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出现的病原体中的应用

基本信息

批准号：
9485904
负责人：
Steven E Bellan
金额：
$ 13.23万
依托单位：
UNIVERSITY OF GEORGIA
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-09-01 至 2019-09-30
项目状态：
已结题

项目摘要

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.

项目摘要/摘要候选人。提出的5年NIAID K01赠款将支持研究和职业发展史蒂文·贝伦（Steven Bellan）博士，史蒂文·贝伦（Steven Bellan），计算生物学与生物信息学中心（CCBB）的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的领导者高性能计算以识别和解决传染病问题。贝伦博士的研究帮助计划并通过考虑研究设计驱动的偏见来解释流行病学研究明确将经验研究模拟为观察过程叠加到传播的观察过程的方法过程。他在流行病学，统计，数学和疾病生态学方面的背景使贝兰博士独特的资格有资格为传染病流行病学做出显着贡献传输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CDC计划了他们最近的埃博拉疫苗试验。贝兰博士在奖项旨在与新的导师和合作者建立关系，以发表科学手稿以促进他已经有很强的出版记录，并在四个新领域进行培训：（1）计算统计；（2）临床试验伦理；（3）决策支持工具开发；（4）艾滋病毒政策。他将通过指导的自学，UT的指导课程，哈佛大学和乔治敦的在线课程获得这项培训以及华盛顿大学的夏季研讨会。贝伦博士的发展成一个成功独立调查员将受到多元化指导委员会的指导，并具有传输方面的专业知识建模，研究设计，计算统计，艾滋病毒流行病学和政策，决策支持工具开发和生物伦理学：博士。 Lauren Meyers（UT），Mike Daniels（UT），Brian Williams（Stellenbosch）和 Rieke van der Graaf（乌得勒支医疗中心）。通过这项培训，贝兰博士还将提高他的培训能力尤其是其他人，通过他的角色教授有关传染病定量方法的讲习班自2009年以来，非洲和美国的学生，研究人员和公共卫生专业人员的流行病学。环境。 UT是Bellan博士获得指导职业奖的绝佳环境强调整合生物学，流行病学和统计研究以了解传染病，正如其CCBB和传染病中心所证明的那样，这促进了研究人员之间的合作来自不同部门。 UT初期戴尔医学院的人口健康部提供了 Bellan博士在一个新生阶段与临床研究人员合作的独特机会生物医学研究中心。最后，UT的计算资源是世界一流。贝伦博士将利用UT的得克萨斯州高级计算中心是世界上最强大的计算资源之一，以带来在传染病流行病学中，过去十年的计算进步。这个独特环境将有助于确保Bellan博士成为成功的独立研究者。研究。拟议研究的目标是阐明艾滋病毒流行病的驱动因素整个撒哈拉以南非洲，建立一个决策支持工具，用于评估统计和道德优点在新兴流行期间的疫苗功效试验设计。这项工作将为艾滋病毒控制政策提供信息，并在未来的新兴病原体爆发中为疫苗研究做准备，例如西非埃博拉病原体流行性。在跨越各种问题的同时，这些目标是通过经典的创新整合而团结的不同的领域：数学建模和流行病学研究设计。 AIM 1：HIV传播率具有几乎完全在遵循感染与未感染之间稳定合作伙伴关系的队列中衡量合作伙伴（夫妻夫妇）。然而，发射高倾向的夫妻表现出详细信息短暂地，减少了他们在此类研究中的代表性和艾滋病毒的下降偏见估计传输速率。要表征HIV传播中异质性，请调整其在偏置传播中的作用费率估算并评估其对艾滋病毒控制策略的影响，这项研究将适合夫妻传播从乌干达拉凯（Rakai 队列的研究设计有关传播，夫妇形成和溶解，损失到遵循的研究设计以及死亡率过程。 AIM 2：艾滋病毒的流行严重程度在撒哈拉以南国家和次国次级水平上差异很大非洲。对这种变异的基础生物学和行为驱动因素的相对作用的了解有限阻碍了成功和本地量身定制的控制策略的发展。这项工作将使用观察到的来自25个非洲国家的人口统计和健康调查的夫妻夫妇分布反事实模拟，以系统地划分升高传输速率的程度。更风险的性混合行为推动了最严重的流行病，并为当地量身定制的控制提供了信息策略。 AIM 3：关于各种试验设计的道德和统计优点的辩论，有助于埃博拉疫苗试验的延迟开始，直到流行病大幅下降。准备更多急性新兴病原体的未来流行病的快速决策能力，这项工作将发展基于仿真的决策支持工具，可以在不同的试验之间结晶道德和统计折衷设计和促进临床医生，流行病学家，建模者和生物伦理学家之间的跨学科对话。