Wastewater Surveillance Research Articles

Wastewater surveillance for antibiotic resistance genes during the late 2020 SARS-CoV-2 peak in two different populations.

The United States Centers for Disease Control and Prevention reported a rise in resistant infections after the coronavirus disease 2019 (COVID-19) pandemic started. How and if the pandemic contributed to antibiotic resistance in the larger population is not well understood. Wastewater treatment plants are good locations for environmental surveillance because they can sample entire populations. This study aimed to validate methods used for COVID-19 wastewater surveillance for bacterial targets and to understand how rising COVID-19 cases from October 2020 to February 2021 in Portugal (PT) and King County, Washington contributed to antibiotic resistance genes in wastewater. Primary influent wastewater was collected from two treatment plants in King County and five treatment plants in PT, and hospital effluent was collected from three hospitals in PT. Genomic extracts were tested with the quantitative polymerase chain reaction for antibiotic resistance genes conferring resistance against antibiotics under threat. Random-effect models were fit for log-transformed gene abundances to assess temporal trends. All samples collected tested positive for multiple resistance genes. During the sampling period, mecA statistically significantly increased in King County and PT. No statistical evidence exists of correlation between samples collected in the same Portuguese metro area.

Optimization and performance analytics of global aircraft-based wastewater surveillance networks.

Aircraft wastewater surveillance has been proposed as a novel approach to monitor the global spread of pathogens. Here we develop a computational framework to provide actionable information for designing and estimating the effectiveness of global aircraft-based wastewater surveillance networks (WWSNs). We study respiratory diseases of varying transmission potentials and find that networks of 10 to 20 strategically placed wastewater sentinel sites can provide timely situational awareness and function effectively as an early warning system. The model identifies potential blind spots and suggests optimization strategies to increase WWSNs effectiveness while minimizing resource use. Our findings highlight that increasing the number of sentinel sites beyond a critical threshold does not proportionately improve WWSNs capabilities, stressing the importance of resource optimization. We show through retrospective analyses that WWSNs can significantly shorten the detection time for emerging pathogens. The presented approach offers a realistic analytic framework for the analysis of WWSNs at airports.

Rapid and extensive SARS-CoV-2 Omicron variant infection wave revealed by wastewater surveillance in Shenzhen following the lifting of a strict COVID-19 strategy

Wastewater-based epidemiology (WBE) has emerged as a promising tool for monitoring the spread of COVID-19, as SARS-CoV-2 can be shed in the faeces of infected individuals, even in the absence of symptoms. This study aimed to optimize a prediction model for estimating COVID-19 infection rates based on SARS-CoV-2 RNA concentrations in wastewater, and reveal the infection trends and variant diversification in Shenzhen, China following the lifting of a strict COVID-19 strategy. Faecal samples (n = 4337) from 1204 SARS-CoV-2 infected individuals hospitalized in a designated hospital were analysed to obtain Omicron variant-specific faecal shedding dynamics. Wastewater samples from 6 wastewater treatment plants (WWTPs) and 9 pump stations, covering 3.55 million people, were monitored for SARS-CoV-2 RNA concentrations and variant abundance. We found that the viral load in wastewater increased rapidly in December 2022 in the two districts, demonstrating a sharp peak in COVID-19 infections in late-December 2022, mainly caused by Omicron subvariants BA.5.2.48 and BF.7.14. The prediction model, based on the mass balance between total viral load in wastewater and individual faecal viral shedding, revealed a surge in the cumulative infection rate from <0.1 % to over 70 % within three weeks after the strict COVID-19 strategy was lifted. Additionally, 39 cryptic SARS-CoV-2 variants were identified in wastewater, in addition to those detected through clinical surveillance. These findings demonstrate the effectiveness of WBE in providing comprehensive and efficient assessments of COVID-19 infection rates and identifying cryptic variants, highlighting its potential for monitoring emerging pathogens with faecal shedding.

The Use of Wastewater Surveillance to Estimate SARS-CoV-2 Fecal Viral Shedding Pattern and Identify Time Periods with Intensified Transmission.

Wastewater-based surveillance is an important tool for monitoring the COVID-19 pandemic. However, it remains challenging to translate wastewater SARS-CoV-2 viral load to infection number, due to unclear shedding patterns in wastewater and potential differences between variants. We utilized comprehensive wastewater surveillance data and estimates of infection prevalence (i.e., the source of the viral shedding) available for New York City (NYC) to characterize SARS-CoV-2 fecal shedding pattern over multiple COVID-19 waves. We collected SARS-CoV-2 viral wastewater measurements in NYC during August 31, 2020 - August 29, 2023 ( N = 3794 samples). Combining with estimates of infection prevalence (number of infectious individuals including those not detected as cases), we estimated the time-lag, duration, and per-infection fecal shedding rate for the ancestral/Iota, Delta, and Omicron variants, separately. We also developed a procedure to identify occasions with intensified transmission. Models suggested fecal viral shedding likely starts around the same time as and lasts slightly longer than respiratory tract shedding. Estimated fecal viral shedding rate was highest during the ancestral/Iota variant wave, at 1.44 (95% CI: 1.35 - 1.53) billion RNA copies in wastewater per day per infection (measured by RT-qPCR), and decreased by ∼20% and 50-60% during the Delta wave and Omicron period, respectively. We identified around 200 occasions during which the wastewater SARS-CoV-2 viral load exceeded the expected level in any of 14 sewersheds. These anomalies disproportionally occurred during late January, late April - early May, early August, and from late-November to late-December, with frequencies exceeding the expectation assuming random occurrence ( P < 0.05; bootstrapping test). These estimates may be useful in understanding changes in underlying infection rate and help quantify changes in COVID-19 transmission and severity over time. We have also demonstrated that wastewater surveillance data can support the identification of time periods with potentially intensified transmission.

Evaluating approach uncertainties of quantitative detection of SARS-CoV-2 in wastewater: Concentration, extraction and amplification

Substantial uncertainties pose challenges to the accuracy of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) quantification in wastewater. We conducted a comprehensive evaluation of two concentration methods, three nucleic acid extraction methods, and the amplification performance of eight primer-probe sets. Our results showed that the two concentration methods exhibited similar recovery rates. Specifically, using a 30 kDa cut-off ultrafilter and a centrifugal force of 2500 g achieved the highest virus recovery rates (27.32 ± 8.06 % and 26.37 ± 7.77 %, respectively), with lower corresponding quantification uncertainties of 29.51 % and 29.47 % in ultrafiltration methods. Similarly, a 15 % PEG concentration with 1.5 M NaCl markedly improved virus recovery (26.76 ± 5.92 % and 28.47 ± 6.74 %, respectively), and reducing variation to 22.16 % and 23.66 % in the PEG precipitation method. Additionally, employing a vigorous bead-beating approach at 6 m/s during viral RNA extraction significantly increased RNA yield, with an efficiency reaching up to 82.18 %. Among the evaluated eight primer-probe sets, the E_Sarbeco primer-probe set provided the most stable and consistent quantitative results across various sample matrices. These findings are crucial for establishing robust viral quantification protocols and enhancing methodological precision for effective wastewater surveillance, enabling sensitive and precise detection of SARS-CoV-2.

Wastewater Surveillance to Confirm Differences in Influenza A Infection between Michigan, USA, and Ontario, Canada, September 2022-March 2023.

Wastewater surveillance is an effective way to track the prevalence of infectious agents within a community and, potentially, the spread of pathogens between jurisdictions. We conducted a retrospective wastewater surveillance study of the 2022-23 influenza season in 2 communities, Detroit, Michigan, USA, and Windsor-Essex, Ontario, Canada, that form North America's largest cross-border conurbation. We observed a positive relationship between influenza-related hospitalizations and the influenza A virus (IAV) wastewater signal in Windsor-Essex (ρ = 0.785; p<0.001) and an association between influenza-related hospitalizations in Michigan and the IAV wastewater signal for Detroit (ρ = 0.769; p<0.001). Time-lagged cross correlation and qualitative examination of wastewater signal in the monitored sewersheds showed the peak of the IAV season in Detroit was delayed behind Windsor-Essex by 3 weeks. Wastewater surveillance for IAV reflects regional differences in infection dynamics which may be influenced by many factors, including the timing of vaccine administration between jurisdictions.

Towards the outbreak tail, what is the public opinion about wastewater surveillance in the United States?

National opinions on a wide variety of public health topics can change over time and have highly contextual nuances. This study is a follow-up to prior inquiries into the knowledge of wastewater-based epidemiology, privacy concerns surrounding sample collection, and the use of data acquired, along with privacy awareness from an online survey conducted in the metropolitan United States during the winter of 2023. Mentions of wastewater-surveillance-related terms in the media remained common. Towards the outbreak tail in 2023, public support for surveillance of toxins (91%), diseases (91%), terrorist threats (87%), illicit drugs (70%), prescription medications (69%), and gun residue (60%) remained high. There was less support for surveillance of alcohol consumption (49%), mental illness (46%), healthy eating (37%), and lifestyle behaviors (35%). In terms of geographic scale, most respondents supported citywide surveillance (85%) with markedly lower levels of support for smaller (less anonymous) geographic scales covered by specific locations. Wastewater surveillance does not receive the public pushback that other COVID-19-related health system actors have witnessed. Instead, the public supports the expansion of wastewater surveillance as a standard to complement public health tools in other areas of health protection.

Focus, vigilance, resilience: towards stronger infectious disease surveillance, threat detection and response in the EU/EEA.

This perspective summarises and explains the long-term surveillance framework 2021-2027 for infectious diseases in the European Union/European Economic Area (EU/EEA) published in April 2023. It shows how shortcomings in the areas of public health focus, vigilance and resilience will be addressed through specific strategies in the coming years and how these strategies will lead to stronger surveillance systems for early detection and monitoring of public health threats as well as informing their effective prevention and control. A sharper public health focus is expected from a more targeted list of notifiable diseases, strictly public-health-objective-driven surveillance standards, and consequently, leaner surveillance systems. Vigilance should improve through mandatory event reporting, more automated epidemic intelligence processing and increased use of genomic surveillance. Finally, EU/EEA surveillance systems should become more resilient by modernising the underlying information technology infrastructure, expanding the influenza sentinel surveillance system to other respiratory viruses for better pandemic preparedness, and increasingly exploiting potentially more robust alternative data sources, such as electronic health records and wastewater surveillance. Continued close collaboration across EU/EEA countries will be key to ensuring the full implementation of this surveillance framework and more effective disease prevention and control.

Wastewater Target Pathogens of Public Health Importance for Expanded Sampling, Houston, Texas, USA.

Building on the success of initiatives put forth during the COVID-19 pandemic response, US health officials are expanding wastewater surveillance programs to track other target pathogens and diseases of public health interest. The Houston Health Department in Houston, Texas, USA, conducted a hypothesis-generating study whereby infectious disease subject matter experts suggested potential targets. This study addressed 2 criteria recommended by the National Academies of Sciences, Engineering, and Medicine for selecting wastewater targets. Results can be used as a basis of a questionnaire for a future population-based study to recommend targets of highest priority to include for expanded wastewater sampling.

A narrative review of wastewater surveillance: pathogens of concern, applications, detection methods, and challenges.

The emergence and resurgence of pathogens have led to significant global health challenges. Wastewater surveillance has historically been used to track water-borne or fecal-orally transmitted pathogens, providing a sensitive means of monitoring pathogens within a community. This technique offers a comprehensive, real-time, and cost-effective approach to disease surveillance, especially for diseases that are difficult to monitor through individual clinical screenings. This narrative review examines the current state of knowledge on wastewater surveillance, emphasizing important findings and techniques used to detect potential pathogens from wastewater. It includes a review of literature on the detection methods, the pathogens of concern, and the challenges faced in the surveillance process. Wastewater surveillance has proven to be a powerful tool for early warning and timely intervention of infectious diseases. It can detect pathogens shed by asymptomatic and pre-symptomatic individuals, providing an accurate population-level view of disease transmission. The review highlights the applications of wastewater surveillance in tracking key pathogens of concern, such as gastrointestinal pathogens, respiratory pathogens, and viruses like SARS-CoV-2. The review discusses the benefits of wastewater surveillance in public health, particularly its role in enhancing existing systems for infectious disease surveillance. It also addresses the challenges faced, such as the need for improved detection methods and the management of antimicrobial resistance. The potential for wastewater surveillance to inform public health mitigation strategies and outbreak response protocols is emphasized. Wastewater surveillance is a valuable tool in the fight against infectious diseases. It offers a unique perspective on the spread and evolution of pathogens, aiding in the prevention and control of disease epidemics. This review underscores the importance of continued research and development in this field to overcome current challenges and maximize the potential of wastewater surveillance in public health.

Wastewater surveillance of severe acute respiratory syndrome coronavirus-2 in open drains of two Indian megacities captures evolutionary lineage transitions: a zonation approach.

Wastewater-based environmental surveillance (WBES) has been proven as proxy tool for monitoring nucleic acids of pathogens shed by infected population before clinical outcomes. The poor sewershed network of low to middle-income countries (LMICs) leads to most of the wastewater flow through open drains. We studied the effectiveness of WBES using open drain samples to monitor the emergence of the SARS-CoV-2 variants in 2 megacities of India having dense population through zonation approach. Samples from 28 locations spanned into 5 zones of Pune region, Maharashtra, India, were collected on a weekly basis during October 2021 to July 2022. Out of 1115 total processed samples, 303 (~ 27%) tested positive for SARS-CoV-2. The periodical rise and fall in the percentage positivity of the samples was found to be in sync with the abundance of SARS-CoV-2 RNA and the reported COVID-19 active cases for Pune city. Sequencing of the RNA obtained from wastewater samples confirmed the presence of SARS-CoV-2. Of 337 sequences, lineage identification for 242 samples revealed 265 distinct SARS-CoV-2 variants including 10 highly transmissible ones. Importantly, transition from Delta to Omicron variant could be detected in wastewater samples 2weeks prior to any clinically reported Omicron cases in India. Thus, this study demonstrates the usefulness of open drain samples for real-time monitoring of a viral pathogen's evolutionary dynamics and could be implemented in LMICs.

Long-term longitudinal monitoring of SARS CoV-2 in urban rivers and sewers of Nepal

In regions without adequate centralized wastewater treatment plants, sample collection from rivers and sewers can be an alternative sampling strategy for wastewater surveillance. This study aimed to assess the feasibility of alternative sampling strategies by testing samples collected from rivers (n = 246) and sewers (n = 244) in the Kathmandu Valley between March 2021 and February 2022. All samples were concentrated using the skimmed-milk flocculation method and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA was quantified using the nucleocapsid (N) and envelope (E) genes qPCR assays. Of the total, 75 % (371/490) of the samples tested positive using at least one qPCR assay, with concentrations ranging from 3.0 to 8.3 log10 gene copies/L. No significant correlation between concentrations of SARS-CoV-2 from both sewers and river with the number of confirmed coronavirus disease 2019 (COVID-19) cases in the Kathmandu valley was observed (p > 0.05). Despite the high concentration of SARS-CoV-2 in rivers and sewers, we hypothesize this finding to be a result of inaccurate number of clinical cases possibly due to inadequate clinical testing. This longitudinal study further supports the statement to consider sampling strategies from sewers and rivers for WBS in Nepal and other low and middle-income countries.

Prospective One-Health investigation into low-abundant extended-spectrum β-lactamase producing Enterobacterales enables detection of potential dissemination events and persistence

BackgroundFollowing a one-health approach, we sought to determine reservoirs of extended-spectrum β-lactamase (ESBL)-producing Enterobacterales (ESBL-PE), other than Escherichia coli or Klebsiella pneumoniae complex species (i.e., low-abundant species), and their associated ESBL genes and plasmid-replicon profiles. MethodsFrom 06/2017–05/2019, ESBL-PE isolates were recovered from clinical samples routinely collected at the University Hospital Basel (Basel, Switzerland), as well as from wastewater and foodstuffs collected monthly at predefined locations throughout the city of Basel. Whole-genome sequencing was performed for characterization of ESBL-PE isolates. ResultsAmong 1634 isolates recovered, 114 (7%) belonged to 17 low-abundant ESBL-PE species.Seven species originated from more than one compartment, mainly from clinical and wastewater samples (6/17). Sixteen different ESBL genes were identified, with blaCTX-M-15 (27%), blaFONA-6 (23%) and blaSHV-12 (16%) being most frequent. The blaCTX-M-1 gene was the only ESBL gene recovered from all three compartments. Putative plasmids constituted 60% of ESBL gene-containing contigs, while chromosomes comprised 40%. Foodstuff isolates showed the highest proportion (91%, 41/45) of ESBL genes located on chromosomes, whereas wastewater isolates had the highest proportion (95%, 37/39) of putative plasmids. Multi-replicon combinations were identified in 81% of the isolates. Epidemiological links were found among some clinical and wastewater isolates. ConclusionsThe dominance of blaCTX-M-15 among low-abundant ESBL-PE species supports its species-independent transmission potential beyond the E. coli and K. pneumoniae complex, and blaCTX-M-1 may be transmitted between strains recovered from different compartments. The substantial overlap between low-abundant ESBL-PE present in wastewater and clinical samples supports the utility of wastewater surveillance for antibiotic resistance monitoring.

Advancing Pathogen Identification: The Role of Digital PCR in Enhancing Diagnostic Power in Different Settings.

Digital polymerase chain reaction (dPCR) has emerged as a groundbreaking technology in molecular biology and diagnostics, offering exceptional precision and sensitivity in nucleic acid detection and quantification. This review highlights the core principles and transformative potential of dPCR, particularly in infectious disease diagnostics and environmental surveillance. Emphasizing its evolution from traditional PCR, dPCR provides accurate absolute quantification of target nucleic acids through advanced partitioning techniques. The review addresses the significant impact of dPCR in sepsis diagnosis and management, showcasing its superior sensitivity and specificity in early pathogen detection and identification of drug-resistant genes. Despite its advantages, challenges such as optimization of experimental conditions, standardization of data analysis workflows, and high costs are discussed. Furthermore, we compare various commercially available dPCR platforms, detailing their features and applications in clinical and research settings. Additionally, the review explores dPCR's role in water microbiology, particularly in wastewater surveillance and monitoring of waterborne pathogens, underscoring its importance in public health protection. In conclusion, future prospects of dPCR, including methodological optimization, integration with innovative technologies, and expansion into new sectors like metagenomics, are explored.

Wastewater-based surveillance of SARS-CoV-2: Short-term projection (forecasting), smoothing and outlier identification using Bayesian smoothing

BackgroundDay-to-day variation in the measurement of SARS-CoV-2 in wastewater can challenge public health interpretation. We assessed a Bayesian smoothing and forecasting method previously used for surveillance and short-term projection of COVID-19 cases, hospitalizations, and deaths. MethodsSARS-CoV-2 viral measurement from the sewershed in Ottawa, Canada, sampled at the municipal wastewater treatment plant from July 1, 2020, to February 15, 2022, was used to assess and internally validate measurement averaging and prediction. External validation was performed using viral measurement data from influent wastewater samples from 15 wastewater treatment plants and municipalities across Ontario. ResultsPlots of SARS-CoV-2 viral measurement over time using Bayesian smoothing visually represented distinct COVID-19 “waves” described by case and hospitalization data in both initial (Ottawa) and external validation in 15 Ontario communities. The time-varying growth rate of viral measurement in wastewater samples approximated the growth rate observed for cases and hospitalization. One-week predicted viral measurement approximated the observed viral measurement throughout the assessment period from December 23, 2020, to August 8, 2022. An uncalibrated model showed underprediction during rapid increases in viral measurement (positive growth) and overprediction during rapid decreases. After recalibration, the model showed a close approximation between observed and predicted estimates. ConclusionBayesian smoothing of wastewater surveillance data of SARS-CoV-2 allows for accurate estimates of COVID-19 growth rates and one- and two-week forecasting of SARS-CoV-2 in wastewater for 16 municipalities in Ontario, Canada. Further assessment is warranted in other communities representing different sewersheds and environmental conditions.

SARS-CoV-2 RNA Detection in Wastewater and Its Effective Correlation with Clinical Data during the Outbreak of COVID-19 in Salamanca.

Wastewater treatment plants (WWTPs) are the final stage of the anthropogenic water cycle where a wide range of chemical and biological markers of human activity can be found. In COVID-19 disease contexts, wastewater surveillance has been used to infer community trends based on viral abundance and SARS-CoV-2 RNA variant composition, which has served to anticipate and establish appropriate protocols to prevent potential viral outbreaks. Numerous studies worldwide have provided reliable and robust tools to detect and quantify SARS-CoV-2 RNA in wastewater, although due to the high dilution and degradation rate of the viral RNA in such samples, the detection limit of the pathogen has been a bottleneck for the proposed protocols so far. The current work provides a comprehensive and systematic study of the different parameters that may affect the detection of SARS-CoV-2 RNA in wastewater and hinder its quantification. The results obtained using synthetic viral RNA as a template allow us to consider that 10 genome copies per µL is the minimum RNA concentration that provides reliable and consistent values for the quantification of SARS-CoV-2 RNA. RT-qPCR analysis of wastewater samples collected at the WWTP in Salamanca (western Spain) and at six pumping stations in the city showed that below this threshold, positive results must be confirmed by sequencing to identify the specific viral sequence. This allowed us to find correlations between the SARS-CoV-2 RNA levels found in wastewater and the COVID-19 clinical data reported by health authorities. The close match between environmental and clinical data from the Salamanca case study has been confirmed by similar experimental approaches in four other cities in the same region. The present methodological approach reinforces the usefulness of wastewater-based epidemiology (WBE) studies in the face of future pandemic outbreaks.

Exploring Canine Picornavirus Diversity in the USA Using Wastewater Surveillance: From High-Throughput Genomic Sequencing to Immuno-Informatics and Capsid Structure Modeling.

The SARS-CoV-2 pandemic resulted in a scale-up of viral genomic surveillance globally. However, the wet lab constraints (economic, infrastructural, and personnel) of translating novel virus variant sequence information to meaningful immunological and structural insights that are valuable for the development of broadly acting countermeasures (especially for emerging and re-emerging viruses) remain a challenge in many resource-limited settings. Here, we describe a workflow that couples wastewater surveillance, high-throughput sequencing, phylogenetics, immuno-informatics, and virus capsid structure modeling for the genotype-to-serotype characterization of uncultivated picornavirus sequences identified in wastewater. Specifically, we analyzed canine picornaviruses (CanPVs), which are uncultivated and yet-to-be-assigned members of the family Picornaviridae that cause systemic infections in canines. We analyzed 118 archived (stored at -20 °C) wastewater (WW) samples representing a population of ~700,000 persons in southwest USA between October 2019 to March 2020 and October 2020 to March 2021. Samples were pooled into 12 two-liter volumes by month, partitioned (into filter-trapped solids [FTSs] and filtrates) using 450 nm membrane filters, and subsequently concentrated to 2 mL (1000×) using 10,000 Da MW cutoff centrifugal filters. The 24 concentrates were subjected to RNA extraction, CanPV complete capsid single-contig RT-PCR, Illumina sequencing, phylogenetics, immuno-informatics, and structure prediction. We detected CanPVs in 58.3% (14/24) of the samples generated 13,824,046 trimmed Illumina reads and 27 CanPV contigs. Phylogenetic and pairwise identity analyses showed eight CanPV genotypes (intragenotype divergence <14%) belonging to four clusters, with intracluster divergence of <20%. Similarity analysis, immuno-informatics, and virus protomer and capsid structure prediction suggested that the four clusters were likely distinct serological types, with predicted cluster-distinguishing B-cell epitopes clustered in the northern and southern rims of the canyon surrounding the 5-fold axis of symmetry. Our approach allows forgenotype-to-serotype characterization of uncultivated picornavirus sequences by coupling phylogenetics, immuno-informatics, and virus capsid structure prediction. This consequently bypasses a major wet lab-associated bottleneck, thereby allowing resource-limited settings to leapfrog from wastewater-sourced genomic data to valuable immunological insights necessary for the development of prophylaxis and other mitigation measures.

Detection of influenza virus in urban wastewater during the season 2022/2023 in Sicily, Italy.

Seasonal influenza generally represents an underestimated public health problem with significant socioeconomic implications. Monitoring and detecting influenza epidemics are important tasks that require integrated strategies. Wastewater-based epidemiology (WBE) is an emerging field that uses wastewater data to monitor the spread of disease and assess the health of a community. It can represent an integrative surveillance tool for better understanding the epidemiology of influenza and prevention strategies in public health. We conducted a study that detected the presence of Influenza virus RNA using a wastewater-based approach. Samples were collected from five wastewater treatment plants in five different municipalities, serving a cumulative population of 555,673 Sicilian inhabitants in Italy. We used the RT-qPCR test to compare the combined weekly average of Influenza A and B viral RNA in wastewater samples with the average weekly incidence of Influenza-like illness (ILI) obtained from the Italian national Influenza surveillance system. We also compared the number of positive Influenza swabs with the viral RNA loads detected from wastewater. Our study investigated 189 wastewater samples. Cumulative ILI cases substantially overlapped with the Influenza RNA load from wastewater samples. Influenza viral RNA trends in wastewater samples were similar to the rise of ILI cases in the population. Therefore, wastewater surveillance confirmed the co-circulation of Influenza A and B viruses during the season 2022/2023, with a similar trend to that reported for the weekly clinically confirmed cases. Wastewater-based epidemiology does not replace traditional epidemiological surveillance methods, such as laboratory testing of samples from infected individuals. However, it can be a valuable complement to obtaining additional information on the incidence of influenza in the population and preventing its spread.

SARS-CoV-2 dynamics in New York City during March 2020-August 2023.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been widespread since 2020 and will likely continue to cause substantial recurring epidemics. However, understanding the underlying infection burden (i.e., including undetected asymptomatic/mild infections) and dynamics, particularly since late 2021 when the Omicron variant emerged, is challenging due to the potential for asymptomatic and repeat SARS-CoV-2 infection, changes in testing practices, and changes in disease reporting. Here, we leverage extensive surveillance data available in New York City (NYC) and a comprehensive model-inference system to reconstruct SARS-CoV-2 dynamics therein from the pandemic onset in March 2020 to August 2023, and further validate the estimates using independent wastewater surveillance data. The validated model-inference estimates indicate a very high infection burden totaling twice the population size (>5 times documented case count) but decreasing infection-fatality risk (a >10-fold reduction) during the first 3.5 years. The detailed estimates also reveal highly complex variant dynamics and immune landscape, changing virus transmissibility, and higher infection risk during winter in NYC over this time period. These transmission dynamics and drivers, albeit based on data in NYC, may be relevant to other populations and inform future planning to help mitigate the public health burden of SARS-CoV-2.

The viral trends and genotype diversity of norovirus in the wastewater of Shenzhen, China

Norovirus (NoV) is the primary cause of acute gastroenteritis (AGE) on a global scale. Numerous studies have demonstrated the immense potential of wastewater surveillance in monitoring the prevalence and spread of NoV within communities. This study employed a one-step reverse transcription-quantitative PCR to quantify NoV GI/GII in wastewater samples (n = 2574), which were collected once or twice a week from 38 wastewater treatment plants from March 2023 to February 2024 in Shenzhen. The concentrations of NoV GI and GII ranged from 5.0 × 104 to 1.7 × 106 copies/L and 4.1 × 105 to 4.5 × 106 copies/L, respectively. The concentrations of NoV GII were higher than those of NoV GI. Spearman's correlation analysis revealed a moderate correlation between the concentration of NoV in wastewater and the detection rates of NoV infections in sentinel hospitals. Baseline values were established for NoV concentrations in Shenzhen's wastewater, providing a crucial reference point for implementing early warning systems and nonpharmaceutical interventions to mitigate the impact of potential outbreaks. A total of 24 NoV genotypes were identified in 100 wastewater samples by sequencing. Nine genotypes of NoV GI were detected, with the major genotypes being GI.4 (38.6 %) and GI.3 (21.8 %); Fifteen genotypes of NoV GII were identified, with GII.4 (53.6 %) and GII.17 (26.0 %) being dominant. The trends in the relative abundance of NoV GI/GII were significantly different, and the trends in the relative abundance of NoV GII.4 over time were similar across all districts, suggesting a potential risk of cross-regional spread. Our findings underscore the effectiveness of wastewater surveillance in reflecting population-level NoV infections, capturing the diverse array of NoV genotypes, and utilizing NoV RNA in wastewater as a specific indicator to supplement clinical surveillance data, ultimately enhancing our ability to predict the timing and intensity of NoV epidemics.