Bioinformatics Framework for Wastewater-based Surveillance of Infectious Diseases

项目摘要 新冠肺炎有望成为美国历史上最大规模的大规模伤亡事件之一 州(美国)。需要对人口中疾病的真实负担进行评估，以预防和 缓解这一情况和未来的病毒疾病暴发。目前，新病例检测(通过拭子/唾液)和 那些以前接触过(通过血清)的人在人群中的接触范围有限。然而，另一种方法是 依靠对社区废水的分析，每周可以对高达70%的美国人口进行筛查 个人临床筛查费用的0.01%。作为一种全人群的传染病 废水流行病学(WBE)这一监测工具可以用来补充目前的监测 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。 我们建议的RADx-rad补充的目标是实现和评估一个接近实时的WBE SARS-CoV-2框架，通过(I)近乎实时地评估新冠状病毒的社区传播，(Ii) 显著增加了接受筛查的美国人口比例，这一检测的频率 完成(每周)和筛选的地理空间分辨率(从全市范围到特定社区)，(3) 废水中新型冠状病毒水平与已报道的感染、疾病和死亡负担的比较 通过当地卫生系统，(Iv)收集关于SARS-CoV-2变异株的高通量序列数据 美国，(V)优化HTS分析管道，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 扩展我们的在线仪表板，这是由求婚团队与伦敦金融城合作开创的 亚利桑那州坦佩 我们将利用以前开发的、经过同行审查的策略，通过以下方式监控人口范围的病毒 逆转录实时聚合酶链式反应(RT-qPCR)、HTS、序列分析和数据 通信最初是为我们的父级奖项开发的，以快速提供数据流和科学 在美国管理新冠肺炎疫情的资源。 在目标1中，为SARS-CoV-2开发基于废水的流行病学生物信息学框架，网址为 国家、城市和下水道内或居民区一级生产RT-qPCR和SARS-CoV-2 RNA-seq 为研究社区内病毒水平和遗传多态的分布提供了数据。在目标2中，我们将 评估WBE生物信息学框架，用于将SARS-CoV-2数据从RT-qPCR和High-QPCR中转换 将吞吐量排序纳入监测人口健康的信息中。 这一生物医学信息学项目的成功完成将为美国提供早期预警 用于SARS-CoV-2检测的系统和公共卫生流行病学家寻求减少 美国新冠肺炎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.