QuBBD: Viral Evolution and Spread of Infectious Disease in Complex Network: Big Data Analysis and Modeling

QuBBD：复杂网络中传染病的病毒进化和传播：大数据分析和建模

• 批准号: 9393052
• 负责人: Pavel Skums
• 金额: $ 30.33万
• 依托单位: GEORGIA INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-12 至 2020-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9393052
• 关键词: Acute; Affect; Algorithm Design; Algorithmic Analysis; Algorithms; Animals; Antibody-Dependent Enhancement; Area; Bayesian Analysis; Big Data; Bioinformatics; Centers for Disease Control and Prevention (U.S.); Chronic; Communicable Diseases; Complex; Computers; Computing Methodologies; Data; Data Analyses; Data Set; Development; Disease; Disease Outbreaks; Drug resistance; Educational workshop; Epidemic; Epidemiologist; Epidemiology; Evolution; Family; Founder Effect; Freezing; GB virus C; Genetic; Genetic Epistasis; Genetic Variation; Genomics; Goals; HIV; Health Personnel; Health Professional; Hepatitis C; Human; Immune; Immune system; Immunity; Individual; Infection; International; Intervention; Investigation; Joints; Journals; Knowledge; Methods; Minor; Modeling; Molecular; Molecular Epidemiology; Morbidity - disease rate; Needle Sharing; Online Systems; Pathway Analysis; Pattern; Plants; Play; Population; Process; Public Health; Publications; RNA Viruses; Recording of previous events; Research; Research Personnel; Role; Sampling; Scientist; Social Network; Software Tools; Source; Structure; Surveillance Methods; Surveillance Program; System; Systems Analysis; Systems Theory; Technology; Time; Variant; Viral; Virulent; Virus; Virus Diseases; base; big biomedical data; cloud based; co-infection; computerized tools; data modeling; data structure; design; dynamic system; empowered; epidemiologic data; epidemiological model; genetic analysis; genomic data; global health; high risk population; immune resistance; immunoreactivity; improved; innovation; mortality; next generation sequencing; novel; open source; pathogen; pressure; public health intervention; reconstruction; resistant strain; simulation; social; software development; symposium; theories; tool; transmission process; treatment program; viral transmission; virology

Overview: Highly mutable RNA viruses, such as human immunodeficiency virus and hepatitis C virus are major causes of morbidity and mortality in the world. The hallmark of RNA viruses is their extremely high genetic diversity that allows them to rapidly establish new infections, escape host's immune system and develop drug resistance. Emergence of next-generation sequencing technologies promises to revolutionize the fields of virology and epidemiology by allowing to sample and characterize millions of intra-host viral variants in thousands of infected individuals. However, our understanding of mechanisms of disease spread and viral evolution are still limited due to the lack of computational methods for processing, integration and analysis of biomedical big data. The overarching goal of this project is to develop a comprehensive family of innovative algorithms and models that allow to describe, analyze, understand and predict complex multidimensional non-linear disease dynamics. Intellectual Merit: The proposed research will be conducted by an interdisciplinary team comprised of biologists, mathematicians, molecular epidemiologists and computer scientists with extensive expertise in the areas relevant to the project. The project will target highly important epidemiological and biomedical problems including development of efficient and scalable computational methods for surveillance of disease spread, modeling of epidemiological dynamics by incorporation of intra-host and inter-host evolutionary dynamics into a single framework and design of computational tools for utilization of data analysis results by health care professionals. Proposed algorithms and models will be validated using massive molecular and epidemiological data generated by project collaborators from CDC and Georgia Tech, as well as available from public sources. The algorithms will be distributed to the researchers and health care workers as free open-source packages and cloud-based online tools. In particular, they will be incorporated in the Global Health Outbreak and Surveillance Technology, a web-based data analysis system currently being developed at CDC. Research findings will be broadly disseminated via journal publications and conference presentations, including the International Symposium on Bioinformatics Research and Applications and Workshop on Computational Advances in Molecular Epidemiology organized by the Pis.

概述：高度可变的RNA病毒，例如人类免疫缺陷病毒和丙型肝炎病毒 世界上发病和死亡率的主要原因。 RNA病毒的标志是它们的高度 遗传多样性使他们能够快速建立新的感染，逃脱宿主的免疫系统和 发展耐药性。下一代测序技术的出现有望革新 病毒学和流行病学领域通过允许样品和表征数百万的宿主内病毒 成千上万受感染个体的变体。但是，我们对疾病传播机制的理解 由于缺乏用于处理，集成和 生物医学大数据的分析。该项目的总体目标是建立一个全面的家庭 允许描述，分析，理解和预测复杂的创新算法和模型 多维非线性疾病动力学。 知识分子优点：拟议的研究将由一个跨学科团队进行 生物学家，数学家，分子流行病学家和计算机科学家，具有广泛专业知识 与项目相关的领域。该项目将针对非常重要的流行病学和生物医学 问题包括开发有效且可扩展的疾病监测方法 通过结合宿主和宿主进化的扩散，流行病学动力学建模 动力学成一个单个框架和计算工具设计，用于利用数据分析结果 医疗保健专业人员。建议的算法和模型将使用大量分子和 CDC和Georgia Tech的项目合作者生成的流行病学数据以及可用的 来自公共来源。这些算法将以免费的 开源软件包和基于云的在线工具。特别是，它们将被纳入全球 健康爆发和监视技术，这是一种基于网络的数据分析系统 在CDC。研究发现将通过期刊出版物和会议广泛传播 演讲，包括关于生物信息学研究和应用的国际研讨会以及 PIS组织的分子流行病学计算进步研讨会。