A Framework for Integrating Multiple Data Sources for Modeling and Forecasting of Infectious Diseases

集成多个数据源以进行传染病建模和预测的框架

• 批准号: 8829434
• 负责人: Elaine O. Nsoesie
• 金额: $ 4.25万
• 依托单位: BOSTON CHILDREN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-29 至 2015-03-01
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8829434
• 关键词: Accounting; Address; Area; Avian Influenza; Big Data; Biological Models; Biomedical Research; Boston; Centers for Disease Control and Prevention (U.S.); Cessation of life; Child; China; Climate; Communicable Diseases; Computer Simulation; Coronavirus; Data; Data Set; Data Sources; Databases; Dengue; Detection; Disease; Disease Outbreaks; Disease model; Emergency Situation; Emerging Communicable Diseases; Environment; Epidemic; Epidemiology; Event; Future; Goals; Health; High Performance Computing; Human; Humidity; Immunization; Incidence; Individual; Influenza; Influenza A Virus, H1N1 Subtype; Influenza A Virus, H7N9 Subtype; Informatics; Institution; International; Internet; Intervention; Label; Linear Models; Machine Learning; Medical; Mentors; Methodology; Middle East; Modeling; Monitor; Outcome; Pattern; Pediatric Hospitals; Poliomyelitis; Population Surveillance; Postdoctoral Fellow; Prevention program; Process; Public Health; Public Policy; Report (account); Reporting; Research; Research Design; Research Personnel; Research Proposals; Research Training; Resolution; Resources; Review Literature; Schools; Science; Series; Source; Statistical Methods; Statistical Models; Stream; Syndrome; Syria; System; Techniques; Temperature; Time; Training; Weight; Work; World Health Organization; base; biomedical informatics; career; computer science; computerized data processing; data acquisition; data integration; data mining; data modeling; digital; disease transmission; disorder control; disorder prevention; disorder risk; epidemiological model; experience; global health; improved; infectious disease model; mathematical model; medical schools; news; novel; pandemic influenza; public health relevance; respiratory; response; skills; social; statistics; tool; trend; web based interface

DESCRIPTION (provided by applicant): I am trained as a computational biologist and statistician, and I am currently a postdoctoral fellow at Boston Children's Hospital, Harvard Medical School. My main career goal is to become an independent researcher at a major research institution. I plan to continue my current research pursuits in global health and infectious diseases. Specifically, I aim to continue developing mathematical and computational approaches for modeling to understand disease transmission, forecasting future dynamics and evaluating interventions for public policy decisions. As a postdoctoral research fellow, I have had the wonderful opportunity of working with data from multiple sources. Although several of these data streams could be labeled as "Big Data", I typically work with the data after it is already processed, filtered and aggregated to a daily or weekly resolution. While I have developed the necessary skills for modeling these already processed data, there are three important areas where I require additional training, mentoring, and experience: (1) advanced computational skills especially in the use of high performance computing and informatics tools, (2) techniques in computational machine learning and data mining necessary for data acquisition and processing, and (3) biostatistical methodology needed for the statistical design of studies involving big data. These three training and mentoring aims would enable me to develop the skills necessary to become an independent investigator in Big Data Science for biomedical research. Boston Children's School and Harvard Medical School are leading institutions in translational biomedical research, thereby making them the ideal environment to pursue the training and research aims in this proposal. The recent emergence of infectious diseases such as the avian influenza H7N9 in China, and re-emergence of diseases such as polio in Syria underscores the importance of strengthening immunization and emergency response programs for the prevention and control of infectious diseases. Researchers have developed computational and mathematical models to capture determinants of infectious disease dynamics and identify factors that support prediction of these dynamics, provide estimates of disease risk, and evaluate various intervention scenarios. While these studies have been extremely useful for the understanding of infectious disease transmission and control, most have been disease specific and solely used data from traditional disease surveillance systems. In contrast, there is a huge amount of internet-based data that have been extensively assessed and validated for public health surveillance in the last decade, but it has been scarcely used in conjunction with other data sources for modeling to predict disease spread. Using these novel digital event-based data sources in combination with climate and case data from traditional disease surveillance systems, we will establish a much needed framework for integrating these disparate data sources for modeling to estimate disease risk and forecasting temporal dynamics of infectious diseases. Our approach will be achieved through three aims. The first objective is to develop an automated process for acquiring, processing and filtering data for modeling (Aim 1). Once we gather this data, we will develop temporal models for the dynamical assessment of the relationship between the various data variables and infectious disease incidence (Aim 2). Finally, we will assess the utility of the modeling approaches developed under Aim 2 for forecasting temporal trends of infectious diseases (Aim 3). Through data acquisition, thorough processing, statistical and epidemiological modeling, and guided by advisers with expertise in biomedical informatics, computer science and statistics, we plan to achieve a comprehensive approach to integrating multiple data streams for modeling to forecast infectious diseases.

简介(申请人提供)：我是一名计算生物学家和统计学家，目前是哈佛医学院波士顿儿童医院的博士后研究员。我的主要职业目标是成为一家大型研究机构的独立研究员。我计划继续我目前在全球健康和传染病方面的研究。具体地说，我的目标是继续开发数学和计算方法，用于建模以了解疾病传播、预测未来动态和评估公共政策决策的干预措施。作为一名博士后研究员，我有机会处理来自多个来源的数据。虽然这些数据流中有几个可以被贴上“大数据”的标签，但我通常在对数据进行处理、过滤和聚合到每日或每周的分辨率之后才处理这些数据。虽然我已经开发了对这些已经处理的数据进行建模的必要技能，但在三个重要领域我需要额外的培训、指导和经验：(1)高级计算技能，特别是在使用高性能计算和信息学工具方面；(2)数据获取和处理所需的计算机器学习和数据挖掘技术；以及(3)涉及大数据的研究的统计设计所需的生物统计学方法。这三个培训和指导目标将使我能够发展必要的技能，成为一名生物医学研究的大数据科学独立研究员。波士顿儿童学校和哈佛医学院是转化式生物医学研究的领先机构，因此它们是实现本提案中的培训和研究目标的理想环境。最近在中国出现的禽流感H7N9等传染病，以及叙利亚再次出现的脊髓灰质炎等疾病，都凸显了加强免疫接种和应急预案对传染病防控的重要性。研究人员开发了计算和数学模型，以捕获传染病动态的决定因素，并确定支持这些动态预测的因素，提供疾病风险估计，并评估各种干预方案。虽然这些研究对了解传染病的传播和控制非常有用，但大多数都是针对疾病的，只使用传统疾病监测系统的数据。相比之下，在过去十年里，有大量基于互联网的数据经过了广泛的评估和验证，可用于公共卫生监测，但很少与其他数据来源一起用于预测疾病传播的建模。使用这些新的基于事件的数字数据源，结合来自传统疾病监测系统的气候和病例数据，我们将建立一个迫切需要的框架，用于集成这些不同的数据源进行建模，以估计疾病风险和预测传染病的时间动态。我们的方针将通过三个目标来实现。第一个目标是开发一个获取、处理和过滤用于建模的数据的自动化程序(目标1)。一旦我们收集了这些数据，我们将开发时间模型，用于动态评估各种数据变量和传染病发病率之间的关系(目标2)。最后，我们将评估在目标2下开发的用于预测传染病时间趋势的建模方法的实用性(目标3)。通过数据收集、彻底处理、统计和流行病学建模，并在具有生物医学信息学、计算机科学和统计学专业知识的顾问的指导下，我们计划实现一种综合方法，将多种数据流整合起来进行建模，以预测传染病。