ABSTRACT
BackgroundRespiratory disease is a major cause of morbidity and mortality; however, current surveillance for circulating respiratory viruses is passive and biased. Seasonal circulation of respiratory viruses changed dramatically during the COVID-19 pandemic. More active methods for understanding respiratory disease dynamics are needed to better inform public health response and to guide clinical decision making. Wastewater-based epidemiology has been used to understand COVID-19, influenza A, and RSV infection rates at a community level, but has not been used to investigate other respiratory viruses. MethodsWe measured concentrations of influenza A and B, RSV A and B, human parainfluenza (1-4), rhinovirus, seasonal human coronaviruses, and human metapneumovirus RNA in wastewater solids three times per week for 17 months spanning the COVID-19 pandemic at a wastewater treatment plant in California, USA. Novel probe-based assays were developed and validated for non-influenza viral targets. We compared viral concentrations to positivity rates for viral infections from clinical specimens submitted to sentinel laboratories. FindingsWe detected RNA from all target viruses in wastewater solids. Human rhinovirus and seasonal coronaviruses were found at highest concentrations. Concentrations of viruses correlated significantly and positively with positivity rates of associated viral diseases from sentinel laboratories. Measurements from wastewater indicated limited circulation of RSV A and influenza B, and human coronavirus OC43 dominated the seasonal human coronavirus infections while human parainfluenza 1 and 4A dominated among parainfluenza infections. InterpretationWastewater-based epidemiology can be used to obtain information on circulation of respiratory viruses at a community level without the need to test many individuals because a single sample of wastewater represents the entire contributing community. Results from wastewater can be available within 24 hours of sample collection, allowing real time information to inform public health response, clinical decision making, and individual behavior modifications.
ABSTRACT
An understanding of circulating SARS-CoV-2 variants can inform pandemic response, vaccine development, disease epidemiology, and use of monoclonal antibody treatments. We developed custom assays targeting characteristic mutations in SARS-CoV-2 variants Omicron BA.1 and BA.2 and confirmed their sensitivity and specificity in silico and in vitro. We then applied these assays to daily wastewater solids samples from eight publicly owned treatment works in the greater Bay Area of California, USA, over four months to obtain a spatially and temporally intensive data set. We documented regional replacement of BA.1 with BA.2 in agreement with, and ahead of, clinical sequencing data. This study highlights the utility of wastewater surveillance for real time tracking of SARS-CoV-2 variant circulation. SynopsisWastewater surveillance was used to document regional emergence of SARS-CoV-2 variant Omicron BA.2 ahead of clinical surveillance. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=103 SRC="FIGDIR/small/22274160v2_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@10e4406org.highwire.dtl.DTLVardef@1941dd9org.highwire.dtl.DTLVardef@133b438org.highwire.dtl.DTLVardef@17ce1c4_HPS_FORMAT_FIGEXP M_FIG C_FIG
ABSTRACT
SARS-CoV-2 RNA concentrations in wastewater settled solids correlate well with COVID-19 incidence rates (IRs). Here, we develop distributed lag models (DLMs) to estimate IRs using concentrations of SARS-CoV-2 RNA from wastewater solids and investigate the impact of sampling frequency on model performance. SARS-CoV-2 N gene and PMMoV RNA concentrations were measured daily at four wastewater treatment plants in California. Artificially reduced datasets were produced for each plant with sampling frequencies of once every 2, 3, 4, and 7 days. Sewershed-specific models that related daily N/PMMoV to IR were fit for each sampling frequency with data from mid-Nov 2020 through mid-July 2021, which included the period of time during which Delta emerged. Models were used to predict IRs during a subsequent out-of-sample time period. When sampling occurred at least once every 4 days, the in- and out-of-sample root mean square error (RMSE) changed less than 7 cases/100,000 compared to daily sampling across sewersheds. This work illustrates that real-time, daily predictions of IR are possible with small error, despite changes in circulating variants, when sampling frequency is once every 4 days or more. However, reduced sampling frequency may not serve other important wastewater surveillance use cases.
ABSTRACT
Changes in the circulation of SARS-CoV-2 variants of concern (VOCs) may require changes in public health response to the COVID-19 pandemic, as they have the potential to evade vaccines and pharmaceutical interventions and may be more transmissive relative to other SARS-CoV-2 variants. As such, it is essential to track and prevent their spread in susceptible communities.We developed digital RT-PCR assays for mutations characteristic of VOCs and used them to quantify those mutations in wastewater settled solids samples collected from a publicly owned treatment works (POTW) during different phases of the COVID-19 pandemic. Wastewater concentrations of single mutations characteristic to each VOC, normalized by the concentration of a conserved SARS-CoV-2 N gene, correlate to regional estimates of the proportion of clinical infections caused by each VOC. These results suggest targeted RT-PCR assays can be used to detect variants circulating in communities and inform public health response to the pandemic. ImportanceWastewater represents a pooled biological sample of the contributing community and thus a resource of assessing community health. Here we show that emergence, spread, and disappearance of SARS-CoV-2 infections caused by variants of concern are reflected in the presence of variant genomic RNA in wastewater settled solids. This work highlights an important public health use case for wastewater.
ABSTRACT
BackgroundThe effective reproductive number, Re, is a critical indicator to monitor disease dynamics, inform regional and national policies, and estimate the effectiveness of interventions. It describes the average number of new infections caused by a single infectious person through time. To date, Re estimates are based on clinical data such as observed cases, hospitalizations, and/or deaths. These estimates are temporarily biased when clinical testing or reporting strategies change. ObjectivesWe show that the dynamics of SARS-CoV-2 RNA in wastewater can be used to estimate Re in near real-time, independent of clinical data and without the associated biases. MethodsWe collected longitudinal measurements of SARS-CoV-2 RNA in wastewater in Zurich, CH, and San Jose (CA), USA. We combined this data with information on the temporal dynamics of shedding (the shedding load distribution) to estimate a time series proportional to the daily COVID-19 infection incidence. We estimated a wastewater-based Re from this incidence. ResultsThe method to estimate Re from wastewater works robustly on data from two different countries and two wastewater matrices. The resulting estimates are as similar to the Re estimates from case report data as Re estimates based on observed cases, hospitalizations, and deaths are among each other. We further provide details on the effect of sampling frequency and the shedding load distribution on the ability to infer Re. DiscussionTo our knowledge, this is the first time Re has been estimated from wastewater. This method provides a low cost, rapid, and independent way to inform SARS-CoV-2 monitoring during the ongoing pandemic and is applicable to future wastewater-based epidemiology targeting other pathogens.
