The COVID-19 pandemic has accelerated the development and adoption of wastewater-based epidemiology. Wastewater samples can provide genomic information for detecting and assessing the spread of SARS-CoV-2 variants in communities and for estimating important epidemiological parameters such as the selection advantage of a viral variant. However, despite demonstrated successes, epidemiological data derived from wastewater suffers from potential biases. Of particular concern are shedding profiles, which can affect the relationship between true viral incidence and viral loads in wastewater. Changes in shedding between variants may decouple the established relationship between wastewater loads and clinical test data. Using mathematical modeling, simulations, and Swiss surveillance data, we demonstrate that estimates of the selection advantage of a variant are not biased by shedding profiles. We show that they are robust to differences in shedding between variants under a wide range of assumptions, and identify specific conditions under which this robustness may break down. Additionally, we demonstrate that differences in shedding only briefly affect estimates of the effective reproduction number. Thus, estimates of selective advantage and reproduction numbers derived from wastewater maintain their advantages over traditional clinical data, even when there are differences in shedding among variants.

Antimicrobial resistance (AMR) is among the top 10 public health threats, with nearly 5 million deaths in 2019 linked to AMR-related bacterial infections. (1) A One Health approach is needed to combat AMR.
Healthcare-based surveillance (HBS) of AMR provides incomplete information about the scope of the AMR threat. HBS screens only patients seeking medical attention, lacking community-level representativeness, and suffers from underreporting. (2) Consequently, researchers are turning to wastewater-based surveillance (WBS) to complement HBS. (3) WBS can provide information about AMR circulating within communities and hospitals, offering a comprehensive understanding of AMR prevalence. However, the surveillance targets and data obtained from WBS are distinct from those derived from HBS, creating uncertainty regarding their utility to the public health sector and ability to yield policy relevant information. In May 2024, participants in a workshop during the 7^th Environmental Dimension of Antimicrobial Resistance (EDAR7) conference (Montréal, Canada) sought to answer four questions aimed at advancing the policy relevance of AMR data generated by WBS.

Measles outbreaks remain a significant public health challenge, despite high vaccination coverage in many regions. Wastewater-based surveillance (WBS) offers a noninvasive and community-level approach to monitoring the circulation of pathogens, including the measles virus. Here, we retrospectively applied a duplex digital PCR assay to distinguish between wild-type and vaccine strains of the measles virus in wastewater samples available from an existing national WBS program. Samples originated from the wastewater treatment plant serving the Lausanne city catchment area, where an outbreak occurred before spreading to the broader Canton Vaud region. Despite high vaccination rates, viral loads of the measles wild type were detected during the first transmission event involving 21 cases identified within a week. However, viral loads were no longer detectable after the initial 21 cases, despite an additional 30 cases reported in the following 3 weeks, possibly due to lower incidence rate or location outside the catchment. Measles vaccine strain was not detected during the outbreak. Our results demonstrate the complementarity of WBS with clinical surveillance and suggest its potential as an early warning system for measles and other vaccine-preventable diseases. Further improvements in the assay sensitivity and integration with epidemiological data could enhance the utility of WBS for outbreak detection and control.

Wastewater as a medium contains information on both circulating pathogens and drug consumption at the population level. This study combines tracking of respiratory viruses and quantification of pharmaceuticals as untargeted indicators of symptoms related to acute respiratory infections and influenza-like illnesses such as coughing, fever and pain. From January 2021 to June 2024, raw wastewater samples from ten locations covering 23% of the Swiss population were analysed. This encompassed 15 pharmaceuticals and four priority respiratory viruses including severe acute respiratory syndrome coronavirus virus-2 (SARS-CoV-2), respiratory syncytial virus (RSV), influenza A and influenza B viruses. The pharmaceutical compounds dextromethorphan, pheniramine, clarithromycin, acetaminophen and codeine showed a strong correlation with respiratory virus loads in wastewater. This enabled the estimation of pathogen-specific and cumulative symptom treatment in the population. In 2021 and 2024, notable increases in pharmaceutical loads without corresponding increases in viral loads signalled high community symptoms linked to unsurveilled pathogens. This study demonstrates that pharmaceutical surveillance can inform respiratory disease burden and highlights the value of integrated surveillance for assessing emerging public health threats beyond those routinely monitored.

Influenza A virus poses significant public health challenges, causing seasonal outbreaks and pandemics. Its rapid evolution motivates continuous monitoring of circulating influenza genomes to inform vaccine and antiviral development. Wastewater-based surveillance offers an unbiased, cost-effective approach for genomic surveillance. We developed a novel tiling amplicon primer panel that covers diversity of influenza A virus, targeting segments of the surface proteins HA, NA, and M of subtypes H1N1 and H3N2. Using this panel, we sequenced nucleic acid extracts from 59 Swiss wastewater samples collected at four locations during the 2022/2023 and 2023/2024 winter seasons. We found that wastewater-based abundance estimates of the dominant H1N1 clades correlated with clinical-based estimates in the 2023/2024 season. Furthermore, wastewater-based sequencing revealed mutations in vaccine and drug target sites, consistent with clinical data. Overall, we demonstrate the effectiveness of wastewater-based genomic surveillance of influenza A, including lineage identification and mutation tracking to inform vaccine and antiviral strategies.

Quantitative polymerase chain reaction (qPCR) and digital PCR (dPCR) are applied for quantifying molecular targets in disease diagnostics, pathogen detection, and ecological monitoring. Uptake of dPCR is increasing due to its higher quantification accuracy relative to qPCR, which stems from its independence from standard curves and its increased resistance to PCR inhibitors. Throughput can be increased through multiplexing, which allows simultaneous quantification of multiple targets. However, multiplexing with dPCR faces unique challenges relative to qPCR. Here, we describe the three-phase development process of non-competing multiplex dPCR assays using target-specific fluorescently labeled hydrolysis probes. We highlight common challenges encountered, along with recommended solutions. Phase 1: In silico assay design; target-specific primers and probes are selected or designed, potential issues with primer and probe interactions are identified, and fluorophores and quenchers are chosen based on dPCR instrumentation. Phase 2: Wet-lab validation; assays are benchmarked using positive controls. Insufficient performance leads to assay redesign, as needed. Phase 3: Assay implementation; assay specificity and sensitivity are validated on relevant sample matrices. Finally, we provide recommendations on the future design and standardization of multiplexed dPCR assays, highlighting the need for better in silico predictions of assay performance, standardizing positive controls, and automating partition classification systems.

Safe drinking water access is a human right, but data on safely managed drinking water services (SMDWS) is lacking for more than half of the global population. We estimate SMDWS use in 135 low- and middle-income countries (LMICs) at subnational levels with a geospatial modeling approach, combining existing household survey data with available global geospatial datasets. We estimate that only one in three people used SMDWS in LMICs in 2020 and identified fecal contamination as the primary limiting factor affecting almost half of the population of LMICs. Our results are relevant for raising awareness about the challenges and limitations of current global monitoring approaches and demonstrating how globally available geospatial data can be leveraged to fill data gaps and identify priority areas in LMICs.

Preventing failures of water treatment barriers can play an important role in meeting the increasing demand for microbiologically safe water. The development and integration of failure prevention strategies into quantitative microbial risk assessment (QMRA) offer opportunities to support the design and operation of treatment trains. This study presents existing failure models and extends them to guide the development of risk-based operational monitoring strategies. For barriers with rapid performance loss, results show that a failure of 15 s should be reliably detected to verify a log reduction value (LRV) of 6.0; thus, detecting and remediating these failures may be beyond current technology. For chemical disinfection with a residual, failure durations in order of minutes should be reliably detected to verify a LRV of 6.0. Short-term failures are buffered because the disinfectant residual concentration sustains a partial reduction performance. Therefore, increasing the contact time and hydraulic mixing reduces the impact of failures. These findings demonstrate the importance of defining precise frequencies to monitor barrier performances during operation. Overall, this study highlights the utility of process-specific models for developing failure prevention strategies for water safety management.

Switzerland has one of the highest annual Legionnaires' disease (LD) notification rates in Europe (7.8 cases/100,000 population in 2021). The main sources of infection and the cause for this high rate remain largely unknown. This hampers the implementation of targeted Legionella spp. control efforts. The SwissLEGIO national case–control and molecular source attribution study investigates risk factors and infection sources for community-acquired LD in Switzerland. Over the duration of one year, the study is recruiting 205 newly diagnosed LD patients through a network of 20 university and cantonal hospitals. Healthy controls matched for age, sex, and residence at district level are recruited from the general population. Risk factors for LD are assessed in questionnaire-based interviews. Clinical and environmental Legionella spp. isolates are compared using whole genome sequencing (WGS). Direct comparison of sero- and sequence types (ST), core genome multilocus sequencing types (cgMLST), and single nucleotide polymorphisms (SNPs) between clinical and environmental isolates are used to investigate the infection sources and the prevalence and virulence of different Legionella spp. strains detected across Switzerland. The SwissLEGIO study innovates in combining case–control and molecular typing approaches for source attribution on a national level outside an outbreak setting. The study provides a unique platform for national Legionellosis and Legionella research and is conducted in an inter- and transdisciplinary, co-production approach involving various national governmental and national research stakeholders.

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

Wastewater-based epidemiology (WBE) has emerged as an effective tool for monitoring SARS-CoV-2 dynamics during the COVID-19 pandemic. Here, we add a spatial component to WBE and use it to investigate SARS-CoV-2 spread in the canton of Ticino during the onset of the pandemic in Switzerland (end of February 2020 to beginning of March 2020). Ticino is located at the border to Northern Italy, where a large COVID-19 outbreak occurred in February 2020. Not surprisingly, Ticino was the site of the first clinically confirmed COVID-19 case in Switzerland. We retrospectively analyzed daily influent samples from nine wastewater treatment plants in Ticino that jointly cover an area of 20 km × 60 km and 351,000 people (>99% of the population). Our result is a fine-grained view of the spatiotemporal evolution of the COVID-19 pandemic in this canton. The wastewater analysis revealed that by February 29, 2020, SARS-CoV-2 had already spread to all catchments. At the same time, only four individual cases had been clinically confirmed across the region served by the treatment plants investigated. Our results demonstrate that WBE could serve as a versatile tool to monitor the introduction and spread of an infectious agent on a regional scale. To fully exploit its utility, WBE should be implemented in real time and become an integral part of future disease surveillance efforts.

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

Increasing incidence of antibiotic resistance in clinical and environmental settings calls for increased scalability in their surveillance. Current screening technologies are limited by the number of samples and genes that can easily be screened. We demonstrate here digital multiplex ligation assay (dMLA) as a low-cost targeted genomic detection workflow capable of highly-parallel screening of bacterial isolates for multiple target gene regions simultaneously. Here, dMLA is used for simultaneous detection of 1187 β-lactamase-encoding genes, including extended spectrum β-lactamase (ESBL) genes, in 74 bacterial isolates. We demonstrate dMLA as a light-weight and cost-efficient workflow which provides a highly scalable tool for antimicrobial resistance surveillance and is also adaptable to genetic screening applications beyond antibiotic resistance.

Background: Infectious diseases can be transmitted via fomites (contaminated surfaces/objects); disinfection can interrupt this transmission route. However, disinfection guidelines for low-resource outbreak settings are inconsistent and not evidence-based.
Methods: A systematic review of surface disinfection efficacy studies was conducted to inform low-resource outbreak guideline development. Due to variation in experimental procedures, outcomes were synthesized in a narrative summary focusing on chlorine-based disinfection against seven pathogens with potential to produce outbreaks in low-resource settings (Mycobacterium tuberculosis, Vibrio cholerae, Salmonella spp., hepatitis A virus, rotavirus, norovirus, Ebola virus).
Results: Data were extracted from 89 laboratory studies and made available, including 20 studies on relevant pathogens used in combination with surrogate data to determine minimum target concentration × time ("CT") factors. Stainless steel (68%) and chlorine-based disinfectants (56%) were most commonly tested. No consistent trend was seen in the influence of chlorine concentration and exposure time on disinfection efficacy. Disinfectant application mode; soil load; and surface type were frequently identified as influential factors in included studies.
Conclusions: This review highlights that surface disinfection efficacy estimates are strongly influenced by each study's experimental conditions. We therefore recommend laboratory testing to be followed by field-based testing/monitoring to ensure effectiveness is achieved in situ.

Escherichia coli is present in multiple hosts and environmental compartments as a normal inhabitant, temporary or persistent colonizer, and as a pathogen. Transmission of E. coli between hosts and with the environment is considered to occur more often in areas with poor sanitation. We performed whole-genome comparative analyses on 60 E. coli isolates from soils and fecal sources (cattle, chickens, and humans) in households in rural Bangladesh. Isolates from household soils were in multiple branches of the reconstructed phylogeny, intermixed with isolates from fecal sources. Pairwise differences between all strain pairs were large (minimum, 189 single nucleotide polymorphisms [SNPs]), suggesting high diversity and heterogeneous origins of the isolates. The presence of multiple virulence and antibiotic resistance genes is indicative of the risk that E. coli from soil and feces represent for the transmission of variants that pose potential harm to people. Analysis of the accessory genomes of the Bangladeshi E. coli relative to E. coli genomes available in NCBI identified a common pool of accessory genes shared among E. coli isolates in this geographic area. Together, these findings indicate that in rural Bangladesh, a high level of E. coli in soil is likely driven by contributions from multiple and diverse E. coli sources (human and animal) that share an accessory gene pool relatively unique to previously published E. coli genomes. Thus, interventions to reduce environmental pathogen or antimicrobial resistance transmission should adopt integrated One Health approaches that consider heterogeneous origins and high diversity to improve effectiveness and reduce prevalence and transmission.

Escherichia coli pathotypes (i.e., enteropathogenic and enterotoxigenic) have been identified among the pathogens most responsible for moderate-to-severe diarrhea in low- and middle-income countries (LMICs). Pathogenic E. coli are transmitted from infected human or animal feces to new susceptible hosts via environmental reservoirs such as hands, water, and soil. Commensal E. coli, which includes nonpathogenic E. coli strains, are widely used as fecal bacteria indicator, with their presence associated with increased likelihood of enteric pathogens and/or diarrheal disease. In this study, we investigated E. coli contamination in environmental reservoirs within households (N = 142) in high-population density communities of Harare, Zimbabwe. We further assessed the interconnectedness of the environmental compartments by investigating associations between, and household-level risk factors for, E. coli contamination. From the data we collected, the source and risk factors for E. coli contamination are not readily apparent. One notable exception is the presence of running tap water on the household plot, which is associated with significantly less E. coli contamination of drinking water, handwashing water, and hands after handwashing. In addition, E. coli levels on hands after washing are significantly associated with handwashing water contamination, hand contamination before washing, and diarrhea incidence. Finally, we observed that animal ownership increases E. coli contamination in soil, and E. coli in soil are correlated with contamination on hands before washing. This study highlights the complexity of E. coli contamination in household environment within LMICs. More, larger, studies are needed to better identify sources and exposure pathways of E. coli—and enteric pathogens generally—to identify effective interventions.

As more countries engage in water reuse, either intended or de facto, there is an urgent need to more comprehensively evaluate resulting environmental and public health concerns. While antibiotic-resistant bacteria (ARB) and antibiotic resistance genes (ARGs) are increasingly coming under the spotlight, as emerging contaminants, existing water reuse regulations and guidelines do not adequately address these concerns. This perspectives paper seeks to frame the various challenges that need to be resolved to identify meaningful and realistic target types and levels of antibiotic resistance benchmarks for water reuse. First, there is the need for standardized and agreed-upon methodologies to identify and quantify ARB and ARGs. Second, even if methodologies are available, identifying which ARB and ARGs to monitor that would best relate to the occurrence of disease burden remains unknown. Third, a framework tailored to assessing the risks associated with ARB and ARGs during reuse is urgently needed. Fourth, similar to protecting drinking water sources, strategies to prevent dissemination of ARB and ARGs via wastewater treatment and reuse are required to ensure that appropriate barriers are emplaced. Finally, current wastewater treatment technologies could benefit from modification or retrofit to more effectively remove ARB and ARGs while also producing a high quality product for water and resource recovery. This perspectives paper highlights the need to consider ARB and ARGs when evaluating the overall safety aspects of water reuse and ways by which this may be accomplished.

Understanding virus transfer between liquid and skin is necessary to estimate transmission during water-related activities. Here, we modeled virus transfer from liquid-to-skin and skin-to-liquid. We performed human subject studies using three bacteriophages as pathogenic virus surrogates: nonenveloped MS2 and Qβ and enveloped Φ6. Our study shows that transfer from liquid-to-skin is describable by a single model based on (1) virus concentration and (2) volume of liquid remaining on skin. Contact times (0.1–30 min), and virus species had little-to-no influence on virus transfer. Likewise, liquid conditions (pH 6–9, ionic strength 10–550 mM) had no influence on transfer as shown for MS2. The model accounts for both, virus adsorbed onto the skin, and virus in the liquid retained on skin. In comparison, virus transfer from skin-to-liquid was influenced by the wetness of the skin and by liquid type (water, saliva). 90 ± 19% of the virus inoculated on the skin are transferred to the water when the skin remains wet compared to 30 ± 17% when the skin is dry. The transfer from skin-to-liquid was 41% higher when the recipient liquid was water as compared with saliva. This study quantifies virus transfer between liquid and skin and guides risk assessments of water-related activities.

Every year, more than half a million children die due to diarrheal diseases. Recent studies have identified the most important etiologies of diarrheal disease are enterotoxigenic and enteropathogenic E. coli, Shigella spp., rotavirus, norovirus and Cryptosporidium spp. These etiologies are unsurprisingly characterized by a combination of high shedding, high infectivity, and transmissibility through multiple environmental reservoirs. The relative importance of the transmission routes is likely site-specific. So the impact of interventions, which typically target only one or two environmental reservoirs, is likely also site-specific. The factors influencing the transmission routes most important for diarrheal disease are complex, including – at a minimum – etiology of endemic disease; and water, sanitation, and hygiene infrastructure and practices. The site-specific nature – and complexity of transmission – helps explain the observed variation in impacts of water, sanitation, and hygiene interventions. It may also render efforts to estimate or quantify global means for interventions' impacts irrelevant. The theme of this Perspective is that greater reductions in diarrheal disease transmission in LMICs can be achieved by designing interventions to interrupt the most important environmental transmission pathways. Intervention choice should be informed by site-specific conditions, most notably: diarrheal etiology and existing water, sanitation, and hygiene infrastructure and practices. The theme is discussed through the lens of the characteristics of the most important diarrheal diseases (shedding, infectivity, growth, and persistence) and the general characteristics of environmental reservoirs (exposure pathways and fecal contamination). The discussion highlights when interventions – and combinations of interventions – will be most effective at reducing diarrheal disease burden.

Diarrheal diseases are a leading cause of under-five mortality and morbidity in sub-Saharan Africa. Quantitative exposure modeling provides opportunities to investigate the relative importance of fecal-oral transmission routes (e.g. hands, water, food) responsible for diarrheal disease. Modeling, however, requires accurate descriptions of individuals’ interactions with the environment (i.e., activity data). Such activity data are largely lacking for people in low-income settings. In the present study, we collected activity data and microbiological sampling data to develop a quantitative microbial exposure model for two female caretakers in peri-urban Tanzania. Activity data were combined with microbiological data of contacted surfaces and fomites (e.g. broom handle, soil, clothing) to develop example exposure profiles describing second-by-second estimates of fecal indicator bacteria (E. coli and enterococci) concentrations on the caretaker’s hands. The study demonstrates the application and utility of video activity data to quantify exposure factors for people in low-income countries and apply these factors to understand fecal contamination exposure pathways. This study provides both a methodological approach for the design and implementation of larger studies, and preliminary data suggesting contacts with dirt and sand may be important mechanisms of hand contamination. Increasing the scale of activity data collection and modeling to investigate individual-level exposure profiles within target populations for specific exposure scenarios would provide opportunities to identify the relative importance of fecal-oral disease transmission routes.