Data integration for global population health through dynamic models

通过动态模型整合全球人口健康数据

• 批准号: 9147593
• 负责人: Willem Gijsbert Van Panhuis
• 金额: $ 15.82万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-30 至 2020-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9147593
• 关键词: Address; Algorithms; Area; Big Data; Big Data to Knowledge; Biological Phenomena; Career Choice; Cells; Colombia; Computational algorithm; Computer Simulation; Computers; Cost Savings; Country; Data; Data Collection; Data Science; Data Set; Dengue; Development; Disease; Endemic Diseases; Epidemic; Epidemiologist; Epidemiology; Expert Systems; Funding; Goals; Grant; Health; Health protection; Improve Access; Individual; Information Sciences; Information Systems; Institutes; Investments; Knowledge; Knowledge Discovery; Laboratories; Lead; Logic; Manuals; Medicine; Mentored Research Scientist Development Award; Mentors; Methods; Modeling; Molecular; Mosquito-borne infectious disease; Ontology; Outcome; Play; Population; Process; Public Health; Public Health Schools; Publications; Readiness; Research; Research Personnel; Research Project Grants; Research Training; Role; Science; Scientist; Specific qualifier value; Standardization; Technical Expertise; Technology; Time; Training; United States National Institutes of Health; Universities; Vector-transmitted infectious disease; Work; Writing; base; biomedical informatics; career; chikungunya; computer program; computer science; data format; data integration; data management; disease transmission; experience; health data; improved; infectious disease model; innovation; interest; new technology; novel; population health; professor; programs; public health research; scale up; simulation; skills; software systems; theories

 DESCRIPTION (provided by applicant) My long term career goal is to accelerate the use of data to improve population health. As an Assistant Professor of Epidemiology, I have focused my previous work on advancing access to biomedical data in public health. From working in several countries around the world, I have become acutely aware of the great potential for new discoveries offered by the vast amount of data on population health that is currently collected by researchers and health agencies. Most of these data are stored in different formats across thousands of data systems and may never be used for new research to better understand health and disease because they cannot be easily integrated (to make the data work together). I aim to redirect my career track from working on one dataset at a time, to improving the availability and use of thousands or millions of datasets at a time by researchers and practitioners around the world. I plan to become an independent investigator and data scientist and to establish my own research group at the interface between Public Health and Big Data. Candidate: This K01 project will help me to achieve my long-term career goal through training in new knowledge and skills. My background in medicine and epidemiology has enabled me to improve access to datasets for epidemiological analysis, but I lack essential technical skills and knowledge to create new technology to improve the integration of population scale data in general. My mentors and I have developed this K01 training and research plan so that I can acquire these skills and knowledge. Training plan: This plan includes formal coursework, seminars, personal mentoring, and an immersive research experience across world-class institutes in Pittsburgh. Throughout this project, I will dedicate 75% effort to K01 training and research in the Department of Biomedical Informatics with my primary mentor Dr. Mike Wagner. Dr. Wagner is a leading expert in the application of intelligent systems and data systems to problems in public health. Dr. Greg Cooper will be my co-mentor and has an established track record in computer and information science and is now the director of the newly created Center for Causal Discovery, funded by the NIH Big Data to Knowledge (BD2K) mechanism. My third mentor, Dr. Mark Roberts, is a practicing clinician and a leader in computer modeling of diseases. He is also the new director of the University of Pittsburgh Public Health Dynamics Laboratory (PHDL) at the Graduate School of Public Health, where I will continue my epidemiological research as co-PI on the NIH Models of Infectious Disease Agent Study (MIDAS) Center of Excellence. My specific training goals during this K01 program are to master: 1) Data standards and ontology development; 2) Logic and logic programming; 3) Computer programming for disease simulation; and 4) Publication and grant writing skills in biomedical informatics and Big Data. I will develop this mastery in the context of the KO1 research project. Research plan: The goal of my K01 research is to improve the integration of population scale data required by epidemic simulators. An epidemic simulator is a software system that can represent epidemics; it typically requires a large diversity of datasets to represent the many interacting processes that result in a particular epidemic. Currently, the use of epidemic simulators is data limited, partly, due to the effort required to integrate datasets. M specific research aims are to: 1) Standardize a wide range of datasets for the mosquito-borne diseases dengue and Chickungunya from a variety of countries; 2) Develop computer algorithms that will search across all available datasets and all available epidemic simulators to identify those epidemics that can be studied by simulation. These algorithms will also identify data gaps; that is, epidemics that could be studied by simulation if a particular datum or dataset were to become available; and 3) Quantify the importance of different datasets for simulation of specific epidemics. This new technology will replace laborious manual processes with fast computer algorithms that can be scaled up to search across millions of datasets and simulators. Impact: Easier and faster discovery of appropriate datasets or data gaps for simulation will expand the use of epidemic simulation for public health research and practice leading to more efficient integration of available data. Using data more efficiently for innovative analyses will lead to new knowledge and discoveries that can improve global population health. Efficient use of data will also lead to cost savings by avoiding redundant data investments. Finally, wider use of epidemic simulators will improve preparedness against new epidemic threats. Outcomes of this project can be used across the biomedical sciences and will prepare me to become an independent investigator at the interface between public health and Big Data. .

 描述（由申请人提供） 我的长期职业目标是加速利用数据来改善人口健康，作为流行病学助理教授，我之前的工作重点是促进公共卫生领域的生物医学数据的获取。已经敏锐地意识到研究人员和卫生机构目前收集的大量人口健康数据所提供的巨大潜力，其中大多数数据以不同格式存储在数千个数据系统中，并且可能永远不会被使用。进行新研究以更好地了解健康和疾病，因为它们无法轻松集成（使数据协同工作），我的目标是将我的职业轨迹从一次处理一个数据集转向提高周围研究人员和从业者一次对数千或数百万个数据集的可用性和使用。我计划成为一名独立调查员和数据科学家，并在公共卫生和大数据之间建立自己的研究小组候选人：这个 K01 项目将帮助我通过新的培训实现我的长期职业目标。我的医学背景和技能。流行病学使我能够更好地获取流行病学分析数据集，但我缺乏必要的技术技能和知识来创造新技术来改善总体人口规模数据的整合，我和我的导师制定了这个 K01 培训和研究计划。我可以获得这些技能和知识：该计划包括正式课程、研讨会、个人指导以及在匹兹堡世界一流机构的沉浸式研究经验，在整个项目中，我将投入 75% 的精力。我的主要导师迈克·瓦格纳 (Mike Wagner) 博士在生物医学信息学系进行 K01 培训和研究。瓦格纳博士是应用智能系统和数据系统解决公共卫生问题的领先专家，格雷格·库珀 (Greg Cooper) 博士将是我的合作伙伴。我的第三位导师马克·罗伯茨 (Mark Roberts) 博士是一名导师，在计算机和信息科学领域拥有丰富的经验，现在是新成立的因果发现中心的主任，该中心由 NIH 大数据知识 (BD2K) 机制资助。执业临床医生和他也是匹兹堡大学公共卫生研究生院公共卫生动态实验室 (PHDL) 的新任主任，我将作为 NIH 传染病模型的联合 PI 继续我的流行病学研究。疾病代理研究 (MIDAS) 卓越中心。我在 K01 项目期间的具体培训目标是掌握：1) 数据标准和本体开发；2) 逻辑和逻辑编程；3) 疾病模拟的计算机编程；我将在 KO1 研究计划的背景下发展这种生物医学信息学和资助写作技能：我的 K01 研究的目标是改进流行病模拟器所需的人口规模数据的整合。流行病模拟器是一种可以代表流行病的软件系统；它通常需要大量的数据集来代表导致特定流行病的许多相互作用的过程，目前，流行病模拟器的使用数据有限，部分原因在于工作量。 M 具体研究目标是：1）对来自不同国家的蚊媒疾病登革热和基孔肯雅热的广泛数据集进行标准化；2）开发可搜索所有可用数据集的计算机算法。流行病模拟器，用于识别可以通过模拟研究的流行病；即，如果特定数据或数据集可用，则可以通过模拟研究流行病；3）量化不同数据集对特定流行病模拟的重要性，这项新技术将用快速的计算机算法取代繁琐的手动流程，这些算法可以扩展到数百万个数据集和模拟器中进行搜索。 影响：更轻松、更快速地发现适当的数据集或数据缺口。模拟将扩大流行病模拟在公共卫生研究和实践中的应用，从而更有效地整合可用数据进行创新分析，将带来新的知识和发现，从而改善全球人口健康。还领导最后，流行病模拟器的广泛使用将提高对新流行病威胁的准备，该项目的成果可以用于整个生物医学科学领域，并使我成为公共卫生领域的独立调查员。和大数据。