Integrating Bioinformatics and Clustering Analysis for Disease Surveillance

 描述（由申请人提供）：生物信息学一直非常关注将实验室数据转化为临床决策的信息和知识。这包括分析人类遗传学以进行个性化医疗和治疗。然而，翻译生物信息学在公共卫生实践中的应用，如新出现/再出现病毒的监测，受到的关注要少得多。这涉及病毒遗传学的数据采集、整合和分析，以推断新菌株出现时的起源、传播和进化。这一实践的相关科学领域包括分子流行病学和生物地理学的某些方面。最近的注意力集中在人畜共患病来源的病毒上，人畜共患病来源的病毒被定义为可在动物和人类之间传播的病原体。除了季节性流感和西尼罗河病毒外，这种病原体分类还包括新型病毒，如中东呼吸综合征和甲型流感H7N9。尽管在文献中强调的成功，国家公共卫生，农业和野生动物机构之间的人畜共患病监测的生物信息学资源和工具的利用很少。以前，这种类型的资源主要限于学术界。 虽然生物信息学很少用于人畜共患病病毒的监测，但国家卫生机构已采用其他应用程序，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（如临床、地理或人类移动数据）生成疾病地图的软件，用于地理编码、聚类或疫情检测等任务。此外，地理空间统计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。然而，这些地理信息系统只考虑传统的流行病学数据，如报告病例的地点和时间，而不是导致疾病的病毒的遗传学。这使卫生机构无法了解病毒基因组的变化及其传播的相关环境如何影响疾病风险。 该提案的长期目标是通过应用生物信息学原理访问、整合和分析病毒遗传学和时空可报告疾病数据，加强对人畜共患病病毒地理空间热点的识别。该项目将包括生物信息学，遗传学，空间统计，地理信息系统和流行病学的方法。为此，我将首先衡量生物信息学资源和工具的利用，以及公共卫生，农业和野生动物的国家机构确定的检测和预测人畜共患病病毒的热点（集群）（目标1）的当前方法和局限性。然后，我将使用这些反馈来开发一个空间决策支持系统，用于检测和预测人畜共患病热点，该系统应用生物信息学原理来访问、整合和分析病毒遗传学、环境和时空可报告疾病数据（目标2）。在目标3中，我将针对一个不考虑病毒遗传学并依赖于传统时空数据的系统来评估我的聚类检测和预测系统，并对预测能力进行验证。额外 将评估用户满意度和系统可用性。

 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.