Viral variant-resolved wastewater surveillance of SARS-CoV-2 at national scale

A gold standard dataset and evaluation of methods for lineage abundance estimation from wastewater

During the SARS-CoV-2 pandemic, genome-based wastewater surveillance sequencing has been a powerful tool for public health to monitor circulating and emerging viral variants. As a medium, wastewater is very complex because of its mixed matrix nature, which makes the deconvolution of wastewater samples more difficult. Here we introduce a gold standard dataset constructed from synthetic viral control mixtures of known composition, spiked into a wastewater RNA matrix and sequenced on the Oxford Nanopore Technologies platform. We compare the performance of eight of the most commonly used deconvolution tools in identifying SARS-CoV-2 variants present in these mixtures. The software evaluated was primarily chosen for its relevance to the CDC wastewater surveillance reporting protocol, which until recently employed a pipeline that incorporates results from four deconvolution methods: Freyja, kallisto, Kraken 2/Bracken, and LCS. We also tested Lollipop, a deconvolution method used by the Swiss SARS-CoV-2 Sequencing Consortium, and three additional methods not used in the C-WAP pipeline: lineagespot, Alcov, and VaQuERo. We found that the commonly used software Freyja outperformed the other CDC pipeline tools in correct identification of lineages present in the control mixtures, and that the VaQuERo method was similarly accurate, with minor differences in the ability of the two methods to avoid false negatives and suppress false positives. Our results also provide insight into the effect of the tiling primer scheme and wastewater RNA extract matrix on viral sequencing and data deconvolution outcomes.

Examining the stability of viral RNA and DNA in wastewater: Effects of storage time, temperature, and freeze-thaw cycles

Wastewater-based epidemiology (WBE) has been demonstrably successful as a relatively unbiased tool for monitoring levels of SARS-CoV-2 virus circulating in communities during the COVID-19 pandemic. Accumulated biobanks of wastewater samples allow retrospective exploration of spatial and temporal trends for public health indicators such as chemicals, viruses, antimicrobial resistance genes, and the possible emergence of novel human or zoonotic pathogens. We investigated virus resilience to time, temperature, and freeze-thaw cycles, plus the optimal storage conditions to maintain the stability of genetic material (RNA/DNA) of viral +ssRNA (Envelope - E, Nucleocapsid - N and Spike protein - S genes of SARS-CoV-2), dsRNA (Phi6 phage) and circular dsDNA (crAssphage) in wastewater. Samples consisted of (i) processed and extracted wastewater samples, (ii) processed and extracted distilled water samples, and (iii) raw, unprocessed wastewater samples. Samples were stored at -80 °C, -20 °C, 4 °C, or 20 °C for 10 days, going through up to 10 freeze-thaw cycles (once per day). Sample stability was measured using reverse transcription quantitative PCR, quantitative PCR, automated electrophoresis, and short-read whole genome sequencing. Exploring different areas of the SARS-CoV-2 genome demonstrated that the S gene in processed and extracted samples showed greater sensitivity to freeze-thaw cycles than the E or N genes. Investigating surrogate and normalisation viruses showed that Phi6 remains a stable comparison for SARS-CoV-2 in a laboratory setting and crAssphage was relatively resilient to temperature variation. Recovery of SARS-CoV-2 in raw unprocessed samples was significantly greater when stored at 4 °C, which was supported by the sequencing data for all viruses - both time and freeze-thaw cycles negatively impacted sequencing metrics. Historical extracts stored at -80 °C that were re-quantified 12, 14 and 16 months after original quantification showed no major changes. This study highlights the importance of the fast processing and extraction of wastewater samples, following which viruses are relatively robust to storage at a range of temperatures.

Enhancement of waterborne pathogen removal by functionalized biochar with ε-polylysine ″dynamic arms″: Potential application in ultrafiltration system

Widespread outbreaks of threatening infections caused by unknown pathogens and water transmission have spawned the development of adsorption methods for pathogen elimination. We proposed a biochar functionalization strategy involving ε-polylysine (PLL), a bio-macromolecular poly(amino acid)s with variable folding conformations, as a "pathogen gripper" on biochar. PLL was successfully bridged onto biochar via polydopamine (PDA) crosslinking. The extension of electropositive side chains within PLL enables the capture of both nanoscale viruses and micrometer-scale bacteria in water, achieving excellent removal performances. This functionalized biochar was tentatively incorporated into ultrafiltration (UF) system, to achieve effective and controllable adsorption and retention of pathogens, and to realize the transfer of pathogens from membrane surface/pore to biochar surface as well as flushing water. The biochar-amended UF systems presents complete retention (∼7 LRV) and hydraulic elution of pathogens into membrane flushing water. Improvements in removal of organics and anti-fouling capability were observed, indicating the broken trade-off in UF pathogen removal dependent on irreversible fouling. Chemical characterizations revealed adsorption mechanisms encompassing electrostatic/hydrophobic interactions, pore filling, electron transfer, chemical bonding and secondary structure transitions. Microscopic and mechanical analyses validated the mechanisms for rapid adsorption and pathogen lysis. Low-concentration alkaline solution for used biochar regeneration, facilitated the deprotonation and transformation of PLL side chain to folded structures (α-helix/β-sheet). Biochar regeneration process also promoted the effective detachment/inactivation of pathogens and protection of functional groups on biochar, corroborated by physicochemical inspection and molecular dynamics simulation. The foldability of poly(amino acid)s acting like dynamic arms, significantly contributed to pathogen capture/desorption/inactivation and biochar regeneration. This study also inspires future investigation for performances of UF systems amended by poly(amino acid)s-functionalized biochar under diverse pressure, temperature, reactive oxygen species of feeds and chemical cleaning solutions, with far-reaching implications for public health, environmental applications of biochar, and UF process improvement.

A One Health framework for exploring zoonotic interactions demonstrated through a case study

The eco-epidemiology of zoonoses is often oversimplified to host-pathogen interactions while findings derived from global datasets are rarely directly transferable to smaller-scale contexts. Through a systematic literature search, we compiled a dataset of naturally occurring zoonotic interactions in Austria, spanning 1975-2022. We introduce the concept of zoonotic web to describe the complex relationships between zoonotic agents, their hosts, vectors, food, and environmental sources. The zoonotic web was explored through network analysis. After controlling for research effort, we demonstrate that, within the projected unipartite source-source network of zoonotic agent sharing, the most influential zoonotic sources are human, cattle, chicken, and some meat products. Analysis of the One Health 3-cliques (triangular sets of nodes representing human, animal, and environment) confirms the increased probability of zoonotic spillover at human-cattle and human-food interfaces. We characterise six communities of zoonotic agent sharing, which assembly patterns are likely driven by highly connected infectious agents in the zoonotic web, proximity to human, and anthropogenic activities. Additionally, we report a frequency of emerging zoonotic diseases in Austria of one every six years. Here, we present a flexible network-based approach that offers insights into zoonotic transmission chains, facilitating the development of locally-relevant One Health strategies against zoonoses.

RBD amplicon sequencing of wastewater reveals patterns of variant emergence and evolution

Rapid evolution of SARS-CoV-2 has resulted in the emergence of numerous variants, posing significant challenges to public health surveillance. Clinical genome sequencing, while valuable, has limitations in capturing the full epidemiological dynamics of circulating variants in the general population. This study utilized receptor-binding domain (RBD) amplicon sequencing of wastewater samples to monitor the SARS-CoV-2 community dynamics and evolution in El Paso, TX. Over 17 months, we identified 91 variants and observed waves of dominant variants transitioning from BA.2 to BA.2.12.1, BA.4&5, BQ.1, and XBB.1.5. Our findings demonstrated early detection of variants and identification of unreported outbreaks, while showing strong consistency with clinical genome sequencing data at the local, state, and national levels. Alpha diversity analyses revealed significant periodical variations, with the highest diversity observed in winter and the outbreak lag phases, likely due to lower competition among variants before the outbreak growth phase. The data underscores the importance of low transmission periods for rapid mutation and variant evolution. This study highlights the effectiveness of integrating RBD amplicon sequencing with wastewater surveillance in tracking viral evolution, understanding variant emergence, and enhancing public health preparedness.

Incursion of SARS-CoV-2 BA.2.86.1 variant into Israel: National-scale wastewater surveillance using a novel quantitative real-time PCR assay

The emergence of the SARS-CoV-2 variant BA.2.86.1 raised a considerable concern, due to the large number of potentially virulent mutations. In this study, we developed a novel assay that specifically detects variant BA.2.86.1, and used it to screen environmental samples from wastewater treatment sites across Israel. By using a multiplex assay that included a general SARS-CoV-2 reaction, together with the BA.2.86.1-specific reaction and a control reaction, we quantified the absolute number of viral copies in each sample and its relative abundance, compared with the total copy number of circulating SARS-CoV-2. Evaluation of the new reactions showed that they are both sensitive and specific, detecting down to four copies per reaction, and maintain specificity in the presence of Omicron variants BA.1, 2 and 4 RNA. Examination of 279 samples from 30 wastewater collection sites during August-September 2023 showed that 35 samples (12.5 %) were positive, from 18 sites. Quantitative analysis of the samples showed that the relative abundance of variant BA.2.86.1 with respect to the total viral load of SARS-CoV-2 was very low and consisted between 0.01 % and 0.6 % of the total SARS-CoV-2 circulation. This study demonstrates the importance of combining wastewater surveillance with the development of specialized diagnostic assays, when clinical testing is insufficient. This approach may be useful for timely response by public health authorities in future outbreaks.

ern: An [Formula: see text] package to estimate the effective reproduction number using clinical and wastewater surveillance data

The effective reproduction number, [Formula: see text], is an important epidemiological metric used to assess the state of an epidemic, as well as the effectiveness of public health interventions undertaken in response. When [Formula: see text] is above one, it indicates that new infections are increasing, and thus the epidemic is growing, while an [Formula: see text] is below one indicates that new infections are decreasing, and so the epidemic is under control. There are several established software packages that are readily available to statistically estimate [Formula: see text] using clinical surveillance data. However, there are comparatively few accessible tools for estimating [Formula: see text] from pathogen wastewater concentration, a surveillance data stream that cemented its utility during the COVID-19 pandemic. We present the [Formula: see text] package ern that aims to perform the estimation of the effective reproduction number from real-world wastewater or aggregated clinical surveillance data in a user-friendly way.

Wastewater-based surveillance is an efficient monitoring tool for tracking influenza A in the community

Around the world, influenza A virus has caused severe pandemics, and the risk of future pandemics remains high. Currently, influenza A virus surveillance is based on the clinical diagnosis and reporting of disease cases. In this study, we apply wastewater-based surveillance to monitor the amount of the influenza A virus RNA at the population level. We report the influenza A virus RNA levels in 10 wastewater treatment plant catchment areas covering 40 % of the Finnish population. Altogether, 251 monthly composite influent wastewater samples (collected between February 2021 and February 2023) were analysed from supernatant fraction using influenza A virus specific RT-qPCR method. During the study period, an influenza A virus epidemic occurred in three waves in Finland. This study shows that the influenza A virus RNA can be detected from the supernatant fraction of 24 h composite influent wastewater samples. The influenza A virus RNA gene copy number in wastewater correlated with the number of confirmed disease cases in the Finnish National Infectious Diseases Register. The median Kendall's τ correlation strength was 0.636 (min= 0.486 and max=0.804) and it was statistically significant in all 10 WTTPs. Wastewater-based surveillance of the influenza A virus RNA is an independent from individual testing method and cost-efficiently reflects the circulation of the virus in the entire population. Thus, wastewater monitoring complements the available, but often too sparse, information from individual testing and improves health care and public health preparedness for influenza A virus pandemics.

Genomic surveillance of SARS-CoV-2 evolution by a centralised pipeline and weekly focused sequencing, Austria, January 2021 to March 2023

BackgroundThe COVID-19 pandemic was largely driven by genetic mutations of SARS-CoV-2, leading in some instances to enhanced infectiousness of the virus or its capacity to evade the host immune system. To closely monitor SARS-CoV-2 evolution and resulting variants at genomic-level, an innovative pipeline termed SARSeq was developed in Austria.AimWe discuss technical aspects of the SARSeq pipeline, describe its performance and present noteworthy results it enabled during the pandemic in Austria.MethodsThe SARSeq pipeline was set up as a collaboration between private and public clinical diagnostic laboratories, a public health agency, and an academic institution. Representative SARS-CoV-2 positive specimens from each of the nine Austrian provinces were obtained from SARS-CoV-2 testing laboratories and processed centrally in an academic setting for S-gene sequencing and analysis.ResultsSARS-CoV-2 sequences from up to 2,880 cases weekly resulted in 222,784 characterised case samples in January 2021-March 2023. Consequently, Austria delivered the fourth densest genomic surveillance worldwide in a very resource-efficient manner. While most SARS-CoV-2 variants during the study showed comparable kinetic behaviour in all of Austria, some, like Beta, had a more focused spread. This highlighted multifaceted aspects of local population-level acquired immunity. The nationwide surveillance system enabled reliable nowcasting. Measured early growth kinetics of variants were predictive of later incidence peaks.ConclusionWith low automation, labour, and cost requirements, SARSeq is adaptable to monitor other pathogens and advantageous even for resource-limited countries. This multiplexed genomic surveillance system has potential as a rapid response tool for future emerging threats.

High-resolution and real-time wastewater viral surveillance by Nanopore sequencing

Wastewater genomic sequencing stands as a pivotal complementary tool for viral surveillance in populations. While long-read Nanopore sequencing is a promising platform to provide real-time genomic data, concerns over the sequencing accuracy of the earlier Nanopore versions have somewhat restrained its widespread application in wastewater analysis. Here, we evaluate the latest improved version of Nanopore sequencing (R10.4.1), using SARS-CoV-2 as the model infectious virus, to demonstrate its effectiveness in wastewater viral monitoring. By comparing amplicon lengths of 400 bp and 1200 bp, we revealed that shorter PCR amplification is more suitable for wastewater samples due to viral genome fragmentation. Utilizing mock wastewater samples, we validated the reliability of Nanopore sequencing for variant identification by comparing it with Illumina sequencing results. The strength of Nanopore sequencing in generating real-time genomic data for providing early warning signals was also showcased, indicating that as little as 0.001 Gb of data can provide accurate results for variant prevalence. Our evaluation also identified optimal alteration frequency cutoffs (>50 %) for precise mutation profiling, achieving >99 % precision in detecting single nucleotide variants (SNVs) and insertions/deletions (indels). Monitoring two major wastewater treatment plants in Hong Kong from September 2022 to April 2023, covering over 4.5 million population, we observed a transition in dominant variants from BA.5 to XBB lineages, with XBB.1.5 being the most prevalent variants. Mutation detection also highlighted the potential of wastewater Nanopore sequencing in uncovering novel mutations and revealed links between signature mutations and specific variants. This study not only reveals the environmental implications of Nanopore sequencing in SARS-CoV-2 surveillance but also underscores its potential in broader applications including environmental health monitoring of other epidemic viruses, which could significantly enhance the field of wastewater-based epidemiology.

Crykey: Rapid identification of SARS-CoV-2 cryptic mutations in wastewater

Wastewater surveillance for SARS-CoV-2 provides early warnings of emerging variants of concerns and can be used to screen for novel cryptic linked-read mutations, which are co-occurring single nucleotide mutations that are rare, or entirely missing, in existing SARS-CoV-2 databases. While previous approaches have focused on specific regions of the SARS-CoV-2 genome, there is a need for computational tools capable of efficiently tracking cryptic mutations across the entire genome and investigating their potential origin. We present Crykey, a tool for rapidly identifying rare linked-read mutations across the genome of SARS-CoV-2. We evaluated the utility of Crykey on over 3,000 wastewater and over 22,000 clinical samples; our findings are three-fold: i) we identify hundreds of cryptic mutations that cover the entire SARS-CoV-2 genome, ii) we track the presence of these cryptic mutations across multiple wastewater treatment plants and over three years of sampling in Houston, and iii) we find a handful of cryptic mutations in wastewater mirror cryptic mutations in clinical samples and investigate their potential to represent real cryptic lineages. In summary, Crykey enables large-scale detection of cryptic mutations in wastewater that represent potential circulating cryptic lineages, serving as a new computational tool for wastewater surveillance of SARS-CoV-2.

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.

Efficient SARS-CoV-2 variant detection and monitoring with Spike Screen next-generation sequencing

The emergence and rapid spread of SARS-CoV-2 prompted the global community to identify innovative approaches to diagnose infection and sequence the viral genome because at several points in the pandemic positive case numbers exceeded the laboratory capacity to characterize sufficient samples to adequately respond to the spread of emerging variants. From week 10, 2020, to week 13, 2023, Slovenian routine complete genome sequencing (CGS) surveillance network yielded 41 537 complete genomes and revealed a typical molecular epidemiology with early lineages gradually being replaced by Alpha, Delta, and finally Omicron. We developed a targeted next-generation sequencing based variant surveillance strategy dubbed Spike Screen through sample pooling and selective SARS-CoV-2 spike gene amplification in conjunction with CGS of individual cases to increase throughput and cost-effectiveness. Spike Screen identifies variant of concern (VOC) and variant of interest (VOI) signature mutations, analyses their frequencies in sample pools, and calculates the number of VOCs/VOIs at the population level. The strategy was successfully applied for detection of specific VOC/VOI mutations prior to their confirmation by CGS. Spike Screen complemented CGS efforts with an additional 22 897 samples sequenced in two time periods: between week 42, 2020, and week 24, 2021, and between week 37, 2021, and week 2, 2022. The results showed that Spike Screen can be applied to monitor VOC/VOI mutations among large volumes of samples in settings with limited sequencing capacity through reliable and rapid detection of novel variants at the population level and can serve as a basis for public health policy planning.

Considerations and Opportunities for Probe Capture Enrichment Sequencing of Emerging Viruses from Wastewater

Until recently, wastewater-based monitoring for pathogens of public health concern primarily used PCR-based quantification methods and targeted sequencing for specific pathogens (e.g., SARS-CoV-2). In the past three years, researchers have expanded sequencing to monitor a broad range of pathogens, applying probe capture enrichment to wastewater. The goals of those studies included (1) monitoring and expanding fundamental knowledge of disease dynamics for known pathogens and (2) evaluating the potential for early detection of emerging diseases resulting from zoonotic spillover or novel viral variants. Several studies using off-the-shelf probe panels designed for clinical and environmental surveillance reported that enrichment increased virus relative abundance but did not recover complete genomes for most nonenteric viruses. Based on our experience and recent results reported by others using these panels for wastewater, clinical, and synthetic samples, we discuss challenges and technical factors that affect the rates of false positive and false negative results. We identify trade-offs and opportunities throughout the workflow, including in wastewater sample processing, probe panel design, and bioinformatic analysis. We suggest tailored methods of virus concentration and background removal, carefully designed probe panels, and multithresholded bioinformatics analysis.

Environmental Surveillance of Flood Control Infrastructure Impacted by Unsheltered Individuals Leads to the Detection of SARS-CoV-2 and Novel Mutations in the Spike Gene

In the United States, the growing number of people experiencing homelessness has become a socioeconomic crisis with public health ramifications, recently exacerbated by the COVID-19 pandemic. We hypothesized that the environmental surveillance of flood control infrastructure may be an effective approach to understand the prevalence of infectious disease. From December 2021 through July 2022, we tested for SARS-CoV-2 RNA from two flood control channels known to be impacted by unsheltered individuals residing in upstream tunnels. Using qPCR, we detected SARS-CoV-2 RNA in these environmental water samples when significant COVID-19 outbreaks were occurring in the surrounding community. We also performed whole genome sequencing to identify SARS-CoV-2 lineages. Variant compositions were consistent with those of geographically and temporally matched municipal wastewater samples and clinical specimens. However, we also detected 10 of 22 mutations specific to the Alpha variant in the environmental water samples collected during January 2022-one year after the Alpha infection peak. We also identified mutations in the spike gene that have never been identified in published reports. Our findings demonstrate that environmental surveillance of flood control infrastructure may be an effective tool to understand public health conditions among unsheltered individuals-a vulnerable population that is underrepresented in clinical surveillance data.

Breakthrough Infections in SARS-CoV-2-Vaccinated Multiple Myeloma Patients Improve Cross-Protection against Omicron Variants

Patients with multiple myeloma (MM) are a heterogenous, immunocompromised group with increased risk for COVID-19 morbidity and mortality but impaired responses to primary mRNA SARS-CoV-2 vaccination. The effects of booster vaccinations and breakthrough infections (BTIs) on antibody (Ab) levels and cross-protection to variants of concern (VOCs) are, however, not sufficiently evaluated. Therefore, we analysed humoral and cellular vaccine responses in MM patients stratified according to disease stage/treatment into group (1) monoclonal gammopathy of undetermined significance, (2) after stem cell transplant (SCT) without immunotherapy (IT), (3) after SCT with IT, and (4) progressed MM, and in healthy subjects (prospective cohort study). In contrast to SARS-CoV-2 hu-1-specific Ab levels, Omicron-specific Abs and their cross-neutralisation capacity remained low even after three booster doses in a majority of MM patients. In particular, progressed MM patients receiving anti-CD38 mAb and those after SCT with IT were Ab low responders and showed delayed formation of spike-specific B memory cells. However, MM patients with hybrid immunity (i.e., vaccination and breakthrough infection) had improved cross-neutralisation capacity against VOCs, yet in the absence of severe COVID-19 disease. Our results indicate that MM patients require frequent variant-adapted booster vaccinations and/or changes to other vaccine formulations/platforms, which might have similar immunological effects as BTIs.

Early Detection of Novel SARS-CoV-2 Variants from Urban and Rural Wastewater through Genome Sequencing and Machine Learning

Genome sequencing from wastewater has emerged as an accurate and cost-effective tool for identifying SARS-CoV-2 variants. However, existing methods for analyzing wastewater sequencing data are not designed to detect novel variants that have not been characterized in humans. Here, we present an unsupervised learning approach that clusters co-varying and time-evolving mutation patterns leading to the identification of SARS-CoV-2 variants. To build our model, we sequenced 3,659 wastewater samples collected over a span of more than two years from urban and rural locations in Southern Nevada. We then developed a multivariate independent component analysis (ICA)-based pipeline to transform mutation frequencies into independent sources with co-varying and time-evolving patterns and compared variant predictions to >5,000 SARS-CoV-2 clinical genomes isolated from Nevadans. Using the source patterns as data-driven reference "barcodes", we demonstrated the model's accuracy by successfully detecting the Delta variant in late 2021, Omicron variants in 2022, and emerging recombinant XBB variants in 2023. Our approach revealed the spatial and temporal dynamics of variants in both urban and rural regions; achieved earlier detection of most variants compared to other computational tools; and uncovered unique co-varying mutation patterns not associated with any known variant. The multivariate nature of our pipeline boosts statistical power and can support accurate and early detection of SARS-CoV-2 variants. This feature offers a unique opportunity for novel variant and pathogen detection, even in the absence of clinical testing.

Systematic SARS-CoV-2 S-gene sequencing in wastewater samples enables early lineage detection and uncovers rare mutations in Portugal

As the COVID-19 pandemic reached its peak, many countries implemented genomic surveillance systems to track the evolution and transmission of SARS-CoV-2. Transition from the pandemic to the endemic phase prioritized alternative testing strategies to maintain effective epidemic surveillance at the population level, with less intensive sequencing efforts. One such promising approach was Wastewater-Based Surveillance (WBS), which offers non-invasive, cost-effective means for analysing virus trends at the sewershed level. From 2020 onwards, wastewater has been recognized as an instrumental source of information for public health, with national and international authorities exploring options to implement national wastewater surveillance systems and increasingly relying on WBS as early warning of potential pathogen outbreaks. In Portugal, several pioneer projects joined the academia, water utilities and Public Administration around WBS. To validate WBS as an effective genomic surveillance strategy, it is crucial to collect long term performance data. In this work, we present one year of systematic SARS-CoV-2 wastewater surveillance in Portugal, representing 35 % of the mainland population. We employed two complementary methods for lineage determination - allelic discrimination by RT-PCR and S-gene sequencing. This combination allowed us to monitor variant evolution in near-real-time and identify low-frequency mutations. Over the course of this year-long study, spanning from May 2022 to April 2023, we successfully tracked the dominant Omicron sub-lineages, their progression and evolution, which aligned with concurrent clinical surveillance data. Our results underscore the effectiveness of WBS as a tracking system for virus variants, with the ability to unveil mutations undetected via massive sequencing of clinical samples from Portugal, demonstrating the ability of WBS to uncover new mutations and detect rare genetic variants. Our findings emphasize that knowledge of the genetic diversity of SARS-CoV-2 at the population level can be extended far beyond via the combination of routine clinical genomic surveillance with wastewater sequencing and genotyping.

Dynamic SARS-CoV-2 surveillance model combining seroprevalence and wastewater concentrations for post-vaccine disease burden estimates

BACKGROUND

Despite wide scale assessments, it remains unclear how large-scale severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccination affected the wastewater concentration of the virus or the overall disease burden as measured by hospitalization rates.

METHODS

We used weekly SARS-CoV-2 wastewater concentration with a stratified random sampling of seroprevalence, and linked vaccination and hospitalization data, from April 2021-August 2021 in Jefferson County, Kentucky (USA). Our susceptible ( S ), vaccinated ( V ), variant-specific infected (I 1 andI 2 ), recovered ( R ), and seropositive ( T ) model ( S V I 2 R T ) tracked prevalence longitudinally. This was related to wastewater concentration.

RESULTS

Here we show the 64% county vaccination rate translate into about a 61% decrease in SARS-CoV-2 incidence. The estimated effect of SARS-CoV-2 Delta variant emergence is a 24-fold increase of infection counts, which correspond to an over 9-fold increase in wastewater concentration. Hospitalization burden and wastewater concentration have the strongest correlation (r = 0.95) at 1 week lag.

CONCLUSIONS

Our study underscores the importance of continuing environmental surveillance post-vaccine and provides a proof-of-concept for environmental epidemiology monitoring of infectious disease for future pandemic preparedness.

Detection of SARS-COV-2 variants and their proportions in wastewater samples using next-generation sequencing in Finland

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variants may have different characteristics, e.g., in transmission, mortality, and the effectiveness of vaccines, indicating the importance of variant detection at the population level. Wastewater-based surveillance of SARS-CoV-2 RNA fragments has been shown to be an effective way to monitor the COVID-19 pandemic at the population level. Wastewater is a complex sample matrix affected by environmental factors and PCR inhibitors, causing insufficient coverage in sequencing, for example. Subsequently, results where part of the genome does not have sufficient coverage are not uncommon. To identify variants and their proportions in wastewater over time, we utilized next-generation sequencing with the ARTIC Network's primer set and bioinformatics pipeline to evaluate the presence of variants in partial genome data. Based on the wastewater data from November 2021 to February 2022, the Delta variant was dominant until mid-December in Helsinki, Finland's capital, and thereafter in late December 2022 Omicron became the most common variant. At the same time, the Omicron variant of SARS-CoV-2 outcompeted the previous Delta variant in Finland in new COVID-19 cases. The SARS-CoV-2 variant findings from wastewater are in agreement with the variant information obtained from the patient samples when visually comparing trends in the sewerage network area. This indicates that the sequencing of wastewater is an effective way to monitor temporal and spatial trends of SARS-CoV-2 variants at the population level.

Using digital PCR to predict ciliate abundance from ribosomal RNA gene copy numbers

Ciliates play a key role in most ecosystems. Their abundance in natural samples is crucial for answering many ecological questions. Traditional methods of quantifying individual species, which rely on microscopy, are often labour-intensive, time-consuming and can be highly biassed. As a result, we investigated the potential of digital polymerase chain reaction (dPCR) for quantifying ciliates. A significant challenge in this process is the high variation in the copy number of the taxonomic marker gene (ribosomal RNA [rRNA]). We first quantified the rRNA gene copy numbers (GCN) of the model ciliate, Paramecium tetraurelia, during different stages of the cell cycle and growth phases. The per-cell rRNA GCN varied between approximately 11,000 and 130,000, averaging around 50,000 copies per cell. Despite these variations in per-cell rRNA GCN, we found a highly significant correlation between GCN and cell numbers. This is likely due to the coexistence of different cellular stages in an uncontrolled (environmental) ciliate population. Thanks to the high sensitivity of dPCR, we were able to detect the target gene in a sample that contained only a single cell. The dPCR approach presented here is a valuable addition to the molecular toolbox in protistan ecology. It may guide future studies in quantifying and monitoring the abundance of targeted (even rare) ciliates in natural samples.

Estimating actual SARS-CoV-2 infections from secondary data

Eminent in pandemic management is accurate information on infection dynamics to plan for timely installation of control measures and vaccination campaigns. Despite huge efforts in diagnostic testing of individuals, the underestimation of the actual number of SARS-CoV-2 infections remains significant due to the large number of undocumented cases. In this paper we demonstrate and compare three methods to estimate the dynamics of true infections based on secondary data i.e., (a) test positivity, (b) infection fatality and (c) wastewater monitoring. The concept is tested with Austrian data on a national basis for the period of April 2020 to December 2022. Further, we use the results of prevalence studies from the same period to generate (upper and lower bounds of) credible intervals for true infections for four data points. Model parameters are subsequently estimated by applying Approximate Bayesian Computation-rejection sampling and Genetic Algorithms. The method is then validated for the case study Vienna. We find that all three methods yield fairly similar results for estimating the true number of infections, which supports the idea that all three datasets contain similar baseline information. None of them is considered superior, as their advantages and shortcomings depend on the specific case study at hand.

Global and genetic diversity of SARS-CoV-2 in wastewater

The analysis of SARS-CoV-2 in wastewater has enabled us to better understand the spread and evolution of the virus worldwide. To deepen our understanding of its epidemiological and genomic characteristics, we analyzed 10,147 SARS-CoV-2 sequences from 5 continents and 21 countries that were deposited in the GISAID database up until January 31, 2023. Our results revealed over 100 independent lineages of the virus circulating in water samples from March 2020 to January 2023, including variants of interest and concern. We observed four clearly defined periods of global distribution of these variants over time, with one variant being replaced by another. Interestingly, we found that SARS-CoV-2 water-borne sequences from different countries had a close phylogenetic relationship. Additionally, 40 SARS-CoV-2 water-borne sequences from Europe and the USA did not show any phylogenetic relationship with SARS-CoV-2 human sequences. We also identified a significant number of non-synonymous mutations, some of which were detected in previously reported cryptic lineages. Among the countries analyzed, France and the USA showed the highest degree of sequence diversity, while Austria reported the highest number of genomes (6,296). Our study provides valuable information about the epidemiological and genomic diversity of SARS-CoV-2 in wastewater, which can be employed to support public health initiatives and preparedness.

Wastewater-based surveillance as a tool for public health action: SARS-CoV-2 and beyond

Wastewater-based surveillance (WBS) has undergone dramatic advancement in the context of the coronavirus disease 2019 (COVID-19) pandemic. The power and potential of this platform technology were rapidly realized when it became evident that not only did WBS-measured SARS-CoV-2 RNA correlate strongly with COVID-19 clinical disease within monitored populations but also, in fact, it functioned as a leading indicator. Teams from across the globe rapidly innovated novel approaches by which wastewater could be collected from diverse sewersheds ranging from wastewater treatment plants (enabling community-level surveillance) to more granular locations including individual neighborhoods and high-risk buildings such as long-term care facilities (LTCF). Efficient processes enabled SARS-CoV-2 RNA extraction and concentration from the highly dilute wastewater matrix. Molecular and genomic tools to identify, quantify, and characterize SARS-CoV-2 and its various variants were adapted from clinical programs and applied to these mixed environmental systems. Novel data-sharing tools allowed this information to be mobilized and made immediately available to public health and government decision-makers and even the public, enabling evidence-informed decision-making based on local disease dynamics. WBS has since been recognized as a tool of transformative potential, providing near-real-time cost-effective, objective, comprehensive, and inclusive data on the changing prevalence of measured analytes across space and time in populations. However, as a consequence of rapid innovation from hundreds of teams simultaneously, tremendous heterogeneity currently exists in the SARS-CoV-2 WBS literature. This manuscript provides a state-of-the-art review of WBS as established with SARS-CoV-2 and details the current work underway expanding its scope to other infectious disease targets.

Development of a reverse transcriptase digital droplet polymerase chain reaction-based approach for SARS-CoV-2 variant surveillance in wastewater

An urgent need for effective surveillance strategies arose due to the global emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Although vaccines and antivirals are available, concerns persist about the evolution of new variants with potentially increased infectivity, transmissibility, and immune evasion. Therefore, variant monitoring is crucial for public health decision-making. Wastewater-based surveillance has proven to be an effective tool to monitor SARS-CoV-2 variants within populations. Specific SARS-CoV-2 variants are detected and quantified in wastewater in this study using a reverse transcriptase digital droplet polymerase chain reaction (RT-ddPCR) approach. The 11 designed assays were first validated in silico using a substantial dataset of high-quality SARS-CoV-2 genomes to ensure comprehensive variant coverage. The assessment of the sensitivity and specificity with reference material showed the capability of the developed assays to reliably identify target mutations while minimizing false positives and false negatives. The applicability of the assays was evaluated using wastewater samples from a wastewater treatment plant in Ghent, Belgium. The quantification of the specific mutations linked to the variants of concern present in these samples was calculated using these assays based on the detection of single mutations, which confirms their use for real-world variant surveillance. In conclusion, this study provides an adaptable protocol to monitor SARS-CoV-2 variants in wastewater with high sensitivity and specificity. Its potential for broader application in other viral surveillance contexts highlights its added value for rapid response to emerging infectious diseases. PRACTITIONER POINTS: Robust RT-ddPCR methodology for specific SARS-CoV-2 variants of concern detection in wastewater. Rigorous validation that demonstrates high sensitivity and specificity. Demonstration of real-world applicability using wastewater samples. Valuable tool for rapid response to emerging infectious diseases.

Prevalence of SARS-CoV-2 variants in Prague wastewater determined by nanopore-based sequencing

The early detection of upcoming disease outbreaks is essential to avoid both health and economic damage. The last four years of COVID-19 pandemic have proven wastewater-based epidemiology is a reliable system for monitoring the spread of SARS-CoV-2, a causative agent of COVID-19, in an urban population. As this monitoring enables the identification of the prevalence of spreading variants of SARS-CoV-2, it could provide a critical tool in the fight against this viral disease. In this study, we evaluated the presence of variants and subvariants of SARS-CoV-2 in Prague wastewater using nanopore-based sequencing. During August 2021, the data clearly showed that the number of identified SARS-CoV-2 RNA copies increased in the wastewater earlier than in clinical samples indicating the upcoming wave of the Delta variant. New SARS-CoV-2 variants consistently prevailed in wastewater samples around a month after they already prevailed in clinical samples. We also analyzed wastewater samples from smaller sub-sewersheds of Prague and detected significant differences in SARS-CoV-2 lineage progression dynamics among individual localities studied, e.g., suggesting faster prevalence of new variants among the sites with highest population density and mobility.

Integrating wastewater and randomised prevalence survey data for national COVID surveillance

During the COVID-19 pandemic, studies in a number of countries have shown how wastewater can be used as an efficient surveillance tool to detect outbreaks at much lower cost than traditional prevalence surveys. In this study, we consider the utilisation of wastewater data in the post-pandemic setting, in which collection of health data via national randomised prevalence surveys will likely be run at a reduced scale; hence an affordable ongoing surveillance system will need to combine sparse prevalence data with non-traditional disease metrics such as wastewater measurements in order to estimate disease progression in a cost-effective manner. Here, we use data collected during the pandemic to model the dynamic relationship between spatially granular wastewater viral load and disease prevalence. We then use this relationship to nowcast local disease prevalence under the scenario that (i) spatially granular wastewater data continue to be collected; (ii) direct measurements of prevalence are only available at a coarser spatial resolution, for example at national or regional scale. The results from our cross-validation study demonstrate the added value of wastewater data in improving nowcast accuracy and reducing nowcast uncertainty. Our results also highlight the importance of incorporating prevalence data at a coarser spatial scale when nowcasting prevalence at fine spatial resolution, calling for the need to maintain some form of reduced-scale national prevalence surveys in non-epidemic periods. The model framework is disease-agnostic and could therefore be adapted to different diseases and incorporated into a multiplex surveillance system for early detection of emerging local outbreaks.

Changes to Public Health Surveillance Methods Due to the COVID-19 Pandemic: Scoping Review

BACKGROUND

Public health surveillance plays a vital role in informing public health decision-making. The onset of the COVID-19 pandemic in early 2020 caused a widespread shift in public health priorities. Global efforts focused on COVID-19 monitoring and contact tracing. Existing public health programs were interrupted due to physical distancing measures and reallocation of resources. The onset of the COVID-19 pandemic intersected with advancements in technologies that have the potential to support public health surveillance efforts.

OBJECTIVE

This scoping review aims to explore emergent public health surveillance methods during the early COVID-19 pandemic to characterize the impact of the pandemic on surveillance methods.

METHODS

A scoping search was conducted in multiple databases and by scanning key government and public health organization websites from March 2020 to January 2022. Published papers and gray literature that described the application of new or revised approaches to public health surveillance were included. Papers that discussed the implications of novel public health surveillance approaches from ethical, legal, security, and equity perspectives were also included. The surveillance subject, method, location, and setting were extracted from each paper to identify trends in surveillance practices. Two public health epidemiologists were invited to provide their perspectives as peer reviewers.

RESULTS

Of the 14,238 unique papers, a total of 241 papers describing novel surveillance methods and changes to surveillance methods are included. Eighty papers were review papers and 161 were single studies. Overall, the literature heavily featured papers detailing surveillance of COVID-19 transmission (n=187). Surveillance of other infectious diseases was also described, including other pathogens (n=12). Other public health topics included vaccines (n=9), mental health (n=11), substance use (n=4), healthy nutrition (n=1), maternal and child health (n=3), antimicrobial resistance (n=2), and misinformation (n=6). The literature was dominated by applications of digital surveillance, for example, by using big data through mobility tracking and infodemiology (n=163). Wastewater surveillance was also heavily represented (n=48). Other papers described adaptations to programs or methods that existed prior to the COVID-19 pandemic (n=9). The scoping search also found 109 papers that discuss the ethical, legal, security, and equity implications of emerging surveillance methods. The peer reviewer public health epidemiologists noted that additional changes likely exist, beyond what has been reported and available for evidence syntheses.

CONCLUSIONS

The COVID-19 pandemic accelerated advancements in surveillance and the adoption of new technologies, especially for digital and wastewater surveillance methods. Given the investments in these systems, further applications for public health surveillance are likely. The literature for surveillance methods was dominated by surveillance of infectious diseases, particularly COVID-19. A substantial amount of literature on the ethical, legal, security, and equity implications of these emerging surveillance methods also points to a need for cautious consideration of potential harm.

Timely Monitoring of SARS-CoV-2 RNA Fragments in Wastewater Shows the Emergence of JN.1 (BA.2.86.1.1, Clade 23I) in Berlin, Germany

The importance of COVID-19 surveillance from wastewater continues to grow since case-based surveillance in the general population has been scaled back world-wide. In Berlin, Germany, quantitative and genomic wastewater monitoring for SARS-CoV-2 is performed in three wastewater treatment plants (WWTP) covering 84% of the population since December 2021. The SARS-CoV-2 Omicron sublineage JN.1 (B.2.86.1.1), was first identified from wastewater on 22 October 2023 and rapidly became the dominant sublineage. This change was accompanied by a parallel and still ongoing increase in the notification-based 7-day-hospitalization incidence of COVID-19 and COVID-19 ICU utilization, indicating increasing COVID-19 activity in the (hospital-prone) population and a higher strain on the healthcare system. In retrospect, unique mutations of JN.1 could be identified in wastewater as early as September 2023 but were of unknown relevance at the time. The timely detection of new sublineages in wastewater therefore depends on the availability of new sequences from GISAID and updates to Pango lineage definitions and Nextclade. We show that genomic wastewater surveillance provides timely public health evidence on a regional level, complementing the existing indicators.

Impact of reference design on estimating SARS-CoV-2 lineage abundances from wastewater sequencing data

BACKGROUND

Sequencing of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA from wastewater samples has emerged as a valuable tool for detecting the presence and relative abundances of SARS-CoV-2 variants in a community. By analyzing the viral genetic material present in wastewater, researchers and public health authorities can gain early insights into the spread of virus lineages and emerging mutations. Constructing reference datasets from known SARS-CoV-2 lineages and their mutation profiles has become state-of-the-art for assigning viral lineages and their relative abundances from wastewater sequencing data. However, selecting reference sequences or mutations directly affects the predictive power.

RESULTS

Here, we show the impact of a mutation- and sequence-based reference reconstruction for SARS-CoV-2 abundance estimation. We benchmark 3 datasets: (i) synthetic "spike-in"' mixtures; (ii) German wastewater samples from early 2021, mainly comprising Alpha; and (iii) samples obtained from wastewater at an international airport in Germany from the end of 2021, including first signals of Omicron. The 2 approaches differ in sublineage detection, with the marker mutation-based method, in particular, being challenged by the increasing number of mutations and lineages. However, the estimations of both approaches depend on selecting representative references and optimized parameter settings. By performing parameter escalation experiments, we demonstrate the effects of reference size and alternative allele frequency cutoffs for abundance estimation. We show how different parameter settings can lead to different results for our test datasets and illustrate the effects of virus lineage composition of wastewater samples and references.

CONCLUSIONS

Our study highlights current computational challenges, focusing on the general reference design, which directly impacts abundance allocations. We illustrate advantages and disadvantages that may be relevant for further developments in the wastewater community and in the context of defining robust quality metrics.

Effectiveness of environmental surveillance of SARS-CoV-2 as an early-warning system: Update of a systematic review during the second year of the pandemic

The aim of this updated systematic review was to offer an overview of the effectiveness of environmental surveillance (ES) of SARS-CoV-2 as a potential early-warning system (EWS) for COVID-19 and new variants of concerns (VOCs) during the second year of the pandemic. An updated literature search was conducted to evaluate the added value of ES of SARS-CoV-2 for public health decisions. The search for studies published between June 2021 and July 2022 resulted in 1,588 publications, identifying 331 articles for full-text screening. A total of 151 publications met our inclusion criteria for the assessment of the effectiveness of ES as an EWS and early detection of SARS-CoV-2 variants. We identified a further 30 publications among the grey literature. ES confirms its usefulness as an EWS for detecting new waves of SARS-CoV-2 infection with an average lead time of 1-2 weeks for most of the publication. ES could function as an EWS for new VOCs in areas with no registered cases or limited clinical capacity. Challenges in data harmonization and variant detection require standardized approaches and innovations for improved public health decision-making. ES confirms its potential to support public health decision-making and resource allocation in future outbreaks.

Evolution of the SARS-CoV-2 Omicron spike

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant of concern, first identified in November 2021, rapidly spread worldwide and diversified into several subvariants. The Omicron spike (S) protein accumulated an unprecedented number of sequence changes relative to previous variants. In this review, we discuss how Omicron S protein structural features modulate host cell receptor binding, virus entry, and immune evasion and highlight how these structural features differentiate Omicron from previous variants. We also examine how key structural properties track across the still-evolving Omicron subvariants and the importance of continuing surveillance of the S protein sequence evolution over time.

Comparison of metagenomic and targeted methods for sequencing human pathogenic viruses from wastewater

IMPORTANCE

Most public health initiatives that monitor viruses in wastewater have utilized quantitative polymerase chain reaction (PCR) and whole genome PCR sequencing, mirroring techniques used for viral epidemiology in individuals. These techniques require prior knowledge of the target viral genome and are limited to monitoring individual or small groups of viruses. Metagenomic sequencing may offer an alternative strategy for monitoring a broad spectrum of viruses in wastewater, including novel and emerging pathogens. In this study, while amplicon sequencing gave high viral genome coverage, untargeted shotgun sequencing of total nucleic acid samples was unable to detect human pathogenic viruses with enough sensitivity for use in genomic epidemiology. Enrichment of shotgun libraries for respiratory viruses using hybrid-capture technology provided genotypic information on a range of viruses simultaneously, indicating strong potential for wastewater surveillance. This type of targeted metagenomics could be used for monitoring diverse targets, such as pathogens or antimicrobial resistance genes, in environmental samples.

Spatiotemporal genome diversity of SARS-CoV-2 in wastewater: a two-year global epidemiological study

Wastewater surveillance locally and globally is important for the investigation of the molecular epidemiological features of SARS-CoV-2 in the environment. The current study investigated the genomic diversity and mutation profile of SARS-CoV-2 variants in wastewater for the period spanning COVID-19 pandemic up to December, 2022. A total of 3618 complete SARS-CoV-2 genome sequences from waste water samples submitted to the GISAID database were retrieved. The SARS-CoV-2 sequences were subjected to pairwise alignment against reference, followed by clade and lineage assignment (based on Nextstrain, GISAID and Pango), distance metric phylogenetic analysis, and detection of substitution mutations. Following GISAID, Nextstrain, and Pango nomenclatures, an overall agreement in clade and lineage determination in wastewater samples was observed. There was successive appearance, dissemination, and disappearance of SARS-CoV-2 lineages along time in wastewater. The SARS-CoV-2 genomes from wastewater were clustered into the variants of concern (VOC) as Alpha GRY (B.1.1.7 + Q.7), Delta GK (B.1.617.2 + AY.*), and Omicron GRA (BA.1*, BA.2* + B.1.1.529, BA.5*). The evolutionary rate was 9.63e-04 substitutions/site/year for SARS-CoV-2 in wastewater. B.1.1.7 was less prevalent than B.1.617.2 in 2021, appeared in succession, and BA.1, BA.2, BA.5 were serially detected in 2022, the latter strain continued to persist in wastewater. The N501Y, E484K/Q, K417N/T, L452R, T478K spike substitutions remained dominant attribute of SARS-CoV-2 VOCs. The study underlines the importance of wastewater surveillance for enumerating spatiotemporal diversity of SARS-CoV-2 variants and mutations, which might pave the way for novel antiviral and vaccine designing towards management and prevention of SARS-CoV-2 infection.

Interlaboratory comparison using inactivated SARS-CoV-2 variants as a feasible tool for quality control in COVID-19 wastewater monitoring

Wastewater-based SARS-CoV-2 epidemiology (WBE) has proven as an excellent tool to monitor pandemic dynamics supporting individual testing strategies. WBE can also be used as an early warning system for monitoring the emergence of novel pathogens or viral variants. However, for a timely transmission of results, sophisticated sample logistics and analytics performed in decentralized laboratories close to the sampling sites are required. Since multiple decentralized laboratories commonly use custom in-house workflows for sample purification and PCR-analysis, comparative quality control of the analytical procedures is essential to report reliable and comparable results. In this study, we performed an interlaboratory comparison at laboratories specialized for PCR and high-throughput-sequencing (HTS)-based WBE analysis. Frozen reserve samples from low COVID-19 incidence periods were spiked with different inactivated authentic SARS-CoV-2 variants in graduated concentrations and ratios. Samples were sent to the participating laboratories for analysis using laboratory specific methods and the reported viral genome copy numbers and the detection of viral variants were compared with the expected values. All PCR-laboratories reported SARS-CoV-2 genome copy equivalents (GCE) for all spiked samples with a mean intra- and inter-laboratory variability of 19 % and 104 %, respectively, largely reproducing the spike-in scheme. PCR-based genotyping was, in dependence of the underlying PCR-assay performance, able to predict the relative amount of variant specific substitutions even in samples with low spike-in amount. The identification of variants by HTS, however, required >100 copies/ml wastewater and had limited predictive value when analyzing at a genome coverage below 60 %. This interlaboratory test demonstrates that despite highly heterogeneous isolation and analysis procedures, overall SARS-CoV-2 GCE and mutations were determined accurately. Hence, decentralized SARS-CoV-2 wastewater monitoring is feasible to generate comparable analysis results. However, since not all assays detected the correct variant, prior evaluation of PCR and sequencing workflows as well as sustained quality control such as interlaboratory comparisons are mandatory for correct variant detection.

Genomic surveillance reveals early detection and transition of delta to omicron lineages of SARS-CoV-2 variants in wastewater treatment plants of Pune, India

The COVID-19 pandemic has emphasized the urgency for rapid public health surveillance methods to detect and monitor the transmission of infectious diseases. The wastewater-based epidemiology (WBE) has emerged as a promising tool for proactive analysis and quantification of infectious pathogens within a population before clinical cases emerge. In the present study, we aimed to assess the trend and dynamics of SARS-CoV-2 variants using a longitudinal approach. Our objective included early detection and monitoring of these variants to enhance our understanding of their prevalence and potential impact. To achieve our goals, we conducted real-time quantitative polymerase chain reaction (RT-qPCR) and Illumina sequencing on 442 wastewater (WW) samples collected from 10 sewage treatment plants (STPs) in Pune city, India, spanning from November 2021 to April 2022. Our comprehensive analysis identified 426 distinct lineages representing 17 highly transmissible variants of SARS-CoV-2. Notably, fragments of Omicron variant were detected in WW samples prior to its first clinical detection in Botswana. Furthermore, we observed highly contagious sub-lineages of the Omicron variant, including BA.1 (~28%), BA.1.X (1.0-72%), BA.2 (1.0-18%), BA.2.X (1.0-97.4%) BA.2.12 (0.8-0.25%), BA.2.38 (0.8-1.0%), BA.2.75 (0.01-0.02%), BA.3 (0.09-6.3%), BA.4 (0.24-0.29%), and XBB (0.01-21.83%), with varying prevalence rates. Overall, the present study demonstrated the practicality of WBE in the early detection of SARS-CoV-2 variants, which could help track future outbreaks of SARS-CoV-2. Such approaches could be implicated in monitoring infectious agents before they appear in clinical cases.

Utility of wastewater genomic surveillance compared to clinical surveillance to track the spread of the SARS-CoV-2 Omicron variant across England

The world has moved into a new stage of managing the SARS-CoV-2 pandemic with minimal restrictions and reduced testing in the population, leading to reduced genomic surveillance of virus variants in individuals. Wastewater-based epidemiology (WBE) can provide an alternative means of tracking virus variants in the population but decision-makers require confidence that it can be applied to a national scale and is comparable to individual testing data. We analysed 19,911 samples from 524 wastewater sites across England at least twice a week between November 2021 and February 2022, capturing sewage from >70% of the English population. We used amplicon-based sequencing and the phylogeny based de-mixing tool Freyja to estimate SARS-CoV-2 variant frequencies and compared these to the variant dynamics observed in individual testing data from clinical and community settings. We show that wastewater data can reconstruct the spread of the Omicron variant across England since November 2021 in close detail and aligns closely with epidemiological estimates from individual testing data. We also show the temporal and spatial spread of Omicron within London. Our wastewater data further reliably track the transition between Omicron subvariants BA1 and BA2 in February 2022 at regional and national levels. Our demonstration that WBE can track the fast-paced dynamics of SARS-CoV-2 variant frequencies at a national scale and closely match individual testing data in time shows that WBE can reliably fill the monitoring gap left by reduced individual testing in a more affordable way.

Wastewater and seroprevalence for pandemic preparedness: variant analysis, vaccination effect, and hospitalization forecasting for SARS-CoV-2, in Jefferson County, Kentucky

Despite wide scale assessments, it remains unclear how large-scale SARS-CoV-2 vaccination affected the wastewater concentration of the virus or the overall disease burden as measured by hospitalization rates. We used weekly SARS-CoV-2 wastewater concentration with a stratified random sampling of seroprevalence, and linked vaccination and hospitalization data, from April 2021-August 2021 in Jefferson County, Kentucky (USA). Our susceptible (S), vaccinated (V), variant-specific infected (I_1 and I_2), recovered (R), and seropositive (T) model (SVI_2 RT) tracked prevalence longitudinally. This was related to wastewater concentration. The 64% county vaccination rate translated into about 61% decrease in SARS-CoV-2 incidence. The estimated effect of SARS-CoV-2 Delta variant emergence was a 24-fold increase of infection counts, which corresponded to an over 9-fold increase in wastewater concentration. Hospitalization burden and wastewater concentration had the strongest correlation (r = 0.95) at 1 week lag. Our study underscores the importance of continued environmental surveillance post-vaccine and provides a proof-of-concept for environmental epidemiology monitoring of infectious disease for future pandemic preparedness.

FAIR+E pathogen data for surveillance and research: lessons from COVID-19

The COVID-19 pandemic has exemplified the importance of interoperable and equitable data sharing for global surveillance and to support research. While many challenges could be overcome, at least in some countries, many hurdles within the organizational, scientific, technical and cultural realms still remain to be tackled to be prepared for future threats. We propose to (i) continue supporting global efforts that have proven to be efficient and trustworthy toward addressing challenges in pathogen molecular data sharing; (ii) establish a distributed network of Pathogen Data Platforms to (a) ensure high quality data, metadata standardization and data analysis, (b) perform data brokering on behalf of data providers both for research and surveillance, (c) foster capacity building and continuous improvements, also for pandemic preparedness; (iii) establish an International One Health Pathogens Portal, connecting pathogen data isolated from various sources (human, animal, food, environment), in a truly One Health approach and following FAIR principles. To address these challenging endeavors, we have started an ELIXIR Focus Group where we invite all interested experts to join in a concerted, expert-driven effort toward sustaining and ensuring high-quality data for global surveillance and research.

Time series modelling for wastewater-based epidemiology of COVID-19: A nationwide study in 40 wastewater treatment plants of Belgium, February 2021 to June 2022

BACKGROUND

Wastewater-based epidemiology (WBE) has been implemented to monitor surges of COVID-19. Yet, multiple factors impede the usefulness of WBE and quantitative adjustment may be required.

AIM

We aimed to model the relationship between WBE data and incident COVID-19 cases, while adjusting for confounders and autocorrelation.

METHODS

This nationwide WBE study includes data from 40 wastewater treatment plants (WWTPs) in Belgium (02/2021-06/2022). We applied ARIMA-based modelling to assess the effect of daily flow rate, pepper mild mottle virus (PMMoV) concentration, a measure of human faeces in wastewater, and variants (alpha, delta, and omicron strains) on SARS-CoV-2 RNA levels in wastewater. Secondly, adjusted WBE metrics at different lag times were used to predict incident COVID-19 cases. Model selection was based on AICc minimization.

RESULTS

In 33/40 WWTPs, RNA levels were best explained by incident cases, flow rate, and PMMoV. Flow rate and PMMoV were associated with -13.0 % (95 % prediction interval: -26.1 to +0.2 %) and +13.0 % (95 % prediction interval: +5.1 to +21.0 %) change in RNA levels per SD increase, respectively. In 38/40 WWTPs, variants did not explain variability in RNA levels independent of cases. Furthermore, our study shows that RNA levels can lead incident cases by at least one week in 15/40 WWTPs. The median population size of leading WWTPs was 85.1 % larger than that of non‑leading WWTPs. In 17/40 WWTPs, however, RNA levels did not lead or explain incident cases in addition to autocorrelation.

CONCLUSION

This study provides quantitative insights into key determinants of WBE, including the effects of wastewater flow rate, PMMoV, and variants. Substantial inter-WWTP variability was observed in terms of explaining incident cases. These findings are of practical importance to WBE practitioners and show that the early-warning potential of WBE is WWTP-specific and needs validation.

Crykey: Rapid Identification of SARS-CoV-2 Cryptic Mutations in Wastewater

We present Crykey, a computational tool for rapidly identifying cryptic mutations of SARS-CoV-2. Specifically, we identify co-occurring single nucleotide mutations on the same sequencing read, called linked-read mutations, that are rare or entirely missing in existing databases, and have the potential to represent novel cryptic lineages found in wastewater. While previous approaches exist for identifying cryptic linked-read mutations from specific regions of the SARS-CoV-2 genome, there is a need for computational tools capable of efficiently tracking cryptic mutations across the entire genome and for tens of thousands of samples and with increased scrutiny, given their potential to represent either artifacts or hidden SARS-CoV-2 lineages. Crykey fills this gap by identifying rare linked-read mutations that pass stringent computational filters to limit the potential for artifacts. We evaluate the utility of Crykey on >3,000 wastewater and >22,000 clinical samples; our findings are three-fold: i) we identify hundreds of cryptic mutations that cover the entire SARS-CoV-2 genome, ii) we track the presence of these cryptic mutations across multiple wastewater treatment plants and over a three years of sampling in Houston, and iii) we find a handful of cryptic mutations in wastewater mirror cryptic mutations in clinical samples and investigate their potential to represent real cryptic lineages. In summary, Crykey enables large-scale detection of cryptic mutations representing potential cryptic lineages in wastewater.

Early detection of the emerging SARS-CoV-2 BA.2.86 lineage through integrated genomic surveillance of wastewater and COVID-19 cases in Sweden, weeks 31 to 38 2023

The SARS-CoV-2 BA.2.86 Omicron subvariant was first detected in wastewater in Sweden in week 31 2023, using 21 highly specific markers from the 50 investigated. We report BA.2.86's introduction and subsequent spread to all 14 regions performing wastewater sampling, and on 70 confirmed COVID-19 cases, along with the emergence of sublineages JN.1 and JN.2. Further, we investigated two novel mutations defining the unique BA.2.86 branching in Sweden. Our integrated approach enabled variant tracking, offering evidence for well-informed public health interventions.

Regional reemergence of a SARS-CoV-2 Delta lineage amid an Omicron wave detected by wastewater sequencing

The implementation and integration of wastewater-based epidemiology constitutes a valuable addition to existing pathogen surveillance systems, such as clinical surveillance for SARS-CoV-2. In the Netherlands, SARS-CoV-2 variant circulation is monitored by performing whole-genome sequencing on wastewater samples. In this manuscript, we describe the detection of an AY.43 lineage (Delta variant) amid a period of BA.5 (Omicron variant) dominance in wastewater samples from two wastewater treatment plants (WWTPs) during the months of August and September of 2022. Our results describe a temporary emergence, which was absent in samples from other WWTPs, and which coincided with peaks in viral load. We show how these lineage estimates can be traced back to lineage-specific substitution patterns. The absence of this variant from reported clinical data, but high associated viral loads suggest cryptic transmission. Our findings highlight the additional value of wastewater surveillance for generating insights into circulating pathogens.

Wastewater genomic sequencing for SARS-CoV-2 variants surveillance in wastewater-based epidemiology applications

Wastewater-based epidemiology (WBE) has been widely used as a complementary approach to SARS-CoV-2 clinical surveillance. Wastewater genomic sequencing could provide valuable information on the genomic diversity of SARS-CoV-2 in the surveyed population. However, reliable detection and quantification of variants or mutations remain challenging. In this study, we used mock wastewater samples created by spiking SARS-CoV-2 variant standard RNA into wastewater RNA to evaluate the impacts of sequencing throughput on various aspects such as genome coverage, mutation detection, and SARS-CoV-2 variant deconvolution. We found that wastewater datasets with sequencing throughput greater than 0.5 Gb yielded reliable results in genomic analysis. In addition, using in silico mock datasets, we evaluated the performance of the adopted pipeline for variant deconvolution. By sequencing 86 wastewater samples covering more than 6 million people over 7 months, we presented two use cases of wastewater genomic sequencing for surveying COVID-19 in Hong Kong in WBE applications, including the replacement of Delta variants by Omicron variants, and the prevalence and development trends of three Omicron sublineages. Importantly, the wastewater genomic sequencing data were able to reveal the variant trends 16 days before the clinical data did. By investigating mutations of the spike (S) gene of the SARS-CoV-2 virus, we also showed the potential of wastewater genomic sequencing in identifying novel mutations and unique alleles. Overall, our study demonstrated the crucial role of wastewater genomic surveillance in providing valuable insights into the emergence and monitoring of new SARS-CoV-2 variants and laid a solid foundation for the development of genomic analysis methodologies for WBE of other novel emerging viruses in the future.

Wastewater-based surveillance identifies start to the pediatric respiratory syncytial virus season in two cities in Ontario, Canada

Introduction

Detection of community respiratory syncytial virus (RSV) infections informs the timing of immunoprophylaxis programs and hospital preparedness for surging pediatric volumes. In many jurisdictions, this relies upon RSV clinical test positivity and hospitalization (RSVH) trends, which are lagging indicators. Wastewater-based surveillance (WBS) may be a novel strategy to accurately identify the start of the RSV season and guide immunoprophylaxis administration and hospital preparedness.

Methods

We compared citywide wastewater samples and pediatric RSVH in Ottawa and Hamilton between August 1, 2022, and March 5, 2023. 24-h composite wastewater samples were collected daily and 5 days a week at the wastewater treatment facilities in Ottawa and Hamilton, Ontario, Canada, respectively. RSV WBS samples were analyzed in real-time for RSV by RT-qPCR.

Results

RSV WBS measurements in both Ottawa and Hamilton showed a lead time of 12 days when comparing the WBS data set to pediatric RSVH data set (Spearman's ρ = 0.90). WBS identify early RSV community transmission and declared the start of the RSV season 36 and 12 days in advance of the provincial RSV season start (October 31) for the city of Ottawa and Hamilton, respectively. The differing RSV start dates in the two cities is likely associated with geographical and regional variation in the incidence of RSV between the cities.

Discussion

Quantifying RSV in municipal wastewater forecasted a 12-day lead time of the pediatric RSVH surge and an earlier season start date compared to the provincial start date. These findings suggest an important role for RSV WBS to inform regional health system preparedness, reduce RSV burden, and understand variations in community-related illness as novel RSV vaccines and monoclonal antibodies become available.

Emerging advances in biosecurity to underpin human, animal, plant, and ecosystem health

One Biosecurity is an interdisciplinary approach to policy and research that builds on the interconnections between human, animal, plant, and ecosystem health to effectively prevent and mitigate the impacts of invasive alien species. To support this approach requires that key cross-sectoral research innovations be identified and prioritized. Following an interdisciplinary horizon scan for emerging research that underpins One Biosecurity, four major interlinked advances were identified: implementation of new surveillance technologies adopting state-of-the-art sensors connected to the Internet of Things, deployable handheld molecular and genomic tracing tools, the incorporation of wellbeing and diverse human values into biosecurity decision-making, and sophisticated socio-environmental models and data capture. The relevance and applicability of these innovations to address threats from pathogens, pests, and weeds in both terrestrial and aquatic ecosystems emphasize the opportunity to build critical mass around interdisciplinary teams at a global scale that can rapidly advance science solutions targeting biosecurity threats.

M. AH, Fernandez S, Huang AT, Vatanaprasan P, Jay DJ, Saethang T, Luk-in S, Storer RJ, Ounjai P, Devanga Ragupathi NK, Kanthawee P, Sano D, Furukawa T, Sei K, Leelahavanichkul A, Kanjanabuch T, Hirankarn N, Higgins PG, Kicic A, Singer AC, Chatsuwan T, Trowsdale S, Abe S, McLellan AD, Ishikawa H. COVID-19 monitoring with sparse sampling of sewered and non-sewered wastewater in urban and rural communities

Equitable SARS-CoV-2 surveillance in low-resource communities lacking centralized sewers is critical as wastewater-based epidemiology (WBE) progresses. However, large-scale studies on SARS-CoV-2 detection in wastewater from low-and middle-income countries is limited because of economic and technical reasons. In this study, wastewater samples were collected twice a month from 186 urban and rural subdistricts in nine provinces of Thailand mostly having decentralized and non-sewered sanitation infrastructure and analyzed for SARS-CoV-2 RNA variants using allele-specific RT-qPCR. Wastewater SARS-CoV-2 RNA concentration was used to estimate the real-time incidence and time-varying effective reproduction number (Re). Results showed an increase in SARS-CoV-2 RNA concentrations in wastewater from urban and rural areas 14-20 days earlier than infected individuals were officially reported. It also showed that community/food markets were "hot spots" for infected people. This approach offers an opportunity for early detection of transmission surges, allowing preparedness and potentially mitigating significant outbreaks at both spatial and temporal scales.

Near full-automation of COPMAN using a LabDroid enables high-throughput and sensitive detection of SARS-CoV-2 RNA in wastewater as a leading indicator

Wastewater-based epidemiology (WBE) is a promising tool to efficiently monitor COVID-19 prevalence in a community. For WBE community surveillance, automation of the viral RNA detection process is ideal. In the present study, we achieved near full-automation of a previously established method, COPMAN (COagulation and Proteolysis method using MAgnetic beads for detection of Nucleic acids in wastewater), which was then applied to detect SARS-CoV-2 in wastewater for half a year. The automation line employed the Maholo LabDroid and an automated-pipetting device to achieve a high-throughput sample-processing capability of 576 samples per week. SARS-CoV-2 RNA was quantified with the automated COPMAN using samples collected from two wastewater treatment plants in the Sagami River basin in Japan between 1 November 2021 and 24 May 2022, when the numbers of daily reported COVID-19 cases ranged from 0 to 130.3 per 100,000 inhabitants. The automated COPMAN detected SARS-CoV-2 RNA from 81 out of 132 samples at concentrations of up to 2.8 × 10⁵ copies/L. These concentrations showed direct correlations with subsequently reported clinical cases (5-13 days later), as determined by Pearson's and Spearman's cross-correlation analyses. To compare the results, we also conducted testing with the EPISENS-S (Efficient and Practical virus Identification System with ENhanced Sensitivity for Solids, Ando et al., 2022), a previously reported detection method. SARS-CoV-2 RNA detected with EPISENS-S correlated with clinical cases only when using Spearman's method. Our automated COPMAN was shown to be an efficient method for timely and large-scale monitoring of viral RNA, making WBE more feasible for community surveillance.

An improved method for determining frequency of multiple variants of SARS-CoV-2 in wastewater using qPCR assays

Wastewater-based surveillance has become an effective tool around the globe for indirect monitoring of COVID-19 in communities. Variants of Concern (VOCs) have been detected in wastewater by use of reverse transcription polymerase chain reaction (RT-PCR) or whole genome sequencing (WGS). Rapid, reliable RT-PCR assays continue to be needed to determine the relative frequencies of VOCs and sub-lineages in wastewater-based surveillance programs. The presence of multiple mutations in a single region of the N-gene allowed for the design of a single amplicon, multiple probe assay, that can distinguish among several VOCs in wastewater RNA extracts. This approach which multiplexes probes designed to target mutations associated with specific VOC's along with an intra-amplicon universal probe (non-mutated region) was validated in singleplex and multiplex. The prevalence of each mutation (i.e. VOC) is estimated by comparing the abundance of the targeted mutation with a non-mutated and highly conserved region within the same amplicon. This is advantageous for the accurate and rapid estimation of variant frequencies in wastewater. The N200 assay was applied to monitor frequencies of VOCs in wastewater extracts from several communities in Ontario, Canada in near real time from November 28, 2021 to January 4, 2022. This includes the period of the rapid replacement of the Delta variant with the introduction of the Omicron variant in these Ontario communities in early December 2021. The frequency estimates using this assay were highly reflective of clinical WGS estimates for the same communities. This style of qPCR assay, which simultaneously measures signal from a non-mutated comparator probe and multiple mutation-specific probes contained within a single qPCR amplicon, can be applied to future assay development for rapid and accurate estimations of variant frequencies.