Impact of reduced sampling frequency of illicit drug wastewater monitoring in the Netherlands.

Drug consumption estimates are traditionally based on surveys or information from police seizures. Alternatively, residues of illicit drugs in untreated wastewater (influent) can be used to calculate mass loads and subsequently estimate drug consumption in the community throughout the week. For this purpose, wastewater is commonly sampled for seven consecutive days within the Sewage analysis CORe group Europe (SCORE), while other sampling schemes may be implemented in long-term studies outside this consortium. The current study demonstrates how sampling frequency of illicit drug residues in the influent of wastewater treatment plants (WWTPs) affects the derived weekly average. Thirty WWTPs were sampled over the course of 12 years and influents were analyzed for five drugs (metabolites): 3,4-methylenedioxymethamphetamine (MDMA), methamphetamine, amphetamine, benzoylecgonine (a metabolite of cocaine), and 11-nor-9-Carboxy tetrahydrocannabinol (THC-COOH). Subsequently, small and large WWTPs were grouped with a threshold of 100,000 inhabitants. After data curation, standardized loads were calculated (mg/d per 1000 inhabitants). Weekly averages of loads of the drug residues were calculated based on six scenarios (sampling one to six weekdays) and compared to the weekly average in the control situation (sampling seven weekdays) in a Monte Carlo simulation. Results indicate that drug residues with more dynamic loads over a week require more frequent sampling. The analysis illustrates that a decreased sampling frequency (4 or 5 days per week) still leads to a representative weekly average for all drugs tested when a deviation up to a factor of 1.25 is deemed acceptable. However, knowledge on typical levels is necessary to define outliers. We therefore recommend to study dynamics in drug residue loads for WWTPs before reducing sampling frequency in long term monitoring programs.

Long-term wastewater monitoring of SARS-CoV-2 viral loads and variants at the major international passenger hub Amsterdam Schiphol Airport: A valuable addition to COVID-19 surveillance.

Wastewater-based epidemiological surveillance at municipal wastewater treatment plants has proven to play an important role in COVID-19 surveillance. Considering international passenger hubs contribute extensively to global transmission of viruses, wastewater surveillance at this type of location may be of added value as well. The aim of this study is to explore the potential of long-term wastewater surveillance at a large passenger hub as an additional tool for public health surveillance during different stages of a pandemic. Here, we present an analysis of SARS-CoV-2 viral loads in airport wastewater by reverse-transcription quantitative polymerase chain reaction (RT-qPCR) from the beginning of the COVID-19 pandemic in Feb 2020, and an analysis of SARS-CoV-2 variants by whole-genome next-generation sequencing from Sep 2020, both until Sep 2022, in the Netherlands. Results are contextualized using (inter)national measures and data sources such as passenger numbers, clinical surveillance data and national wastewater surveillance data. Our findings show that wastewater surveillance was possible throughout the study period, irrespective of measures, as viral loads were detected and quantified in 98.6 % (273/277) of samples. Emergence of SARS-CoV-2 variants, identified in 91.0 % (161/177) of sequenced samples, coincided with increases in viral loads. Furthermore, trends in viral load and variant detection in airport wastewater closely followed, and in some cases preceded, trends in national daily average viral load in wastewater and variants detected in clinical surveillance. Wastewater-based epidemiology at a large international airport is a valuable addition to classical COVID-19 surveillance and the developed expertise can be applied in pandemic preparedness plans for other (emerging) pathogens in the future.

Inferring hospital admissions from SARS-CoV-2 virus loads in wastewater in The Netherlands, August 2020 - February 2022.

Wastewater-based surveillance enables tracking of SARS-CoV-2 circulation at a local scale in near-real time. Here we investigate the relation between virus loads and the number of hospital admissions in the Netherlands. Inferred virus loads from August 2020 until February 2022 in each of the 344 Dutch municipalities are analysed in a Bayesian multilevel Poisson regression to relate virus loads to daily age-stratified (in groups of 20 years) hospital admissions. Covariates include municipal vaccination coverages stratified by age and dose (first, second, and booster) and prevalence of the circulating coronavirus variants (wildtype, Alpha, Delta, and Omicron (BA.1 and BA.2)). Our model captures the relation between hospital admissions and virus loads well. Estimated hospitalisation rates per 1,000,000 persons per day at a virus load of 1013 particles range from 0.18 (95 % Prediction Interval (PI): 0.046-0.48) in children (0-19 years) to 20.1 (95 % PI: 9.46-36.8) in the oldest age group (80 years and older) in an unvaccinated population with only wildtype SARS-CoV-2 circulation. The analyses indicate a nearly twofold (1.92 (95 % PI: 1.78-2.05)) decrease in the expected number of hospitalisations at a given virus load between the Alpha and the Omicron variant. Our analyses show that virus load estimates in wastewater are closely related to the expected number of hospitalisations and provide an attractive tool to detect increased SARS-CoV-2 circulation at a local scale, even when there are few hospital admissions. Our analyses enable integration of data at the municipality level into meaningful conversion rates to translate virus loads at a local level into expected numbers of hospital admissions, which would allow for a better interpretation of virus loads detected in wastewater.

PCR standard curve quantification in an extensive wastewater surveillance program: results from the Dutch SARS-CoV-2 wastewater surveillance.

Since the start of the COVID-19 pandemic in 2020, wastewater surveillance programs were established, or upscaled, in many countries around the world and have proven to be a cost-effective way of monitoring infectious disease pathogens. Many of these programs use RT-qPCR, and quantify the viral concentrations in samples based on standard curves, by including preparations of a reference material with known nucleic acid or virus concentrations in the RT-qPCR analyses. In high-throughput monitoring programs it is possible to combine data from multiple previous runs, circumventing the need for duplication and resulting in decreased costs and prolonged periods during which the reference material is obtained from the same batch. However, over time, systematic shifts in standard curves are likely to occur. This would affect the reliability and usefulness of wastewater surveillance as a whole. We aim to find an optimal combination of standard curve data to compensate for run-to-run measurement variance while ensuring enough flexibility to capture systematic longitudinal shifts. Based on more than 4000 observations obtained with the CDC N1 and N2 assays, taken as a part of the National Sewage Surveillance program at the Dutch National Institute for Public Health and the Environment, we show that seasonal and long-term shifts in RT-qPCR efficiency and sensitivity occur. We find that in our setting, using five days of standard-curve data to quantify, results in the least error prone curve or best approximation. This results in differences up to 100% in quantified viral loads when averaged out over a nationwide program of >300 treatment plants. Results show that combining standard curves from a limited set of runs can be a valid approach to quantification without obscuring the trends in the viral load of interest.

Patterns of SARS-CoV-2 circulation revealed by a nationwide sewage surveillance programme, the Netherlands, August 2020 to February 2022.

BackgroundSurveillance of SARS-CoV-2 in wastewater offers a near real-time tool to track circulation of SARS-CoV-2 at a local scale. However, individual measurements of SARS-CoV-2 in sewage are noisy, inherently variable and can be left-censored.AimWe aimed to infer latent virus loads in a comprehensive sewage surveillance programme that includes all sewage treatment plants (STPs) in the Netherlands and covers 99.6% of the Dutch population.MethodsWe applied a multilevel Bayesian penalised spline model to estimate time- and STP-specific virus loads based on water flow-adjusted SARS-CoV-2 qRT-PCR data for one to four sewage samples per week for each of the more than 300 STPs.ResultsThe model captured the epidemic upsurges and downturns in the Netherlands, despite substantial day-to-day variation in the measurements. Estimated STP virus loads varied by more than two orders of magnitude, from ca 1012 virus particles per 100,000 persons per day in the epidemic trough in August 2020 to almost 1015 per 100,000 in many STPs in January 2022. The timing of epidemics at the local level was slightly shifted between STPs and municipalities, which resulted in less pronounced peaks and troughs at the national level.ConclusionAlthough substantial day-to-day variation is observed in virus load measurements, wastewater-based surveillance of SARS-CoV-2 that is performed at high sampling frequency can track long-term progression of an epidemic at a local scale in near real time.