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.

项目概要 SARS-CoV-2 现已成为全球大流行病，全球已有 420 万例病例和 29 万例死亡（截至 2020 年 5 月 12 日）。 美国有超过 130 万例病例和 8.1 万人死亡，亚利桑那州当地有超过 1.1 万例病例和 562 人死亡。 为了应对这一公共卫生紧急情况，已经发表了几项研究来描述死亡情况。 此外，流行病学家和临床终点方面的患者特征。 传染病研究人员利用下一代测序技术产生了完整的 病毒基因组用于临床和流行病学研究使科学家能够进行基因组流行病学研究。 了解本地化传播，同时确定不同地区引入的地理来源 然而，大多数 SARS-CoV-2（以及其他病毒）的测序是 在州或地方卫生部门（例如疾病控制和预防中心）之外进行 （疾病预防控制中心）、大学或私人实验室一旦测序，就很难将病原体与病原体联系起来。 卫生部门收集的用于病例调查的数据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 因此，尽管在这次大流行期间收集了大量的临床和流行病学数据， 需要更多的信息学研究来了解如何将病毒遗传和流行病学数据联系起来 证明其对于疾病监测的价值。 本补充文件的目的是将亚利桑那州 COVID-19 阳性患者的流行病学数据与 来自测序分离株的病毒遗传学，以更好地了解病毒遗传学与病毒之间的关系 我们将通过利用亚利桑那州的疾病监测来实现这一目标。 我们将使用在线核酸数据库中发布的系统以及可用序列和元数据。 不同的概率匹配策略来链接两个不同的源（目标 1），然后使用贝叶斯 流行病学家可以使用系统发生学和系统发育地理学来研究流行病学病例的聚类（目标 2）。 这些发现是为了了解当地病毒如何在基因上与特定病毒相关 虽然疾病的严重程度取决于个体的免疫反应和临床病例。 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 为未来动物模型中的反向遗传学和免疫学研究提供了下一代。