Optimizing SARS-CoV-2 wastewater based surveillance in urban and university campus settings.

优化城市和大学校园环境中基于 SARS-CoV-2 废水的监测。

基本信息

批准号：
10320993
负责人：
Kartik Chandran
金额：
$ 230.41万
依托单位：
COLUMBIA UNIVERSITY HEALTH SCIENCES
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-01-01 至 2024-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10320993
关键词：
2019-nCoV Acute Area Biological Assay COVID-19 COVID-19 detection COVID-19 diagnostic COVID-19 monitoring COVID-19 pandemic COVID-19 risk COVID-19 surveillance COVID-19 test COVID-19 testing Cessation of life Characteristics Cities Clinical Collection Communities Consult Country County Data Detection Development Disease Outbreaks Employee Engineering Environmental Protection Feces Filtration Frequencies Funding Goals Hospitals Incidence Individual Infectious Disease Epidemiology Measures Medical Methods Microfluidics Modality Modeling Morbidity - disease rate New York City Pathogen detection Patients Phase Plant Model Plants Population Population Density Positioning Attribute Precipitation Prevalence Property Protocols documentation Quantitative Reverse Transcriptase PCR RADx RADx Radical RNA Viruses Research Research Personnel SARS-CoV-2 transmission Safety Saliva Sampling Site Stream Students Surveillance Program System Test Result Testing Time Tissue Sample United States United States National Institutes of Health Universities Viral Viral Load result Virus Inactivation base betacoronavirus cohort cost cost effective detection assay detection limit detection sensitivity experience improved inner city innovation metatranscriptomics microbial model building mortality nanopore nasopharyngeal swab novel novel coronavirus novel strategies pandemic disease pathogenic virus point of care point of care testing research clinical testing research facility residence response social stigma surveillance strategy tertiary care transmission process undergraduate student urban setting viral genomics wastewater surveillance wastewater testing wasting

项目摘要

The novel coronavirus SARS-CoV-2 is causing significant morbidity and mortality. Current approaches to SARS- CoV-2 testing are costly, inconsistently implemented, and fail to rapidly identify evolving outbreaks. Innovative surveillance programs are urgently needed to better measure baseline transmission dynamics and anticipate new localized outbreaks. Wastewater based testing (WBT) has the potential to enable population level surveillance, trigger earlier regional responses to acute outbreaks, and overcome barriers to individual testing such as stigma and lack of access. WBT could therefore enable faster and cheaper pathogen detection and improve population-level estimates of prevalence. Reliable capture approaches for this novel coronavirus using WBT are currently undefined. Viral dynamics during wastewater transport must be considered, and correlation of WBT with clinical testing must be systematically evaluated at multiple scales. Here, we propose to optimize WBT surveillance protocols of waste streams at an urban university campus encompassing dorms, research facilities and a tertiary care hospital, surrounding sewershed and wastewater treatment plant. We will detect SARS-CoV-2 using qRT-PCR to estimate prevalence and viral panel-enriched metatranscriptomics to characterize viral diversity. We will model case counts using normalized WBT data and develop point-of-use microfluidics systems for WBT. Our team of investigators is uniquely positioned for this study, with expertise in infectious diseases, epidemiology, microbial characterization using WBT at national scales, and point-of-care testing. We will implement three complimentary specific aims. In Aim 1, we will optimize (1a) collection and processing to determine sensitivity and safety of WBT. This includes grab vs. composite sampling;) filtration- vs. precipitation-based enrichment; and viral inactivation protocols. We will further optimize scale and frequency of sampling (1b) at the building/sewer pit, campus, sewershed, and WWTP, and across various frequencies. Presence of SARS-CoV-2 will be ascertained by qRT-PCR and long-read spiked-primer enriched metatranscriptomics. WBT results will be integrated with clinical case-loads, existing surveillance cohorts and expanded employee surveillance. In Aim 2. we will improve modeling of SARS-CoV-2 case dynamics using extrapolated WBT data and site-specific normalization factors. We will correlate modeled building-, campus- and community-level case counts with existing clinical incidence data and campus surveillance using ensemble Kalman filter (EnKF) dynamic modeling incorporating both qRT-PCR and metatranscriptomics data. We will compare normalization methods factoring in wastewater residence time, per capita viral load equivalents (PCVLEs), and other waste flow parameters to reduce model error. Finally, in Aim 3, we will adapt point-of-use testing capabilities using microfluidics based on optimized WBT protocols. We will apply existing RADx development of a photothermal amplification system for SARS-CoV-2 detection to optimized WBT practices. We will develop a modular system for WBT samples and determine assay detection thresholds using viral controls.

新型冠状病毒 SARS-CoV-2 正在导致显着的发病率和死亡率。目前应对 SARS 的方法 CoV-2 检测成本高昂、实施不一致，并且无法快速识别不断变化的疫情。创新的迫切需要监测计划来更好地测量基线传播动态并预测新的局部疫情。基于废水的测试 (WBT) 有潜力提高人口水平监测，尽早引发区域对急性疫情的反应，并克服个人检测的障碍例如耻辱和缺乏接触机会。因此，WBT 可以实现更快、更便宜的病原体检测，改进人口层面的患病率估计。使用这种新型冠状病毒的可靠捕获方法 WBT 目前尚未定义。必须考虑废水运输过程中的病毒动力学，以及相关性 WBT 与临床测试的结合必须在多个层面进行系统评估。在此，我们建议优化城市大学校园（包括宿舍、研究室）废物流的 WBT 监测协议设施和一家三级护理医院，周围有下水道和废水处理厂。我们将检测使用 qRT-PCR 估计 SARS-CoV-2 患病率和病毒组富集宏转录组学表征病毒多样性。我们将使用标准化 WBT 数据对病例数进行建模并开发使用点 WBT 的微流体系统。我们的研究团队在这项研究中拥有独特的优势，拥有以下方面的专业知识：在国家范围内使用 WBT 进行传染病、流行病学、微生物表征以及护理点测试。我们将实现三个互补的具体目标。在目标 1 中，我们将优化 (1a) 收集和确定 WBT 的敏感性和安全性的处理。这包括抓取与复合采样；）过滤与过滤。基于降水的富集；和病毒灭活方案。我们将进一步优化活动规模和频次在建筑物/下水道坑、校园、下水道和污水处理厂采样 (1b)，并以不同的频率进行采样。 SARS-CoV-2 的存在将通过 qRT-PCR 和长读长尖峰引物富集来确定宏转录组学。 WBT 结果将与临床病例数、现有监测队列和扩大员工监控。在目标 2 中，我们将使用以下方法改进 SARS-CoV-2 病例动态建模推断 WBT 数据和特定地点标准化因子。我们将把模型建筑、校园和利用现有临床发病率数据进行社区级病例计数，并使用整体进行校园监测卡尔曼滤波器 (EnKF) 动态建模结合了 qRT-PCR 和宏转录组数据。我们将比较考虑废水停留时间、人均病毒载量当量的标准化方法 (PCVLE) 和其他废物流参数，以减少模型误差。最后，在目标 3 中，我们将调整使用点使用基于优化的 WBT 协议的微流体进行测试能力。我们将应用现有的 RADx 开发用于 SARS-CoV-2 检测的光热放大系统，以优化 WBT 实践。我们将开发用于 WBT 样本的模块化系统，并使用病毒对照确定测定检测阈值。