Key considerations for pathogen surveillance in wastewater

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.

Defining alarm thresholds for the load of pathogenic viruses in wastewater for decision making: An application to three French cities

Wastewater monitoring has the potential to complement infectious disease surveillance systems. However, the absence of predefined viral signal thresholds in wastewater is often presented as a limiting factor in triggering public health action. To overcome this issue, the feasibility of defining alarm threshold for viral loads in wastewater samples was assessed by quantifying genome fragments of SARS-CoV-2, influenza A virus (IAV), respiratory syncytial virus (RSV), norovirus (NoV), and rotavirus (RoV) by RT-digital PCR (dPCR) in untreated wastewater samples from three treatment plants. Cut-point values were calculated for periods with a high rate of visits to emergency rooms or at-home visits by SOS Médecins for the related diseases. ROC curves were constructed, and the values of alarm threshold in wastewater were defined using the Youden index. For each targeted virus, alarm thresholds were close to each other across the three WWTPs. As indicated by likelihood ratios, evidence to rule in the diagnosis of high rate of visits when the alarm threshold was exceeded ranged from weak to strong and was highest for RSV and SARS-CoV-2. Evidence to rule out the diagnosis when the alarm threshold was not exceeded was strong or moderate for IAV, SARS-CoV-2 and RSV. Diagnostic performance of the test was not as high for NoV and RoV. Positive predictive value was highest for SARS-CoV-2 and RSV. For SARS-CoV-2 and RSV, the definition of an alarm threshold in wastewater could substantially inform the diagnosis of a period with a high rate of medical visits for COVID-19 and bronchiolitis, respectively.

Wastewater-based epidemiology framework: Collaborative modeling for sustainable disease surveillance

Many wastewater-based epidemiology (WBE) programs are being implemented worldwide due to their usefulness in monitoring residents' health. Modeling wastewater dynamics in outbreak scenarios can provide important data for designing wastewater surveillance plans. For outbreak modeling to be effective, researchers must coordinate with public health authorities and laboratory services, using frameworks to ensure that their modeling and output data are relevant for informed decision-making. However, theoretical and institutional frameworks typically omit modeling, and the connection between theoretical frameworks and models is often unrecognized. A framework that surpasses theoretical conceptualization for promoting collaboration between actors by integrating modeling can achieve the required synchrony toward sustainable wastewater surveillance plans. First, we build on an existing theoretical framework to create a collaborative framework that integrates modeling and suggests stakeholder activities for designing WBE programs. Then, we demonstrate our framework for developing a WBE plan via a COVID-19 case study where we answer when, how often, and where to sample wastewater to detect and monitor an outbreak. We evaluate the results in space and time for three outbreak phases (early detection, peak, and tail). The modeling outputs indicate the need for different sampling strategies for these outbreak phases. Our results also quantify the differences in the likelihood of capturing viral events in wastewater between the sampling hours at different disease phases for COVID-19 and various spatial locations in the sewer network. This framework lays the foundation for sustainable WBE to improve the detection efficiency of wastewater surveillance plans.

Integrating agent-based disease, mobility and wastewater models for the study of the spread of communicable diseases

Wastewater-based epidemiology was utilized during the COVID-19 outbreak to monitor the circulation of SARS-CoV-2, the virus causing this disease. However, this approach is limited by the need for additional methods to accurately translate virus concentrations in wastewater to disease-positive human counts. Combined modelling of COVID-19 disease cases and the concentration of its causative virus, SARS-CoV-2, in wastewater will necessarily deepen our understanding. However, this requires addressing the technical differences between disease, population mobility and wastewater models. To that end, we developed an integrated Agent-Based Model (ABM) that facilitates analysis in space and time at various temporal resolutions, including disease spread, population mobility and wastewater production, while also being sufficiently generic for different types of infectious diseases or pathogens. The integrated model replicates the epidemic curve for COVID-19 and can estimate the daily infections at the household level, enabling the monitoring of the spatial patterns of infection intensity. Additionally, the model allows monitoring the estimated production of infected wastewater over time and spatially across the sewage and treatment plant. The model addresses differences between resolutions and can potentially support Early Warning Systems (EWS) for future pandemics.

Emergence and Global Spread of Mpox Clade Ib: Challenges and the Role of Wastewater and Environmental Surveillance

Several African countries, mainly the Democratic Republic of Congo, Burundi, and Uganda, are facing highly transmissible mpox clade Ib epidemics, prompting the World Health Organization to declare a Public Health Emergency of International Concern. It has spread to key travel hubs like Kinshasa, Bujumbura, and Kampala, increasing international spread risks. Current mitigation efforts focus mainly on medical care, diagnostics, vaccination, and infection prevention, but overlook wastewater and environmental surveillance (WES). WES can be effective in detecting hotspots and enabling rapid response through enhanced data collection and genomic sequencing. This perspective article reviews the latest outbreak situation and advocates integrating WES into response strategies.

Community case study: an academia-industry-government partnership that monitors and predicts outbreaks in Tri-County Detroit area since 2017

The Tri-County Detroit Area (TCDA) is the 12th most populous metropolitan area in the United States with over three million people. Multiple communicable diseases are endemic in the TCDA. In 2017, to explore innovative methods that may provide early warnings of outbreaks affecting populations in the TCDA, an exploratory partnership that was funded by a U.S. National Science Foundation Early-concept Grant for Exploratory Research (EAGER) began. Since 2017, a project team including the College of Engineering at Michigan State University (MSU), the City of Detroit, the Great Lakes Water Authority (GLWA), industry, and local government and health departments, has been testing municipal wastewater from the TCDA to survey and predict surges in communicable diseases in the area. This ongoing effort started years before wastewater-based epidemiology became a widespread method in public health practice, due to the COVID-19 pandemic, and is now supported by the U.S. Centers for Disease Control and Prevention (CDC). The work of the partnership led to significant breakthroughs in the field of wastewater surveillance/wastewater epidemiology. The results of our surveillance efforts are used to assist local health departments in their understanding and response efforts for health issues in the TCDA, facilitating public health messaging for local awareness, targeted clinical testing, and increased vaccination efforts. Our data are available to the local health departments, and our methodological advancements are published and have been used by other communities nationwide and beyond. This paper describes the partnership, lessons learned, significant achievements, and provides a look into the future. The successful implementations and advancements of wastewater surveillance in the TCDA advocate the importance of frequent communications and interactions within the partnership, idea generations from each stakeholder for decision-making, maintenance of scientific rigor, ethical awareness, and more.

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.

Surveillance of Emerging Rodent-Borne Pathogens in Wastewater in Taiwan: A One Health Approach

Leptospirosis and hantavirus syndrome are two major rodent-borne diseases in Taiwan. Rocahepevirus ratii (RHEV), a virus closely related to hepatitis E virus (HEV, Paslahepevirus balayani), is emerging and has been reported to cause hepatitis in humans. We employed wastewater-based epidemiology to actively monitor rodent-borne pathogens, and the correlations with human cases were evaluated. Wastewater was collected using grab sampling at 11 sites along a sewer system including influents and effluents at a wastewater treatment plant in Tamsui, New Taipei City, Taiwan, monthly during June 2023 to May 2024. The presence of pathogens was examined by reverse transcription-polymerase chain reaction (RT-PCR). The result showed an overall positivity rate of 38.2% (50/131). Leptospira was detected most often (48/131, 36.6%), and RHEV and hantaviruses were found once each during the study period. Sequencing identified Leptospira interrogans close to isolates from rodents and human cases, while sequences of hantavirus and RHEV were most similar to isolates from rodents. No significant correlation was found with human cases or positive samples for rodent DNA. Here, we present an example of a One Health approach applying wastewater to environmental surveillance for the early detection and prevention of emerging diseases.

Prevalence of hepatitis A and E viruses in wastewater in Asian countries

Hepatitis A and E viruses (HAV and HEV, respectively) remain a significant global health concern despite advancements in healthcare and vaccination programs. Regular monitoring and vaccine efficacy of HAV are still lacking in different countries. This study aimed to investigate HAV and HEV prevalence in developed, developing, and least-developed Asian countries using wastewater as a surveillance tool. A total of 232 untreated wastewater samples were collected from six wastewater treatment plants, a sewage treatment plant, or an open drainage in six countries [Nepal (n = 51), Indonesia (n = 37), Thailand (n = 30), Vietnam (n = 27), the Philippines (n = 17), and Japan (n = 70)] between April and October 2022. Viruses in wastewater were concentrated by simple centrifugation or polyethylene glycol precipitation method, followed by viral RNA extraction and reverse transcription-quantitative polymerase chain reaction. HAV and HEV RNA were detected in the samples from Nepal (51 % for HAV and 2 % for HEV), Thailand (3 % for both viruses), and Japan (1 % for HAV and 24 % for HEV). Only HAV RNA was found in 11 % of the samples in Indonesia, whereas only HEV RNA was detected in Vietnam and the Philippines, with a positive ratio of 15 % and 12 %, respectively. These results highlighted the geographic variability in HAV and HEV prevalence, underscoring the need for localized public health strategies to address specific viral hepatitis challenges in each country.

Wastewater-associated plastispheres: A hidden habitat for microbial pathogens?

Wastewater treatment plants (WWTPs) receive wastewater from various sources. Despite wastewater treatment aiming to remove contaminants, microplastics persist. Plastic surfaces are quickly colonized by microbial biofilm ("plastispheres"). Plastisphere communities are suggested to promote the spread and survival of potential human pathogens, suggesting that the transfer of plastispheres from wastewater to the environment could pose a risk to human and environmental health. The study aimed to identify pathogens in wastewater plastispheres, specifically food-borne pathogens, in addition to characterizing the taxonomic diversity and composition of the wastewater plastispheres. Plastispheres that accumulated on polypropylene (PP), polyvinyl chloride (PVC), and high-density polyethylene propylene (HDPE) surfaces exposed to raw and treated wastewater were analyzed via cultivation methods, quantitative reverse transcription PCR (RT‒qPCR) and 16S rRNA amplicon sequencing. RT‒qPCR revealed the presence of potential foodborne pathogenic bacteria and viruses, such as Listeria monocytogenes, Escherichia coli, norovirus, and adenovirus. Viable isolates of the emerging pathogenic species Klebsiella pneumoniae and Acinetobacter spp. were identified in the plastispheres from raw and treated wastewater, indicating that potential pathogenic bacteria might survive in the plastispheres during the wastewater treatment. These findings underscore the potential of plastispheres to harbor and disseminate pathogenic species, posing challenges to water reuse initiatives. The taxonomic diversity and composition of the plastispheres, as explored through 16S rRNA amplicon sequencing, were significantly influenced by the wastewater environment and the duration of time the plastic spent in the wastewater. In contrast, the specific plastic material did not influence the bacterial composition, while the bacterial diversity was affected. Without efficient wastewater treatment and proper plastic waste management, wastewater could act as a source of transferring plastic-associated pathogens into the food chain and possibly pose a threat to human health. Continued research and innovation are essential to improve the removal of microplastics and associated pathogenic microorganisms in wastewater.

Longitudinal fecal shedding of SARS-CoV-2, pepper mild mottle virus, and human mitochondrial DNA in COVID-19 patients

Since the coronavirus disease 2019 (COVID-19) pandemic, wastewater-based epidemiology (WBE) has been widely applied in many countries and regions for monitoring COVID-19 transmission in the population through testing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in wastewater. However, the amount of virus shed by individuals over time based on the stage of infection and accurate number of infections in the community creates challenges in predicting COVID-19 prevalence in the population and interpreting WBE results. In this study, we measured SARS-CoV-2, pepper mild mottle virus (PMMoV), and human mitochondrial DNA (mtDNA) in longitudinal fecal samples collected from 42 COVID-19 patients for up to 42 days after diagnosis. SARS-CoV-2 RNA was detected in 73.1% (19/26) of inpatient study participants in at least one of the collected fecal specimens during the sampling period. Most participants shed the virus within 3 weeks after diagnosis, but five inpatient participants still shed the virus between 20 and 60 days after diagnosis. The median concentration of SARS-CoV-2 in positive fecal samples was 1.08 × 105 genome copies (GC)/gram dry fecal material. PMMoV and mtDNA were detected in 99.4% (154/155) and 100% (155/155) of all fecal samples, respectively. The median concentrations of PMMoV RNA and mtDNA in fecal samples were 1.73 × 107 and 2.49 × 108 GC/dry gram, respectively. These results provide important information about the dynamics of fecal shedding of SARS-CoV-2 and two human fecal indicators in COVID-19 patients. mtDNA showed higher positive rates, higher concentrations, and less variability between and within individuals than PMMoV, suggesting that mtDNA could be a better normalization factor for WBE results than PMMoV.

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.