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: 9123353
• 负责人: Elaine O. Nsoesie
• 金额: $ 10.75万
• 依托单位: UNIVERSITY OF WASHINGTON
• 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 2018-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9123353
• 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; Data; Data Science; Data Set; Data Sources; Databases; Dengue; Detection; Disease; Disease Outbreaks; Disease model; Emergency response; 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 Respiratory Syndrome Coronavirus; Modeling; Monitor; Outcome; Pattern; Pediatric Hospitals; Poliomyelitis; Population Surveillance; Postdoctoral Fellow; Prevention program; Process; Public Health; Public Policy; Reporting; Research; Research Design; Research Personnel; Research Proposals; Research Training; Resolution; Resources; Review Literature; Schools; Series; Source; Statistical Methods; Statistical Models; Stream; Syria; System; Techniques; Temperature; Time; Training; Weight; Work; World Health Organization; base; biomedical informatics; career; climate data; 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; model building; news; novel; pandemic influenza; skills; social media; 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.

描述（由申请人提供）：我接受了计算生物学家和统计学家的培训，目前，我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主要研究机构的独立研究人员。我计划继续我当前的全球健康和传染病研究。具体而言，我旨在继续开发数学和计算方法，以建模以了解疾病传播，预测未来的动态并评估公共政策决策的干预措施。作为博士后研究员，我有一个很棒的机会，可以与来自多个来源的数据合作。尽管这些数据流中的几个可以标记为“大数据”，但在已经处理，过滤和汇总到每日或每周分辨率的数据后，我通常会使用数据流。虽然我已经开发了对这些已经处理过的数据进行建模的必要技能，但在三个重要领域中，我需要额外的培训，指导和经验：（1）高级计算技能，尤其是在使用高性能计算和信息学工具时，（2）计算机学习和数据挖掘中所需的数据获取和处理所需的技术挖掘技术，以及（3）对统计学设计的生物统计学方法，包括大量的研究。这三个培训和指导目标将使我能够发展成为生物医学研究大数据科学独立研究者所必需的技能。波士顿儿童学校和哈佛医学院是转化生物医学研究的领先机构，从而使它们成为追求培训和研究目标的理想环境。传染病的最近出现，例如中国的鸟类流感H7N9，以及叙利亚脊髓灰质炎等疾病的重新出现，强调了加强免疫接种和紧急反应计划的重要性，以预防和控制感染性疾病。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，提供疾病风险的估计并评估各种干预措施。尽管这些研究对于理解传染病的传播和控制非常有用，但大多数研究是特定于疾病的，并且完全使用了传统疾病监测系统的数据。相比之下，在过去的十年中，已经对公共卫生监视进行了广泛的评估和验证，但几乎没有与其他数据源一起使用以预测疾病扩散的建模。使用这些新型的基于数字事件的数据源，结合了来自传统疾病监视系统的气候和病例数据，我们将建立一个急需的框架，以整合这些不同的数据源以估算疾病风险和预测传染病的时间动态。我们的方法将通过三个目标来实现。第一个目标是开发一个自动化过程，用于获取，处理和过滤用于建模的数据（AIM 1）。一旦收集了这些数据，我们将开发时间模型，以动态评估各种数据变量与传染病发生率之间的关系（AIM 2）。最后，我们将评估AIM 2下开发的建模方法的实用性，以预测传染病的时间趋势（AIM 3）。通过数据获取，彻底的处理，统计和流行病学建模，并由具有生物医学信息学，计算机科学和统计专业知识的顾问进行指导，我们计划实现一种全面的方法，以整合多个数据流以预测传染病。

项目成果

A Platform for Crowdsourced Foodborne Illness Surveillance: Description of Users and Reports.

• DOI: 10.2196/publichealth.7076
• 发表时间: 2017-07-05
• 期刊: JMIR public health and surveillance
• 影响因子: 8.5
• 作者: Quade P;Nsoesie EO
• 通讯作者: Nsoesie EO

Forecasting influenza-like illness trends in Cameroon using Google Search Data.

• DOI: 10.1038/s41598-021-85987-9
• 发表时间: 2021-03-24
• 期刊: Scientific reports
• 影响因子: 4.6
• 作者: Nsoesie EO;Oladeji O;Abah ASA;Ndeffo-Mbah ML
• 通讯作者: Ndeffo-Mbah ML

Social Media as a Sentinel for Disease Surveillance: What Does Sociodemographic Status Have to Do with It?

社交媒体作为疾病监测的哨兵：社会人口状况与之有何关系？

• DOI: 10.1371/currents.outbreaks.cc09a42586e16dc7dd62813b7ee5d6b6
• 发表时间: 2016
• 期刊: PLoS currents
• 作者: Nsoesie,ElaineO;Flor,Luisa;Hawkins,Jared;Maharana,Adyasha;Skotnes,Tobi;Marinho,Fatima;Brownstein,JohnS
• 通讯作者: Brownstein,JohnS

Temporal Topic Modeling to Assess Associations between News Trends and Infectious Disease Outbreaks.

用于评估新闻趋势与传染病爆发之间关联的时间主题建模。

• DOI: 10.1038/srep40841
• 发表时间: 2017-01-19
• 期刊: Scientific reports
• 影响因子: 4.6
• 作者: Ghosh S;Chakraborty P;Nsoesie EO;Cohn E;Mekaru SR;Brownstein JS;Ramakrishnan N
• 通讯作者: Ramakrishnan N