# Using wastewater surveillance data to study SARS-CoV-2 dynamics and predict COVID-19 outcomes

> **NIH NIH R21** · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2024 · $205,625

## Abstract

Using wastewater surveillance data to study SARS-CoV-2 dynamics and predict COVID-19 outcomes
Due to the continued evolution of SARS-CoV-2 and emergence of new variants, COVID-19 will likely continue
to impose a substantial public health burden in the United States in the future. Yet, the rollback of clinical
testing programs and increased use of at-home tests nationwide will exacerbate under-detection of SARS-
CoV-2 infections, hindering timely public health situation awareness and intervention. Thus, development of
modeling tools to tackle this surveillance challenge is urgently needed and the goal of this application. We
propose to use wastewater surveillance data to study SARS-CoV-2 dynamics and predict COVID-19 cases,
hospitalizations, and deaths 1 to 6 weeks in the future. The proposed core model-inference/prediction system
will combine mechanistic models depicting SARS-CoV-2 transmission in the general population and the
ensemble adjustment Kalman filter (EAKF) to incorporate SARS-CoV-2 wastewater surveillance data for
inference. We will pilot-test this system using both rich data (wastewater surveillance and multiple COVID-19
outcomes) and detailed model estimates (e.g., infection prevalence) available for New York City (Aim 1). We
will then expand and test the system on 50+ counties across the United States (Aim 2). Using these models,
we will further create an easy-to-use modeling tool for public health officials (Aim 3). The proposed work is
Innovative and Robust in that 1) SARS-CoV-2 concentration in wastewater represents a composite measure
of SARS-CoV-2 presence in the population, regardless of individual testing behavior; 2) We will build prediction
systems that go beyond the situation awareness afforded by wastewater surveillance alone. We will design the
model-prediction system to be 3) flexible using modularized model components to accommodate diverse data
availability across locations and 4) robust by leveraging detailed data and estimates for New York City and 50+
counties to test and improve various model forms and quantify the uncertainty and accuracy of each model.
Further, the Investigator Team has synthesized expertise in wastewater surveillance and modeling, and will
work closely with public health officials to tailor the modeling system to public health need. With SARS-CoV-2
wastewater surveillance widely adopted in many communities (currently representing 100+ million Americans),
the model-prediction system developed here can support more proactive COVID-19 planning in the future.

## Key facts

- **NIH application ID:** 10762470
- **Project number:** 5R21AI175747-02
- **Recipient organization:** COLUMBIA UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** Wan Yang
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $205,625
- **Award type:** 5
- **Project period:** 2023-01-10 → 2025-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10762470

## Citation

> US National Institutes of Health, RePORTER application 10762470, Using wastewater surveillance data to study SARS-CoV-2 dynamics and predict COVID-19 outcomes (5R21AI175747-02). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10762470. Licensed CC0.

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