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Boogaerts T, Van Wichelen N, Quireyns M, Burgard D, Bijlsma L, Delputte P, Gys C, Covaci A, van Nuijs ALN. Current state and future perspectives on de facto population markers for normalization in wastewater-based epidemiology: A systematic literature review. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 935:173223. [PMID: 38761943 DOI: 10.1016/j.scitotenv.2024.173223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Revised: 05/10/2024] [Accepted: 05/11/2024] [Indexed: 05/20/2024]
Abstract
Wastewater-based epidemiology (WBE) and wastewater surveillance have become a valuable complementary data source to collect information on community-wide exposure through the measurement of human biomarkers in influent wastewater (IWW). In WBE, normalization of data with the de facto population that corresponds to a wastewater sample is crucial for a correct interpretation of spatio-temporal trends in exposure and consumption patterns. However, knowledge gaps remain in identifying and validating suitable de facto population biomarkers (PBs) for refinement of WBE back-estimations. WBE studies that apply de facto PBs (including hydrochemical parameters, utility consumption data sources, endo- and exogenous chemicals, biological biomarkers and signalling records) for relative trend analysis and absolute population size estimation were systematically reviewed from three databases (PubMed, Web of Science, SCOPUS) according to the PRISMA guidelines. We included in this review 81 publications that accounted for daily variations in population sizes by applying de facto population normalization. To date, a wide range of PBs have been proposed for de facto population normalization, complicating the comparability of normalized measurements across WBE studies. Additionally, the validation of potential PBs is complicated by the absence of an ideal external validator, magnifying the overall uncertainty for population normalization in WBE. Therefore, this review proposes a conceptual tier-based cross-validation approach for identifying and validating de facto PBs to guide their integration for i) relative trend analysis, and ii) absolute population size estimation. Furthermore, this review also provides a detailed evaluation of the uncertainty observed when comparing different de jure and de facto population estimation approaches. This study shows that their percentual differences can range up to ±200 %, with some exceptions showing even larger variations. This review underscores the need for collaboration among WBE researchers to further streamline the application of de facto population normalization and to evaluate the robustness of different PBs in different socio-demographic communities.
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Affiliation(s)
- Tim Boogaerts
- Toxicological Centre, University of Antwerp, Universiteitsplein 1, 2610 Antwerp, Belgium; Exposome Center of Excellence, University of Antwerp, Universiteitsplein 1, 2610 Antwerp, Belgium
| | - Natan Van Wichelen
- Toxicological Centre, University of Antwerp, Universiteitsplein 1, 2610 Antwerp, Belgium; Exposome Center of Excellence, University of Antwerp, Universiteitsplein 1, 2610 Antwerp, Belgium
| | - Maarten Quireyns
- Toxicological Centre, University of Antwerp, Universiteitsplein 1, 2610 Antwerp, Belgium; Exposome Center of Excellence, University of Antwerp, Universiteitsplein 1, 2610 Antwerp, Belgium
| | - Dan Burgard
- Department of Chemistry and Biochemistry, University of Puget Sound, Tacoma, WA, USA
| | - Lubertus Bijlsma
- Environmental and Public Health Analytical Chemistry, Research Institute for Pesticides and Water, University Jaume I, Castellón, Spain
| | - Peter Delputte
- Laboratory for Microbiology, Parasitology and Hygiene, University of Antwerp, Universiteitsplein 1, 2610 Antwerp, Belgium; Infla-Med Center of Excellence, University of Antwerp, Universiteitsplein 1, 2610 Antwerp, Belgium
| | - Celine Gys
- Toxicological Centre, University of Antwerp, Universiteitsplein 1, 2610 Antwerp, Belgium; Exposome Center of Excellence, University of Antwerp, Universiteitsplein 1, 2610 Antwerp, Belgium
| | - Adrian Covaci
- Toxicological Centre, University of Antwerp, Universiteitsplein 1, 2610 Antwerp, Belgium; Exposome Center of Excellence, University of Antwerp, Universiteitsplein 1, 2610 Antwerp, Belgium
| | - Alexander L N van Nuijs
- Toxicological Centre, University of Antwerp, Universiteitsplein 1, 2610 Antwerp, Belgium; Exposome Center of Excellence, University of Antwerp, Universiteitsplein 1, 2610 Antwerp, Belgium
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Haalck I, Székely A, Ramne S, Sonestedt E, von Brömssen C, Eriksson E, Lai FY. Are we using more sugar substitutes? Wastewater analysis reveals differences and rising trends in artificial sweetener usage in Swedish urban catchments. ENVIRONMENT INTERNATIONAL 2024; 190:108814. [PMID: 38917625 DOI: 10.1016/j.envint.2024.108814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Revised: 05/07/2024] [Accepted: 06/10/2024] [Indexed: 06/27/2024]
Abstract
The market for artificial sweeteners as substitutes for conventional sugar (sucrose) is growing, despite potential health risks associated with their intake. Estimating population usage of artificial sweeteners is therefore crucial, and wastewater analysis can serve as a complement to existing methods. This study evaluated spatial and temporal usage of artificial sweeteners in five Swedish communities based on wastewater analysis. We further compared their levels measured in wastewater with the restrictions during the COVID-19 pandemic in Sweden and assessed health risks to the Swedish population. Influent wastewater samples (n = 194) collected in March 2019-February 2022 from communities in central and southern Sweden were analyzed for acesulfame, saccharin, and sucralose using liquid-chromatography coupled with tandem mass spectrometry. Spatial differences in loads for individual artificial sweetener were observed, with sucralose being higher in Kalmar (southern Sweden), and acesulfame and saccharin in Enköping and Östhammar (central Sweden). Based on sucrose equivalent doses, all communities showed a consistent prevalence pattern of sucralose > acesulfame > saccharin. Four communities with relatively short monitoring periods showed no apparent temporal changes in usage, but the four-year monitoring in Uppsala revealed a significant (p < 0.05) annual increase of ∼19 % for sucralose, ∼9 % for acesulfame and ∼8 % for saccharin. This trend showed no instant or delayed effects from COVID-19 restrictions, reflecting positively on the studied population which retained similar exposure to the artificial sweeteners despite potential pandemic stresses. Among the three artificial sweeteners, only acesulfame's levels were at the lower end of the health-related threshold for consumption of artificially sweetened beverages; yet, all were far below the acceptable daily intake, indicating no appreciable health risks. Our study provided valuable, pilot insights into the spatio-temporal usage of artificial sweeteners in Sweden and their associated health risks. This shows the usefulness of wastewater analysis for public health authorities wishing to assess future relevant interventions.
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Affiliation(s)
- Inga Haalck
- Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences (SLU), Uppsala SE 75007, Sweden; Department of Exposure Science, Helmholtz Centre for Environmental Research (UFZ), 04318 Leipzig, Germany
| | - Anna Székely
- Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences (SLU), Uppsala SE 75007, Sweden
| | - Stina Ramne
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Nutritional Epidemiology, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund University, Malmö, Sweden
| | - Emily Sonestedt
- Nutritional Epidemiology, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund University, Malmö, Sweden; Department of Food and Meal Science and the Research Environment MEAL, Faculty of Natural Science, Kristianstad University, SE 29188 Kristianstad, Sweden
| | - Claudia von Brömssen
- Department of Energy and Technology, Swedish University of Agricultural Sciences (SLU), Uppsala SE 75007, Sweden
| | - Elin Eriksson
- Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences (SLU), Uppsala SE 75007, Sweden
| | - Foon Yin Lai
- Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences (SLU), Uppsala SE 75007, Sweden.
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3
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Ryon MG, Langan LM, Brennan C, O'Brien ME, Bain FL, Miller AE, Snow CC, Salinas V, Norman RS, Bojes HK, Brooks BW. Influences of 23 different equations used to calculate gene copies of SARS-CoV-2 during wastewater-based epidemiology. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 917:170345. [PMID: 38272099 DOI: 10.1016/j.scitotenv.2024.170345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Revised: 12/01/2023] [Accepted: 01/19/2024] [Indexed: 01/27/2024]
Abstract
Following the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in late 2019, the use of wastewater-based surveillance (WBS) has increased dramatically along with associated infrastructure globally. However, due to the global nature of its application, and various workflow adaptations (e.g., sample collection, water concentration, RNA extraction kits), numerous methods for back-calculation of gene copies per volume (gc/L) of sewage have also emerged. Many studies have considered the comparability of processing methods (e.g., water concentration, RNA extraction); however, for equations used to calculate gene copies in a wastewater sample and subsequent influences on monitoring viral trends in a community and its association with epidemiological data, less is known. Due to limited information on how many formulas exist for the calculation of SARS-CoV-2 gene copies in wastewater, we initially attempted to quantify how many equations existed in the referred literature. We identified 23 unique equations, which were subsequently applied to an existing wastewater dataset. We observed a range of gene copies based on use of different equations, along with variability of AUC curve values, and results from correlation and regression analyses. Though a number of individual laboratories appear to have independently converged on a similar formula for back-calculation of viral load in wastewater, and share similar relationships with epidemiological data, differential influences of various equations were observed for variation in PCR volumes, RNA extraction volumes, or PCR assay parameters. Such observations highlight challenges when performing comparisons among WBS studies when numerous methodologies and back-calculation methods exist. To facilitate reproducibility among studies, the different gc/L equations were packaged as an R Shiny app, which provides end users the ability to investigate variability within their datasets and support comparisons among studies.
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Affiliation(s)
- Mia G Ryon
- Department of Environmental Science, Baylor University, One Bear Place #97266, Waco, TX 76798, USA; Center for Reservoir and Aquatic Systems Research, Baylor University, One Bear Place #97178, Waco, TX 76798, USA
| | - Laura M Langan
- Department of Environmental Science, Baylor University, One Bear Place #97266, Waco, TX 76798, USA; Center for Reservoir and Aquatic Systems Research, Baylor University, One Bear Place #97178, Waco, TX 76798, USA.
| | - Christopher Brennan
- Department of Entomology, Texas A&M University, TAMU 2475, College Station, TX 77843-2475, USA
| | - Megan E O'Brien
- Department of Environmental Science, Baylor University, One Bear Place #97266, Waco, TX 76798, USA; Center for Reservoir and Aquatic Systems Research, Baylor University, One Bear Place #97178, Waco, TX 76798, USA
| | - Fallon L Bain
- Department of Environmental Science, Baylor University, One Bear Place #97266, Waco, TX 76798, USA; Center for Reservoir and Aquatic Systems Research, Baylor University, One Bear Place #97178, Waco, TX 76798, USA
| | - Aubree E Miller
- Department of Environmental Science, Baylor University, One Bear Place #97266, Waco, TX 76798, USA; Center for Reservoir and Aquatic Systems Research, Baylor University, One Bear Place #97178, Waco, TX 76798, USA
| | - Christine C Snow
- Department of Environmental Science, Baylor University, One Bear Place #97266, Waco, TX 76798, USA; Center for Reservoir and Aquatic Systems Research, Baylor University, One Bear Place #97178, Waco, TX 76798, USA
| | - Victoria Salinas
- Environmental Epidemiology and Disease Registries, Texas Department of State Health Services, Austin, TX 78756, USA
| | - R Sean Norman
- Department of Environmental Health Sciences, Arnold School of Public Health, University of South Carolina, 921 Assembly St., Columbia, SC 28208, USA
| | - Heidi K Bojes
- Environmental Epidemiology and Disease Registries, Texas Department of State Health Services, Austin, TX 78756, USA
| | - Bryan W Brooks
- Department of Environmental Science, Baylor University, One Bear Place #97266, Waco, TX 76798, USA; Center for Reservoir and Aquatic Systems Research, Baylor University, One Bear Place #97178, Waco, TX 76798, USA; Department of Public Health, Baylor University, One Bear Place #97343, Waco, TX 76798, USA.
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Baz Lomba JA, Pires J, Myrmel M, Arnø JK, Madslien EH, Langlete P, Amato E, Hyllestad S. Effectiveness of environmental surveillance of SARS-CoV-2 as an early-warning system: Update of a systematic review during the second year of the pandemic. JOURNAL OF WATER AND HEALTH 2024; 22:197-234. [PMID: 38295081 PMCID: wh_2023_279 DOI: 10.2166/wh.2023.279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/02/2024]
Abstract
The aim of this updated systematic review was to offer an overview of the effectiveness of environmental surveillance (ES) of SARS-CoV-2 as a potential early-warning system (EWS) for COVID-19 and new variants of concerns (VOCs) during the second year of the pandemic. An updated literature search was conducted to evaluate the added value of ES of SARS-CoV-2 for public health decisions. The search for studies published between June 2021 and July 2022 resulted in 1,588 publications, identifying 331 articles for full-text screening. A total of 151 publications met our inclusion criteria for the assessment of the effectiveness of ES as an EWS and early detection of SARS-CoV-2 variants. We identified a further 30 publications among the grey literature. ES confirms its usefulness as an EWS for detecting new waves of SARS-CoV-2 infection with an average lead time of 1-2 weeks for most of the publication. ES could function as an EWS for new VOCs in areas with no registered cases or limited clinical capacity. Challenges in data harmonization and variant detection require standardized approaches and innovations for improved public health decision-making. ES confirms its potential to support public health decision-making and resource allocation in future outbreaks.
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Affiliation(s)
- Jose Antonio Baz Lomba
- Department of Infection Control and Preparedness, Norwegian Institute of Public Health, Oslo, Norway E-mail:
| | - João Pires
- Department of Infection Control and Preparedness, Norwegian Institute of Public Health, Oslo, Norway; ECDC fellowship Programme, Public Health Microbiology path (EUPHEM), European Centre for Disease Prevention and Control (ECDC), Solna, Sweden
| | - Mette Myrmel
- Faculty of Veterinary Medicine, Virology Unit, Norwegian University of Life Science (NMBU), Oslo, Norway
| | - Jorunn Karterud Arnø
- Department of Infection Control and Preparedness, Norwegian Institute of Public Health, Oslo, Norway
| | - Elisabeth Henie Madslien
- Department of Infection Control and Preparedness, Norwegian Institute of Public Health, Oslo, Norway
| | - Petter Langlete
- Department of Infection Control and Preparedness, Norwegian Institute of Public Health, Oslo, Norway
| | - Ettore Amato
- Department of Infection Control and Preparedness, Norwegian Institute of Public Health, Oslo, Norway
| | - Susanne Hyllestad
- Department of Infection Control and Preparedness, Norwegian Institute of Public Health, Oslo, Norway
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Wiesner-Friedman C, Brinkman NE, Wheaton E, Nagarkar M, Hart C, Keely SP, Varughese E, Garland J, Klaver P, Turner C, Barton J, Serre M, Jahne M. Characterizing Spatial Information Loss for Wastewater Surveillance Using crAssphage: Effect of Decay, Temperature, and Population Mobility. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:20802-20812. [PMID: 38015885 DOI: 10.1021/acs.est.3c05587] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2023]
Abstract
Populations contribute information about their health status to wastewater. Characterizing how that information degrades in transit to wastewater sampling locations (e.g., wastewater treatment plants and pumping stations) is critical to interpret wastewater responses. In this work, we statistically estimate the loss of information about fecal contributions to wastewater from spatially distributed populations at the census block group resolution. This was accomplished with a hydrologically and hydraulically influenced spatial statistical approach applied to crAssphage (Carjivirus communis) load measured from the influent of four wastewater treatment plants in Hamilton County, Ohio. We find that we would expect to observe a 90% loss of information about fecal contributions from a given census block group over a travel time of 10.3 h. This work demonstrates that a challenge to interpreting wastewater responses (e.g., during wastewater surveillance) is distinguishing between a distal but large cluster of contributions and a near but small contribution. This work demonstrates new modeling approaches to improve measurement interpretation depending on sewer network and wastewater characteristics (e.g., geospatial layout, temperature variability, population distribution, and mobility). This modeling can be integrated into standard wastewater surveillance methods and help to optimize sewer sampling locations to ensure that different populations (e.g., vulnerable and susceptible) are appropriately represented.
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Affiliation(s)
- Corinne Wiesner-Friedman
- Oak Ridge Institute for Science and Education, 26 West Martin Luther King Drive, Cincinnati, Ohio 45268, United States
| | - Nichole E Brinkman
- Office of Research and Development, U.S. Environmental Protection Agency, 26 West Martin Luther King Drive, Cincinnati, Ohio 45268, United States
| | - Emily Wheaton
- Office of Research and Development, U.S. Environmental Protection Agency, 26 West Martin Luther King Drive, Cincinnati, Ohio 45268, United States
| | - Maitreyi Nagarkar
- Office of Research and Development, U.S. Environmental Protection Agency, 26 West Martin Luther King Drive, Cincinnati, Ohio 45268, United States
| | - Chloe Hart
- Office of Research and Development, U.S. Environmental Protection Agency, 26 West Martin Luther King Drive, Cincinnati, Ohio 45268, United States
| | - Scott P Keely
- Office of Research and Development, U.S. Environmental Protection Agency, 26 West Martin Luther King Drive, Cincinnati, Ohio 45268, United States
| | - Eunice Varughese
- Office of Research and Development, U.S. Environmental Protection Agency, 26 West Martin Luther King Drive, Cincinnati, Ohio 45268, United States
| | - Jay Garland
- Office of Research and Development, U.S. Environmental Protection Agency, 26 West Martin Luther King Drive, Cincinnati, Ohio 45268, United States
| | - Peter Klaver
- LimnoTech, 501 Avis Drive, Ann Arbor, Michigan 48108, United States
| | - Carrie Turner
- LimnoTech, 501 Avis Drive, Ann Arbor, Michigan 48108, United States
| | - John Barton
- Metropolitan Sewer District of Greater Cincinnati, 1081 Woodrow Street, Cincinnati, Ohio 45204, United States
| | - Marc Serre
- Gillings School of Global Public Health, Department of Environmental Sciences and Engineering, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Michael Jahne
- Office of Research and Development, U.S. Environmental Protection Agency, 26 West Martin Luther King Drive, Cincinnati, Ohio 45268, United States
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Thapar I, Langan LM, Davis H, Norman RS, Bojes HK, Brooks BW. Influence of storage conditions and multiple freeze-thaw cycles on N1 SARS-CoV-2, PMMoV, and BCoV signal. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 896:165098. [PMID: 37392884 PMCID: PMC10307669 DOI: 10.1016/j.scitotenv.2023.165098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 06/16/2023] [Accepted: 06/21/2023] [Indexed: 07/03/2023]
Abstract
Wastewater-based epidemiology/wastewater-based surveillance (WBE/WBS) continues to serve as an effective means of monitoring various diseases, including COVID-19 and the emergence of SARS-CoV-2 variants, at the population level. As the use of WBE expands, storage conditions of wastewater samples will play a critical role in ensuring the accuracy and reproducibility of results. In this study, the impacts of water concentration buffer (WCB), storage temperature, and freeze-thaw cycles on the detection of SARS-CoV-2 and other WBE-related gene targets were examined. Freeze-thawing of concentrated samples did not significantly affect (p > 0.05) crossing/cycle threshold (Ct) value for any of the gene targets studied (SARS-CoV-2 N1, PMMoV, and BCoV). However, use of WCB during concentration resulted in a significant (p < 0.05) decrease in Ct for all targets, and storage at -80 °C (in contrast to -20 °C) appeared preferable for wastewater storage signal stability based on decreased Ct values, although this was only significantly different (p < 0.05) for the BCoV target. Interestingly, when Ct values were converted to gene copies per influent sample, no significant differences (p > 0.05) were observed in any of the targets examined. Stability of RNA targets in concentrated wastewater against freeze-thaw degradation supports archiving of concentrated samples for use in retrospective examination of COVID-19 trends and tracing SARS-CoV-2 variants and potentially other viruses, and provides a starting point for establishing a consistent procedure for specimen collection and storage for the WBE/WBS community.
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Affiliation(s)
- Isha Thapar
- Department of Environmental Science, Baylor University, One Bear Place #97266, Waco, TX 76798, USA
| | - Laura M Langan
- Department of Environmental Science, Baylor University, One Bear Place #97266, Waco, TX 76798, USA; Center for Reservoir and Aquatic Systems Research, Baylor University, One Bear Place #97178, Waco, TX 76798, USA.
| | - Haley Davis
- Department of Environmental Science, Baylor University, One Bear Place #97266, Waco, TX 76798, USA; Harbor Branch Oceanographic Institute, Florida Atlantic University, 5600 US-1, Fort Pierce, FL 34946, USA
| | - R Sean Norman
- Department of Environmental Health Sciences, Arnold School of Public Health, South Carolina, 921 Assembly St., Columbia, SC 29208, USA
| | - Heidi K Bojes
- Environmental Epidemiology and Disease Registries Section, Texas Department of State Health Services, Austin, TX 78756, USA
| | - Bryan W Brooks
- Department of Environmental Science, Baylor University, One Bear Place #97266, Waco, TX 76798, USA; Center for Reservoir and Aquatic Systems Research, Baylor University, One Bear Place #97178, Waco, TX 76798, USA; Institute of Biomedical Studies, Baylor University, One Bear Place #97224, Waco, TX 76798, USA
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7
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Rioux MD, Guillemette F, Lemarchand K, Doiron K, Lemay JF, Maere T, Dolcé P, Quessy P, Abonnenc N, Vanrolleghem PA, Frigon D. Wastewater-based epidemiology: the crucial role of viral shedding dynamics in small communities. Front Public Health 2023; 11:1141837. [PMID: 37601171 PMCID: PMC10433918 DOI: 10.3389/fpubh.2023.1141837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 06/30/2023] [Indexed: 08/22/2023] Open
Abstract
Background Wastewater surveillance (WWS) of pathogens is a rapidly evolving field owing to the 2019 coronavirus disease pandemic, which brought about a paradigm shift in public health authorities for the management of pathogen outbreaks. However, the interpretation of WWS in terms of clinical cases remains a challenge, particularly in small communities where large variations in pathogen concentrations are routinely observed without a clear relation to clinical incident cases. Methods Results are presented for WWS from six municipalities in the eastern part of Canada during the spring of 2021. We developed a numerical model based on viral kinetics reduction functions to consider both prevalent and incident cases to interpret the WWS data in light of the reported clinical cases in the six surveyed communities. Results The use of the proposed numerical model with a viral kinetics reduction function drastically increased the interpretability of the WWS data in terms of the clinical cases reported for the surveyed community. In line with our working hypothesis, the effects of viral kinetics reduction modeling were more important in small communities than in larger communities. In all but one of the community cases (where it had no effect), the use of the proposed numerical model led to a change from a +1.5% (for the larger urban center, Quebec City) to a +48.8% increase in the case of a smaller community (Drummondville). Conclusion Consideration of prevalent and incident cases through the proposed numerical model increases the correlation between clinical cases and WWS data. This is particularly the case in small communities. Because the proposed model is based on a biological mechanism, we believe it is an inherent part of any wastewater system and, hence, that it should be used in any WWS analysis where the aim is to relate WWS measurement to clinical cases.
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Affiliation(s)
- Marc-Denis Rioux
- Department of Mathematics and Engineering, Université du Québec à Rimouski, Quebec, QC, Canada
| | - François Guillemette
- Department of Environmental Science, Université du Québec à Trois-Rivière, Quebec, QC, Canada
| | - Karine Lemarchand
- Institut des Sciences de la Mer, Université du Québec à Rimouski, Quebec, QC, Canada
| | - Kim Doiron
- Northern Institute for Research in Environment and Occupational Health and Safety, Quebec, QC, Canada
| | - Jean-François Lemay
- Centre National en Électrochimie et Technologies Environnementales, Cegep of Shawinigan, Quebec, QC, Canada
| | - Thomas Maere
- modelEAU, Département de génie civil et de génie des eaux, Université Laval, Quebec, QC, Canada
| | - Patrick Dolcé
- Centre Intégré de Santé et de services sociaux du Bas-Saint-Laurent, Quebec, QC, Canada
| | - Patrik Quessy
- Centre National en Électrochimie et Technologies Environnementales, Cegep of Shawinigan, Quebec, QC, Canada
| | - Nanouk Abonnenc
- Centre National en Électrochimie et Technologies Environnementales, Cegep of Shawinigan, Quebec, QC, Canada
| | - Peter A. Vanrolleghem
- modelEAU, Département de génie civil et de génie des eaux, Université Laval, Quebec, QC, Canada
| | - Dominic Frigon
- Department of Civil Engineering, McGill University, Quebec, QC, Canada
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Keshaviah A, Diamond MB, Wade MJ, Scarpino SV. Wastewater monitoring can anchor global disease surveillance systems. Lancet Glob Health 2023; 11:e976-e981. [PMID: 37202030 DOI: 10.1016/s2214-109x(23)00170-5] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 02/28/2023] [Accepted: 03/27/2023] [Indexed: 05/20/2023]
Abstract
To inform the development of global wastewater monitoring systems, we surveyed programmes in 43 countries. Most programmes monitored predominantly urban populations. In high-income countries (HICs), composite sampling at centralised treatment plants was most common, whereas grab sampling from surface waters, open drains, and pit latrines was more typical in low-income and middle-income countries (LMICs). Almost all programmes analysed samples in-country, with an average processing time of 2·3 days in HICs and 4·5 days in LMICs. Whereas 59% of HICs regularly monitored wastewater for SARS-CoV-2 variants, only 13% of LMICs did so. Most programmes share their wastewater data internally, with partnering organisations, but not publicly. Our findings show the richness of the existing wastewater monitoring ecosystem. With additional leadership, funding, and implementation frameworks, thousands of individual wastewater initiatives can coalesce into an integrated, sustainable network for disease surveillance-one that minimises the risk of overlooking future global health threats.
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Affiliation(s)
| | | | - Matthew J Wade
- Analytics & Data Science Directorate, UK Health Security Agency, London, UK
| | - Samuel V Scarpino
- Institute for Experiential AI, Network Science Institute, Department of Health Sciences, and Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA; Santa Fe Institute, Santa Fe, NM, USA; Vermont Complex Systems Center, University of Vermont, Burlington, VT, USA.
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9
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Rainey AL, Liang S, Bisesi JH, Sabo-Attwood T, Maurelli AT. A multistate assessment of population normalization factors for wastewater-based epidemiology of COVID-19. PLoS One 2023; 18:e0284370. [PMID: 37043469 PMCID: PMC10096268 DOI: 10.1371/journal.pone.0284370] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 03/29/2023] [Indexed: 04/13/2023] Open
Abstract
Wastewater-based epidemiology (WBE) has become a valuable tool for monitoring SARS-CoV-2 infection trends throughout the COVID-19 pandemic. Population biomarkers that measure the relative human fecal contribution to normalize SARS-CoV-2 wastewater concentrations are needed for improved analysis and interpretation of community infection trends. The Centers for Disease Control and Prevention National Wastewater Surveillance System (CDC NWSS) recommends using the wastewater flow rate or human fecal indicators as population normalization factors. However, there is no consensus on which normalization factor performs best. In this study, we provided the first multistate assessment of the effects of flow rate and human fecal indicators (crAssphage, F+ Coliphage, and PMMoV) on the correlation of SARS-CoV-2 wastewater concentrations and COVID-19 cases using the CDC NWSS dataset of 182 communities across six U.S. states. Flow normalized SARS-CoV-2 wastewater concentrations produced the strongest correlation with COVID-19 cases. The correlation from the three human fecal indicators were significantly lower than flow rate. Additionally, using reverse transcription droplet digital polymerase chain reaction (RT-ddPCR) significantly improved correlation values over samples that were analyzed with real-time reverse transcription quantitative polymerase chain reaction (rRT-qPCR). Our assessment shows that utilizing flow normalization with RT-ddPCR generate the strongest correlation between SARS-CoV-2 wastewater concentrations and COVID-19 cases.
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Affiliation(s)
- Andrew L. Rainey
- Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, Florida, United States of America
- Emerging Pathogens Institute, University of Florida, Gainesville, Florida, United States of America
| | - Song Liang
- Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, Florida, United States of America
- Emerging Pathogens Institute, University of Florida, Gainesville, Florida, United States of America
| | - Joseph H. Bisesi
- Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, Florida, United States of America
- Emerging Pathogens Institute, University of Florida, Gainesville, Florida, United States of America
- Center for Environmental and Human Toxicology, University of Florida, Gainesville, Florida, United States of America
| | - Tara Sabo-Attwood
- Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, Florida, United States of America
- Emerging Pathogens Institute, University of Florida, Gainesville, Florida, United States of America
- Center for Environmental and Human Toxicology, University of Florida, Gainesville, Florida, United States of America
| | - Anthony T. Maurelli
- Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, Florida, United States of America
- Emerging Pathogens Institute, University of Florida, Gainesville, Florida, United States of America
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10
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Lamba S, Ganesan S, Daroch N, Paul K, Joshi SG, Sreenivas D, Nataraj A, Srikantaiah V, Mishra R, Ramakrishnan U, Ishtiaq F. SARS-CoV-2 infection dynamics and genomic surveillance to detect variants in wastewater - a longitudinal study in Bengaluru, India. THE LANCET REGIONAL HEALTH. SOUTHEAST ASIA 2023; 11:100151. [PMID: 36688230 PMCID: PMC9847225 DOI: 10.1016/j.lansea.2023.100151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Revised: 11/01/2022] [Accepted: 01/06/2023] [Indexed: 01/19/2023]
Abstract
Background Environmental surveillance (ES) of a pathogen is crucial for understanding the community load of disease. As an early warning system, ES for SARS-CoV-2 has complemented routine diagnostic surveillance by capturing near real-time virus circulation at a population level. Methods In this longitudinal study conducted between January 2022 and June 2022 in 28 sewershed sites in Bengaluru city (∼11 million inhabitants), we quantified weekly SARS-CoV-2 RNA concentrations to track infection dynamics and provide evidence of change in the relative abundance of emerging variants. Findings We describe an early warning system using the exponentially weighted moving average control chart and demonstrate how SARS-CoV-2 RNA concentrations in wastewater correlated with clinically diagnosed new COVID-19 cases, with the trends appearing 8-14 days earlier in wastewater than in clinical data. This was further corroborated by showing that the estimated number of infections is strongly correlated with SARS-CoV-2 RNA copies detected in the wastewater. Using a deconvolution matrix, we detected emerging variants of concern up to two months earlier in wastewater samples. In addition, we found a huge diversity in variants detected in wastewater compared to clinical samples. The findings from this study have been discussed regularly with local authorities to inform policy-making decisions. Interpretation Our study highlights that quantifying viral titre, correlating it with a known number of cases in the area, and combined with genomic surveillance helps in tracking variants of concern (VOC) over time and space, enabling timely and making informed policy decisions. Funding This work has been supported by funding from the Rockefeller Foundation grant to National Centre for Biological Sciences, TIFR) and the Indian Council of Medical Research grant to (FI) Tata Institute for Genetics and Society and Tata Trusts.
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Affiliation(s)
- Sanjay Lamba
- Tata Institute for Genetics and Society, GKVK Post, Bellary Road, Bengaluru, 560065, India
| | - Sutharsan Ganesan
- Tata Institute for Genetics and Society, GKVK Post, Bellary Road, Bengaluru, 560065, India
| | - Namrta Daroch
- Tata Institute for Genetics and Society, GKVK Post, Bellary Road, Bengaluru, 560065, India
| | - Kiran Paul
- Tata Institute for Genetics and Society, GKVK Post, Bellary Road, Bengaluru, 560065, India
| | - Soumya Gopal Joshi
- Tata Institute for Genetics and Society, GKVK Post, Bellary Road, Bengaluru, 560065, India
| | - Darshan Sreenivas
- National Centre for Biological Sciences, TIFR, Bellary Road, Bengaluru, 560065, India
| | - Annamalai Nataraj
- Tata Institute for Genetics and Society, GKVK Post, Bellary Road, Bengaluru, 560065, India
| | | | - Rakesh Mishra
- Tata Institute for Genetics and Society, GKVK Post, Bellary Road, Bengaluru, 560065, India
| | - Uma Ramakrishnan
- National Centre for Biological Sciences, TIFR, Bellary Road, Bengaluru, 560065, India
| | - Farah Ishtiaq
- Tata Institute for Genetics and Society, GKVK Post, Bellary Road, Bengaluru, 560065, India,Corresponding author. Tata Institute for Genetics and Society, GKVK Post, Bellary Road, Bengaluru, 560065, India
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11
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Saingam P, Li B, Nguyen Quoc B, Jain T, Bryan A, Winkler MKH. Wastewater surveillance of SARS-CoV-2 at intra-city level demonstrated high resolution in tracking COVID-19 and calibration using chemical indicators. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 866:161467. [PMID: 36626989 PMCID: PMC9825140 DOI: 10.1016/j.scitotenv.2023.161467] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Revised: 12/17/2022] [Accepted: 01/04/2023] [Indexed: 05/12/2023]
Abstract
Wastewater-based epidemiology has proven to be a supportive tool to better comprehend the dynamics of the COVID-19 pandemic. As the disease moves into endemic stage, the surveillance at wastewater sub-catchments such as pump station and manholes is providing a novel mechanism to examine the reemergence and to take measures that can prevent the spread. However, there is still a lack of understanding when it comes to wastewater-based epidemiology implementation at the smaller intra-city level for better granularity in data, and dilution effect of rain precipitation at pump stations. For this study, grab samples were collected from six areas of Seattle between March-October 2021. These sampling sites comprised five manholes and one pump station with population ranging from 2580 to 39,502 per manhole/pump station. The wastewater samples were analyzed for SARS-CoV-2 RNA concentrations, and we also obtained the daily COVID-19 cases (from individual clinical testing) for each corresponding sewershed, which ranged from 1 to 12 and the daily incidence varied between 3 and 64 per 100,000 of population. Rain precipitation lowered viral RNA levels and sensitivity of viral detection but wastewater total ammonia (NH4+-N) and phosphate (PO43--P) were shown as potential chemical indicators to calibrate/level out the dilution effect. These chemicals showed the potential in improving the wastewater surveillance capacity of COVID-19.
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Affiliation(s)
- Prakit Saingam
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195, USA.
| | - Bo Li
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195, USA
| | - Bao Nguyen Quoc
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195, USA
| | - Tanisha Jain
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195, USA
| | - Andrew Bryan
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98195, USA
| | - Mari K H Winkler
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195, USA.
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12
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Perez-Zabaleta M, Archer A, Khatami K, Jafferali MH, Nandy P, Atasoy M, Birgersson M, Williams C, Cetecioglu Z. Long-term SARS-CoV-2 surveillance in the wastewater of Stockholm: What lessons can be learned from the Swedish perspective? THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 858:160023. [PMID: 36356735 PMCID: PMC9640212 DOI: 10.1016/j.scitotenv.2022.160023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 10/14/2022] [Accepted: 11/03/2022] [Indexed: 06/16/2023]
Abstract
Wastewater-based epidemiology (WBE) can be used to track the spread of SARS-CoV-2 in a population. This study presents the learning outcomes from over two-year long monitoring of SARS-CoV-2 in Stockholm, Sweden. The three main wastewater treatment plants in Stockholm, with a total of six inlets, were monitored from April 2020 until June 2022 (in total 600 samples). This spans five major SARS-CoV-2 waves, where WBE data provided early warning signals for each wave. Further, the measured SARS-CoV-2 content in the wastewater correlated significantly with the level of positive COVID-19 tests (r = 0.86; p << 0.0001) measured by widespread testing of the population. Moreover, as a proof-of-concept, six SARS-CoV-2 variants of concern were monitored using hpPCR assay, demonstrating that variants can be traced through wastewater monitoring. During this long-term surveillance, two sampling protocols, two RNA concentration/extraction methods, two calculation approaches, and normalization to the RNA virus Pepper mild mottle virus (PMMoV) were evaluated. In addition, a study of storage conditions was performed, demonstrating that the decay of viral RNA was significantly reduced upon the addition of glycerol to the wastewater before storage at -80 °C. Our results provide valuable information that can facilitate the incorporation of WBE as a prediction tool for possible future outbreaks of SARS-CoV-2 and preparations for future pandemics.
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Affiliation(s)
- Mariel Perez-Zabaleta
- Department of Industrial Biotechnology, KTH Royal Institute of Technology, AlbaNova University Center, SE-10691 Stockholm, Sweden; Department of Chemical Engineering, KTH Royal Institute of Technology, SE-10044, Sweden
| | - Amena Archer
- Department of Protein Science, KTH Royal Institute of Technology, Science for Life Laboratory, Solna, Sweden
| | - Kasra Khatami
- Department of Industrial Biotechnology, KTH Royal Institute of Technology, AlbaNova University Center, SE-10691 Stockholm, Sweden; Department of Chemical Engineering, KTH Royal Institute of Technology, SE-10044, Sweden
| | - Mohammed Hakim Jafferali
- Department of Protein Science, KTH Royal Institute of Technology, Science for Life Laboratory, Solna, Sweden
| | - Prachi Nandy
- Department of Chemical Engineering, KTH Royal Institute of Technology, SE-10044, Sweden
| | - Merve Atasoy
- Department of Chemical Engineering, KTH Royal Institute of Technology, SE-10044, Sweden
| | - Madeleine Birgersson
- Department of Protein Science, KTH Royal Institute of Technology, Science for Life Laboratory, Solna, Sweden
| | - Cecilia Williams
- Department of Protein Science, KTH Royal Institute of Technology, Science for Life Laboratory, Solna, Sweden
| | - Zeynep Cetecioglu
- Department of Industrial Biotechnology, KTH Royal Institute of Technology, AlbaNova University Center, SE-10691 Stockholm, Sweden; Department of Chemical Engineering, KTH Royal Institute of Technology, SE-10044, Sweden.
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13
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Maal-Bared R, Qiu Y, Li Q, Gao T, Hrudey SE, Bhavanam S, Ruecker NJ, Ellehoj E, Lee BE, Pang X. Does normalization of SARS-CoV-2 concentrations by Pepper Mild Mottle Virus improve correlations and lead time between wastewater surveillance and clinical data in Alberta (Canada): comparing twelve SARS-CoV-2 normalization approaches. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 856:158964. [PMID: 36167131 PMCID: PMC9508694 DOI: 10.1016/j.scitotenv.2022.158964] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 09/13/2022] [Accepted: 09/19/2022] [Indexed: 05/02/2023]
Abstract
Wastewater-based surveillance (WBS) data normalization is an analyte measurement correction that addresses variations resulting from dilution of fecal discharge by non-sanitary sewage, stormwater or groundwater infiltration. No consensus exists on what WBS normalization parameters result in the strongest correlations and lead time between SARS-CoV-2 WBS data and COVID-19 cases. This study compared flow, population size and biomarker normalization impacts on the correlations and lead times for ten communities in twelve sewersheds in Alberta (Canada) between September 2020 and October 2021 (n = 1024) to determine if normalization by Pepper Mild Mottle Virus (PMMoV) provides any advantages compared to other normalization parameters (e.g., flow, reported and dynamic population sizes, BOD, TSS, NH3, TP). PMMoV concentrations (GC/mL) corresponded with plant influent flows and were highest in the urban centres. SARS-CoV-2 target genes E, N1 and N2 were all negatively associated with wastewater influent pH, while PMMoV was positively associated with temperature. Pooled data analysis showed that normalization increased ρ-values by almost 0.1 and was highest for ammonia, TKN and TP followed by PMMoV. Normalization by other parameters weakened associations. None of the differences were statistically significant. Site-specific correlations showed that normalization of SARS-CoV-2 data by PMMoV only improved correlations significantly in two of the twelve systems; neither were large sewersheds or combined sewer systems. In five systems, normalization by traditional wastewater strength parameters and dynamic population estimates improved correlations. Lead time ranged between 1 and 4 days in both pooled and site-specific comparisons. We recommend that WBS researchers and health departments: a) Investigate WWTP influent properties (e.g., pH) in the WBS planning phase and use at least two parallel approaches for normalization only if shown to provide value; b) Explore normalization by wastewater strength parameters and dynamic population size estimates further; and c) Evaluate purchasing an influent flow meter in small communities to support long-term WBS efforts and WWTP management.
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Affiliation(s)
- Rasha Maal-Bared
- Quality Assurance and Environment, EPCOR Water, Edmonton, Alberta, Canada.
| | - Yuanyuan Qiu
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, Alberta, Canada
| | - Qiaozhi Li
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, Alberta, Canada
| | - Tiejun Gao
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, Alberta, Canada
| | - Steve E Hrudey
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, Alberta, Canada
| | - Sudha Bhavanam
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, Alberta, Canada
| | - Norma J Ruecker
- Water Quality Services, City of Calgary, Calgary, Alberta, Canada
| | - Erik Ellehoj
- Ellehoj Redmond Consulting, Edmonton, Alberta, Canada
| | - Bonita E Lee
- Department of Paediatrics, University of Alberta, Edmonton, Alberta, Canada
| | - Xiaoli Pang
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, Alberta, Canada; Public Health Laboratories (ProvLab), Alberta Precision Laboratories (APL), Edmonton, Alberta, Canada
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14
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Hopkins L, Persse D, Caton K, Ensor K, Schneider R, McCall C, Stadler LB. Citywide wastewater SARS-CoV-2 levels strongly correlated with multiple disease surveillance indicators and outcomes over three COVID-19 waves. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 855:158967. [PMID: 36162580 PMCID: PMC9507781 DOI: 10.1016/j.scitotenv.2022.158967] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 09/16/2022] [Accepted: 09/19/2022] [Indexed: 06/16/2023]
Abstract
Public health surveillance systems for COVID-19 are multifaceted and include multiple indicators reflective of different aspects of the burden and spread of the disease in a community. With the emergence of wastewater disease surveillance as a powerful tool to track infection dynamics of SARS-CoV-2, there is a need to integrate and validate wastewater information with existing disease surveillance systems and demonstrate how it can be used as a routine surveillance tool. A first step toward integration is showing how it relates to other disease surveillance indicators and outcomes, such as case positivity rates, syndromic surveillance data, and hospital bed use rates. Here, we present an 86-week long surveillance study that covers three major COVID-19 surges. City-wide SARS-CoV-2 RNA viral loads in wastewater were measured across 39 wastewater treatment plants and compared to other disease metrics for the city of Houston, TX. We show that wastewater levels are strongly correlated with positivity rate, syndromic surveillance rates of COVID-19 visits, and COVID-19-related general bed use rates at hospitals. We show that the relative timing of wastewater relative to each indicator shifted across the pandemic, likely due to a multitude of factors including testing availability, health-seeking behavior, and changes in viral variants. Next, we show that individual WWTPs led city-wide changes in SARS-CoV-2 viral loads, indicating a distributed monitoring system could be used to enhance the early-warning capability of a wastewater monitoring system. Finally, we describe how the results were used in real-time to inform public health response and resource allocation.
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Affiliation(s)
- Loren Hopkins
- Houston Health Department, 8000 N. Stadium Dr., Houston, TX, United States of America; Department of Statistics, Rice University, 6100 Main Street MS 138, Houston, TX, United States of America
| | - David Persse
- Houston Health Department, 8000 N. Stadium Dr., Houston, TX, United States of America; Department of Medicine and Surgery, Baylor College of Medicine, Houston, TX, United States of America; City of Houston Emergency Medical Services, Houston, TX, United States of America
| | - Kelsey Caton
- Houston Health Department, 8000 N. Stadium Dr., Houston, TX, United States of America
| | - Katherine Ensor
- Department of Statistics, Rice University, 6100 Main Street MS 138, Houston, TX, United States of America
| | - Rebecca Schneider
- Houston Health Department, 8000 N. Stadium Dr., Houston, TX, United States of America
| | - Camille McCall
- Department of Civil and Environmental Engineering, Rice University, 6100 Main Street MS-519, Houston, TX, United States of America
| | - Lauren B Stadler
- Department of Civil and Environmental Engineering, Rice University, 6100 Main Street MS-519, Houston, TX, United States of America.
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15
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Schumann VF, de Castro Cuadrat RR, Wyler E, Wurmus R, Deter A, Quedenau C, Dohmen J, Faxel M, Borodina T, Blume A, Freimuth J, Meixner M, Grau JH, Liere K, Hackenbeck T, Zietzschmann F, Gnirss R, Böckelmann U, Uyar B, Franke V, Barke N, Altmüller J, Rajewsky N, Landthaler M, Akalin A. SARS-CoV-2 infection dynamics revealed by wastewater sequencing analysis and deconvolution. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 853:158931. [PMID: 36228784 PMCID: PMC9549760 DOI: 10.1016/j.scitotenv.2022.158931] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 08/27/2022] [Accepted: 09/18/2022] [Indexed: 06/16/2023]
Abstract
The use of RNA sequencing from wastewater samples is a valuable way for estimating infection dynamics and circulating lineages of SARS-CoV-2. This approach is independent from testing individuals and can therefore become the key tool to monitor this and potentially other viruses. However, it is equally important to develop easily accessible and scalable tools which can highlight critical changes in infection rates and dynamics over time across different locations given sequencing data from wastewater. Here, we provide an analysis of lineage dynamics in Berlin and New York City using wastewater sequencing and present PiGx SARS-CoV-2, a highly reproducible computational analysis pipeline with comprehensive reports. This end-to-end pipeline includes all steps from raw data to shareable reports, additional taxonomic analysis, deconvolution and geospatial time series analyses. Using simulated datasets (in silico generated and spiked-in samples) we could demonstrate the accuracy of our pipeline calculating proportions of Variants of Concern (VOC) from environmental as well as pre-mixed samples (spiked-in). By applying our pipeline on a dataset of wastewater samples from Berlin between February 2021 and January 2022, we could reconstruct the emergence of B.1.1.7(alpha) in February/March 2021 and the replacement dynamics from B.1.617.2 (delta) to BA.1 and BA.2 (omicron) during the winter of 2021/2022. Using data from very-short-reads generated in an industrial scale setting, we could see even higher accuracy in our deconvolution. Lastly, using a targeted sequencing dataset from New York City (receptor-binding-domain (RBD) only), we could reproduce the results recovering the proportions of the so-called cryptic lineages shown in the original study. Overall our study provides an in-depth analysis reconstructing virus lineage dynamics from wastewater. While applying our tool on a wide range of different datasets (from different types of wastewater sample locations and sequenced with different methods), we show that PiGx SARS-CoV-2 can be used to identify new mutations and detect any emerging new lineages in a highly automated and scalable way. Our approach can support efforts to establish continuous monitoring and early-warning projects for detecting SARS-CoV-2 or any other pathogen.
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Affiliation(s)
- Vic-Fabienne Schumann
- Bioinformatics & Omics Data Science Platform, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine Berlin, Berlin, Germany
| | - Rafael Ricardo de Castro Cuadrat
- Bioinformatics & Omics Data Science Platform, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine Berlin, Berlin, Germany
| | - Emanuel Wyler
- RNA Biology and Posttranscriptional Regulation, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine Berlin, Berlin, Germany
| | - Ricardo Wurmus
- Bioinformatics & Omics Data Science Platform, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine Berlin, Berlin, Germany
| | - Aylina Deter
- RNA Biology and Posttranscriptional Regulation, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine Berlin, Berlin, Germany
| | - Claudia Quedenau
- Genomics Platform, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine Berlin, Berlin, Germany
| | - Jan Dohmen
- Bioinformatics & Omics Data Science Platform, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine Berlin, Berlin, Germany
| | - Miriam Faxel
- Bioinformatics & Omics Data Science Platform, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine Berlin, Berlin, Germany
| | - Tatiana Borodina
- Genomics Platform, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine Berlin, Berlin, Germany
| | - Alexander Blume
- Bioinformatics & Omics Data Science Platform, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine Berlin, Berlin, Germany
| | - Jonas Freimuth
- Bioinformatics & Omics Data Science Platform, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine Berlin, Berlin, Germany
| | | | | | | | | | | | | | | | - Bora Uyar
- Bioinformatics & Omics Data Science Platform, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine Berlin, Berlin, Germany
| | - Vedran Franke
- Bioinformatics & Omics Data Science Platform, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine Berlin, Berlin, Germany
| | - Niclas Barke
- RNA Biology and Posttranscriptional Regulation, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine Berlin, Berlin, Germany
| | - Janine Altmüller
- Genomics Platform, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine Berlin, Berlin, Germany
| | - Nikolaus Rajewsky
- Systems Biology of Gene Regulatory Elements, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine Berlin, Berlin, Germany.
| | - Markus Landthaler
- RNA Biology and Posttranscriptional Regulation, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine Berlin, Berlin, Germany.
| | - Altuna Akalin
- Bioinformatics & Omics Data Science Platform, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine Berlin, Berlin, Germany.
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16
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Hsu SY, Bayati M, Li C, Hsieh HY, Belenchia A, Klutts J, Zemmer SA, Reynolds M, Semkiw E, Johnson HY, Foley T, Wieberg CG, Wenzel J, Johnson MC, Lin CH. Biomarkers selection for population normalization in SARS-CoV-2 wastewater-based epidemiology. WATER RESEARCH 2022; 223:118985. [PMID: 36030667 PMCID: PMC9376872 DOI: 10.1016/j.watres.2022.118985] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 07/15/2022] [Accepted: 08/13/2022] [Indexed: 05/29/2023]
Abstract
Wastewater-based epidemiology (WBE) has been one of the most cost-effective approaches to track the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) levels in the communities since the coronavirus disease 2019 (COVID-19) outbreak in 2020. Normalizing SARS-CoV-2 concentrations by the population biomarkers in wastewater is critical for interpreting the viral loads, comparing the epidemiological trends among the sewersheds, and identifying the vulnerable communities. In this study, five population biomarkers, pepper mild mottle virus (PMMoV), creatinine (CRE), 5-hydroxyindoleacetic acid (5-HIAA), caffeine (CAF) and its metabolite paraxanthine (PARA) were investigated and validated for their utility in normalizing the SARS-CoV-2 loads through two normalizing approaches using the data from 64 wastewater treatment plants (WWTPs) in Missouri. Their utility in assessing the real-time population contributing to the wastewater was also evaluated. The best performing candidate was further tested for its capacity for improving correlation between normalized SARS-CoV-2 loads and the clinical cases reported in the City of Columbia, Missouri, a university town with a constantly fluctuating population. Our results showed that, except CRE, the direct and indirect normalization approaches using biomarkers allow accounting for the changes in wastewater dilution and differences in relative human waste input over time regardless flow volume and population of the given WWTP. Among selected biomarkers, PARA is the most reliable population biomarker in determining the SARS-CoV-2 load per capita due to its high accuracy, low variability, and high temporal consistency to reflect the change in population dynamics and dilution in wastewater. It also demonstrated its excellent utility for real-time assessment of the population contributing to the wastewater. In addition, the viral loads normalized by the PARA-estimated population significantly improved the correlation (rho=0.5878, p < 0.05) between SARS-CoV-2 load per capita and case numbers per capita. This chemical biomarker complements the current normalization scheme recommended by CDC and helps us understand the size, distribution, and dynamics of local populations for forecasting the prevalence of SARS-CoV2 within each sewershed.
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Affiliation(s)
- Shu-Yu Hsu
- School of Natural Resources, University of Missouri, Columbia, MO 65201, USA; Center for Agroforestry, University of Missouri, Columbia, MO 65201, USA
| | - Mohamed Bayati
- School of Natural Resources, University of Missouri, Columbia, MO 65201, USA
| | - Chenhui Li
- School of Natural Resources, University of Missouri, Columbia, MO 65201, USA
| | - Hsin-Yeh Hsieh
- School of Natural Resources, University of Missouri, Columbia, MO 65201, USA
| | - Anthony Belenchia
- Bureau of Environmental Epidemiology, Division of Community and Public Health, Missouri Department of Health and Senior Services, Jefferson City, MO, USA
| | - Jessica Klutts
- Water Protection Program, Missouri Department of Natural Resources, Jefferson City, MO, USA
| | - Sally A Zemmer
- Water Protection Program, Missouri Department of Natural Resources, Jefferson City, MO, USA
| | - Melissa Reynolds
- Bureau of Environmental Epidemiology, Division of Community and Public Health, Missouri Department of Health and Senior Services, Jefferson City, MO, USA
| | - Elizabeth Semkiw
- Bureau of Environmental Epidemiology, Division of Community and Public Health, Missouri Department of Health and Senior Services, Jefferson City, MO, USA
| | - Hwei-Yiing Johnson
- Bureau of Environmental Epidemiology, Division of Community and Public Health, Missouri Department of Health and Senior Services, Jefferson City, MO, USA
| | - Trevor Foley
- Missouri Department of Corrections, Jefferson City, MO, USA
| | - Chris G Wieberg
- Water Protection Program, Missouri Department of Natural Resources, Jefferson City, MO, USA
| | - Jeff Wenzel
- Bureau of Environmental Epidemiology, Division of Community and Public Health, Missouri Department of Health and Senior Services, Jefferson City, MO, USA
| | - Marc C Johnson
- Department of Molecular Microbiology and Immunology, University of Missouri, School of Medicine and the Christopher S. Bond Life Sciences Center, Columbia, MO 65201, USA
| | - Chung-Ho Lin
- School of Natural Resources, University of Missouri, Columbia, MO 65201, USA; Center for Agroforestry, University of Missouri, Columbia, MO 65201, USA.
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17
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A Two-Step Data Normalization Approach for Improving Classification Accuracy in the Medical Diagnosis Domain. MATHEMATICS 2022. [DOI: 10.3390/math10111942] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Data normalization is a data preprocessing task and one of the first to be performed during intellectual analysis, particularly in the case of tabular data. The importance of its implementation is determined by the need to reduce the sensitivity of the artificial intelligence model to the values of the features in the dataset to increase the studied model’s adequacy. This paper focuses on the problem of effectively preprocessing data to improve the accuracy of intellectual analysis in the case of performing medical diagnostic tasks. We developed a new two-step method for data normalization of numerical medical datasets. It is based on the possibility of considering both the interdependencies between the features of each observation from the dataset and their absolute values to improve the accuracy when performing medical data mining tasks. We describe and substantiate each step of the algorithmic implementation of the method. We also visualize the results of the proposed method. The proposed method was modeled using six different machine learning methods based on decision trees when performing binary and multiclass classification tasks. We used six real-world, freely available medical datasets with different numbers of vectors, attributes, and classes to conduct experiments. A comparison between the effectiveness of the developed method and that of five existing data normalization methods was carried out. It was experimentally established that the developed method increases the accuracy of the Decision Tree and Extra Trees Classifier by 1–5% in the case of performing the binary classification task and the accuracy of the Bagging, Decision Tree, and Extra Trees Classifier by 1–6% in the case of performing the multiclass classification task. Increasing the accuracy of these classifiers only by using the new data normalization method satisfies all the prerequisites for its application in practice when performing various medical data mining tasks.
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