算统计、艾滋病毒流行病学和政策、决策支持工具 发展和生物伦理学：劳伦·迈耶斯博士(UT)、迈克·丹尼尔斯(Mike Daniels)(UT)、布莱恩·威廉姆斯(Brian Williams)(StellenBosch)和 Rieke van der Graaf(乌得勒支医疗中心)。通过这次培训，贝兰博士还将提高他的培训能力 其他人，特别是通过他在传染病定量方法方面的角色教学研讨会 自2009年以来，向非洲和美国的学生、研究人员和公共卫生专业人员提供流行病学培训。 环境。得克萨斯大学是向贝兰博士颁发职业指导奖的绝佳环境，因为它 强调整合生物学、流行病学和统计学研究以了解传染病， 其CCBB和传染病中心就是明证，它们促进了研究人员之间的合作 来自不同的部门。该部门。德克萨斯大学初创的戴尔医学院的人口健康部门提供 贝兰博士有独特的机会与临床研究人员在一项 生物医学研究中心。最后，UT的计算资源是世界级的；Bellan博士将利用UT的 得克萨斯高级计算中心，世界上最强大的计算资源之一，为 过去十年在传染病流行病学关键问题上的计算进展。这个独一无二的 环境将有助于确保贝兰博士发展成为一名成功的独立调查员。 研究。这项拟议研究的目标是阐明艾滋病毒流行变异的驱动因素。 并建立一个决策支持工具，以评估统计和伦理方面的优点 在新出现的流行病期间疫苗功效试验设计。这项工作将为艾滋病毒控制政策和 为未来爆发西非埃博拉等新病原体时的疫苗研究做好准备 流行病。虽然跨越了不同的问题，但这些目标通过创新地整合经典的 不同领域：数学建模和流行病学研究设计。目标1：艾滋病毒的传播率 几乎只在感染和未感染之间建立稳定伙伴关系的队列中进行测量 伴侣(血清不一致的夫妇)。然而，有很高传播倾向的夫妇表现出血清不一致 转瞬即逝，减少了他们在这类研究中的代表性，并下调了对艾滋病毒的估计 传输率。为了表征HIV传播的异质性，调整其在偏向传播中的作用 估计比率，并评估其对艾滋病毒控制策略的影响，这项研究将适合夫妇传播 来自乌干达拉凯的长达20年的人口队列数据集的模型，该数据集叠加了 关于传播、夫妻形成和解体、失访和死亡率的队列研究设计 流程。目标2：撒哈拉以南国家和国家以下各级的艾滋病毒流行严重程度差别很大 非洲。对这种变异背后的生物和行为驱动因素的相对作用的了解有限 妨碍制定成功的、因地制宜的控制战略。这项工作将使用观察到的 来自25个非洲国家和地区的人口和健康调查的夫妇血清状况分布 反事实模拟，以系统地划分传输速率提高与 风险更高的性交行为会导致最严重的流行病，并为当地量身定做的控制提供信息 战略。目标3：关于不同试验设计的伦理和统计学优点的辩论促成了 推迟启动埃博拉疫苗试验，直到疫情大幅下降之后。要做更多准备 对未来急性新出现病原体流行的快速决策能力，这项工作将开发一种 基于模拟的决策支持工具，明确了不同试验之间的伦理和统计权衡 设计并促进临床医生、流行病学家、模型师和生物伦理学家之间的跨学科对话。

PROJECT SUMMARY/ABSTRACT CANDIDATE. This proposed 5-year NIAID K01 grant will support the research and career development of Dr. Steven Bellan, a Postdoctoral Fellow in the Center for Computational Biology and Bioinformatics (CCBB) at The University of Texas at Austin (UT). Dr. Bellan's long-term career goal is to become a leader in the use of high performance computing to identify and solve infectious disease problems. Dr. Bellan's research helps plan and interpret epidemiological studies by accounting for study design-driven biases with innovative simulation methods that explicitly model empirical studies as observation processes superimposed over transmission processes. His background in epidemiology, statistics, mathematics, and disease ecology make Dr. Bellan uniquely qualified to contribute significantly to infectious disease epidemiology at the nexus between transmission modeling and epidemiological study design. His research has already led to key insights into HIV epidemiology and helped the CDC plan their recent Ebola vaccine trial. Dr. Bellan's short-term goals during the award are to build relationships with new mentors and collaborators, to publish scientific manuscripts to boost his already strong publication record, and to develop training in four new areas: (1) cutting-edge methods in computational statistics; (2) clinical trial ethics; (3) decision-support tool development; and (4) HIV policy. He will gain this training through guided self-study, courses at UT, online courses from Harvard and Georgetown, and a summer workshop at the University of Washington. Dr. Bellan's development into a successful independent investigator will be guided by a diverse mentorship committee with expertise in transmission modeling, study design, computational statistics, HIV epidemiology and policy, decision-support tool development, and bioethics: Drs. Lauren Meyers (UT), Mike Daniels (UT), Brian Williams (Stellenbosch), and Rieke van der Graaf (Utrecht Medical Center). With this training, Dr. Bellan will also advance his ability to train others, in particular, through his role teaching workshops on quantitative methods in infectious disease epidemiology to students, researchers, and public health professionals in Africa and the US since 2009. ENVIRONMENT. UT is an excellent setting for a mentored career award to Dr. Bellan because of its emphasis on integrating biological, epidemiological, and statistical research to understand infectious diseases, as evidenced by its CCBB and Center for Infectious Diseases, which foster collaboration between researchers from diverse departments. The Dept. of Population Health at UT's incipient Dell Medical School provides a unique opportunity for Dr. Bellan to forge collaborations with clinical researchers during the nascent stage of a biomedical research hub. Finally, UT's computational resources are world class; Dr. Bellan will leverage UT's Texas Advanced Computing Center, one of the most powerful computing resources in the world, to bring the computational advances of the last decade to key questions in infectious disease epidemiology. This unique environment will help ensure that Dr. Bellan develops into a successful independent investigator. RESEARCH. The goals of the proposed research are to illuminate the drivers of HIV epidemic variation across sub-Saharan Africa and to build a decision-support tool for evaluating the statistical and ethical merits of vaccine efficacy trial designs during emerging epidemics. This work will inform HIV control policies and prepare for vaccine research during future outbreaks of emerging pathogens like the West African Ebola epidemic. While spanning diverse questions, these goals are united by their innovative integration of classically distinct fields: mathematical modeling and epidemiological study design. Aim 1: The HIV transmission rate has been measured almost exclusively in cohorts that follow stable partnerships between infected and uninfected partners (serodiscordant couples). Yet, couples with a high propensity to transmit exhibit serodiscordance fleetingly, reducing their representation in such studies and downwards-biasing estimates of the HIV transmission rate. To characterize heterogeneity in HIV transmission, adjust for its role in biasing transmission rate estimates, and assess its impact on HIV control strategies, this research will fit a couples transmission model to a 20-year long population cohort data set from Rakai, Uganda that superimposes a model of the cohort's study design over transmission, couple formation and dissolution, loss-to-follow up, and mortality processes. Aim 2: HIV epidemic severity varies widely at both national and subnational levels in sub-Saharan Africa. Limited understanding of the relative role of biological and behavioral drivers underlying this variation hampers the development of successful and locally tailored control strategies. This work will use observed couple serostatus distributions from Demographic and Health Surveys in 25 African countries and counterfactual simulations to systematically partition out the extent to which elevated transmission rates vs. riskier sexual mixing behaviors drive the most severe epidemics and to inform locally-tailored control strategies. Aim 3: Debates on the ethical and statistical merits of diverse trial designs contributed to the delayed initiation of Ebola vaccine trials until after the epidemic had substantially declined. To prepare more rapid decision-making capabilities for future epidemics of acute emerging pathogens, this work will develop a simulation-based decision-support tool that crystallizes ethical and statistical tradeoffs between diverse trial designs and facilitates interdisciplinary dialogue between clinicians, epidemiologists, modelers and bioethicists.