During the COVID-19 pandemic, wastewater surveillance was widely used to monitor temporal and geographical infection trends. Using this as a foundation, a statewide program for routine wastewater monitoring of gastrointestinal pathogens was established in Oklahoma. The results from 18 months of surveillance showed that wastewater concentrations of Salmonella, Campylobacter, and norovirus exhibit similar seasonal patterns to those observed in reported human cases (F = 4–29, p < 0.05) and that wastewater can serve as an early warning tool for increases in cases, offering between one- and two-weeks lead time. Approximately one third of outbreak alerts in wastewater correlated in time with confirmed outbreaks of Salmonella or Campylobacter and our results further indicated that several outbreaks are likely to go undetected through the traditional surveillance approach currently in place. Better understanding of the true distribution and burden of gastrointestinal infections ultimately facilitates better disease prevention and control and reduces the overall socioeconomic and healthcare related impact of these pathogens. In this respect, wastewater represents a unique opportunity for monitoring infections in real-time, without the need for individual human testing. With increasing demands for sustainable and low-cost disease surveillance, the usefulness of wastewater as a long-term method for tracking infectious disease transmission is likely to become even more pronounced.
more »
« less
Wastewater surveillance for infectious disease preparedness
Wastewater surveillance for infectious disease preparednessThe University of Oklahoma Wastewater Based Epidemiology (OU WBE) team highlights successes from their three years of wastewater surveillance in Oklahoma & how this surveillance approach can be used as next-level monitoring for infectious disease preparedness. The OU WBE team, founded by Bradley Stevenson, Jason Vogel, and Katrin Gaardbo Kuhn in response to the COVID-19 pandemic in Summer 2020, has expanded to one of the most extensive wastewater monitoring networks in the world with a team that has included over 50 faculty, students and staff. In a paper published in 1942, Drs. James Trask and John Paul described a study to detect poliovirus in wastewater samples collected in New York and New Haven. They concluded, “It is likely that the periodic sampling of sewage for pathogenic viruses or bacteria may be a method of epidemiological value”. (1) Since then, wastewater surveillance has been used to detect sporadic outbreaks or clusters of various infectious pathogens, reaching new levels of routine utilization during the COVID-19 pandemic.(2)
more »
« less
- Award ID(s):
- 2200299
- PAR ID:
- 10528306
- Publisher / Repository:
- Open Access Government
- Date Published:
- Journal Name:
- Open Access Government
- Volume:
- 40
- Issue:
- 1
- ISSN:
- 2516-3817
- Page Range / eLocation ID:
- 22 to 23
- Subject(s) / Keyword(s):
- wastewater surveillance
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Elkins, Christopher A (Ed.)ABSTRACT Wastewater-based epidemiology (WBE) expanded rapidly in response to the COVID-19 pandemic. As the public health emergency has ended, researchers and practitioners are looking to shift the focus of existing wastewater surveillance programs to other targets, including bacteria. Bacterial targets may pose some unique challenges for WBE applications. To explore the current state of the field, the National Science Foundation-funded Research Coordination Network (RCN) on Wastewater Based Epidemiology for SARS-CoV-2 and Emerging Public Health Threats held a workshop in April 2023 to discuss the challenges and needs for wastewater bacterial surveillance. The targets and methods used in existing programs were diverse, with twelve different targets and nine different methods listed. Discussions during the workshop highlighted the challenges in adapting existing programs and identified research gaps in four key areas: choosing new targets, relating bacterial wastewater data to human disease incidence and prevalence, developing methods, and normalizing results. To help with these challenges and research gaps, the authors identified steps the larger community can take to improve bacteria wastewater surveillance. This includes developing data reporting standards and method optimization and validation for bacterial programs. Additionally, more work is needed to understand shedding patterns for potential bacterial targets to better relate wastewater data to human infections. Wastewater surveillance for bacteria can help provide insight into the underlying prevalence in communities, but much work is needed to establish these methods. IMPORTANCEWastewater surveillance was a useful tool to elucidate the burden and spread of SARS-CoV-2 during the pandemic. Public health officials and researchers are interested in expanding these surveillance programs to include bacterial targets, but many questions remain. The NSF-funded Research Coordination Network for Wastewater Surveillance of SARS-CoV-2 and Emerging Public Health Threats held a workshop to identify barriers and research gaps to implementing bacterial wastewater surveillance programs.more » « less
-
Abstract Traditional health surveillance methods play a critical role in public health safety but are limited by the data collection speed, coverage, and resource requirements. Wastewater‐based epidemiology (WBE) has emerged as a cost‐effective and rapid tool for detecting infectious diseases through sewage analysis of disease biomarkers. Recent advances in big data analytics have enhanced public health monitoring by enabling predictive modeling and early risk detection. This paper explores the application of machine learning (ML) in WBE data analytics, with a focus on infectious disease surveillance and forecasting. We highlight the advantages of ML‐driven WBE prediction models, including their ability to process multimodal data, predict disease trends, and evaluate policy impacts through scenario simulations. We also examine challenges such as data quality, model interpretability, and integration with existing public health infrastructure. The integration of ML WBE data analytics enables rapid health data collection, analysis, and interpretation that are not feasible in current surveillance approaches. By leveraging ML and WBE, decision makers can reduce cognitive biases and enhance data‐driven responses to public health threats. As global health risks evolve, the synergy between WBE, ML, and data‐driven decision‐making holds significant potential for improving public health outcomes.more » « less
-
Abstract Since the start of the coronavirus disease-2019 (COVID-19) pandemic, there has been interest in using wastewater monitoring as an approach for disease surveillance. A significant uncertainty that would improve the interpretation of wastewater monitoring data is the intensity and timing with which individuals shed RNA from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) into wastewater. By combining wastewater and case surveillance data sets from a university campus during a period of heightened surveillance, we inferred that individual shedding of RNA into wastewater peaks on average 6 days (50% uncertainty interval (UI): 6–7; 95% UI: 4–8) following infection, and that wastewater measurements are highly overdispersed [negative binomial dispersion parameter, k = 0.39 (95% credible interval: 0.32–0.48)]. This limits the utility of wastewater surveillance as a leading indicator of secular trends in SARS-CoV-2 transmission during an epidemic, and implies that it could be most useful as an early warning of rising transmission in areas where transmission is low or clinical testing is delayed or of limited capacity.more » « less
-
Wastewater surveillance for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA has demonstrated useful correlation with both coronavirus disease 2019 (COVID-19) cases and clinical testing positivity at the community level. Wastewater surveillance on college campuses has also demonstrated promising predictive capacity for the presence and absence of COVID-19 cases. However, to date, such monitoring has most frequently relied upon composite samplers and reverse transcription quantitative PCR (RT-qPCR) techniques, which limits the accessibility and scalability of wastewater surveillance, particularly in low-resource settings. In this study, we trialed the use of tampons as passive swabs for sample collection and reverse transcription loop-mediated isothermal amplification (RT-LAMP), which does not require sophisticated thermal cycling equipment, to detect SARS-CoV-2 RNA in wastewater. Results for the workflow were available within three hours of sample collection. The RT-LAMP assay is approximately 20 times less analytically sensitive than RT-droplet digital PCR. Nonetheless, during a building-level wastewater surveillance campaign concurrent with independent weekly clinical testing of all students, the method demonstrated a three-day positive predictive value (PPV) of 75% (excluding convalescent cases) and same-day negative predictive value (NPV) of 80% for incident COVID-19 cases. These predictive values are comparable to that reported by wastewater monitoring using RT-qPCR. These observations suggest that even with lower analytical sensitivity the tampon swab and RT-LAMP workflow offers a cost-effective and rapid approach that could be leveraged for scalable building-level wastewater surveillance for COVID-19 potentially even in low-resource settings.more » « less
An official website of the United States government

