Community‐level wastewater surveillance with machine learning methods to assess underreporting of COVID‐19 case counts

2025/12/01 by Nathan Szeto, Jianfeng Wu, Yili Wang +9 · 1 voice
Medicine · Environmental Science · Mathematics · #SARS-CoV-2 detection and testing #Fecal contamination and water quality #COVID-19 epidemiological studies

paper · pdf · doi:10.1002/mlf2.70055

openalex publication_date 2025/12/01 · openalex created_date 2025/12/03 · openalex updated_date 2026/07/23

Abstract

COVID-19 remains an ongoing threat to public health, and reliable, continuous disease monitoring programs are essential for preventing future surges of infection. However, without mandated COVID-19 testing, accurate data of confirmed cases are unavailable. Instead, COVID-19 viruses may be tracked via wastewater samples from sewage manholes in areas of high social connectivity, where captured viral RNA data are biomarkers useful for monitoring and predicting community-level COVID-19 prevalence through machine learning techniques. We construct a prediction model of high sensitivity and specificity to provide evidence of significant underreporting of COVID-19 cases for the time period following the lifting of testing mandates.

Citations

• Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV-2)

Discussions

• New in #mLife Using wastewater RNA and machine learning, this work developed a predictive model that showed significant underreporting of COVID-19 cases in a university community after testing mandate [bsky, 2 points, 0 comments]

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