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Statistical and Quantitative Training in Big Data Health Science

大数据健康科学统计与定量培训

基本信息

批准号：
9901569
负责人：
John Quackenbush
金额：
$ 29.28万
依托单位：
HARVARD SCHOOL OF PUBLIC HEALTH
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：
2016-04-01 至 2021-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9901569
关键词：
Big Data Health Sciences Training

项目摘要

DESCRIPTION (provided by applicant): Unprecedented advances in digital technology during the second half of the 20th century have produced a revolution that is transforming science, including health and biomedical research, by providing data of unprecedented complexity in volumes and at a rate that was previously unimaginable. Members of National Research Council's (NRC's) Committee on Massive Data Analysis concluded in their 2013 "Frontiers of Massive Data Analysis" report that the challenges associated with "Big Data" go far beyond the technical aspects of data management and emphasized that development of rigorous quantitative and statistical methods was crucial if we are to use these data to their advantage. In this application we describe an integrated program designed to provide students with training in the quantitative and computational skills and communication and interdisciplinary research skills-and their application-required for those students to become the next generation of leading Big Data scientists in health and biomedical research. At the Harvard TH Chan School of Public Health, we have made a substantial investment is addressing these challenges, including launching a new formal Master's Degree program in Computational Biology and Quantitative Genomics, revamping the curriculum in Biostatistics to include a greater emphasis on computational methods and Big Data, a proposal undergoing internal review to include computation as an area of core competency for our students, and the inclusion of Big Data analytics as a central focus of the School's ongoing capital campaign. We are requesting support for six pre-doctoral students who will emerge from the program with expertise in cutting-edge statistical and computational methods development, a thorough understanding of fundamental basic science, public health, and clinical science, and demonstrated skills in the application of those methods in a wide range of areas in health and biomedical research. Our students will participate in a program designed to provide them with interdisciplinary research experience, to train them to collaborate and communicate effectively, and to understand the importance of data provenance and reproducible research. The training program involves active participation by accomplished and experienced multidisciplinary faculty members, including biostatisticians, bioinformatics scientists and computational biologists, computer scientists, molecular biologists, public health researchers, and clinicians. It combines elements of training in coursework, lab rotations in biostatistics, computational biology, computer science, molecular biology, population science and clinical science. Students will participate in directed and independent methodological research, will be involved in broad-based collaborative research projects, and will have rich career development opportunities in a stimulating and nurturing interdisciplinary environment that will prepare them to be leaders in quantitative Big Data health science research.

描述（由申请人提供）：在20世纪世纪后半叶，数字技术的前所未有的进步已经产生了一场革命，通过提供前所未有的复杂数据量和以前无法想象的速度，正在改变科学，包括健康和生物医学研究。美国国家研究理事会（NRC）海量数据分析委员会的成员在2013年《大数据分析前沿》报告中总结说，与“大数据”相关的挑战远远超出了数据管理的技术层面，并强调，如果我们要利用这些数据，开发严格的定量和统计方法至关重要。在本申请描述了一个综合计划，旨在为学生提供定量和计算技能以及沟通和跨学科研究技能及其应用方面的培训，这些学生需要成为下一代健康和生物医学研究领域领先的大数据科学家。在哈佛TH Chan公共卫生学院，我们已经做出了大量投资来应对这些挑战，包括推出一个新的计算生物学和定量基因组学的正式硕士学位课程，修改生物统计学课程，以更加强调计算方法和大数据，一项正在进行内部审查的提案，将计算作为我们学生的核心竞争力领域，以及将大数据分析作为学校正在进行的资本活动的中心焦点。我们正在请求支持六名博士前学生，他们将从该计划中脱颖而出，具有尖端统计和计算方法开发的专业知识，对基础基础科学，公共卫生和临床科学的透彻理解，并在健康和生物医学研究的广泛领域中应用这些方法的技能。我们的学生将参加一个旨在为他们提供跨学科研究经验的计划，培养他们有效地合作和沟通，并了解数据来源和可复制研究的重要性。培训计划涉及由完成和经验丰富的多学科教师，包括生物统计学家，生物信息学家和计算生物学家，计算机科学家，分子生物学家，公共卫生研究人员和临床医生的积极参与。它结合了课程培训，生物统计学，计算生物学，计算机科学，分子生物学，人口科学和临床科学的实验室轮换的元素。学生将参与指导和独立的方法研究，将参与基础广泛的合作研究项目，并将在激励和培养跨学科环境中获得丰富的职业发展机会，这将使他们成为定量大数据健康科学研究的领导者。

项目成果

期刊论文数量（13）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Syndromic surveillance using monthly aggregate health systems information data: methods with application to COVID-19 in Liberia.

DOI：
10.1093/ije/dyab094
发表时间：
2021-08-30
期刊：
International journal of epidemiology
影响因子：
7.7
作者：
Fulcher IR;Boley EJ;Gopaluni A;Varney PF;Barnhart DA;Kulikowski N;Mugunga JC;Murray M;Law MR;Hedt-Gauthier B;Cross-site COVID-19 Syndromic Surveillance Working Group
通讯作者：
Cross-site COVID-19 Syndromic Surveillance Working Group

An early warning approach to monitor COVID-19 activity with multiple digital traces in near real time.

DOI：
10.1126/sciadv.abd6989
发表时间：
2021-03
期刊：
Science advances
影响因子：
13.6
作者：
Kogan NE;Clemente L;Liautaud P;Kaashoek J;Link NB;Nguyen AT;Lu FS;Huybers P;Resch B;Havas C;Petutschnig A;Davis J;Chinazzi M;Mustafa B;Hanage WP;Vespignani A;Santillana M
通讯作者：
Santillana M

Human-in-the-Loop Interpretability Prior.

人在环可解释性优先。