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.