What is it about?

Wastewater monitoring has provided health officials with early warnings for new COVID-19 outbreaks, but to date, no approach has been validated to distinguish signal (sustained surges) from noise (background variability) in wastewater data to alert officials to the need for heightened public health response. After considering patterns across several metrics (detectability, percent change, and the magnitude of viral concentrations), Mathematica researchers developed a simple algorithm that successfully identified the start of the Delta and Omicron surges, with a true positive rate of 82%, false positive rate of 7%.

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Why is it important?

Many public health officials struggle with interpreting wastewater data, in part because commonly reported wastewater metrics do not indicate when action is needed. The Covid-SURGE algorithm fills that gap by providing a reliable way to flag community-level surges in real time. The algorithm showed strong performance for two very different variant-based surges, in sites with small and large wastewater treatment plants, and across multiple states.


This algorithm is an exciting step towards the type of predictive wastewater analytics that empower public health officials act early to suppress disease outbreaks. Our goal with this work was to translate complex lab measures into a simple alert that indicates when action may be needed. The logical criteria we lay forth provide a foundation to finetune Covid-SURGE for other biomarkers, states, and as COVID-19 becomes endemic.

Aparna Keshaviah
Mathematica Inc

Read the Original

This page is a summary of: Separating signal from noise in wastewater data: An algorithm to identify community-level COVID-19 surges in real time, Proceedings of the National Academy of Sciences, July 2023, Proceedings of the National Academy of Sciences, DOI: 10.1073/pnas.2216021120.
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