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.
ABSTRACT
Wastewater surveillance is a useful complement to clinical testing for managing COVID-19. While good agreement has been found between community-scale wastewater and clinical data, little is known about sub-community relationships between the two data types. Moreover, effects of non-detects in qPCR wastewater data have been largely overlooked. We used data collected from September 2020-June 2021 in Davis, California (USA) to address these gaps. By applying a predictive probability model to spatially disaggregate clinical results, we compared wastewater and clinical data at the community scale, in 16 sampling zones isolating city sub-regions, and in seven zones isolating high-priority building complexes or neighborhoods. We found reasonable agreement between wastewater and clinical data at all scales. Greater activity (i.e., more frequent detections) in clinical data tended to be mirrored in wastewater data. Small, isolated clinical-data spikes were often matched as well. We also developed a method for handling such non-detects using multiple imputation and compared results to (i) single imputation using half the qPCR limit of detection, (ii) single imputation using maximum qPCR cycle number, and (iii) non-detect censoring. Apparent wastewater trends were significantly influenced by non-detect handling. Multiple imputation improved correlation relative to single imputation, though not necessarily relative to censoring. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=78 SRC="FIGDIR/small/22269911v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@5a57f5org.highwire.dtl.DTLVardef@144cf73org.highwire.dtl.DTLVardef@8fa56borg.highwire.dtl.DTLVardef@b52587_HPS_FORMAT_FIGEXP M_FIG C_FIG
ABSTRACT
Testing surfaces in school classrooms for the presence of SARS-CoV-2, the virus that causes COVID-19, can provide public-health information that complements clinical testing. We monitored the presence of SARS-CoV-2 RNA in five schools (96 classrooms) in Davis, California (USA) by collecting weekly surface-swab samples from classroom floors and/or portable high-efficiency particulate air (HEPA) units. Twenty-two surfaces tested positive, with qPCR cycle threshold (Ct) values ranging from 36.07-38.01. Intermittent repeated positives in a single room were observed for both floor and HEPA filter samples for up to 52 days, even following regular cleaning and HEPA filter replacement after a positive result. We compared the two environmental sampling strategies by testing one floor and two HEPA filter samples in 57 classrooms at Schools D and E. HEPA filter sampling yielded 3.02% and 0.41% positivity rates per filter sample collected for Schools D and E, respectively, while floor sampling yielded 0.48% and 0% positivity rates. Our results indicate that HEPA filter swabs are more sensitive than floor swabs at detecting SARS-CoV-2 RNA in interior spaces. During the study, all schools were offered weekly free COVID-19 clinical testing. On-site clinical testing was offered in Schools D and E, and upticks in testing participation were observed following a confirmed positive environmental sample. However, no confirmed COVID-19 cases were identified among students associated with classrooms yielding positive environmental samples. The positive samples detected in this study appeared to reflect relic viral RNA from individuals infected before the monitoring program started and/or RNA transported into classrooms via fomites. The high-Ct positive results from environmental swabs further suggest the absence of active infections. Additional research is needed to differentiate between fresh and relic SARS-CoV-2 RNA in environmental samples and to determine what types of results should trigger interventions.
