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

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. Innovativesurveillance programs are urgently needed to better measure baseline transmission dynamics and anticipatenew localized outbreaks. Wastewater based testing (WBT) has the potential to enable population levelsurveillance, trigger earlier regional responses to acute outbreaks, and overcome barriers to individual testingsuch as stigma and lack of access. WBT could therefore enable faster and cheaper pathogen detection andimprove population-level estimates of prevalence. Reliable capture approaches for this novel coronavirus usingWBT are currently undefined. Viral dynamics during wastewater transport must be considered, and correlationof WBT with clinical testing must be systematically evaluated at multiple scales. Here, we propose to optimizeWBT surveillance protocols of waste streams at an urban university campus encompassing dorms, researchfacilities and a tertiary care hospital, surrounding sewershed and wastewater treatment plant. We will detectSARS-CoV-2 using qRT-PCR to estimate prevalence and viral panel-enriched metatranscriptomics tocharacterize viral diversity. We will model case counts using normalized WBT data and develop point-of-usemicrofluidics systems for WBT. Our team of investigators is uniquely positioned for this study, with expertise ininfectious diseases, epidemiology, microbial characterization using WBT at national scales, and point-of-caretesting. We will implement three complimentary specific aims. In Aim 1, we will optimize (1a) collection andprocessing 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 ofsampling (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 enrichedmetatranscriptomics. WBT results will be integrated with clinical case-loads, existing surveillance cohorts andexpanded employee surveillance. In Aim 2. we will improve modeling of SARS-CoV-2 case dynamics usingextrapolated WBT data and site-specific normalization factors. We will correlate modeled building-, campus- andcommunity-level case counts with existing clinical incidence data and campus surveillance using ensembleKalman filter (EnKF) dynamic modeling incorporating both qRT-PCR and metatranscriptomics data. We willcompare 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-usetesting capabilities using microfluidics based on optimized WBT protocols. We will apply existing RADxdevelopment of a photothermal amplification system for SARS-CoV-2 detection to optimized WBT practices. Wewill develop a modular system for WBT samples and determine assay detection thresholds using viral controls.