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

新型冠状病毒SARS-CoV-2正在造成严重的发病率和死亡率。目前应对SARS的方法-- CoV-2检测成本高昂、实施不一致，而且无法快速识别不断演变的疫情。创新型 迫切需要监测方案，以更好地衡量基线传播动态并预测 新的局部暴发。基于废水的检测(WBT)有可能使人口水平 监测，启动对急性疫情的更早区域反应，并克服个别检测的障碍 例如耻辱和缺乏准入。因此，WBT可以实现更快、更便宜的病原体检测和 改进对流行率的人口水平估计。这种新型冠状病毒的可靠捕获方法 WBT目前还没有定义。必须考虑污水运输过程中的病毒动态，并将其与 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 包括宿舍在内的城市大学校园垃圾流的WBT监测协议，研究 设施和一所三级护理医院，周围有污水处理厂和污水处理厂。我们会检测到 用qRT-PCR评估SARS-CoV-2的流行率和病毒群富集型转位手术 描述病毒的多样性。我们将使用标准化的WBT数据对病例计数进行建模，并开发使用点 用于WBT的微流控系统。我们的调查团队在这项研究中处于独特的地位，具有以下专业知识 传染病、流行病学、在国家范围内使用WBT的微生物特征，以及医疗点 测试。我们将实施三个相辅相成的具体目标。在目标1中，我们将优化(1a)收集和 处理以确定WBT的敏感性和安全性。这包括抓取与复合抽样；)过滤与 基于沉淀的浓缩和病毒灭活方案。我们将进一步优化规模和频率。 在建筑物/下水道坑、校园、污水池和污水处理厂以及不同频率上进行采样(1b)。 用QRT-PCR和长片段扩增技术确定SARS-CoV-2的存在 变态剪辑学。WBT结果将与临床病例量、现有监测队列和 扩大了对员工的监控。在目标2中，我们将改进sars-cov-2病例动力学的建模。 外推WBT数据和特定地点的归一化因素。我们将把模型化建筑、校园和 利用现有临床发病率数据进行社区级病例计数，并使用系综进行校园监测 卡尔曼滤波(EnKF)动态模型结合了qRT-PCR和元转录组学数据。我们会 比较考虑废水停留时间、人均病毒载量当量的归一化方法 (PCVLEs)和其他垃圾流动参数，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WBT协议的微流控技术测试能力。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WBT样本的模块化系统，并使用病毒控制来确定化验检测阈值。

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.