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速数据的使用，以改善人口健康。作为流行病学的助理教授，我把我以前的工作重点放在推进公共卫生中的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提供了巨大的新发现潜力。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，可能永远不会用于新的研究，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生涯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对数千或数百万数据集的可用性和使用。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。候选人：这个K 01项目将通过新知识和技能的培训帮助我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访问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建新技术，以改善总体人口规模数据的整合。我的导师和我已经制定了这个K 01培训和研究计划，以便我可以获得这些技能和知识。培训计划：该计划包括正式的课程，研讨会，个人指导，以及在匹兹堡世界级研究所的沉浸式研究体验。在整个项目中，我将与我的主要导师Mike瓦格纳博士一起投入75%的精力在生物医学信息学系进行K 01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。Greg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良好的记录，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（BD 2K）机制资助。我的第三位导师马克·罗伯茨博士是一位执业临床医生，也是疾病计算机建模的领导者。他也是匹兹堡大学公共卫生动力学实验室（PHDL）在公共卫生研究生院的新主任，在那里我将继续我的流行病学研究作为共同PI对传染病病原体研究（MIDAS）卓越中心的NIH模型。我在这个K 01计划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）疾病模拟计算机编程;和4）生物医学信息学和大数据的出版和授予写作技能。我将在KO 1研究项目的背景下发展这种掌握。研究计划：我的K 01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。流行病模拟器是一个可以表示流行病的软件系统;它通常需要大量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。M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Chickungunya的广泛数据集; 2）开发计算机算法，搜索所有可用的数据集和所有可用的流行病模拟器，以确定可以通过模拟研究的流行病。这些算法还将确定数据缺口，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; 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费力的手动过程，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：更快、更快地发现用于模拟的适当数据集或数据缺口，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。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。数据的有效使用还将通过避免冗余的数据投资来节省成本。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防备。这个项目的成果可以在整个生物医学科学中使用，并将使我成为公共卫生和大数据之间接口的独立调查员。.

 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. .