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。

• 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
• 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

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正在导致显著的发病率和死亡率。目前对SARS的处理方法- CoV-2检测成本高，实施不一致，无法快速识别不断演变的疫情。创新 迫切需要监测项目来更好地测量基线传播动态， 新的局部爆发。基于废水的测试（WBT）有可能使人口水平 监测，触发对急性疫情的早期区域反应，并克服个人检测的障碍 例如耻辱和缺乏机会。因此，WBT可以实现更快，更便宜的病原体检测， 改进人口一级流行率估计数。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， 必须在多个尺度上系统地评估WBT与临床试验的关系。在这里，我们建议优化 WBT监测协议的废物流在城市大学校园，包括宿舍，研究 设施和三级保健医院，周围的污水处理厂和废水处理厂。我们会发现 使用qRT-PCR估计SARS-CoV-2的流行率，使用病毒组富集的元转录组学估计 表征病毒多样性。我们将使用规范化的WBT数据对病例计数进行建模，并开发使用点 用于WBT的微流体系统。我们的研究者团队在这项研究中处于独特的地位，他们拥有以下方面的专业知识： 传染病、流行病学、在全国范围内使用WBT进行微生物表征，以及床旁检测 试验.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， 处理以确定WBT的敏感性和安全性。这包括抓取与复合采样;）过滤与 基于沉淀的富集;和病毒灭活方案。我们将进一步优化规模和频率， 在建筑物/下水道坑、校园、下水道和污水处理厂以及不同频率进行采样（1b）。 将通过qRT-PCR和长读段加标引物富集来确定SARS-CoV-2的存在 元转录组学WBT结果将与临床病例负荷、现有监测队列和 扩大员工监控范围在目标2中。我们将改进SARS-CoV-2病例动态的建模， 外推的WBT数据和站点特定的归一化因子。我们将把模型建筑、校园和 社区一级病例计数与现有的临床发病率数据和校园监测使用合奏 卡尔曼滤波器（EnKF）动态建模结合qRT-PCR和元转录组学数据。我们将 比较归一化方法，考虑废水停留时间、人均病毒载量当量 （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 基于优化的WBT协议，使用微流体测试能力。我们将应用现有的RADx 开发用于SARS-CoV-2检测的光热扩增系统，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检测阈值。