Data integration for global population health through dynamic models

 描述(由申请人提供) 我的长期职业目标是加快数据的使用，以改善人口健康。作为一名流行病学助理教授，我之前的工作重点是推进公共卫生领域生物医学数据的获取。通过在世界各地的几个国家工作，我敏锐地意识到，研究人员和卫生机构目前收集的大量人口健康数据带来了新发现的巨大潜力。这些数据中的大多数以不同的格式存储在数千个数据系统中，可能永远不会用于更好地了解健康和疾病的新研究，因为它们不容易集成(使数据一起工作)。我的目标是改变我的职业轨迹，从一次处理一个数据集，转向提高世界各地的研究人员和从业者一次使用数千或数百万个数据集的可用性和使用率。我计划成为一名独立的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。应聘者：这个K01项目将帮助我通过新知识和技能的培训来实现我的长期职业目标。我在医学和流行病学方面的背景使我能够更好地获得用于流行病学分析的数据集，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，这样我就可以获得这些技能和知识。培训计划：该计划包括正式课程、研讨会、个人指导，以及匹兹堡世界级学院的身临其境的研究体验。在整个项目中，我将与我的主要导师Mike Wagner博士一起，将75%的精力投入到生物医学信息学系的K01培训和研究中。瓦格纳博士是智能系统和数据系统在解决公共卫生问题方面的领先专家。格雷格·库珀博士将是我的共同导师，他在计算机和信息科学方面有着成熟的记录，现在是新成立的因果发现中心的主任，该中心由NIH大数据到知识(BD2K)机制资助。我的第三位导师马克·罗伯茨博士是一名执业临床医生，也是疾病计算机建模领域的领军人物。他还是匹兹堡大学公共卫生研究生院公共卫生动力学实验室(PHDL)的新任主任，在那里我将继续作为NIH传染病病原体研究模型(MIDAS)卓越中心的共同PI进行流行病学研究。我在这个K01项目中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；4)生物医学信息学和大数据的出版和授权写作技能。我将在KO1研究项目的背景下发展这种掌握。研究计划：我的K01研究的目标是提高流行病模拟器所需的人口规模数据的集成度。流行病模拟器是可以表示流行病的软件系统；它通常需要大量多样化的数据集来表示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，部分原因是整合数据集所需的努力。具体的研究目标是：1)标准化来自不同国家的蚊媒疾病登革热和基孔肯雅病的广泛数据集；2)开发计算机算法，在所有可用的数据集和所有可用的流行病模拟器中进行搜索，以确定那些可以通过模拟研究的流行病。这些算法还将识别数据差距；即，如果特定数据或数据集可用，可以通过模拟研究的流行病；以及3)量化不同数据集对模拟特定流行病的重要性。这项新技术将用快速的计算机算法取代繁琐的人工过程，这些算法可以扩大规模，在数百万个数据集和模拟器中进行搜索。影响：更容易和更快地发现适当的数据集或用于模拟的数据缺口，将扩大流行病模拟在公共卫生研究和实践中的使用，从而更有效地整合现有数据。更有效地利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。通过避免重复的数据投资，数据的高效利用还将带来成本节约。最后，更广泛地使用流行病模拟器将改善对新的流行病威胁的准备。这个项目的成果可以应用于生物医学科学，并将使我做好准备，成为公共卫生和大数据之间的接口的独立研究员。。

 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. .