United States Influenza 2022-2023 Season Characteristics as Inferred from Wastewater Solids, Influenza Hospitalization, and Syndromic Data

Influenza RNA fluxes monitoring in wastewater as a complementary epidemiological surveillance indicator: A four-year nationwide study in Luxembourg

Wastewater surveillance has demonstrated success in monitoring SARS-CoV-2 in communities, indicating potential for extension to other respiratory viruses. This study investigates influenza A and B viruses (IAV; IBV) in raw urban wastewater over a 4-year period, introducing two key concepts: the use of viral RNA fluxes instead of concentration measurements and the determination of epidemiological parameters directly from wastewater data. The estimation of daily fluxes, representing the number of viral genome copies per day per 100,000 inhabitants, offers an integrative approach that combines microbiological and hydrological measurements to better assess viral particle dynamics in a water system. A total of 1013 wastewater samples collected between March 2020 and March 2024 from Luxembourg's four largest wastewater treatment plants (covering about 52 % of the population) were analysed using RT-qPCR and RT-droplet digital PCR (RT-ddPCR), following concentration of viral particles by ultrafiltration. Data on the presence of IAV and IBV were expressed as either detection rates or fluxes. Significant correlations were observed between the number of laboratory-confirmed influenza cases and both wastewater detection rates (RT-qPCR: Spearman ρ = 0.52; RT-ddPCR: ρ = 0.61, p-value <10-13) and viral RNA fluxes (RT-ddPCR: ρ = 0.64, p-value <10-15). More importantly, our results demonstrated that critical influenza seasonality parameters (start, peak and end weeks of the epidemic) can be effectively determined from wastewater data. These findings establish wastewater surveillance as a cost-effective, non-invasive approach to support and complement existing influenza surveillance programs, with potential applications for other respiratory pathogens.

Wastewater-based epidemiology of influenza viruses: a systematic review

INTRODUCTION

Wastewater-based epidemiology (WBE) has emerged as a valuable public health tool for monitoring the circulation of many pathogens, including influenza viruses (IVs). The general aim of this study is to systematically retrieve and summarize evidence on the use of WBE for supporting influenza surveillance. Specific objectives are: (i) to map influenza monitoring activities using WBE; (ii) to assess the performance of viral recovery methods; (iii) to explore association with clinical data; (iv) to evaluate the feasibility of typing/subtyping IVs directly from wastewater.

METHODS

We conducted a systematic review following the PRISMA guidelines, focusing on original data from peer-reviewed studies identified through PubMed/Medline, Scopus, and Web of Science.

RESULTS

Of 882 identified citations, 42 studies were included in the review. IVs detection was reported in all but one study, although typically at lower concentration than SARS-CoV-2. Thirteen studies (38.09 %) performed comparative analysis of different protocols, with mostly inconclusive results. Detection of IVs in the solid fraction of wastewater samples generally outperformed detection in the supernatant/liquid. Additionally, we describe the findings from 22 studies (52.38 %) that examined the link between environmental viral concentrations and clinical data, and 14 studies (33.33 %) that described IVs subtyping in wastewater.

CONCLUSION

WBE has the potential to monitor influenza circulation in humans and animals, offering insights into outbreak size and circulating IVs subtypes. However, several key areas remain unexplored. Further research is needed to refine experimental techniques and standardize protocols, and to understand how to successfully integrate WBE data into public health strategies for influenza control.

Advances in Wastewater-Based Epidemiology for Pandemic Surveillance: Methodological Frameworks and Future Perspectives

Wastewater-based epidemiology (WBE) has emerged as a transformative approach for community-level health monitoring, particularly during the COVID-19 pandemic. This review critically examines the methodological framework of WBE systems through the following three core components: (1) sampling strategies that address spatial-temporal variability in wastewater systems, (2) comparative performance of different platforms in pathogen detection, and (3) predictive modeling integrating machine learning approaches. We systematically analyze how these components collectively overcome the limitations of conventional surveillance methods through early outbreak detection, asymptomatic case identification, and population-level trend monitoring. While highlighting technical breakthroughs in viral concentration methods and variant tracking through sequencing, the review also identifies persistent challenges, including data standardization, cost-effectiveness concerns in resource-limited settings, and ethical considerations in public health surveillance. Drawing insights from global implementation cases, we propose recommendations for optimizing each operational phase and discuss emerging applications beyond pandemic response. This review highlights WBE as an indispensable tool for modern public health, whose methodological refinements and cross-disciplinary integration are critical for transforming pandemic surveillance from reactive containment to proactive population health management.

Respiratory Virus Season Surveillance in the United States Using Wastewater Metrics, 2023-2024

Wastewater measurements represent an entire contributing population and can be available within 24 h. Enhanced information about disease occurrence can improve population health through better timing of policies and interventions. We aimed to infer seasonal occurrence patterns for common respiratory viruses alongside transmission dynamics for SARS-CoV-2 across the USA using wastewater samples. We used wastewater RNA concentrations of influenza A and B (IAV/IBV), respiratory syncytial virus (RSV), human metapneumovirus (HMPV), and SARS-CoV-2 from 175 treatment plants (July 2023-June 2024). For IAV, IBV, RSV, and HMPV, we determined epidemic onset, offset, peak, and duration at national and subnational scales. For SARS-CoV-2, we categorized wastewater measurements based on recent wastewater levels and trends. Epidemic onset occurred in November for IAV and RSV which aligned with prepandemic norms. Onset occurred in January for IBV and April for HMPV which were later than expected according to historical data. Duration was longer for IAV and shorter for IBV, RSV, and HMPV than expected based on historical data. Epidemic peak dates were consistent with prepandemic norms for all viruses. Peak dates for influenza and RSV coincided with high, upward trending SARS-CoV-2 RNA concentrations, suggesting potential co-occurrence of SARS-CoV-2 with these viruses.

Wastewater-based estimation of temporal variation in shedding amount of influenza A virus and clinically identified cases using the PRESENS model

Wastewater-based estimation of infectious disease prevalence in real-time assists public health authorities in developing effective responses to current outbreaks. However, wastewater-based estimation for IAV remains poorly demonstrated, partially because of a lack of knowledge about temporal variation in shedding amount of an IAV-infected person. In this study, we applied two mathematical models to previously collected wastewater and clinical data from four U.S. states during the 2022/2023 influenza season, dominated by the H3N2 subtype. First, we modeled the relationship between the detection probability of IAV in wastewater and FluA case counts, using a logistic function. The model revealed that a 50 % probability of IAV detection in wastewater corresponds to 0.53 (95 % CrI: 0.35-0.78) cases per 100,000 people, as observed in clinical surveillance over two weeks. Next, we applied the previously developed PRESENS model to IAV wastewater concentration data from California, revealing rapid and prolonged virus shedding patterns. The estimated shedding model was incorporated into an extended version of the PRESENS model to assess the variability in the relationship between IAV concentrations and case numbers across other states, including Massachusetts, New Jersey, and Utah. As a result, our analysis demonstrated the effectiveness of normalizing IAV concentrations with PMMoV (Pepper mild mottle virus) to accurately understand spatial distribution patterns of IAV prevalence. We successfully estimated FluA case counts from wastewater concentrations within a factor of two for 80 % of data from a state where 34 % of the state population was monitored by wastewater surveillance. Importantly, wastewater-based estimates provided real-time or leading insights (0-2 days) compared to clinical case detection in the three states, enabling early understanding of the incidence trends by limiting delays in data publication. These findings highlight the potential of wastewater surveillance to detect IAV outbreaks in near real-time and enhance efficiency of the infectious disease management.

Wastewater-based effective reproduction number and prediction under the absence of shedding information

Estimating effective reproduction number (Re) and predicting disease incidences are essential to formulate effective strategies for disease control. Although recent studies developed models for inferring Re from wastewater-based data, they require information on shedding dynamics. Here, we proposed a framework of Re estimation and prediction without shedding information. The framework consists of a space-state model for smoothing wastewater-based data and a renewal equation modified for wastewater-based data. The applicability of the framework was tested with simulated data and real-world data on Influenza A virus (IAV) and SARS-CoV-2 concentration in wastewater in 2022/2023 season in the USA. We confirmed the state-space model effectively fits various simulated epidemic curves and real-world data. In simulations, we found wastewater-based Re (Reww) closely aligns with instantaneous clinical Re when shedding dynamics are rapid. For more prolonged shedding, Reww approximates a smoothed Re over time. We also observed the necessary sampling frequency to trace dynamics of wastewater concentration and Reww accurately in the framework varies depending on the precision of detection methods, the epidemic status, the transmissibility of infectious diseases, and shedding dynamics. By applying our framework to real-world data, we found Reww for SARS-CoV-2 showed similar trend and values to clinically-based Re. Reww for IAV ranged from 0.66 to 1.52 with a clear peak in the winter season, which agrees with previously reported Re. We also succeeded in predicting wastewater concentration in a few weeks from available wastewater-based data. These results indicate that our framework potentially enables near real-time monitoring of approximated Re and prediction of infectious disease dynamics through wastewater surveillance, which limits the delay between infection and reporting. Our framework is useful especially for regions where reliable clinical surveillance is not available and notifiable surveillance is abolished, and can be expanded to multiple infectious diseases that have been detected from wastewater.

Estimating epidemic trajectories of SARS-CoV-2 and influenza A virus based on wastewater monitoring and a novel machine learning algorithm

The COVID-19 pandemic has altered the circulation of non-SARS-CoV-2 respiratory viruses. In this study, we carried out wastewater surveillance of SARS-CoV-2 and influenza A virus (IAV) in three key port cities in China through real-time quantitative PCR (RT-qPCR). Next, a novel machine learning algorithm (MLA) based on Gaussian model and random forest model was used to predict the epidemic trajectories of SARS-CoV-2 and IAV. The results showed that from February 2023 to January 2024, three port cities experienced two waves of SARS-CoV-2 infection, which peaked in late-May and late-August 2023, respectively. Two waves of IAV were observed in the spring and winter of 2023, respectively with considerable variations in terms of onset/offset date and duration. Furthermore, we employed MLA to extract the key features of epidemic trajectories of SARS-CoV-2 and IAV from February 3rd, to October 15th, 2023, and thereby predicted the epidemic trends of SARS-CoV-2 and IAV from October 16th, 2023 to April 22nd, 2024, which showed high consistency with the observed values. These collective findings offer an important understanding of SARS-CoV-2 and IAV epidemics, suggesting that wastewater surveillance together with MLA emerges as a powerful tool for risk assessment of respiratory viral diseases and improving public health preparedness.

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.

Human pathogen nucleic acids in wastewater solids from 191 wastewater treatment plants in the United States

We measured concentrations of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its variants, influenza A and B viruses, respiratory syncytial virus, human metapneumovirus, enterovirus D68, human parainfluenza types 1, 2, 3, 4a, and 4b in aggregate, norovirus genotype II, rotavirus, Candida auris, hepatitis A virus, human adenovirus, mpox virus, H5 influenza A virus, and pepper mild mottle virus nucleic acids in wastewater solids prospectively at 191 wastewater treatment plants in 40 states across the United States plus Washington DC. Measurements were made two to seven times per week from 1 January 2022 to 30 June 2024, depending on wastewater treatment plant staff availability. Measurements were made using droplet digital (reverse-transcription-) polymerase chain reaction (ddRT-PCR) following best practices for making environmental molecular biology measurements. These data can be used to better understand disease occurrence in communities contributing to the wastewater.

Wastewater Surveillance for Influenza A Virus and H5 Subtype Concurrent with the Highly Pathogenic Avian Influenza A(H5N1) Virus Outbreak in Cattle and Poultry and Associated Human Cases - United States, May 12-July 13, 2024

As part of the response to the highly pathogenic avian influenza A(H5N1) virus outbreak in U.S. cattle and poultry and the associated human cases, CDC and partners are monitoring influenza A virus levels and detection of the H5 subtype in wastewater. Among 48 states and the District of Columbia that performed influenza A testing of wastewater during May 12-July 13, 2024, a weekly average of 309 sites in 38 states had sufficient data for analysis, and 11 sites in four states reported high levels of influenza A virus. H5 subtype testing was conducted at 203 sites in 41 states, with H5 detections at 24 sites in nine states. For each detection or high level, CDC and state and local health departments evaluated data from other influenza surveillance systems and partnered with wastewater utilities and agriculture departments to investigate potential sources. Among the four states with high influenza A virus levels detected in wastewater, three states had corresponding evidence of human influenza activity from other influenza surveillance systems. Among the 24 sites with H5 detections, 15 identified animal sources within the sewershed or adjacent county, including eight milk-processing inputs. Data from these early investigations can help health officials optimize the use of wastewater surveillance during the upcoming respiratory illness season.

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.

Influenza A Virus Utilizes the Nasolacrimal System to Establish Respiratory Infection after Ocular Exposure in the Swine Model

Influenza A virus (IAV) can rapidly disseminate among animals through various transmission routes, with emerging evidence suggesting the ocular surface as an important entrance. However, it remains unclear how the virus invades the respiratory tract after ocular exposure. Here, we demonstrated that H1N1 (A/swine/Guangdong/1/2011) utilizes the nasolacrimal system to rapidly spread from the ocular surface to the respiratory tract in the porcine model. In vivo and ex vivo, IAV could efficiently attach and replicate in conjunctiva epithelium, which has abundance of α-2,6-linked and α-2,3-linked sialic acid. After ocular inoculation, infectious virions swiftly migrate to the nasolacrimal duct of piglets and, via continual drainage, disseminate to the respiratory tract. Moreover, the detection of continual virus shedding as well as the successful isolation of virus from conjunctiva and respiratory tract tissue indicated the establishment of productive infection after the transocular route. This study presents evidence suggesting that IAVs could utilize the nasolacrimal system to swiftly spread to the respiratory tract following ocular exposure, which contributes to understanding the modes of transocular transmission of IAVs.

Design, synthesis and biological evaluation of sulfamethazine derivatives as potent neuraminidase inhibitors

Aim: The purpose of this study is to design and synthesize a new series of sulfamethazine derivatives as potent neuraminidase inhibitors. Materials & methods: A sulfamethazine lead compound, ZINC670537, was first identified by structure-based virtual screening technique, then some novel inhibitors X1-X10 based on ZINC670537 were designed and synthesized. Results: Compound X3 exerts the most good potency in inhibiting the wild-type H5N1 NA (IC50 = 6.74 μM) and the H274Y mutant NA (IC50 = 21.09 μM). 150-cavity occupation is very important in determining activities of these inhibitors. The sulfamethazine moiety also plays an important role. Conclusion: Compound X3 maybe regard as a good anti-influenza candidate to preform further study.

Observations of Respiratory Syncytial Virus (RSV) Nucleic Acids in Wastewater Solids Across the United States in the 2022-2023 Season: Relationships with RSV Infection Positivity and Hospitalization Rates

Respiratory syncytial virus (RSV) is a leading cause of respiratory illness and hospitalization, but clinical surveillance detects only a minority of cases. Wastewater surveillance could determine the onset and extent of RSV circulation in the absence of sensitive case detection, but to date, studies of RSV in wastewater are few. We measured RSV RNA concentrations in wastewater solids from 176 sites during the 2022-2023 RSV season and compared those to publicly available RSV infection positivity and hospitalization rates. Concentrations ranged from undetectable to 107 copies per gram. RSV RNA concentration aggregated at state and national levels correlated with infection positivity and hospitalization rates. RSV season onset was determined using both wastewater and clinical positivity rates using independent algorithms for 14 states where both data were available at the start of the RSV season. In 4 of 14 states, wastewater and clinical surveillance identified RSV season onset during the same week; in 3 states, wastewater onset preceded clinical onset, and in 7 states, wastewater onset occurred after clinical onset. Wastewater concentrations generally peaked in the same week as hospitalization rates but after case positivity rates peaked. Differences in onset and peaks in wastewater versus clinical data may reflect inherent differences in the surveillance approaches.

Early Detection of the Emerging SARS-CoV-2 BA.2.86 Lineage Through Wastewater Surveillance Using a Mediator Probe PCR Assay - Shenzhen City, Guangdong Province, China, 2023

Introduction

The emergence of the new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron sublineage, BA.2.86, has sparked global public health concerns for its potential heightened transmissibility and immune evasion. Utilizing data from Shenzhen's city-wide wastewater surveillance system, we highlight the presence of the BA.2.86 lineage in Shenzhen.

Methods

A mediator probe polymerase chain reaction (PCR) assay was developed to detect the BA.2.86 lineage in wastewater by targeting a specific mutation (Spike: A264D). Between September 19 and December 10, 2023, 781 wastewater samples from 38 wastewater treatment plants (WWTPs) and 9 pump stations in ten districts of Shenzhen were examined. Through multiple short-amplicon sequencing, three positive samples were identified.

Results

The BA.2.86 lineage was identified in the wastewater of Futian and Nanshan districts in Shenzhen on December 2, 2023. From December 2 to 10, a total of 21 BA.2.86-positive wastewater samples were found across 6 districts (Futian, Nanshan, Longhua, Baoan, Longgang, and Luohu) in Shenzhen. The weighted average viral load of the BA.2.86 lineage in Shenzhen's wastewater was 43.5 copies/L on December 2, increased to 219.8 copies/L on December 4, and then decreased to approximately 100 copies/L on December 6, 8, and 10.

Conclusions

The mediator probe PCR assay, designed for swift detection of low viral concentrations of the BA.2.86 lineage in wastewater samples, shows promise for detecting different SARS-CoV-2 variants. Wastewater surveillance could serve as an early detection system for promptly identifying specific SARS-CoV-2 variants as they emerge.