# Improved scalability, sensitivity, and interpretability of pathogen detection, including SARS-CoV-2, in wastewater using high-throughput, highly multiplexed digital array PCR technology

> **NIH NIH U01** · UNIV OF NORTH CAROLINA CHAPEL HILL · 2021 · $997,507

## Abstract

PROJECT ABSTRACT
Presently, the application of molecular technology such as RT-qPCR and digital PCR (dPCR) to quantify
SARS-CoV-2 and related targets in wastewater is cumbersome, time consuming, and costly. While
progress has been made on the development of methods and the interpretation of data, much remains to
be improved for the technology to be used as a public health management tool. A major drawback in the
current approaches are 1) the lack of streamlined and consistent pre-analytical processing steps, 2)
coverage across the relevant targets requires a high number of reactions (>20) from any single sample to
provide quantitative information, and 3) a lack of vision on the development of a pathogen/marker panel,
much like those used in clinical arenas, for interpretation of the data across different states, regions and
nations. The goal of this project will be to successfully navigate these three limitations toward
development of a public health warning system that is not dependent on clinical testing and has the ability
to rapidly address novel pathogen threats in the future.

## Key facts

- **NIH application ID:** 10264252
- **Project number:** 1U01DA053899-01
- **Recipient organization:** UNIV OF NORTH CAROLINA CHAPEL HILL
- **Principal Investigator:** Rachel Todd Noble
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $997,507
- **Award type:** 1
- **Project period:** 2021-01-01 → 2022-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10264252

## Citation

> US National Institutes of Health, RePORTER application 10264252, Improved scalability, sensitivity, and interpretability of pathogen detection, including SARS-CoV-2, in wastewater using high-throughput, highly multiplexed digital array PCR technology (1U01DA053899-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10264252. Licensed CC0.

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