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

> **NIH NIH U01** · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2022 · $2,304,069

## Abstract

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
d...

## Key facts

- **NIH application ID:** 10320993
- **Project number:** 4U01DA053949-02
- **Recipient organization:** COLUMBIA UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** Kartik Chandran
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $2,304,069
- **Award type:** 4N
- **Project period:** 2021-01-01 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10320993

## Citation

> US National Institutes of Health, RePORTER application 10320993, Optimizing SARS-CoV-2 wastewater based surveillance in urban and university campus settings. (4U01DA053949-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10320993. Licensed CC0.

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