Bioinformatics Framework for Wastewater-based Surveillance of Infectious Diseases

项目摘要 SARS-CoV-2现在是一种全球大流行，全球有420万病例和29万人死亡(截至2020年5月12日)。 在美国，有超过130万个病例和81K人死亡。在当地，亚利桑那州有超过11K例和562例 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，描述了 患者在体征、症状和临床终点方面的特征。此外，流行病学家和 传染病研究人员利用下一代测序技术生产出完整的 用于临床和流行病学调查的病毒基因组。基因组流行病学使科学家能够 了解本地化传播，同时确定不同来源的引种的地理来源 州和国家。然而，SARS-CoV-2(以及其他病毒)的大部分测序是 在州或地方卫生部门之外进行，如疾病控制和预防中心 (CDC)、大学或私人实验室。然后，一旦对病原体进行测序，就很难将其与 卫生部门为病例调查收集的数据。在以下情况下，这会抑制基因组流行病学 病毒分离株序列和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；特别是对于 新冠肺炎。因此，尽管在这次大流行期间收集了丰富的临床和流行病学数据， 需要进行更多的信息学研究，以了解如何将病毒遗传和流行病学数据联系起来 证明这对疾病监测的价值。 本附录的目的是将亚利桑那州新冠肺炎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和 流行病学和临床表型。我们将利用亚利桑那州的疾病监测来实现这一点 系统以及在线核酸数据库中公布的可用序列和元数据。我们将使用 使用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 系统发育学和系统地理学，研究流行病学病例的聚集性(目标2)。流行病学家可以使用 这些发现有助于了解本地病毒如何在基因上聚集性与特定的 流行病学和临床病例。虽然疾病的严重程度取决于个人的免疫反应和 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 为未来动物模型的反向遗传学和免疫学研究而生。

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.