Bioinformatics Framework for Wastewater-based Surveillance of Infectious Diseases

基于废水的传染病监测的生物信息学框架

• 批准号: 10166255
• 负责人: ROLF U HALDEN
• 金额: $ 7.47万
• 依托单位: ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2021-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10166255
• 关键词: 2019-nCoV; Algorithms; Animal Model; Arizona; Award; Back; Bioinformatics; COVID-19; Case Study; Centers for Disease Control and Prevention (U.S.); Cessation of life; Characteristics; Clinical; Clinical Data; Collection; Communicable Diseases; Country; County; DNA Sequence; Data; Data Set; Databases; Disease; Disease Surveillance; Environmental Risk Factor; Epidemiologist; Epidemiology; Expert Systems; Future Generations; Genbank; General Population; Genome; Geographic Locations; Geography; Goals; Health; Health Services; Immune response; Immunologics; Individual; Informatics; Investigation; Link; Location; Manuals; Medical; Metadata; Methodology; Modification; Nucleic Acid Databases; Parents; Patients; Phenotype; Phylogenetic Analysis; Phylogeny; Population Surveillance; Privatization; Publishing; Records; Research; Research Personnel; Sampling; Scientist; Severities; Severity of illness; Signs and Symptoms; Source; State or Local Health Department; Structure; System; Technology; Testing; United States; Universities; Validation; Viral; Viral Genome; Virus; Work; base; biomedical informatics; clinical investigation; clinical phenotype; epidemiologic data; epidemiology study; genomic data; genomic epidemiology; health data; improved; next generation sequencing; pandemic disease; pathogen; public health emergency; reconstruction; response; reverse genetics; trait; transmission process; virus genetics

Project Summary SARS-CoV-2 is now a global pandemic with 4.2M cases and 290K deaths worldwide (as of May 12, 2020). In the United States, there are over 1.3M cases and 81K deaths. Locally, Arizona has over 11K cases and 562 deaths. In response to this public health emergency, several studies have been published that describe patient characteristics in terms of signs, symptoms, and clinical endpoints. In addition, epidemiologists and infectious disease researchers have utilized next-generation sequencing technology to produce complete genomes of the virus for clinical and epidemiologic investigation. Genomic epidemiology has enabled scientists to understanding localized transmission while determining geographic sources of introductions from different states and countries. However, most of the sequencing for SARS-CoV-2 (as well as for other viruses) is performed outside of state or local health departments such as the Centers for Disease Control and Prevention (CDC), universities, or private labs. It can then be difficult to link the pathogen, once sequenced, back to the data collected by the health department for case investigation. This can inhibit genomic epidemiology when there is no link between sequences of viral isolates and epidemiologic case data. There is limited research in how to link pathogen sequences to epidemiologic case data; especially for COVID-19. Thus, despite the abundance of clinical and epidemiologic data collected during this pandemic, more informatics research is needed to understand how to link viral genetic and epidemiological data and demonstrate the value of this for disease surveillance. The goal of this supplement is to link epidemiologic data from COVID-19 positive patients in Arizona with viral genetics from sequenced isolates to better understand the relationship between viral genetics and epidemiologic and clinical phenotypes. We will accomplish this by utilizing Arizona’s disease surveillance system and available sequences and metadata that are published in online nucleic acid databases. We will use different probabilistic matching strategies to link the two different sources (Aim 1) and then use Bayesian phylogenetics and phylogeography to study clustering of epidemiologic cases (Aim 2). Epidemiologists can use these findings to gain an understanding of how local viruses genetically cluster in relation to specific epidemiologic and clinical cases. While disease severity is dependent on individual immune response and environmental factors, linking viral genetics to its proper epidemiologic case could also support hypothesis generation for future reverse genetics and immunological studies in animal models.

项目摘要 SARS-COV-2现在是全球大流行病，全球为420万例和29万人死亡（截至2020年5月12日）。 在美国，有超过130万例和81K死亡。在当地，亚利桑那州有11k案件和562个案件 死亡人数。为了应对这一公共卫生紧急情况，已经发表了一些描述的研究 在体征，符号和临床终点方面的患者特征。此外，流行病学家和 传染病研究人员利用下一代测序技术来生产完整的 用于临床和流行病学研究的病毒基因组。基因组流行病学使科学家能够 在确定来自不同的介绍的地理来源的同时了解本地传输 国家和国家。但是，SARS-COV-2（以及其他病毒）的大多数测序是 在州或地方卫生部门以外进行，例如疾病控制与预防中心 （CDC），大学或私人实验室。然后很难将病原体连接起来，一旦测序， 卫生部门收集的数据进行案例调查。这可以抑制基因组流行病学 病毒分离株序列与流行病学病例数据之间没有联系。 关于如何将病原体序列与流行病学病例数据联系起来的研究有限。特别是 新冠肺炎。这是在此大流行期间收集的大量临床和流行病学数据的丰富性 需要更多的信息研究来了解如何将病毒遗传学和流行病学数据联系起来以及 证明这对疾病监测的价值。 该补充的目的是将来自亚利桑那州共同199阳性患者的流行病学数据与 从测序分离株中的病毒遗传学，以更好地了解病毒遗传学与 流行病学和临床表型。我们将通过使用亚利桑那州的疾病监测来实现这一目标 在线核酸数据库中发表的系统和可用序列和元数据。我们将使用 不同的概率匹配策略将两个不同来源连接起来（AIM 1），然后使用贝叶斯 系统发育学和植物地理学研究流行病学病例的聚类（AIM 2）。流行病学家可以使用 这些发现是为了了解局部病毒与特定的基因群体如何簇 流行病学和临床病例。而疾病的严重程度取决于个体免疫响应和 环境因素，将病毒遗传学与其适当的流行病学病例联系起来也可以支持假设 生成动物模型中未来反向遗传学和免疫学研究。