Bioinformatics Framework for Wastewater-based Surveillance of Infectious Diseases

项目摘要 COVID-19预计将成为美国历史上最大的大规模伤亡事件之一 美国）。需要评估人口中疾病的真实负担， 缓解这种和未来的病毒性疾病爆发。目前，对新病例的检测（通过拭子/唾液）和 以前接触过（通过血清）的人在人口中的影响有限。然而，另一种方法 依靠对社区废水的分析，每周可以筛查多达70%的美国人口。 根据<0.01%的个人临床筛查费用。作为一种人群性传染病 一种监测工具--基于废水的流行病学（WBE）可用于补充目前的监测 更好地了解疾病负担以及这些负担在不同社区之间的差异的方法。 我们提出的RADx-rad补充的目标是实现和评估近实时WBE SARS-CoV-2的框架，（i）近实时评估新型冠状病毒的社区传播，（ii） 显着增加美国人口的比例筛选，这种测试的频率正在 完成（每周）和筛选的地理空间分辨率（从全市范围到具体社区），㈢ 将废水中的新型冠状病毒水平与报告的感染、疾病和死亡率负担进行比较 （iv）收集SARS-CoV-2变异体的高通量序列（HTS）数据， 美国，(v)优化高温超导分析的管道，以及（vi）立即分享所获得的任何新知识 与RADx-rad数据协调中心（DCC），研究社区和公众通过一个 扩展我们的在线仪表板，这是由提议团队与城市合作开创的。 滕佩，亚利桑那州。 我们将利用先前开发的、经过同行评审的策略，通过以下方式进行全人群的病毒监测： 逆转录实时聚合酶链反应（RT-qPCR）、HTS、序列分析和数据 通信最初是为我们的父母奖开发的，以快速提供数据流和科学 管理美国COVID-19疫情的资源 在目标1中，为SARS-CoV-2建立一个基于废水的流行病学（WBE）生物信息学框架， 国家、城市和下水道内或社区一级生产RT-qPCR和SARS-CoV-2 RNA-seq 用于研究社区中病毒水平和遗传多态性分布的数据。在目标2中，我们 评估WBE生物信息学框架，用于从RT-qPCR和高通量PCR中翻译SARS-CoV-2数据。 将生产量排序为监测人口健康的信息。 这个生物医学信息学项目的成功完成将为美国提供早期预警 SARS-CoV-2检测系统和公共卫生流行病学家的跟踪援助， 美国COVID-19等传染病的发病率和死亡率。

Project Summary COVID-19 is expected to become one of the largest mass casualty events in the history of the United States (U.S.). Assessment of the true burden of disease in the population is needed for the prevention and mitigation of this and future viral disease outbreaks. Currently, testing of new cases (via swabs / saliva) and those previously exposed (via serum) has limited reach in the population. However, an alternative approach relying on the analysis of community wastewater can screen up to 70% of the U.S. population on a weekly basis at <0.01% of the cost of clinical screening of individuals. As a population-wide infectious disease surveillance tool, wastewater-based epidemiology (WBE) can be used to complement current surveillance methods to better understand disease burden and how these burdens differ across communities. The goal of our proposed RADx-rad supplement is to implement and evaluate a near real-time WBE framework for SARS-CoV-2 by (i) assessing in near real-time community spread of the new coronavirus, (ii) significantly increasing the fraction of the U.S. population screened, the frequency at which this testing is being completed (weekly) and the geospatial resolution of screening (from city-wide to neighborhood-specific), (iii) comparing novel coronavirus levels in wastewater with the burdens of infection, disease and mortality reported by local health systems, (iv) harvesting high throughput sequence (HTS) data on SARS-CoV-2 variants across the U.S., (v) optimizing pipelines for HTS analysis, and (vi) immediately sharing any new knowledge gained with the RADx-rad Data Coordinating Center (DCC), research community, and the general public via an expansion of our online dashboard that was pioneered by the proposing team in collaboration with the City of Tempe, AZ. We will leverage previously developed, peer-reviewed strategies for population-wide virus monitoring via reverse transcription real-time polymerase chain reaction (RT-qPCR), HTS, sequence analysis, and data communication originally developed for our parent award to quickly provide a data stream and scientific resource for managing the COVID-19 epidemic in the U.S. In Aim 1, develop a wastewater-based epidemiology (WBE) bioinformatics framework for SARS-CoV-2 at the national, city and intra-sewershed or neighborhood-level to produce RT-qPCR and SARS-CoV-2 RNA-seq data for studying the distribution of viral levels and genetic polymorphisms in the community. In Aim 2, we will evaluate a WBE bioinformatics framework for translating SARS-CoV-2 data from RT-qPCR and high- throughput sequencing into information for monitoring population health. Successful completion of this biomedical informatics project will provide the U.S. with an early warning system for SARS-CoV-2 detection and a tracking aid for public health epidemiologists seeking to reduce morbidity and mortality from infectious diseases like COVID-19 in the U.S.