ABSTRACT
BackgroundWastewater-based epidemiology (WBE) has been deployed broadly as an early warning tool for emerging COVID-19 outbreaks. WBE can inform targeted interventions and identify communities with high transmission, enabling quick and effective response. As wastewater becomes an increasingly important indicator for COVID-19 transmission, more robust methods and metrics are needed to guide public health decision making. ObjectivesThe aim of this research was to develop and implement a mathematical framework to infer incident cases of COVID-19 from SARS-CoV-2 levels measured in wastewater. We propose a classification scheme to assess the adequacy of model training periods based on clinical testing rates and assess the sensitivity of model predictions to training periods. MethodsWe present a Bayesian deconvolution method and linear regression to estimate COVID-19 cases from wastewater data. We described an approach to characterize adequacy in testing during specific time periods and provided evidence to highlight the importance of model training periods on the projection of cases. We estimated the effective reproductive number (Re) directly from observed cases and from the reconstructed incidence of cases from wastewater. The proposed modeling framework was applied to three Northern California communities served by distinct wastewater treatment plants. ResultsBoth deconvolution and linear regression models consistently projected robust estimates of prevalent cases and Re from wastewater influent samples when assuming training periods with adequate testing. Case estimates from models that used poorer-quality training periods consistently underestimated observed cases. DiscussionWastewater surveillance data requires robust statistical modeling methods to provide actionable insight for public health decision-making. We propose and validate a modeling framework that can provide estimates of COVID-19 cases and Re from wastewater data that can be used as tool for disease surveillance including quality assessment for potential training data.
