Integrating Bioinformatics and Clustering Analysis for Disease Surveillance

整合生物信息学和聚类分析进行疾病监测

• 批准号: 9050106
• 负责人: Rachel Beard
• 金额: $ 3.66万
• 依托单位: ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-12-21 至 2018-12-20
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9050106
• 关键词: Academia; Address; Agriculture; Algorithm Design; Algorithms; Animals; Area; Attention; Biodiversity; Bioinformatics; Case Study; Clinical; Cluster Analysis; Communicable Diseases; Computer software; Data; Databases; Decision Support Systems; Detection; Disease; Disease Outbreaks; Ecology; Environment; Epidemiology; Evaluation; Evolution; Feedback; Future; Genbank; Genetic; Geographic Information Systems; Goals; Health; Human; Human Genetics; Infection; Influenza; Influenza A Virus, H7N9 Subtype; Influenza A virus; Knowledge; Literature; Location; Machine Learning; Maps; Measures; Metadata; Modeling; Molecular Epidemiology; Molecular Evolution; Pattern; Population; Public Health; Public Health Practice; Questionnaire Designs; Questionnaires; Research; Resources; Retrospective Studies; Risk; Satellite Viruses; Scanning; Sequence Alignment; Syndrome; System; Time; Translations; Validation; Validity and Reliability; Viral; Viral Genome; Virus; Virus Diseases; West Nile virus; Work; clinical decision-making; data acquisition; disorder risk; health data; high risk; novel virus; pathogen; personalized medicine; prevent; respiratory; satisfaction; seasonal influenza; spatiotemporal; statistics; success; tool; usability; virus classification; virus genetics

 DESCRIPTION (provided by applicant): There has been a tremendous focus in bioinformatics on translation of data from the bench into information and knowledge for clinical decision-making. This includes analysis of human genetics for personalized medicine and treatment. However, there has been much less attention on translational bioinformatics for public health practice such as surveillance of emerging/re-emerging viruses. This involves data acquisition, integration, and analyses of viral genetics to infer origin, spread, and evolution suc as the emergence of new strains. The relevant scientific fields for this practice include certain aspects of molecular epidemiology and phylogeography. Recent attention has focused on viruses of zoonotic origin, which are defined as pathogens that are transmittable between animals and humans. In addition to seasonal influenza and West Nile virus, this classification of pathogens includes novel viruses such as Middle Eastern Respiratory Syndrome and influenza A H7N9. Despite the successes highlighted in the literature, there has been little utilization of bioinformatics resources and tools among state public health, agriculture, and wildlife agencies for zoonotic surveillance. Previously this type of resource has been restricted primarily to those in academia. While bioinformatics has been sparsely used for surveillance of zoonotic viruses, other applications such as Geospatial Information Systems (GIS) have been employed by state health agencies to analyze spatial patterns of infection. This includes software to produce disease maps using an array of data types such as clinical, geographical, or human mobility data for tasks such as, geocoding, clustering, or outbreak detection. In addition, advances in geospatial statistics have enabled health agencies to perform more powerful space-time analyses to infer spatiotemporal patterns. However, these GIS consider only traditional epidemiological data such as location and timing of reported cases and not the genetics of the virus that causes the disease. This prevents health agencies from understanding how changes in the genome of the virus and the associated environment in which it disseminates impacts disease risk. The long-term goal of this proposal is to enhance the identification of geospatial hotspots of zoonotic viruses by applying bioinformatics principles to access, integrate, and analyze viral genetics and spatiotemporal reportable disease data. This project will include approaches from bioinformatics, genetics, spatial statistics, GIS, and epidemiology. To do this, I will first measue the utilization of bioinformatics resources and tools as well as the current approaches and limitations identified by state agencies of public health, agriculture, and wildlife for detecting nd predicting hotspots (clusters) of zoonotic viruses (Aim 1). I will then use this feedback to develo a spatial decision support system for detecting and predicting zoonotic hotspots that applies bioinformatics principles to access, integrate, and analyze viral genetics, environmental, and spatiotemporal reportable disease data (Aim 2). In Aim 3, I will then evaluate my system for cluster detection and prediction against a system that does not consider viral genetics and relies on traditional spatiotemporal data, and perform validation of the predictive capability. Additional evaluation of the user's satisfaction and system usability will be evaluated.

 描述（由应用程序提供）：生物信息学上非常重点，将数据从台上的数据转换为临床决策的信息和知识。这包括对个性化医学和治疗的人类遗传学分析。但是，人们对公共卫生实践的转化生物信息学的关注要少得多，例如对新兴/重新出现病毒的监视。这涉及对病毒遗传学的数据获取，整合和分析，以推断出来源，传播和进化作为新菌株的出现。这种实践的相关科学领域包括分子流行病学和植物地理学的某些方面。最近的注意力集中在人畜共患病的病毒上，这些病毒被定义为在动物和人类之间传播的病原体。除了季节性影响和西尼罗河病毒外，这种病原体的分类还包括新的病毒，例如中东呼吸道综合征和影响力A H7N9。尽管文献中取得了成功，但在州公共卫生，农业和野生动植物机构中，生物信息学资源和工具几乎没有利用。以前，这种类型的资源主要限于学术界的资源。 虽然生物信息学已被稀疏用于人畜共患病毒的监视，但国家卫生机构已经采用了其他应用，例如地理空间信息系统（GIS）来分析感染的空间模式。这包括使用一系列数据类型来生成疾病图的软件，例如临床，地理或人类流动性数据，例如地理编码，聚类或爆发检测。此外，地理空间统计数据的进步使卫生机构能够执行更强大的时空分析以推断时空模式。但是，这些GI仅考虑传统的流行病学数据，例如报告病例的位置和时间安排，而不是导致疾病的病毒的遗传学。这样可以防止卫生机构了解病毒基因组的变化以及它传播的相关环境会影响疾病风险。 该提案的长期目标是通过应用生物信息学原理来访问，整合和分析病毒遗传学和空间时间可报告疾病数据，从而增强人畜共患病毒的地理空间热点。该项目将包括生物信息学，遗传学，空间统计，GIS和流行病学的方法。为此，我将首先测量生物信息学资源和工具的利用，以及公共卫生，商定和野生动植物国家机构确定的当前方法和局限性，以检测可测量的人畜共患病毒的热点（簇）（AIM 1）。然后，我将使用此反馈来开发一种空间决策支持系统，用于检测和预测应用生物信息学原理以访问，整合和分析病毒遗传学，环境和空间时间可报告疾病数据的人畜共患病（AIM 2）。在AIM 3中，我将评估我的系统，以针对不考虑病毒遗传学并依赖传统空间时间数据的系统进行聚类检测和预测，并对预测能力进行验证。额外的 评估用户的满意度和系统可用性。