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

研究设计和传播动力学的综合建模，以推断流行病驱动因素并为决策提供信息：在艾滋病毒和其他新兴病原体中的应用

• 批准号: 9164944
• 负责人: Steven E Bellan
• 金额: $ 1.62万
• 依托单位: UNIVERSITY OF TEXAS AT AUSTIN
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-06-23 至 2016-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9164944
• 关键词: Accounting; Acute; Address; Africa; Africa South of the Sahara; African; Area; Award; Behavioral; Bioethics; Bioethics Consultants; Bioinformatics; Biological; Biomedical Research; Case Fatality Rates; Centers for Disease Control and Prevention (U.S.); Cessation of life; Clinical; Clinical Research; Clinical Trials; Cohort Studies; Collaborations; Communicable Diseases; Communities; Computational Biology; Country; Couples; Data; Data Set; Decision Making; Demographic and Health Surveys; Development; Dimensions; Disease; Disease Outbreaks; Ebola Vaccines; Ebola virus; Ecology; Educational process of instructing; Educational workshop; Ensure; Environment; Epidemic; Epidemiologic Studies; Epidemiologist; Epidemiology; Equilibrium; Equipoise; Ethics; Exhibits; Familiarity; Fostering; Future; Gap Junctions; Goals; Grant; HIV; Health Professional; Heterogeneity; High Performance Computing; Human; Human immunodeficiency virus test; Individual; Infection; Infectious Disease Epidemiology; Infectious Diseases Research; Learning; Link; Manuscripts; Mathematics; Measures; Medical; Medical center; Mentors; Mentorship; Methods; Modeling; National Institute of Allergy and Infectious Disease; Policies; Population; Postdoctoral Fellow; Predisposition; Process; Public Health; Publications; Publishing; Qualifying; Relationship-Building; Research; Research Design; Research Personnel; Resolution; Role; Severities; Speed; Staging; Statistical Methods; Students; Surveys; Texas; Time; Training; Uganda; Universities; Vaccine Research; Variant; Washington; Work; abstracting; arm; austin; base; career; career development; cohort; computing resources; design; disease natural history; driving behavior; efficacy trial; follow-up; forging; global health; innovation; insight; mathematical model; medical schools; mortality; novel; online course; pathogen; population health; research and development; risk benefit ratio; simulation; statistics; support tools; tool development; transmission process; trial design; vaccine efficacy; vaccine trial

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.

项目总结/摘要 候选人这项拟议的5年NIAID K 01赠款将支持研究和职业发展 Steven Bellan博士，计算生物学和生物信息学中心（CCBB）的博士后研究员 德克萨斯大学奥斯汀分校（UT）Bellan博士的长期职业目标是成为使用 高性能计算来识别和解决传染病问题。贝兰博士的研究有助于 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 明确地将经验研究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是 传播建模和流行病学研究设计。他的研究已经导致了对艾滋病毒的关键见解 流行病学，并帮助疾病预防控制中心计划他们最近的埃博拉疫苗试验。贝兰博士的短期目标 奖项旨在与新的导师和合作者建立关系，发表科学手稿，以促进 他已经强大的出版记录，并在四个新领域开展培训：（1） 计算统计学;（2）临床试验伦理学;（3）决策支持工具开发;（4）艾滋病毒政策。他 将通过引导自学，UT课程，哈佛和乔治敦的在线课程， 以及华盛顿大学的暑期研讨会。贝兰博士发展成一个成功的 独立调查员将由一个在传播方面具有专门知识的多元化指导委员会指导 建模，研究设计，计算统计，艾滋病流行病学和政策，决策支持工具 发展和生物伦理学：Lauren Meyers（UT），Mike Daniels（UT），Brian威廉姆斯（Stellenbosch）， Rieke货车der Graaf（乌得勒支医疗中心）。通过这种训练，贝兰博士也将提高他的训练能力， 其他人，特别是通过他在传染病定量方法教学研讨会上的作用， 自2009年以来，流行病学在非洲和美国的学生，研究人员和公共卫生专业人员。 环境UT是一个很好的设置为指导职业奖博士贝兰，因为它的 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和传染病中心证明了这一点，这促进了研究人员之间的合作 来自不同部门六处人口健康在UT的初期戴尔医学院提供了一个 这是一个独特的机会，让Bellan博士在一个新的阶段与临床研究人员建立合作， 生物医学研究中心最后，UT的计算资源是世界一流的; Bellan博士将利用UT的 德州高级计算中心，世界上最强大的计算资源之一，带来了 在传染病流行病学的关键问题的计算进展的过去十年。这种独特 环境将有助于确保贝兰博士发展成为一个成功的独立调查员。 RESEARCH.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，用于评估统计和道德价值 疫苗有效性试验的设计。这项工作将为艾滋病毒控制政策提供信息， 为未来西非埃博拉等新出现的病原体爆发期间的疫苗研究做准备 疫情虽然跨越不同的问题，这些目标是统一的，他们的创新整合的经典 不同的领域：数学建模和流行病学研究设计。目标1：艾滋病毒传播率 几乎只在感染者和未感染者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，具有高传播倾向的夫妇表现出血清不一致性， 短暂地减少了他们在这些研究中的代表性，并降低了对艾滋病毒的估计 传输速率描述HIV传播的异质性，调整其在偏倚传播中的作用 率估计，并评估其对艾滋病控制策略的影响，这项研究将适合一对夫妇传播 乌干达Rakai的20年人口队列数据集的模型， 队列研究设计对传播、夫妇形成和解散、失访和死亡率的影响 流程.目标2：撒哈拉以南非洲国家和国家以下各级艾滋病毒流行病的严重程度差别很大 非洲对这种变化背后的生物和行为驱动因素的相对作用的理解有限 阻碍制定成功的、适合当地情况的控制战略。这项工作将使用观察 25个非洲国家人口和健康调查的夫妇血清状况分布， 反事实模拟系统分区的程度，提高传输速率与 风险更大的性混合行为导致最严重的流行病，并为当地量身定制的控制提供信息 战略布局目的3：关于不同试验设计的伦理和统计学优点的辩论有助于 将埃博拉疫苗试验的启动推迟到疫情大幅下降之后。准备更多 快速决策能力的急性新兴病原体的未来流行病，这项工作将制定一个 一种基于模拟的决策支持工具，可以明确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、建模者和生物伦理学家之间的跨学科对话。