Wastewater-Based Estimation of the Effective Reproductive Number of SARS-CoV-2

Comparison of Three Viral Nucleic Acid Preamplification Pipelines for Sewage Viral Metagenomics

Viral metagenomics is a useful tool for detecting multiple human viruses in urban sewage. However, more refined protocols are required for its effective use in disease surveillance. In this study, we investigated the performance of three different preamplification pipelines (specific to RNA viruses, DNA viruses or both) for viral genome sequencing using spiked-in Phosphate Buffered Saline and sewage samples containing known concentrations of viruses. We found that compared to the pipeline targeting all genome types, the RNA pipeline performed better in detecting RNA viruses in both spiked and unspiked sewage samples, allowing the detection of various mammalian viruses including members from the Reoviridae, Picornaviridae, Astroviridae and Caliciviridae. However, the DNA-specific pipeline did not improve the detection of mammalian DNA viruses. We also measured viral recovery by quantitative reverse transcription polymerase chain reaction and assessed the impact of genetic background (non-viral genetic material) on viral coverage. Our results indicate that viral recoveries were generally lower in sewage (average of 11.0%) and higher in Phosphate Buffered Saline (average of 23.4%) for most viruses. Additionally, spiked-in viruses showed lower genome coverage in sewage, demonstrating the negative effect of genetic background on sequencing. Finally, correlation analysis revealed a relationship between virus concentration and genome normalized reads per million, indicating that viral metagenomic sequencing can be semiquantitative.

Nationwide Trends in COVID-19 Cases and SARS-CoV-2 RNA Wastewater Concentrations in the United States

Wastewater-based epidemiology has emerged as a promising technology for population-level surveillance of COVID-19. In this study, we present results of a large nationwide SARS-CoV-2 wastewater monitoring system in the United States. We profile 55 locations with at least six months of sampling from April 2020 to May 2021. These locations represent more than 12 million individuals across 19 states. Samples were collected approximately weekly by wastewater treatment utilities as part of a regular wastewater surveillance service and analyzed for SARS-CoV-2 RNA concentrations. SARS-CoV-2 RNA concentrations were normalized to pepper mild mottle virus, an indicator of fecal matter in wastewater. We show that wastewater data reflect temporal and geographic trends in clinical COVID-19 cases and investigate the impact of normalization on correlations with case data within and across locations. We also provide key lessons learned from our broad-scale implementation of wastewater-based epidemiology, which can be used to inform wastewater-based epidemiology approaches for future emerging diseases. This work demonstrates that wastewater surveillance is a feasible approach for nationwide population-level monitoring of COVID-19 disease. With an evolving epidemic and effective vaccines against SARS-CoV-2, wastewater-based epidemiology can serve as a passive surveillance approach for detecting changing dynamics or resurgences of the virus.

SARS-CoV-2 RNA Wastewater Settled Solids Surveillance Frequency and Impact on Predicted COVID-19 Incidence Using a Distributed Lag Model

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA concentrations in wastewater settled solids correlate well with coronavirus disease 2019 (COVID-19) incidence rates (IRs). Here, we develop distributed lag models 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 pepper mild mottle virus (PMMoV) RNA concentrations were measured daily at four wastewater treatment plants in California. Artificially reduced data sets 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-November 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 changed by <7 cases/100 000 compared to daily sampling across sewersheds. This work illustrates that real-time, daily predictions of IR are possible with small errors, 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.

Persistence of Endogenous SARS-CoV-2 and Pepper Mild Mottle Virus RNA in Wastewater-Settled Solids

Limited information is available on the decay rate of endogenous SARS-CoV-2 and pepper mild mottle virus (PMMoV) RNA in wastewater and primary settled solids, potentially limiting an understanding of how transit or holding times within wastewater infrastructure might impact RNA measurements and their relationship to community COVID-19 infections. In this study, primary settled solids samples were collected from two wastewater treatment plants in the San Francisco Bay Area. Samples were thoroughly mixed, aliquoted into subsamples, and stored at 4, 22, and 37 °C for 10 days. The concentrations of SARS-CoV-2 (N1 and N2 targets) and PMMoV RNA were measured using an RT-ddPCR. Limited decay (<1 log10 reduction) was observed in the detection of viral RNA targets at all temperature conditions, suggesting that SARS-CoV-2 and PMMoV RNA can be highly persistent in solids. First-order decay rate constants ranged from 0.011 to 0.098 day-1 for SARS-CoV-2 RNA and from 0.010 to 0.091 day-1 for PMMoV RNA depending on the temperature conditions. A slower decay was observed for SARS-CoV-2 RNA in primary settled solids compared to previously reported decay in wastewater influent. Further research is needed to understand if solid content and wastewater characteristics might influence the persistence of viral RNA targets.

Nationwide Trends in COVID-19 Cases and SARS-CoV-2 RNA Wastewater Concentrations in the United States

Wastewater-based epidemiology has emerged as a promising technology for population-level surveillance of COVID-19. In this study, we present results of a large nationwide SARS-CoV-2 wastewater monitoring system in the United States. We profile 55 locations with at least six months of sampling from April 2020 to May 2021. These locations represent more than 12 million individuals across 19 states. Samples were collected approximately weekly by wastewater treatment utilities as part of a regular wastewater surveillance service and analyzed for SARS-CoV-2 RNA concentrations. SARS-CoV-2 RNA concentrations were normalized to pepper mild mottle virus, an indicator of fecal matter in wastewater. We show that wastewater data reflect temporal and geographic trends in clinical COVID-19 cases and investigate the impact of normalization on correlations with case data within and across locations. We also provide key lessons learned from our broad-scale implementation of wastewater-based epidemiology, which can be used to inform wastewater-based epidemiology approaches for future emerging diseases. This work demonstrates that wastewater surveillance is a feasible approach for nationwide population-level monitoring of COVID-19 disease. With an evolving epidemic and effective vaccines against SARS-CoV-2, wastewater-based epidemiology can serve as a passive surveillance approach for detecting changing dynamics or resurgences of the virus.

Estimation and worldwide monitoring of the effective reproductive number of SARS-CoV-2

The effective reproductive number R_e is a key indicator of the growth of an epidemic. Since the start of the SARS-CoV-2 pandemic, many methods and online dashboards have sprung up to monitor this number through time. However, these methods are not always thoroughly tested, correctly placed in time, or are overly confident during high incidence periods. Here, we present a method for timely estimation of R_e, applied to COVID-19 epidemic data from 170 countries. We thoroughly evaluate the method on simulated data, and present an intuitive web interface for interactive data exploration. We show that, in early 2020, in the majority of countries the estimated R_e dropped below 1 only after the introduction of major non-pharmaceutical interventions. For Europe the implementation of non-pharmaceutical interventions was broadly associated with reductions in the estimated R_e. Globally though, relaxing non-pharmaceutical interventions had more varied effects on subsequent R_e estimates. Our framework is useful to inform governments and the general public on the status of epidemics in their country, and is used as the official source of R_e estimates for SARS-CoV-2 in Switzerland. It further allows detailed comparison between countries and in relation to covariates such as implemented public health policies, mobility, behaviour, or weather data.

Estimation and worldwide monitoring of the effective reproductive number of SARS-CoV-2

The effective reproductive number R_e is a key indicator of the growth of an epidemic. Since the start of the SARS-CoV-2 pandemic, many methods and online dashboards have sprung up to monitor this number through time. However, these methods are not always thoroughly tested, correctly placed in time, or are overly confident during high incidence periods. Here, we present a method for timely estimation of R_e, applied to COVID-19 epidemic data from 170 countries. We thoroughly evaluate the method on simulated data, and present an intuitive web interface for interactive data exploration. We show that, in early 2020, in the majority of countries the estimated R_e dropped below 1 only after the introduction of major non-pharmaceutical interventions. For Europe the implementation of non-pharmaceutical interventions was broadly associated with reductions in the estimated R_e. Globally though, relaxing non-pharmaceutical interventions had more varied effects on subsequent R_e estimates. Our framework is useful to inform governments and the general public on the status of epidemics in their country, and is used as the official source of R_e estimates for SARS-CoV-2 in Switzerland. It further allows detailed comparison between countries and in relation to covariates such as implemented public health policies, mobility, behaviour, or weather data.

Over the past two and a half years, countries around the globe have struggled to control the transmission of the SARS-CoV-2 virus within their borders. To manage the situation, it is important to have an accurate picture of how fast the virus is spreading. This can be achieved by calculating the effective reproductive number (Re), which describes how many people, on average, someone with COVID-19 is likely to infect. If the Re is greater than one, the virus is infecting increasingly more people, but if it is smaller than one, the number of cases is declining.

Scientists use various strategies to estimate the Re, which each have their own strengths and weaknesses. One of the main difficulties is that infections are typically recorded only when people test positive for COVID-19, are hospitalized with the virus, or die. This means that the data provides a delayed representation of when infections are happening. Furthermore, changes in these records occur later than measures that change the infection dynamics. As a result, researchers need to take these delays into account when estimating Re.

Here, Huisman, Scire et al. have developed a new method for estimating the Re based on available data records, statistically taking into account the above-mentioned delays. An online dashboard with daily updates was then created so that policy makers and the population could monitor the values over time.

For over two years, Huisman, Scire et al. have been applying their tool and dashboard to COVID-19 data from 170 countries. They found that public health interventions, such as mask requirements and lockdowns, did help reduce the Re in Europe. But the effects were not uniform across the globe, likely because of variations in how restrictions were implemented and followed during the pandemic. In early 2020, the Re only dropped below one after countries put lockdowns or other severe measures in place.

The Re values added to the dashboard over the last two years have been used pro-actively to inform public health policies in Switzerland and to monitor the spread of SARS-CoV-2 in South Africa. The team has also recently released programming software based on this method that can be used to track future disease outbreaks, and extended the method to estimate the Re using SARS-CoV-2 levels in wastewater.

Early detection and surveillance of SARS-CoV-2 genomic variants in wastewater using COJAC

The continuing emergence of SARS-CoV-2 variants of concern and variants of interest emphasizes the need for early detection and epidemiological surveillance of novel variants. We used genomic sequencing of 122 wastewater samples from three locations in Switzerland to monitor the local spread of B.1.1.7 (Alpha), B.1.351 (Beta) and P.1 (Gamma) variants of SARS-CoV-2 at a population level. We devised a bioinformatics method named COJAC (Co-Occurrence adJusted Analysis and Calling) that uses read pairs carrying multiple variant-specific signature mutations as a robust indicator of low-frequency variants. Application of COJAC revealed that a local outbreak of the Alpha variant in two Swiss cities was observable in wastewater up to 13 d before being first reported in clinical samples. We further confirmed the ability of COJAC to detect emerging variants early for the Delta variant by analysing an additional 1,339 wastewater samples. While sequencing data of single wastewater samples provide limited precision for the quantification of relative prevalence of a variant, we show that replicate and close-meshed longitudinal sequencing allow for robust estimation not only of the local prevalence but also of the transmission fitness advantage of any variant. We conclude that genomic sequencing and our computational analysis can provide population-level estimates of prevalence and fitness of emerging variants from wastewater samples earlier and on the basis of substantially fewer samples than from clinical samples. Our framework is being routinely used in large national projects in Switzerland and the UK.

Making waves: Wastewater surveillance of SARS-CoV-2 in an endemic future

Wastewater-based surveillance (WBS) has been widely used as a public health tool to monitor the emergence and spread of SARS-CoV-2 infections in populations during the COVID-19 pandemic. Coincident with the global vaccination efforts, the world is also enduring new waves of SARS-CoV-2 variants. Reinfections and vaccine breakthroughs suggest an endemic future where SARS-CoV-2 continues to persist in the general population. In this treatise, we aim to explore the future roles of wastewater surveillance. Practically, WBS serves as a relatively affordable and non-invasive tool for mass surveillance of SARS-CoV-2 infection while minimizing privacy concerns, attributes that make it extremely suited for its long-term usage. In an endemic future, the utility of WBS will include 1) monitoring the trend of viral loads of targets in wastewater for quantitative estimate of changes in disease incidence; 2) sampling upstream for pinpointing infections in neighborhoods and at the building level; 3) integrating wastewater and clinical surveillance for cost-efficient population surveillance; and 4) genome sequencing wastewater samples to track circulating and emerging variants in the population. We further discuss the challenges and future developments of WBS to reduce inconsistencies in wastewater data worldwide, improve its epidemiological inference, and advance viral tracking and discovery as a preparation for the next viral pandemic.

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.

Model-based assessment of COVID-19 epidemic dynamics by wastewater analysis

Continuous surveillance of COVID-19 diffusion remains crucial to control its diffusion and to anticipate infection waves. Detecting viral RNA load in wastewater samples has been suggested as an effective approach for epidemic monitoring and the development of an effective warning system. However, its quantitative link to the epidemic status and the stages of outbreak is still elusive. Modelling is thus crucial to address these challenges. In this study, we present a novel mechanistic model-based approach to reconstruct the complete epidemic dynamics from SARS-CoV-2 viral load in wastewater. Our approach integrates noisy wastewater data and daily case numbers into a dynamical epidemiological model. As demonstrated for various regions and sampling protocols, it quantifies the case numbers, provides epidemic indicators and accurately infers future epidemic trends. Following its quantitative analysis, we also provide recommendations for wastewater data standards and for their use as warning indicators against new infection waves. In situations of reduced testing capacity, our modelling approach can enhance the surveillance of wastewater for early epidemic prediction and robust and cost-effective real-time monitoring of local COVID-19 dynamics.

Monitoring COVID-19 spread in Prague local neighborhoods based on the presence of SARS-CoV-2 RNA in wastewater collected throughout the sewer network

Many reports have documented that the presence of SARS-CoV-2 RNA in the influents of municipal wastewater treatment plants (WWTP) correlates with the actual epidemic situation in a given city. However, few data have been reported thus far on measurements upstream of WWTPs, i.e. throughout the sewer network. In this study, the monitoring of the presence of SARS-CoV-2 RNA in Prague wastewater was carried out at selected locations of the Prague sewer network from August 2020 through May 2021. Various locations such as residential areas of various sizes, hospitals, city center areas, student dormitories, transportation hubs (airport, bus terminal), and commercial areas were monitored together with four of the main Prague sewers. The presence of SARS-CoV-2 RNA was determined by reverse transcription - multiplex quantitative polymerase chain reaction (RT-mqPCR) after the precipitation of nucleic acids with PEG 8,000 and RNA isolation with TRIzol™ Reagent. The number of copies of the gene encoding SARS-CoV-2 nucleocapsid (N1) per liter of wastewater was compared with the number of officially registered COVID-19 cases in Prague. Although the data obtained by sampling wastewater from the major Prague sewers were more consistent than those obtained from the small sewers, the correlation between wastewater-based and clinical-testing data was also good for the residential areas with more than 7,000 registered inhabitants. It was shown that monitoring SARS-CoV-2 RNA in wastewater sampled from small sewers could identify isolated occurrences of COVID-19-positive cases in local neighborhoods. This can be very valuable while tracking COVID-19 hotspots within large cities.

Factors influencing SARS-CoV-2 RNA concentrations in wastewater up to the sampling stage: A systematic review

Wastewater-based surveillance (WBS) for SARS-CoV-2 RNA is a promising complementary approach to monitor community viral circulation. A myriad of factors, however, can influence RNA concentrations in wastewater, impeding its epidemiological value. This article aims to provide an overview and discussion of factors up to the sampling stage that impact SARS-CoV-2 RNA concentration estimates in wastewater. To this end, a systematic review was performed in three databases (MEDLINE, Web of Science and Embase) and two preprint servers (MedRxiv and BioRxiv). Two authors independently screened and selected articles published between January 1, 2019 and May 4, 2021. A total of 22 eligible articles were included in this systematic review. The following factors up to sampling were identified to have an influence on SARS-CoV-2 RNA concentrations in wastewater and its interpretation: (i) shedding-related factors, including faecal shedding parameters (i.e. shedding pattern, recovery, rate, and load distribution), (ii) population size, (iii) in-sewer factors, including solid particles, organic load, travel time, flow rate, wastewater pH and temperature, and (iv) sampling strategy. In conclusion, factors influencing SARS-CoV-2 RNA concentration estimates in wastewater were identified and research gaps were discussed. The identification of these factors supports the need for further research on WBS for COVID-19.

Detection of SARS-CoV-2 Variants Mu, Beta, Gamma, Lambda, Delta, Alpha, and Omicron in Wastewater Settled Solids Using Mutation-Specific Assays Is Associated with Regional Detection of Variants in Clinical Samples

Changes in the circulation of SARS-CoV-2 variants of concern (VOCs) may require changes in the public health response to the COVID-19 pandemic, as they have the potential to evade vaccines and pharmaceutical interventions and may be more transmissive than other SARS-CoV-2 variants. As such, it is essential to track and prevent their spread in susceptible communities. We developed digital reverse transcription (RT)-PCR assays for mutations characteristic of VOCs and used them to quantify those mutations in samples of wastewater settled solids collected from a publicly owned treatment works (POTW) during different phases of the COVID-19 pandemic. Wastewater concentrations of single mutations characteristic of each VOC, normalized by the concentration of a conserved SARS-CoV-2 N gene, correlate with regional estimates of the proportion of clinical infections caused by each VOC. These results suggest that targeted RT-PCR assays can be used to detect variants circulating in communities and inform the public health response to the pandemic. IMPORTANCE Wastewater represents a pooled biological sample of the contributing community and thus a resource for 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.

High Sensitivity and Specificity of Dormitory-Level Wastewater Surveillance for COVID-19 during Fall Semester 2020 at Syracuse University, New York

A residential building’s wastewater presents a potential non-invasive method of surveilling numerous infectious diseases, including SARS-CoV-2. We analyzed wastewater from 16 different residential locations at Syracuse University (Syracuse, NY, USA) during fall semester 2020, testing for SARS-CoV-2 RNA twice weekly and compared the presence of clinical COVID-19 cases to detection of the viral RNA in wastewater. The sensitivity of wastewater surveillance to correctly identify dormitories with a case of COVID-19 ranged from 95% (95% confidence interval [CI] = 76–100%) on the same day as the case was diagnosed to 73% (95% CI = 53–92%), with 7 days lead time of wastewater. The positive predictive value ranged from 20% (95% CI = 13–30%) on the same day as the case was diagnosed to 50% (95% CI = 40–60%) with 7 days lead time. The specificity of wastewater surveillance to correctly identify dormitories without a case of COVID-19 ranged from 60% (95% CI = 52–67%) on the day of the wastewater sample to 67% (95% CI = 58–74%) with 7 days lead time. The negative predictive value ranged from 99% (95% CI = 95–100%) on the day of the wastewater sample to 84% (95% CI = 77–91%) with 7 days lead time. Wastewater surveillance for SARS-CoV-2 at the building level is highly accurate in determining if residents have a COVID-19 infection. Particular benefit is derived from negative wastewater results that can confirm a building is COVID-19 free.

SARS-CoV-2 RNA is enriched by orders of magnitude in primary settled solids relative to liquid wastewater at publicly owned treatment works

Wastewater-based epidemiology has gained attention throughout the world for detection of SARS-CoV-2 RNA in wastewater to supplement clinical testing. Raw wastewater consists of small particles, or solids, suspended in liquid. Methods have been developed to measure SARS-CoV-2 RNA in the liquid and the solid fraction of wastewater, with some studies reporting higher concentrations in the solid fraction. To investigate this relationship further, six laboratories collaborated to conduct a study across five publicly owned treatment works (POTWs) where both primary settled solids obtained from primary clarifiers and raw wastewater influent samples were collected and quantified for SARS-CoV-2 RNA. Settled solids and influent samples were processed by participating laboratories using their respective methods and retrospectively paired based on date of collection. SARS-CoV-2 RNA concentrations, on a mass equivalent basis, were higher in settled solids than in influent by approximately three orders of magnitude. Concentrations in matched settled solids and influent were positively and significantly correlated at all five POTWs. RNA concentrations in both settled solids and influent were correlated to COVID-19 incidence rates in the sewersheds and thus representative of disease occurrence; the settled solids methods appeared to produce a comparable relationship between SARS-CoV-2 RNA concentration measurements and incidence rates across all POTWs. Settled solids and influent methods showed comparable sensitivity, N gene detection frequency, and calculated empirical incidence rate lower limits. Analysis of settled solids for SARS-CoV-2 RNA has the advantage of using less sample volume to achieve similar sensitivity to influent methods.

Inferring transmission fitness advantage of SARS-CoV-2 variants of concern from wastewater samples using digital PCR, Switzerland, December 2020 through March 2021

BackgroundThroughout the COVID-19 pandemic, SARS-CoV-2 genetic variants of concern (VOCs) have repeatedly and independently arisen. VOCs are characterised by increased transmissibility, increased virulence or reduced neutralisation by antibodies obtained from prior infection or vaccination. Tracking the introduction and transmission of VOCs relies on sequencing, typically whole genome sequencing of clinical samples. Wastewater surveillance is increasingly used to track the introduction and spread of SARS-CoV-2 variants through sequencing approaches.AimHere, we adapt and apply a rapid, high-throughput method for detection and quantification of the relative frequency of two deletions characteristic of the Alpha, Beta, and Gamma VOCs in wastewater.MethodsWe developed drop-off RT-dPCR assays and an associated statistical approach implemented in the R package WWdPCR to analyse temporal dynamics of SARS-CoV-2 signature mutations (spike Δ69-70 and ORF1a Δ3675-3677) in wastewater and quantify transmission fitness advantage of the Alpha VOC.ResultsBased on analysis of Zurich wastewater samples, the estimated transmission fitness advantage of SARS-CoV-2 Alpha based on the spike Δ69-70 was 0.34 (95% confidence interval (CI): 0.30-0.39) and based on ORF1a Δ3675-3677 was 0.53 (95% CI: 0.49-0.57), aligning with the transmission fitness advantage of Alpha estimated by clinical sample sequencing in the surrounding canton of 0.49 (95% CI: 0.38-0.61).ConclusionDigital PCR assays targeting signature mutations in wastewater offer near real-time monitoring of SARS-CoV-2 VOCs and potentially earlier detection and inference on transmission fitness advantage than clinical sequencing.

Understanding and managing uncertainty and variability for wastewater monitoring beyond the pandemic: Lessons learned from the United Kingdom national COVID-19 surveillance programmes

The COVID-19 pandemic has put unprecedented pressure on public health resources around the world. From adversity, opportunities have arisen to measure the state and dynamics of human disease at a scale not seen before. In the United Kingdom, the evidence that wastewater could be used to monitor the SARS-CoV-2 virus prompted the development of National wastewater surveillance programmes. The scale and pace of this work has proven to be unique in monitoring of virus dynamics at a national level, demonstrating the importance of wastewater-based epidemiology (WBE) for public health protection. Beyond COVID-19, it can provide additional value for monitoring and informing on a range of biological and chemical markers of human health. A discussion of measurement uncertainty associated with surveillance of wastewater, focusing on lessons-learned from the UK programmes monitoring COVID-19 is presented, showing that sources of uncertainty impacting measurement quality and interpretation of data for public health decision-making, are varied and complex. While some factors remain poorly understood, we present approaches taken by the UK programmes to manage and mitigate the more tractable sources of uncertainty. This work provides a platform to integrate uncertainty management into WBE activities as part of global One Health initiatives beyond the pandemic.

SARS-CoV-2 wastewater surveillance data can predict hospitalizations and ICU admissions

We measured SARS-CoV-2 RNA load in raw wastewater in Attica, Greece, by RT-qPCR for the environmental surveillance of COVID-19 for 6 months. The lag between RNA load and pandemic indicators (COVID-19 hospital and intensive care unit (ICU) admissions) was calculated using a grid search. Our results showed that RNA load in raw wastewater is a leading indicator of positive COVID-19 cases, new hospitalization and admission into ICUs by 5, 8 and 9 days, respectively. Modelling techniques based on distributed/fixed lag modelling, linear regression and artificial neural networks were utilized to build relationships between SARS-CoV-2 RNA load in wastewater and pandemic health indicators. SARS-CoV-2 mutation analysis in wastewater during the third pandemic wave revealed that the alpha-variant was dominant. Our results demonstrate that clinical and environmental surveillance data can be combined to create robust models to study the on-going COVID-19 infection dynamics and provide an early warning for increased hospital admissions.

Inferring SARS-CoV-2 RNA shedding into wastewater relative to the time of infection

Since the start of the coronavirus disease-2019 (COVID-19) pandemic, there has been interest in using wastewater monitoring as an approach for disease surveillance. A significant uncertainty that would improve the interpretation of wastewater monitoring data is the intensity and timing with which individuals shed RNA from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) into wastewater. By combining wastewater and case surveillance data sets from a university campus during a period of heightened surveillance, we inferred that individual shedding of RNA into wastewater peaks on average 6 days (50% uncertainty interval (UI): 6-7; 95% UI: 4-8) following infection, and that wastewater measurements are highly overdispersed [negative binomial dispersion parameter, k = 0.39 (95% credible interval: 0.32-0.48)]. This limits the utility of wastewater surveillance as a leading indicator of secular trends in SARS-CoV-2 transmission during an epidemic, and implies that it could be most useful as an early warning of rising transmission in areas where transmission is low or clinical testing is delayed or of limited capacity.

Inferring SARS-CoV-2 RNA shedding into wastewater relative to the time of infection

Since the start of the coronavirus disease-2019 (COVID-19) pandemic, there has been interest in using wastewater monitoring as an approach for disease surveillance. A significant uncertainty that would improve the interpretation of wastewater monitoring data is the intensity and timing with which individuals shed RNA from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) into wastewater. By combining wastewater and case surveillance data sets from a university campus during a period of heightened surveillance, we inferred that individual shedding of RNA into wastewater peaks on average 6 days (50% uncertainty interval (UI): 6-7; 95% UI: 4-8) following infection, and that wastewater measurements are highly overdispersed [negative binomial dispersion parameter, k = 0.39 (95% credible interval: 0.32-0.48)]. This limits the utility of wastewater surveillance as a leading indicator of secular trends in SARS-CoV-2 transmission during an epidemic, and implies that it could be most useful as an early warning of rising transmission in areas where transmission is low or clinical testing is delayed or of limited capacity.

Wastewater surveillance demonstrates high predictive value for COVID-19 infection on board repatriation flights to Australia

Controlling importation and transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from overseas travelers is essential for countries, such as Australia, New Zealand, and other island nations, that have adopted a suppression strategy to manage very low community transmission. Wastewater surveillance of SARS-CoV-2 RNA has emerged as a promising tool employed in public health response in many countries globally. This study aimed to establish whether the surveillance of aircraft wastewater can be used to provide an additional layer of information to augment individual clinical testing. Wastewater from 37 long-haul flights chartered to repatriate Australians was tested for the presence of SARS-CoV-2 RNA. Children 5 years or older on these flights tested negative for coronavirus disease 19 (COVID-19) (deep nasal and oropharyngeal reverse-transcription (RT)-PCR swab) 48 h before departure. All passengers underwent mandatory quarantine for 14-day post arrival in Howard Springs, NT, Australia. Wastewater from 24 (64.9 %) of the 37 flights tested positive for SARS-CoV-2 RNA. During the 14 day mandatory quarantine, clinical testing identified 112 cases of COVID-19. Surveillance for SARS-CoV-2 RNA in repatriation flight wastewater using pooled results from three RT-qPCR assays demonstrated a positive predictive value (PPV) of 87.5 %, a negative predictive value (NPV) of 76.9 % and 83.7% accuracy for COVID-19 cases during the post-arrival 14-day quarantine period. The study successfully demonstrates that the surveillance of wastewater from aircraft for SARS-CoV-2 can provide an additional and effective tool for informing the management of returning overseas travelers and for monitoring the importation of SARS CoV-2 and other clinically significant pathogens.

Wastewater Surveillance for SARS-CoV-2 RNA in Canada

Wastewater surveillance for SARS-CoV-2 RNA is a relatively recent adaptation of long-standing wastewater surveillance for infectious and other harmful agents. Individuals infected with COVID-19 were found to shed SARS-CoV-2 in their faeces. Researchers around the world confirmed that SARS-CoV-2 RNA fragments could be detected and quantified in community wastewater. Canadian academic researchers, largely as volunteer initiatives, reported proof-of-concept by April 2020. National collaboration was initially facilitated by the Canadian Water Network.

Many public health officials were initially skeptical about actionable information being provided by wastewater surveillance even though experience has shown that public health surveillance for a pandemic has no single, perfect approach. Rather, different approaches provide different insights, each with its own strengths and limitations. Public health science must triangulate among different forms of evidence to maximize understanding of what is happening or may be expected. Well-conceived, resourced, and implemented wastewater-based platforms can provide a cost-effective approach to support other conventional lines of evidence. Sustaining wastewater monitoring platforms for future surveillance of other disease targets and health states is a challenge. Canada can benefit from taking lessons learned from the COVID-19 pandemic to develop forward-looking interpretive frameworks and capacity to implement, adapt, and expand such public health surveillance capabilities.

A nationwide indicator to smooth and normalize heterogeneous SARS-CoV-2 RNA data in wastewater

Since many infected people experience no or few symptoms, the SARS-CoV-2 epidemic is frequently monitored through massive virus testing of the population, an approach that may be biased and may be difficult to sustain in low-income countries. Since SARS-CoV-2 RNA can be detected in stool samples, quantifying SARS-CoV-2 genome by RT-qPCR in wastewater treatment plants (WWTPs) has been carried out as a complementary tool to monitor virus circulation among human populations. However, measuring SARS-CoV-2 viral load in WWTPs can be affected by many experimental and environmental factors. To circumvent these limits, we propose here a novel indicator, the wastewater indicator (WWI), that partly reduces and corrects the noise associated with the SARS-CoV-2 genome quantification in wastewater (average noise reduction of 19%). All data processing results in an average correlation gain of 18% with the incidence rate. The WWI can take into account the censorship linked to the limit of quantification (LOQ), allows the automatic detection of outliers to be integrated into the smoothing algorithm, estimates the average measurement error committed on the samples and proposes a solution for inter-laboratory normalization in the absence of inter-laboratory assays (ILA). This method has been successfully applied in the context of Obépine, a French national network that has been quantifying SARS-CoV-2 genome in a representative sample of French WWTPs since March 5th 2020. By August 26th, 2021, 168 WWTPs were monitored in the French metropolitan and overseas territories of France. We detail the process of elaboration of this indicator, show that it is strongly correlated to the incidence rate and that the optimal time lag between these two signals is only a few days, making our indicator an efficient complement to the incidence rate. This alternative approach may be especially important to evaluate SARS-CoV-2 dynamics in human populations when the testing rate is low.

High-Frequency, High-Throughput Quantification of SARS-CoV-2 RNA in Wastewater Settled Solids at Eight Publicly Owned Treatment Works in Northern California Shows Strong Association with COVID-19 Incidence

A number of recent retrospective studies have demonstrated that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA concentrations in wastewater are associated with coronavirus disease 2019 (COVID-19) cases in the corresponding sewersheds. Implementing high-resolution, prospective efforts across multiple plants depends on sensitive measurements that are representative of COVID-19 cases, scalable for high-throughput analysis, and comparable across laboratories. We conducted a prospective study across eight publicly owned treatment works (POTWs). A focus on SARS-CoV-2 RNA in solids enabled us to scale up our measurements with a commercial lab partner. Samples were collected daily, and results were posted to a website within 24 h. SARS-CoV-2 RNA in daily samples correlated with the incidence of COVID-19 cases in the sewersheds; a 1 log10 increase in SARS-CoV-2 RNA in settled solids corresponds to a 0.58 log10 (4×) increase in sewershed incidence rate. SARS-CoV-2 RNA signals measured with the commercial laboratory partner were comparable across plants and comparable to measurements conducted in a university laboratory when normalized by pepper mild mottle virus (PMMoV) RNA. Results suggest that SARS-CoV-2 RNA should be detectable in settled solids for COVID-19 incidence rates of >1/100,000 (range, 0.8 to 2.3 cases per 100,000). These sensitive, representative, scalable, and comparable methods will be valuable for future efforts to scale up wastewater-based epidemiology. IMPORTANCE Access to reliable, rapid monitoring data is critical to guide response to an infectious disease outbreak. For pathogens that are shed in feces or urine, monitoring wastewater can provide a cost-effective snapshot of transmission in an entire community via a single sample. In order for a method to be useful for ongoing COVID-19 monitoring, it should be sensitive for detection of low concentrations of SARS-CoV-2, representative of incidence rates in the community, scalable to generate data quickly, and comparable across laboratories. This paper presents a method utilizing wastewater solids to meet these goals, producing measurements of SARS-CoV-2 RNA strongly associated with COVID-19 cases in the sewershed of a publicly owned treatment work. Results, provided within 24 h, can be used to detect incidence rates as low as approximately 1/100,000 cases and can be normalized for comparison across locations generating data using different methods.

High-Frequency, High-Throughput Quantification of SARS-CoV-2 RNA in Wastewater Settled Solids at Eight Publicly Owned Treatment Works in Northern California Shows Strong Association with COVID-19 Incidence

A number of recent retrospective studies have demonstrated that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA concentrations in wastewater are associated with coronavirus disease 2019 (COVID-19) cases in the corresponding sewersheds. Implementing high-resolution, prospective efforts across multiple plants depends on sensitive measurements that are representative of COVID-19 cases, scalable for high-throughput analysis, and comparable across laboratories. We conducted a prospective study across eight publicly owned treatment works (POTWs). A focus on SARS-CoV-2 RNA in solids enabled us to scale up our measurements with a commercial lab partner. Samples were collected daily, and results were posted to a website within 24 h. SARS-CoV-2 RNA in daily samples correlated with the incidence of COVID-19 cases in the sewersheds; a 1 log₁₀ increase in SARS-CoV-2 RNA in settled solids corresponds to a 0.58 log₁₀ (4×) increase in sewershed incidence rate. SARS-CoV-2 RNA signals measured with the commercial laboratory partner were comparable across plants and comparable to measurements conducted in a university laboratory when normalized by pepper mild mottle virus (PMMoV) RNA. Results suggest that SARS-CoV-2 RNA should be detectable in settled solids for COVID-19 incidence rates of >1/100,000 (range, 0.8 to 2.3 cases per 100,000). These sensitive, representative, scalable, and comparable methods will be valuable for future efforts to scale up wastewater-based epidemiology. IMPORTANCE Access to reliable, rapid monitoring data is critical to guide response to an infectious disease outbreak. For pathogens that are shed in feces or urine, monitoring wastewater can provide a cost-effective snapshot of transmission in an entire community via a single sample. In order for a method to be useful for ongoing COVID-19 monitoring, it should be sensitive for detection of low concentrations of SARS-CoV-2, representative of incidence rates in the community, scalable to generate data quickly, and comparable across laboratories. This paper presents a method utilizing wastewater solids to meet these goals, producing measurements of SARS-CoV-2 RNA strongly associated with COVID-19 cases in the sewershed of a publicly owned treatment work. Results, provided within 24 h, can be used to detect incidence rates as low as approximately 1/100,000 cases and can be normalized for comparison across locations generating data using different methods.

Effect of storage conditions on SARS-CoV-2 RNA quantification in wastewater solids

SARS-CoV-2 RNA in wastewater settled solids is associated with COVID-19 incidence in sewersheds and therefore, there is a strong interest in using these measurements to augment traditional disease surveillance methods. A wastewater surveillance program should provide rapid turn around for sample measurements (ideally within 24 hours), but storage of samples is necessary for a variety of reasons including biobanking. Here we investigate how storage of wastewater solids at 4 °C, -20 °C, and -80 °C affects measured concentrations of SARS-CoV-2 RNA. We find that short term (7 or 8 d) storage of raw solids at 4 °C has little effect on measured concentrations of SARS-CoV-2 RNA, whereas longer term storage at 4 °C (35-122 d) or freezing reduces measurements by 60%, on average. We show that normalizing SARS-CoV-2 RNA concentrations by concentrations of pepper mild mottle virus (PMMoV) RNA, an endogenous wastewater virus, can correct for changes during storage as storage can have a similar effect on PMMoV RNA as on SARS-CoV-2 RNA. The reductions in SARS-CoV-2 RNA in solids during freeze thaws is less than those reported for the same target in liquid influent by several authors.