Artificial neural network-based estimation of COVID-19 case numbers and effective reproduction rate using wastewater-based epidemiology.

As a cost-effective and objective population-wide surveillance tool, wastewater-based epidemiology (WBE) has been widely implemented worldwide to monitor the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA concentration in wastewater. However, viral concentrations or loads in wastewater often correlate poorly with clinical case numbers. To date, there is no reliable method to back-estimate the coronavirus disease 2019 (COVID-19) case numbers from SARS-CoV-2 concentrations in wastewater. This greatly limits WBE in achieving its full potential in monitoring the unfolding pandemic. The exponentially growing SARS-CoV-2 WBE dataset, on the other hand, offers an opportunity to develop data-driven models for the estimation of COVID-19 case numbers (both incidence and prevalence) and transmission dynamics (effective reproduction rate). This study developed artificial neural network (ANN) models by innovatively expanding a conventional WBE dataset to include catchment, weather, clinical testing coverage and vaccination rate. The ANN models were trained and evaluated with a comprehensive state-wide wastewater monitoring dataset from Utah, USA during May 2020 to December 2021. In diverse sewer catchments, ANN models were found to accurately estimate the COVID-19 prevalence and incidence rates, with excellent precision for prevalence rates. Also, an ANN model was developed to estimate the effective reproduction number from both wastewater data and other pertinent factors affecting viral transmission and pandemic dynamics. The established ANN model was successfully validated for its transferability to other states or countries using the WBE dataset from Wisconsin, USA.

Correlation of SARS-CoV-2 RNA in wastewater with COVID-19 disease burden in sewersheds.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes the coronavirus disease (COVID-19), is shed in feces and the viral ribonucleic acid (RNA) is detectable in wastewater. A nine-week wastewater epidemiology study of ten wastewater facilities, serving 39% of the state of Utah or 1.26 M individuals was conducted in April and May of 2020. COVID-19 cases were tabulated from within each sewershed boundary. RNA from SARS-CoV-2 was detectable in 61% of 126 wastewater samples. Urban sewersheds serving >100,000 individuals and tourist communities had higher detection frequencies. An outbreak of COVID-19 across two communities positively correlated with an increase in wastewater SARS-CoV-2 RNA, while a decline in COVID-19 cases preceded a decline in RNA. SARS-CoV-2 RNA followed a first order decay rate in wastewater, while 90% of the RNA was present in the liquid phase of the influent. Infiltration and inflow, virus decay and sewershed characteristics should be considered during correlation analysis of SAR-CoV-2 with COVID-19 cases. These results provide evidence of the utility of wastewater epidemiology to assist in public health responses to COVID-19.