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.

新型的冠状病毒SARS-COV-2正在引起明显的发病率和死亡率。当前的SARS- COV-2测试是昂贵的，不一致的，并且无法快速识别不断发展的爆发。创新的 迫切需要进行监视计划，以更好地衡量基线传输动态并预测 新的局部暴发。基于废水的测试（WBT）有可能使人口水平 监视，触发急性暴发的早期区域反应，并克服对个体测试的障碍 例如污名和缺乏访问权限。因此，WBT可以使病原体检测更快，更便宜 改善人口级别的估计。使用这种新型冠状病毒的可靠捕获方法 WBT目前未定义。必须考虑废水运输过程中的病毒动态，并相关性 必须在多个尺度上系统地评估具有临床测试的WBT的。在这里，我们建议优化 城市大学校园中废物流的WBT监视协议，包括宿舍，研究 设施和三级护理医院，周围的下水道和废水处理厂。我们将检测到 SARS-COV-2使用QRT-PCR估计患病率和富含病毒面板的元文字组学 特征病毒多样性。我们将使用归一化的WBT数据对案例计数进行建模并开发使用点 WBT的微流体系统。我们的调查人员团队在这项研究方面具有独特的位置，并具有专业知识 传染病，流行病学，使用WBT在国家尺度上的微生物表征和护理点 测试。我们将实施三个免费的特定目标。在AIM 1中，我们将优化（1a）集合，并 处理以确定WBT的灵敏度和安全性。这包括抓取与复合抽样的;）过滤-VS。 基于降水的富集；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，校园，下水道和WWTP以及各种频率上进行采样（1B）。 SARS-COV-2的存在将通过QRT-PCR和长阅读的尖刺富集来确定 metatranscriptomics。 WBT结果将与临床病例负载，现有的监视队列和 扩大的员工监视。在AIM 2中。我们将使用使用SARS-COV-2案例动态的建模 推断的WBT数据和特定于现场的归一化因子。我们将将建模的建筑物 - 校园和 社区级案例计数现有的临床发病率数据和校园监视 Kalman Filter（ENKF）的动态建模均包含QRT-PCR和Metatranscriptomics数据。我们将 比较废水停留时间中的标准化方法，人均病毒载荷等效物 （PCVLE）和其他废物流参数，以减少模型误差。最后，在AIM 3中，我们将适应使用点 使用基于优化的WBT协议的微流体学测试功能。我们将应用现有的RADX 开发用于优化WBT实践的SARS-COV-2检测的光热扩增系统。我们 将开发用于WBT样品的模块化系统，并使用病毒对照确定测定检测阈值。