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

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.