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Updated: Jan 7, 2026

Quantification and Whole Genome Characterization of SARS-CoV-2 RNA in Wastewater and Air Samples
Published on: June 30, 2023
Community-level wastewater surveillance with machine learning methods to assess underreporting of COVID-19 case
Nathan Szeto1, Jianfeng Wu2, Yili Wang1,3
1Department of Biostatistics University of Michigan School of Public Health Ann Arbor Michigan USA.
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.
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