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Malla B, Shrestha S, Sthapit N, Hirai S, Raya S, Rahmani AF, Angga MS, Siri Y, Ruti AA, Haramoto E. Beyond COVID-19: Wastewater-based epidemiology for multipathogen surveillance and normalization strategies. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 946:174419. [PMID: 38960169 DOI: 10.1016/j.scitotenv.2024.174419] [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: 04/27/2024] [Revised: 06/29/2024] [Accepted: 06/29/2024] [Indexed: 07/05/2024]
Abstract
Wastewater-based epidemiology (WBE) is a critical tool for monitoring community health. Although much attention has focused on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a causative agent of coronavirus disease 2019 (COVID-19), other pathogens also pose significant health risks. This study quantified the presence of SARS-CoV-2, influenza A virus (Inf-A), and noroviruses of genogroups I (NoV-GI) and II (NoV-GII) in wastewater samples collected weekly (n = 170) from July 2023 to February 2024 from five wastewater treatment plants (WWTPs) in Yamanashi Prefecture, Japan, by quantitative PCR. Inf-A RNA exhibited localized prevalence with positive ratios of 59 %-82 % in different WWTPs, suggesting regional outbreaks within specific areas. NoV-GI (94 %, 160/170) and NoV-GII (100 %, 170/170) RNA were highly prevalent, with NoV-GII (6.1 ± 0.8 log10 copies/L) consistently exceeding NoV-GI (5.4 ± 0.7 log10 copies/L) RNA concentrations. SARS-CoV-2 RNA was detected in 100 % of the samples, with mean concentrations of 5.3 ± 0.5 log10 copies/L in WWTP E and 5.8 ± 0.4 log10 copies/L each in other WWTPs. Seasonal variability was evident, with higher concentrations of all pathogenic viruses during winter. Non-normalized and normalized virus concentrations by fecal indicator bacteria (Escherichia coli and total coliforms), an indicator virus (pepper mild mottle virus (PMMoV)), and turbidity revealed significant positive associations with the reported disease cases. Inf-A and NoV-GI + GII RNA concentrations showed strong correlations with influenza and acute gastroenteritis cases, particularly when normalized to E. coli (Spearman's ρ = 0.70-0.81) and total coliforms (ρ = 0.70-0.81), respectively. For SARS-CoV-2, non-normalized concentrations showed a correlation of 0.61, decreasing to 0.31 when normalized to PMMoV, suggesting that PMMoV is unsuitable. Turbidity normalization also yielded suboptimal results. This study underscored the importance of selecting suitable normalization parameters tailored to specific pathogens for accurate disease trend monitoring using WBE, demonstrating its utility beyond COVID-19 surveillance.
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Affiliation(s)
- Bikash Malla
- Interdisciplinary Center for River Basin Environment, University of Yamanashi, 4-3-11 Takeda, Kofu, Yamanashi 400-8511, Japan
| | - Sadhana Shrestha
- Interdisciplinary Center for River Basin Environment, University of Yamanashi, 4-3-11 Takeda, Kofu, Yamanashi 400-8511, Japan
| | - Niva Sthapit
- Interdisciplinary Center for River Basin Environment, University of Yamanashi, 4-3-11 Takeda, Kofu, Yamanashi 400-8511, Japan
| | - Soichiro Hirai
- Department of Engineering, University of Yamanashi, 4-3-11 Takeda, Kofu, Yamanashi 400-8511, Japan
| | - Sunayana Raya
- Department of Engineering, University of Yamanashi, 4-3-11 Takeda, Kofu, Yamanashi 400-8511, Japan
| | - Aulia Fajar Rahmani
- Department of Engineering, University of Yamanashi, 4-3-11 Takeda, Kofu, Yamanashi 400-8511, Japan
| | - Made Sandhyana Angga
- Department of Engineering, University of Yamanashi, 4-3-11 Takeda, Kofu, Yamanashi 400-8511, Japan
| | - Yadpiroon Siri
- Department of Engineering, University of Yamanashi, 4-3-11 Takeda, Kofu, Yamanashi 400-8511, Japan
| | - Annisa Andarini Ruti
- Department of Engineering, University of Yamanashi, 4-3-11 Takeda, Kofu, Yamanashi 400-8511, Japan
| | - Eiji Haramoto
- Interdisciplinary Center for River Basin Environment, University of Yamanashi, 4-3-11 Takeda, Kofu, Yamanashi 400-8511, Japan.
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Tiwari A, Radu E, Kreuzinger N, Ahmed W, Pitkänen T. Key considerations for pathogen surveillance in wastewater. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 945:173862. [PMID: 38876348 DOI: 10.1016/j.scitotenv.2024.173862] [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: 04/09/2024] [Revised: 06/03/2024] [Accepted: 06/06/2024] [Indexed: 06/16/2024]
Abstract
Wastewater surveillance (WWS) has received significant attention as a rapid, sensitive, and cost-effective tool for monitoring various pathogens in a community. WWS is employed to assess the spatial and temporal trends of diseases and identify their early appearances and reappearances, as well as to detect novel and mutated variants. However, the shedding rates of pathogens vary significantly depending on factors such as disease severity, the physiology of affected individuals, and the characteristics of pathogen. Furthermore, pathogens may exhibit differential fate and decay kinetics in the sewerage system. Variable shedding rates and decay kinetics may affect the detection of pathogens in wastewater. This may influence the interpretation of results and the conclusions of WWS studies. When selecting a pathogen for WWS, it is essential to consider it's specific characteristics. If data are not readily available, factors such as fate, decay, and shedding rates should be assessed before conducting surveillance. Alternatively, these factors can be compared to those of similar pathogens for which such data are available.
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Affiliation(s)
- Ananda Tiwari
- Finnish Institute for Health and Welfare, Department of Health Security, Kuopio, Finland; University of Helsinki, Faculty of Veterinary Medicine, Helsinki, Finland.
| | - Elena Radu
- Institute for Water Quality and Resource Management, Vienna University of Technology, Karlsplatz 13/226, 1040 Vienna, Austria; Stefan S. Nicolau Institute of Virology, Department of Cellular and Molecular Pathology, 285 Mihai Bravu Avenue, 030304 Bucharest, Romania; University of Medicine and Pharmacy Carol Davila, Department of Virology, 37 Dionisie Lupu Street, 020021 Bucharest, Romania.
| | - Norbert Kreuzinger
- Institute for Water Quality and Resource Management, Vienna University of Technology, Karlsplatz 13/226, 1040 Vienna, Austria.
| | - Warish Ahmed
- CSIRO Environment, Ecosciences Precinct, 41 Boggo Road, Dutton Park, QLD 4102, Australia.
| | - Tarja Pitkänen
- Finnish Institute for Health and Welfare, Department of Health Security, Kuopio, Finland; University of Helsinki, Faculty of Veterinary Medicine, Helsinki, Finland.
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Mohring J, Leithäuser N, Wlazło J, Schulte M, Pilz M, Münch J, Küfer KH. Estimating the COVID-19 prevalence from wastewater. Sci Rep 2024; 14:14384. [PMID: 38909097 PMCID: PMC11193770 DOI: 10.1038/s41598-024-64864-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 06/13/2024] [Indexed: 06/24/2024] Open
Abstract
Wastewater based epidemiology has become a widely used tool for monitoring trends of concentrations of different pathogens, most notably and widespread of SARS-CoV-2. Therefore, in 2022, also in Rhineland-Palatinate, the Ministry of Science and Health has included 16 wastewater treatment sites in a surveillance program providing biweekly samples. However, the mere viral load data is subject to strong fluctuations and has limited value for political deciders on its own. Therefore, the state of Rhineland-Palatinate has commissioned the University Medical Center at Johannes Gutenberg University Mainz to conduct a representative cohort study called SentiSurv, in which an increasing number of up to 12,000 participants have been using sensitive antigen self-tests once or twice a week to test themselves for SARS-CoV-2 and report their status. This puts the state of Rhineland-Palatinate in the fortunate position of having time series of both, the viral load in wastewater and the prevalence of SARS-CoV-2 in the population. Our main contribution is a calibration study based on the data from 2023-01-08 until 2023-10-01 where we identified a scaling factor ( 0.208 ± 0.031 ) and a delay ( 5.07 ± 2.30 days) between the virus load in wastewater, normalized by the pepper mild mottle virus (PMMoV), and the prevalence recorded in the SentiSurv study. The relation is established by fitting an epidemiological model to both time series. We show how that can be used to estimate the prevalence when the cohort data is no longer available and how to use it as a forecasting instrument several weeks ahead of time. We show that the calibration and forecasting quality and the resulting factors depend strongly on how wastewater samples are normalized.
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Affiliation(s)
- Jan Mohring
- Fraunhofer Institute for Industrial Mathematics, 67663, Kaiserslautern, Germany.
| | - Neele Leithäuser
- Fraunhofer Institute for Industrial Mathematics, 67663, Kaiserslautern, Germany
| | - Jarosław Wlazło
- Fraunhofer Institute for Industrial Mathematics, 67663, Kaiserslautern, Germany
| | - Marvin Schulte
- Fraunhofer Institute for Industrial Mathematics, 67663, Kaiserslautern, Germany
| | - Maximilian Pilz
- Fraunhofer Institute for Industrial Mathematics, 67663, Kaiserslautern, Germany
| | - Johanna Münch
- Fraunhofer Institute for Industrial Mathematics, 67663, Kaiserslautern, Germany
| | - Karl-Heinz Küfer
- Fraunhofer Institute for Industrial Mathematics, 67663, Kaiserslautern, Germany
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Tiwari A, Lehto KM, Paspaliari DK, Al-Mustapha AI, Sarekoski A, Hokajärvi AM, Länsivaara A, Hyder R, Luomala O, Lipponen A, Oikarinen S, Heikinheimo A, Pitkänen T. Developing wastewater-based surveillance schemes for multiple pathogens: The WastPan project in Finland. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 926:171401. [PMID: 38467259 DOI: 10.1016/j.scitotenv.2024.171401] [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: 12/02/2023] [Revised: 02/28/2024] [Accepted: 02/28/2024] [Indexed: 03/13/2024]
Abstract
Wastewater comprises multiple pathogens and offers a potential for wastewater-based surveillance (WBS) to track the prevalence of communicable diseases. The Finnish WastPan project aimed to establish wastewater-based pandemic preparedness for multiple pathogens (viruses, bacteria, parasites, fungi), including antimicrobial resistance (AMR). This article outlines WastPan's experiences in this project, including the criteria for target selection, sampling locations, frequency, analysis methods, and results communication. Target selection relied on epidemiological and microbiological evidence and practical feasibility. Within the WastPan framework, wastewater samples were collected between 2021 and 2023 from 10 wastewater treatment plants (WWTPs) covering 40 % of Finland's population. WWTP selection was validated for reported cases of Extended Spectrum Beta-lactamase-producing bacterial pathogens (Escherichia coli and Klebsiella pneumoniae) from the National Infectious Disease Register. The workflow included 24-h composite influent samples, with one fraction for culture-based analysis (bacteria and fungi) and the rest of the sample was reserved for molecular analysis (viruses, bacteria, antibiotic resistance genes, and parasites). The reproducibility of the monitoring workflow was assessed for SARS-CoV-2 through inter-laboratory comparisons using the N2 and N1 assays. Identical protocols were applied to same-day samples, yielding similar positivity trends in the two laboratories, but the N2 assay achieved a significantly higher detection rate (Laboratory 1: 91.5 %; Laboratory 2: 87.4 %) than the N1 assay (76.6 %) monitored only in Laboratory 2 (McNemar, p < 0.001 Lab 1, = 0.006 Lab 2). This result indicates that the selection of monitoring primers and assays may impact monitoring sensitivity in WBS. Overall, the current study recommends that the selection of sampling frequencies and population coverage of the monitoring should be based on pathogen-specific epidemiological characteristics. For example, pathogens that are stable over time may need less frequent annual sampling, while those that are occurring across regions may require reduced sample coverage. Here, WastPan successfully piloted WBS for monitoring multiple pathogens, highlighting the significance of one-litre community composite wastewater samples for assessing community health. The infrastructure established for COVID-19 WBS is valuable for monitoring various pathogens. The prioritization of the monitoring targets optimizes resource utilization. In the future legislative support in target selection, coverage determination, and sustained funding for WBS is recomended.
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Affiliation(s)
- Ananda Tiwari
- Finnish Institute for Health and Welfare, Department of Health Security, Kuopio and Helsinki, Finland.
| | - Kirsi-Maarit Lehto
- Tampere University, Faculty of Medicine and Health Technology, Tampere, Finland.
| | - Dafni K Paspaliari
- Finnish Institute for Health and Welfare, Department of Health Security, Kuopio and Helsinki, Finland; ECDC Fellowship Programme, Public Health Microbiology path (EUPHEM), European Centre for Disease Prevention and Control (ECDC), Solna, Sweden
| | - Ahmad I Al-Mustapha
- University of Helsinki, Faculty of Veterinary Medicine, Helsinki, Finland; Department of Veterinary Public Health and Preventive Medicine, Faculty of Veterinary Medicine, University of Ibadan, Ibadan, Nigeria.
| | - Anniina Sarekoski
- Finnish Institute for Health and Welfare, Department of Health Security, Kuopio and Helsinki, Finland; University of Helsinki, Faculty of Veterinary Medicine, Helsinki, Finland.
| | - Anna-Maria Hokajärvi
- Finnish Institute for Health and Welfare, Department of Health Security, Kuopio and Helsinki, Finland.
| | - Annika Länsivaara
- Tampere University, Faculty of Medicine and Health Technology, Tampere, Finland.
| | - Rafiqul Hyder
- Tampere University, Faculty of Medicine and Health Technology, Tampere, Finland.
| | - Oskari Luomala
- Finnish Institute for Health and Welfare, Department of Health Security, Kuopio and Helsinki, Finland.
| | - Anssi Lipponen
- Finnish Institute for Health and Welfare, Department of Health Security, Kuopio and Helsinki, Finland.
| | - Sami Oikarinen
- Tampere University, Faculty of Medicine and Health Technology, Tampere, Finland.
| | - Annamari Heikinheimo
- University of Helsinki, Faculty of Veterinary Medicine, Helsinki, Finland; Finnish Food Authority, Seinäjoki, Finland.
| | - Tarja Pitkänen
- Finnish Institute for Health and Welfare, Department of Health Security, Kuopio and Helsinki, Finland; University of Helsinki, Faculty of Veterinary Medicine, Helsinki, Finland.
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Islam G, Gedge A, Ibrahim R, de Melo T, Lara-Jacobo L, Dlugosz T, Kirkwood AE, Simmons D, Desaulniers JP. The role of catchment population size, data normalization, and chronology of public health interventions on wastewater-based COVID-19 viral trends. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 937:173272. [PMID: 38763190 DOI: 10.1016/j.scitotenv.2024.173272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Revised: 04/28/2024] [Accepted: 05/13/2024] [Indexed: 05/21/2024]
Abstract
The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) pandemic presented the most challenging global crisis in recent times. A pandemic caused by a novel pathogen such as SARS-CoV-2 necessitated the development of innovative techniques for the monitoring and surveillance of COVID-19 infections within communities. Wastewater surveillance (WWS) is recognized as a non-invasive, cost-effective, and valuable epidemiological tool to monitor the prevalence of COVID-19 infections in communities. Seven municipal wastewater sampling sites representing distinct sewershed communities were selected for the surveillance of the SARS-CoV-2 virus in Durham Region, Ontario, Canada over 8 months from March 2021 to October 2021. Viral RNA fragments of SARS-CoV-2 and the normalization target pepper mild mottle virus (PMMoV) were concentrated from wastewater influent using the PEG/NaCl superspeed centrifugation method and quantified using RT-qPCR. Strong significant correlations (Spearman's rs = 0.749 to 0.862, P < 0.001) were observed between SARS-CoV-2 gene copies/mL of wastewater and clinical cases reported in each delineated sewershed by onset date. Although raw wastewater offered higher correlation coefficients with clinical cases by onset date compared to PMMoV normalized data, only one site had a statistically significantly higher Spearman's correlation coefficient value for raw data than normalized data. Implementation of community stay-at-home orders and vaccinations over the course of the study period in 2021 were found to strongly correspond to decreasing SARS-CoV-2 wastewater trends in the wastewater treatment plants and upstream pumping stations.
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Affiliation(s)
- Golam Islam
- Faculty of Science, Ontario Tech University, 2000 Simcoe St N, Oshawa, ON L1G 0C5, Canada.
| | - Ashley Gedge
- Faculty of Science, Ontario Tech University, 2000 Simcoe St N, Oshawa, ON L1G 0C5, Canada
| | - Reeta Ibrahim
- Faculty of Science, Ontario Tech University, 2000 Simcoe St N, Oshawa, ON L1G 0C5, Canada
| | - Tomas de Melo
- Faculty of Science, Ontario Tech University, 2000 Simcoe St N, Oshawa, ON L1G 0C5, Canada
| | - Linda Lara-Jacobo
- Faculty of Science, Ontario Tech University, 2000 Simcoe St N, Oshawa, ON L1G 0C5, Canada
| | - Thomas Dlugosz
- Faculty of Science, Ontario Tech University, 2000 Simcoe St N, Oshawa, ON L1G 0C5, Canada
| | - Andrea E Kirkwood
- Faculty of Science, Ontario Tech University, 2000 Simcoe St N, Oshawa, ON L1G 0C5, Canada
| | - Denina Simmons
- Faculty of Science, Ontario Tech University, 2000 Simcoe St N, Oshawa, ON L1G 0C5, Canada
| | - Jean-Paul Desaulniers
- Faculty of Science, Ontario Tech University, 2000 Simcoe St N, Oshawa, ON L1G 0C5, Canada
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6
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Pilz M, Küfer KH, Mohring J, Münch J, Wlazło J, Leithäuser N. Statistical analysis of three data sources for Covid-19 monitoring in Rhineland-Palatinate, Germany. Sci Rep 2024; 14:10245. [PMID: 38702453 PMCID: PMC11068884 DOI: 10.1038/s41598-024-60973-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 04/29/2024] [Indexed: 05/06/2024] Open
Abstract
In Rhineland-Palatinate, Germany, a system of three data sources has been established to track the Covid-19 pandemic. These sources are the number of Covid-19-related hospitalizations, the Covid-19 genecopies in wastewater, and the prevalence derived from a cohort study. This paper presents an extensive comparison of these parameters. It is investigated whether wastewater data and a cohort study can be valid surrogate parameters for the number of hospitalizations and thus serve as predictors for coming Covid-19 waves. We observe that this is possible in general for the cohort study prevalence, while the wastewater data suffer from a too large variability to make quantitative predictions by a purely data-driven approach. However, the wastewater data and the cohort study prevalence are able to detect hospitalizations waves in a qualitative manner. Furthermore, a detailed comparison of different normalization techniques of wastewater data is provided.
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Affiliation(s)
- Maximilian Pilz
- Fraunhofer Institute for Industrial Mathematics, Kaiserslautern, Germany.
| | - Karl-Heinz Küfer
- Fraunhofer Institute for Industrial Mathematics, Kaiserslautern, Germany
| | - Jan Mohring
- Fraunhofer Institute for Industrial Mathematics, Kaiserslautern, Germany
| | - Johanna Münch
- Fraunhofer Institute for Industrial Mathematics, Kaiserslautern, Germany
| | - Jarosław Wlazło
- Fraunhofer Institute for Industrial Mathematics, Kaiserslautern, Germany
| | - Neele Leithäuser
- Fraunhofer Institute for Industrial Mathematics, Kaiserslautern, Germany
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7
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Holm RH, Rempala GA, Choi B, Brick JM, Amraotkar AR, Keith RJ, Rouchka EC, Chariker JH, Palmer KE, Smith T, Bhatnagar A. Dynamic SARS-CoV-2 surveillance model combining seroprevalence and wastewater concentrations for post-vaccine disease burden estimates. COMMUNICATIONS MEDICINE 2024; 4:70. [PMID: 38594350 PMCID: PMC11004132 DOI: 10.1038/s43856-024-00494-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 03/28/2024] [Indexed: 04/11/2024] Open
Abstract
BACKGROUND Despite wide scale assessments, it remains unclear how large-scale severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccination affected the wastewater concentration of the virus or the overall disease burden as measured by hospitalization rates. METHODS We used weekly SARS-CoV-2 wastewater concentration with a stratified random sampling of seroprevalence, and linked vaccination and hospitalization data, from April 2021-August 2021 in Jefferson County, Kentucky (USA). Our susceptible ( S ), vaccinated ( V ), variant-specific infected (I 1 andI 2 ), recovered ( R ), and seropositive ( T ) model ( S V I 2 R T ) tracked prevalence longitudinally. This was related to wastewater concentration. RESULTS Here we show the 64% county vaccination rate translate into about a 61% decrease in SARS-CoV-2 incidence. The estimated effect of SARS-CoV-2 Delta variant emergence is a 24-fold increase of infection counts, which correspond to an over 9-fold increase in wastewater concentration. Hospitalization burden and wastewater concentration have the strongest correlation (r = 0.95) at 1 week lag. CONCLUSIONS Our study underscores the importance of continuing environmental surveillance post-vaccine and provides a proof-of-concept for environmental epidemiology monitoring of infectious disease for future pandemic preparedness.
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Grants
- P20 GM103436 NIGMS NIH HHS
- This study was supported by Centers for Disease Control and Prevention (75D30121C10273), Louisville Metro Government, James Graham Brown Foundation, Owsley Brown II Family Foundation, Welch Family, Jewish Heritage Fund for Excellence, the National Institutes of Health, (P20GM103436), the Rockefeller Foundation, the National Sciences Foundation (DMS-2027001), and the Basic Science Research Program National Research Foundation of Korea (NRF) (RS-2023-00245056).
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Affiliation(s)
- Rochelle H Holm
- Christina Lee Brown Envirome Institute, School of Medicine, University of Louisville, Louisville, KY, 40202, USA
| | - Grzegorz A Rempala
- Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, OH, 43210, USA
| | - Boseung Choi
- Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, OH, 43210, USA
- Division of Big Data Science, Korea University, Sejong, South Korea
- Biomedical Mathematics Group, Institute for Basic Science, Daejeon, South Korea
| | | | - Alok R Amraotkar
- Christina Lee Brown Envirome Institute, School of Medicine, University of Louisville, Louisville, KY, 40202, USA
| | - Rachel J Keith
- Christina Lee Brown Envirome Institute, School of Medicine, University of Louisville, Louisville, KY, 40202, USA
| | - Eric C Rouchka
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Louisville, Louisville, KY, 40202, USA
- KY INBRE Bioinformatics Core, University of Louisville, Louisville, KY, 40202, USA
| | - Julia H Chariker
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Louisville, Louisville, KY, 40202, USA
- KY INBRE Bioinformatics Core, University of Louisville, Louisville, KY, 40202, USA
| | - Kenneth E Palmer
- Center for Predictive Medicine for Biodefense and Emerging Infectious Diseases, University of Louisville, Louisville, KY, 40202, USA
- Department of Pharmacology and Toxicology, School of Medicine, University of Louisville, Louisville, KY, 40202, USA
| | - Ted Smith
- Christina Lee Brown Envirome Institute, School of Medicine, University of Louisville, Louisville, KY, 40202, USA
| | - Aruni Bhatnagar
- Christina Lee Brown Envirome Institute, School of Medicine, University of Louisville, Louisville, KY, 40202, USA.
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Li Y, Ash K, Alamilla I, Joyner D, Williams DE, McKay PJ, Green B, DeBlander S, North C, Kara-Murdoch F, Swift C, Hazen TC. COVID-19 trends at the University of Tennessee: predictive insights from raw sewage SARS-CoV-2 detection and evaluation and PMMoV as an indicator for human waste. Front Microbiol 2024; 15:1379194. [PMID: 38605711 PMCID: PMC11007199 DOI: 10.3389/fmicb.2024.1379194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 03/19/2024] [Indexed: 04/13/2024] Open
Abstract
Wastewater-based epidemiology (WBE) has become a valuable tool for monitoring the prevalence of SARS-CoV-2 on university campuses. However, concerns about effectiveness of raw sewage as a COVID-19 early warning system still exist, and it's not clear how useful normalization by simultaneous comparison of Pepper Mild Mottle Virus (PMMoV) is in addressing variations resulting from fecal discharge dilution. This study aims to contribute insights into these aspects by conducting an academic-year field trial at the student residences on the University of Tennessee, Knoxville campus, raw sewage. This was done to investigate the correlations between SARS-CoV-2 RNA load, both with and without PMMoV normalization, and various parameters, including active COVID-19 cases, self-isolations, and their combination among all student residents. Significant positive correlations between SARS-CoV-2 RNA load a week prior, during the monitoring week, and the subsequent week with active cases. Despite these correlations, normalization by PMMoV does not enhance these associations. These findings suggest the potential utility of SARS-CoV-2 RNA load as an early warning indicator and provide valuable insights into the application and limitations of WBE for COVID-19 surveillance specifically within the context of raw sewage on university campuses.
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Affiliation(s)
- Ye Li
- Department of Civil and Environmental Sciences, University of Tennessee, Knoxville, TN, United States
| | - Kurt Ash
- Department of Civil and Environmental Sciences, University of Tennessee, Knoxville, TN, United States
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States
| | | | - Dominique Joyner
- Department of Civil and Environmental Sciences, University of Tennessee, Knoxville, TN, United States
| | - Daniel Edward Williams
- Center for Environmental Biotechnology, University of Tennessee, Knoxville, TN, United States
| | - Peter J. McKay
- Battelle Memorial Institute, Columbus, OH, United States
| | - Brianna Green
- Department of Microbiology, University of Tennessee, Knoxville, TN, United States
| | - Sydney DeBlander
- College of Natural Science, Michigan State University, East Lansing, MI, United States
| | - Carman North
- Student Health Center, University of Tennessee, Knoxville, TN, United States
| | - Fadime Kara-Murdoch
- Department of Civil and Environmental Sciences, University of Tennessee, Knoxville, TN, United States
- Battelle Memorial Institute, Columbus, OH, United States
- Center for Environmental Biotechnology, University of Tennessee, Knoxville, TN, United States
| | - Cynthia Swift
- Department of Civil and Environmental Sciences, University of Tennessee, Knoxville, TN, United States
- Center for Environmental Biotechnology, University of Tennessee, Knoxville, TN, United States
| | - Terry C. Hazen
- Department of Civil and Environmental Sciences, University of Tennessee, Knoxville, TN, United States
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States
- Department of Microbiology, University of Tennessee, Knoxville, TN, United States
- Department of Earth and Planetary Sciences, University of Tennessee, Knoxville, TN, United States
- Institute for a Secure and Sustainable Environment, University of Tennessee, Knoxville, TN, United States
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9
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Nash D, Ellmen I, Knapp JJ, Menon R, Overton AK, Cheng J, Lynch MDJ, Nissimov JI, Charles TC. A Novel Tiled Amplicon Sequencing Assay Targeting the Tomato Brown Rugose Fruit Virus (ToBRFV) Genome Reveals Widespread Distribution in Municipal Wastewater Treatment Systems in the Province of Ontario, Canada. Viruses 2024; 16:460. [PMID: 38543825 PMCID: PMC10974707 DOI: 10.3390/v16030460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 03/13/2024] [Accepted: 03/15/2024] [Indexed: 05/23/2024] Open
Abstract
Tomato Brown Rugose Fruit Virus (ToBRFV) is a plant pathogen that infects important Solanaceae crop species and can dramatically reduce tomato crop yields. The ToBRFV has rapidly spread around the globe due to its ability to escape detection by antiviral host genes which confer resistance to other tobamoviruses in tomato plants. The development of robust and reproducible methods for detecting viruses in the environment aids in the tracking and reduction of pathogen transmission. We detected ToBRFV in municipal wastewater influent (WWI) samples, likely due to its presence in human waste, demonstrating a widespread distribution of ToBRFV in WWI throughout Ontario, Canada. To aid in global ToBRFV surveillance efforts, we developed a tiled amplicon approach to sequence and track the evolution of ToBRFV genomes in municipal WWI. Our assay recovers 95.7% of the 6393 bp ToBRFV RefSeq genome, omitting the terminal 5' and 3' ends. We demonstrate that our sequencing assay is a robust, sensitive, and highly specific method for recovering ToBRFV genomes. Our ToBRFV assay was developed using existing ARTIC Network resources, including primer design, sequencing library prep, and read analysis. Additionally, we adapted our lineage abundance estimation tool, Alcov, to estimate the abundance of ToBRFV clades in samples.
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Affiliation(s)
- Delaney Nash
- Department of Biology, University of Waterloo, Waterloo, ON N2L 3G1, Canada; (I.E.); (J.J.K.); (R.M.); (A.K.O.); (J.C.); (M.D.J.L.); (J.I.N.); (T.C.C.)
- Metagenom Bio Life Science Inc., Waterloo, ON N2L 5V4, Canada
| | - Isaac Ellmen
- Department of Biology, University of Waterloo, Waterloo, ON N2L 3G1, Canada; (I.E.); (J.J.K.); (R.M.); (A.K.O.); (J.C.); (M.D.J.L.); (J.I.N.); (T.C.C.)
- Metagenom Bio Life Science Inc., Waterloo, ON N2L 5V4, Canada
| | - Jennifer J. Knapp
- Department of Biology, University of Waterloo, Waterloo, ON N2L 3G1, Canada; (I.E.); (J.J.K.); (R.M.); (A.K.O.); (J.C.); (M.D.J.L.); (J.I.N.); (T.C.C.)
| | - Ria Menon
- Department of Biology, University of Waterloo, Waterloo, ON N2L 3G1, Canada; (I.E.); (J.J.K.); (R.M.); (A.K.O.); (J.C.); (M.D.J.L.); (J.I.N.); (T.C.C.)
| | - Alyssa K. Overton
- Department of Biology, University of Waterloo, Waterloo, ON N2L 3G1, Canada; (I.E.); (J.J.K.); (R.M.); (A.K.O.); (J.C.); (M.D.J.L.); (J.I.N.); (T.C.C.)
| | - Jiujun Cheng
- Department of Biology, University of Waterloo, Waterloo, ON N2L 3G1, Canada; (I.E.); (J.J.K.); (R.M.); (A.K.O.); (J.C.); (M.D.J.L.); (J.I.N.); (T.C.C.)
- Metagenom Bio Life Science Inc., Waterloo, ON N2L 5V4, Canada
| | - Michael D. J. Lynch
- Department of Biology, University of Waterloo, Waterloo, ON N2L 3G1, Canada; (I.E.); (J.J.K.); (R.M.); (A.K.O.); (J.C.); (M.D.J.L.); (J.I.N.); (T.C.C.)
- Metagenom Bio Life Science Inc., Waterloo, ON N2L 5V4, Canada
| | - Jozef I. Nissimov
- Department of Biology, University of Waterloo, Waterloo, ON N2L 3G1, Canada; (I.E.); (J.J.K.); (R.M.); (A.K.O.); (J.C.); (M.D.J.L.); (J.I.N.); (T.C.C.)
| | - Trevor C. Charles
- Department of Biology, University of Waterloo, Waterloo, ON N2L 3G1, Canada; (I.E.); (J.J.K.); (R.M.); (A.K.O.); (J.C.); (M.D.J.L.); (J.I.N.); (T.C.C.)
- Metagenom Bio Life Science Inc., Waterloo, ON N2L 5V4, Canada
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10
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Leisman KP, Owen C, Warns MM, Tiwari A, Bian GZ, Owens SM, Catlett C, Shrestha A, Poretsky R, Packman AI, Mangan NM. A modeling pipeline to relate municipal wastewater surveillance and regional public health data. WATER RESEARCH 2024; 252:121178. [PMID: 38309063 DOI: 10.1016/j.watres.2024.121178] [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: 09/05/2023] [Revised: 12/18/2023] [Accepted: 01/22/2024] [Indexed: 02/05/2024]
Abstract
As COVID-19 becomes endemic, public health departments benefit from improved passive indicators, which are independent of voluntary testing data, to estimate the prevalence of COVID-19 in local communities. Quantification of SARS-CoV-2 RNA from wastewater has the potential to be a powerful passive indicator. However, connecting measured SARS-CoV-2 RNA to community prevalence is challenging due to the high noise typical of environmental samples. We have developed a generalized pipeline using in- and out-of-sample model selection to test the ability of different correction models to reduce the variance in wastewater measurements and applied it to data collected from treatment plants in the Chicago area. We built and compared a set of multi-linear regression models, which incorporate pepper mild mottle virus (PMMoV) as a population biomarker, Bovine coronavirus (BCoV) as a recovery control, and wastewater system flow rate into a corrected estimate for SARS-CoV-2 RNA concentration. For our data, models with BCoV performed better than those with PMMoV, but the pipeline should be used to reevaluate any new data set as the sources of variance may change across locations, lab methods, and disease states. Using our best-fit model, we investigated the utility of RNA measurements in wastewater as a leading indicator of COVID-19 trends. We did this in a rolling manner for corrected wastewater data and for other prevalence indicators and statistically compared the temporal relationship between new increases in the wastewater data and those in other prevalence indicators. We found that wastewater trends often lead other COVID-19 indicators in predicting new surges.
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Affiliation(s)
- Katelyn Plaisier Leisman
- Department of Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, IL, USA
| | - Christopher Owen
- Department of Biological Sciences, University of Illinois Chicago, Chicago, IL, USA
| | - Maria M Warns
- Department of Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, IL, USA
| | - Anuj Tiwari
- Discovery Partners Institute, University of Illinois Chicago, Chicago, IL, USA
| | - George Zhixin Bian
- Department of Computer Science, Northwestern University, Evanston, IL, USA
| | - Sarah M Owens
- Biosciences, Argonne National Laboratory, Lemont, IL, USA
| | - Charlie Catlett
- Discovery Partners Institute, University of Illinois Chicago, Chicago, IL, USA; Computing, Environment, and Life Sciences, Argonne National Laboratory, Lemont, IL, USA
| | - Abhilasha Shrestha
- Division of Environmental and Occupational Health Sciences, School of Public Health, University of Illinois Chicago, Chicago, IL, USA
| | - Rachel Poretsky
- Department of Biological Sciences, University of Illinois Chicago, Chicago, IL, USA
| | - Aaron I Packman
- Center for Water Research, Northwestern University, Evanston, IL, USA; Department of Civil and Environmental Engineering, Northwestern University, Evanston, IL, USA
| | - Niall M Mangan
- Department of Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, IL, USA; Center for Water Research, Northwestern University, Evanston, IL, USA.
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11
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Parkins MD, Lee BE, Acosta N, Bautista M, Hubert CRJ, Hrudey SE, Frankowski K, Pang XL. Wastewater-based surveillance as a tool for public health action: SARS-CoV-2 and beyond. Clin Microbiol Rev 2024; 37:e0010322. [PMID: 38095438 PMCID: PMC10938902 DOI: 10.1128/cmr.00103-22] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/16/2024] Open
Abstract
Wastewater-based surveillance (WBS) has undergone dramatic advancement in the context of the coronavirus disease 2019 (COVID-19) pandemic. The power and potential of this platform technology were rapidly realized when it became evident that not only did WBS-measured SARS-CoV-2 RNA correlate strongly with COVID-19 clinical disease within monitored populations but also, in fact, it functioned as a leading indicator. Teams from across the globe rapidly innovated novel approaches by which wastewater could be collected from diverse sewersheds ranging from wastewater treatment plants (enabling community-level surveillance) to more granular locations including individual neighborhoods and high-risk buildings such as long-term care facilities (LTCF). Efficient processes enabled SARS-CoV-2 RNA extraction and concentration from the highly dilute wastewater matrix. Molecular and genomic tools to identify, quantify, and characterize SARS-CoV-2 and its various variants were adapted from clinical programs and applied to these mixed environmental systems. Novel data-sharing tools allowed this information to be mobilized and made immediately available to public health and government decision-makers and even the public, enabling evidence-informed decision-making based on local disease dynamics. WBS has since been recognized as a tool of transformative potential, providing near-real-time cost-effective, objective, comprehensive, and inclusive data on the changing prevalence of measured analytes across space and time in populations. However, as a consequence of rapid innovation from hundreds of teams simultaneously, tremendous heterogeneity currently exists in the SARS-CoV-2 WBS literature. This manuscript provides a state-of-the-art review of WBS as established with SARS-CoV-2 and details the current work underway expanding its scope to other infectious disease targets.
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Affiliation(s)
- Michael D. Parkins
- Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, Calgary, Alberta, Canada
- Department of Medicine, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- O’Brien Institute of Public Health, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Bonita E. Lee
- Department of Pediatrics, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, Canada
| | - Nicole Acosta
- Department of Medicine, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Maria Bautista
- Department of Biological Sciences, Faculty of Science, University of Calgary, Calgary, Alberta, Canada
| | - Casey R. J. Hubert
- Department of Biological Sciences, Faculty of Science, University of Calgary, Calgary, Alberta, Canada
| | - Steve E. Hrudey
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, Alberta, Canada
| | - Kevin Frankowski
- Advancing Canadian Water Assets, University of Calgary, Calgary, Alberta, Canada
| | - Xiao-Li Pang
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, Alberta, Canada
- Provincial Health Laboratory, Alberta Health Services, Calgary, Alberta, Canada
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12
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Gerrity D, Crank K, Oh EC, Quinones O, Trenholm RA, Vanderford BJ. Wastewater surveillance of high risk substances in Southern Nevada: Sucralose normalization to translate data for potential public health action. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 908:168369. [PMID: 37951274 DOI: 10.1016/j.scitotenv.2023.168369] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 11/01/2023] [Accepted: 11/04/2023] [Indexed: 11/13/2023]
Abstract
The COVID-19 pandemic highlighted the value of wastewater surveillance in providing unbiased assessments of incidence/prevalence for infectious disease targets, ultimately leading to the development of local, state, and national programs across the United States. To address the growing epidemic of drug abuse, there have been calls to extend these programs to high risk substances (HRS) and metabolites, while leveraging the experience gained during the pandemic and from ongoing efforts in other countries. This study further advances the science of wastewater surveillance for HRS by (1) highlighting analytical and sewer transport considerations, (2) proposing sucralose normalization to adjust for varying human urine/fecal load and confounded population estimates (e.g., high tourism areas), and (3) characterizing temporal and geographic trends in HRS use. This one-year study across eight sewersheds in Southern Nevada (208 total samples) monitored concentrations of 17 pharmaceuticals and personal care products (PPCPs) and 22 HRS and metabolites, including natural, semi-synthetic, and synthetic opioids. The data indicated a ∼200 % increase in heroin and methamphetamine use since 2010, a stark increase in fentanyl consumption beginning in October 2022, and statistically significant differences in HRS consumption patterns between sewersheds and on certain dates. Notably, the latter outcome highlights the potential for wastewater surveillance data to be strategically translated into public health action to reduce and/or more rapidly respond to overdoses.
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Affiliation(s)
- Daniel Gerrity
- Applied Research and Development Center, Southern Nevada Water Authority, P.O. Box 99954, Las Vegas, NV 89193, United States.
| | - Katherine Crank
- Applied Research and Development Center, Southern Nevada Water Authority, P.O. Box 99954, Las Vegas, NV 89193, United States
| | - Edwin C Oh
- Laboratory of Neurogenetics and Precision Medicine, Nevada Institute of Personalized Medicine, Department of Internal Medicine, UNLV School of Medicine, University of Nevada, Las Vegas, Las Vegas, NV 89154, United States
| | - Oscar Quinones
- Applied Research and Development Center, Southern Nevada Water Authority, P.O. Box 99954, Las Vegas, NV 89193, United States
| | - Rebecca A Trenholm
- Applied Research and Development Center, Southern Nevada Water Authority, P.O. Box 99954, Las Vegas, NV 89193, United States
| | - Brett J Vanderford
- Applied Research and Development Center, Southern Nevada Water Authority, P.O. Box 99954, Las Vegas, NV 89193, United States
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13
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Kanchan S, Ogden E, Kesheri M, Skinner A, Miliken E, Lyman D, Armstrong J, Sciglitano L, Hampikian G. COVID-19 hospitalizations and deaths predicted by SARS-CoV-2 levels in Boise, Idaho wastewater. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 907:167742. [PMID: 37852488 DOI: 10.1016/j.scitotenv.2023.167742] [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: 07/22/2023] [Revised: 09/22/2023] [Accepted: 10/09/2023] [Indexed: 10/20/2023]
Abstract
The viral load of COVID-19 in untreated wastewater from Idaho's capital city Boise, ID (Ada County) has been used to predict changes in hospital admissions (statewide in Idaho) and deaths (Ada County) using distributed fixed lag modeling and artificial neural networks (ANN). The wastewater viral counts were used to determine the lag time between peaks in wastewater viral counts and COVID-19 hospitalizations as well as deaths (14 and 23 days, respectively). Quantitative measurement of SARS-CoV-2 viral RNA counts in the untreated wastewater was determined three times a week using RT-qPCR over a span of 13 months. To mitigate the effects of PCR inhibitors in wastewater, a series of dilution tests were conducted, and the 1/4 dilution was used to generate the most successful model. Wastewater SARS-CoV-2 viral RNA counts and hospitalization from June 7, 2021 to December 29, 2021 were used as training data to predict hospitalizations; and wastewater SARS-CoV-2 viral RNA counts and deaths from June 7, 2021 to December 20, 2021 were used as training data to predict deaths. These training data were used to make predictive ANN models for future hospitalizations and deaths. To the best of our knowledge, this is the first report of prediction of deaths from COVID-19 based on wastewater SARS-CoV-2 viral RNA counts using machine learning-based multilayered ANN. The applied modeling demonstrates that wastewater surveillance data can be combined with hospitalizations and death data to generate machine learning-based ANN models that predict future COVID-19 hospital admissions and deaths, providing an early warning for medical response teams and healthcare policymakers.
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Affiliation(s)
- Swarna Kanchan
- Department of Biological Sciences, Boise State University, Boise, Idaho, 83725, United States of America; Department of Biomedical Sciences, Joan C. Edwards School of Medicine, Marshall University, Huntington, West Virginia, 25701, United States of America
| | - Ernie Ogden
- Department of Biological Sciences, Boise State University, Boise, Idaho, 83725, United States of America
| | - Minu Kesheri
- Department of Biological Sciences, Boise State University, Boise, Idaho, 83725, United States of America; Department of Biomedical Sciences, Joan C. Edwards School of Medicine, Marshall University, Huntington, West Virginia, 25701, United States of America
| | - Alexis Skinner
- Department of Biological Sciences, Boise State University, Boise, Idaho, 83725, United States of America
| | - Erin Miliken
- Department of Biological Sciences, Boise State University, Boise, Idaho, 83725, United States of America
| | - Devyn Lyman
- Department of Biological Sciences, Boise State University, Boise, Idaho, 83725, United States of America
| | - Jacob Armstrong
- Department of Biological Sciences, Boise State University, Boise, Idaho, 83725, United States of America
| | - Lawrence Sciglitano
- Department of Biological Sciences, Boise State University, Boise, Idaho, 83725, United States of America
| | - Greg Hampikian
- Department of Biological Sciences, Boise State University, Boise, Idaho, 83725, United States of America.
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14
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Kuroita T, Yoshimura A, Iwamoto R, Ando H, Okabe S, Kitajima M. Quantitative analysis of SARS-CoV-2 RNA in wastewater and evaluation of sampling frequency during the downward period of a COVID-19 wave in Japan. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 906:166526. [PMID: 37647962 DOI: 10.1016/j.scitotenv.2023.166526] [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: 06/05/2023] [Revised: 08/06/2023] [Accepted: 08/22/2023] [Indexed: 09/01/2023]
Abstract
Wastewater-based epidemiology (WBE) is a practical approach for detecting the presence of SARS-CoV-2 infections and assessing the epidemic trend of the coronavirus disease 2019 (COVID-19). The purpose of this study was to evaluate the minimum sampling frequency required to properly identify the COVID-19 trend during the downward epidemic period when using a highly sensitive RNA detection method. WBE was conducted using the Efficient and Practical virus Identification System with ENhanced Sensitivity for Solids (EPISENS-S), a highly sensitive SARS-CoV-2 RNA detection method, at nine neighboring wastewater treatment plants (WWTPs). These WWTPs were in the same prefecture in Japan, and they had different sewer types, sampling methods, and sampling frequencies. The overall detection rate of SARS-CoV-2 RNA was 97.8 % during the entire study period when the geometric means of new COVID-19 cases per 100,000 inhabitants were between 3.3 and 7.7 in each WWTP. The maximum SARS-CoV-2 RNA concentration in wastewater was 2.14 × 104 copies/L, which corresponded to pepper mild mottle virus (PMMoV)-normalized concentrations of 6.54 × 10-3. We evaluated the effect of sampling frequencies on the probability of a significant correlation with the number of newly reported COVID-19 cases by hypothetically reducing the sampling frequency in the same dataset. When the wastewater sampling frequency occurred 5, 3, 2, and 1 times per week, these results exhibited significant correlations of 100 % (5/5), 89 % (8/9), 85 % (23/27), and 48 % (13/27), respectively. To achieve significant correlation with a high probability of over 85 %, a minimum sampling frequency of twice per week is required, even if sampling methods and sewer types are different. WBE using the EPISENS-S method and a sampling frequency of more than twice a week can be used to properly monitor COVID-19 wave epidemic trends, even during downward periods.
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Affiliation(s)
- Tomohiro Kuroita
- AdvanSentinel Inc., 3-1-8, Doshomachi, Chuo-ku, Osaka 541-0045, Japan; Shionogi & Co., Ltd., 3-1-8, Doshomachi, Chuo-ku, Osaka 541-0045, Japan
| | - Akimasa Yoshimura
- Shionogi & Co., Ltd., 3-1-8, Doshomachi, Chuo-ku, Osaka 541-0045, Japan
| | - Ryo Iwamoto
- AdvanSentinel Inc., 3-1-8, Doshomachi, Chuo-ku, Osaka 541-0045, Japan; Shionogi & Co., Ltd., 3-1-8, Doshomachi, Chuo-ku, Osaka 541-0045, Japan
| | - Hiroki Ando
- Division of Environmental Engineering, Faculty of Engineering, Hokkaido University, North 13 West 8, Kita-ku, Sapporo, Hokkaido 060-8628, Japan
| | - Satoshi Okabe
- Division of Environmental Engineering, Faculty of Engineering, Hokkaido University, North 13 West 8, Kita-ku, Sapporo, Hokkaido 060-8628, Japan
| | - Masaaki Kitajima
- Division of Environmental Engineering, Faculty of Engineering, Hokkaido University, North 13 West 8, Kita-ku, Sapporo, Hokkaido 060-8628, Japan.
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15
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Marin-Ramirez A, Mahoney T, Smith T, Holm RH. Predicting wastewater treatment plant influent in mixed, separate, and combined sewers using nearby surface water discharge for better wastewater-based epidemiology sampling design. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 906:167375. [PMID: 37774884 DOI: 10.1016/j.scitotenv.2023.167375] [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: 07/07/2023] [Revised: 08/28/2023] [Accepted: 09/24/2023] [Indexed: 10/01/2023]
Abstract
For wastewater sample collection approaches supporting public health applications, few high hydrologic activity normalizing guidelines currently consider readily available environmental flow data that may earlier capture information regarding periods of influent mixing and dilution of wastewater with groundwater and runoff. This study aimed to identify wastewater sampling rules for high hydrological activity events, allowing for an earlier decision point in the control of dilution before sample collection. We defined the sampling rules via data-driven models (Random Forest and linear regression) using environmental data (i.e., wastewater treatment facility influent rates, nearby stream discharge flow, and precipitation). These models were applied to five treatment plants in Jefferson County, Kentucky (USA) in mixed, separate, and combined sewers with different population sizes. We proposed cutoffs of 10 %, 25 %, and 50 % flow conditions for orientation towards public health samples. The results showed a strong nonlinear relationship between nearby stream discharge and treatment facility flow rates, which was used to infer the hydrological conditions that produce high volumes of diluted wastewater in the sewer system. Accumulated Local Effects and SHapley Additive exPlanations aided in deciphering the relationship between the predictors and response variables of the Random Forest models. The influent rate to the treatment plant from the previous day and two USGS stream gages were needed to adequately predict the degree of infiltration and inflow mixing on a given day. Surface water discharge data can be used to provide an earlier workflow decision point during wet weather periods to improve understanding of flow conditions for wastewater-based epidemiological studies to inform laboratory analysis and data interpretation. Not only total flow, but also the specific proportions of infiltration and inflow to wastewater volume in influent should be considered when analyzing data for normalization purposes, and our method provides a starting point for doing so rapidly and at low cost.
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Affiliation(s)
- Arlex Marin-Ramirez
- Department of Civil and Environmental Engineering, J. B. Speed School of Engineering, University of Louisville, 132 E. Pkwy., Louisville, KY 40202, United States
| | - Tyler Mahoney
- Department of Civil and Environmental Engineering, J. B. Speed School of Engineering, University of Louisville, 132 E. Pkwy., Louisville, KY 40202, United States
| | - Ted Smith
- Christina Lee Brown Envirome Institute, School of Medicine, University of Louisville, 302 E. Muhammad Ali Blvd., Louisville, KY 40202, United States
| | - Rochelle H Holm
- Christina Lee Brown Envirome Institute, School of Medicine, University of Louisville, 302 E. Muhammad Ali Blvd., Louisville, KY 40202, United States.
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16
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Holm RH, Rempala G, Choi B, Brick JM, Amraotkar A, Keith R, Rouchka EC, Chariker JH, Palmer K, Smith TR, Bhatnagar A. Wastewater and seroprevalence for pandemic preparedness: variant analysis, vaccination effect, and hospitalization forecasting for SARS-CoV-2, in Jefferson County, Kentucky. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.01.06.23284260. [PMID: 36656780 PMCID: PMC9844017 DOI: 10.1101/2023.01.06.23284260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Despite wide scale assessments, it remains unclear how large-scale SARS-CoV-2 vaccination affected the wastewater concentration of the virus or the overall disease burden as measured by hospitalization rates. We used weekly SARS-CoV-2 wastewater concentration with a stratified random sampling of seroprevalence, and linked vaccination and hospitalization data, from April 2021-August 2021 in Jefferson County, Kentucky (USA). Our susceptible (S), vaccinated (V), variant-specific infected (I_1 and I_2), recovered (R), and seropositive (T) model (SVI_2 RT) tracked prevalence longitudinally. This was related to wastewater concentration. The 64% county vaccination rate translated into about 61% decrease in SARS-CoV-2 incidence. The estimated effect of SARS-CoV-2 Delta variant emergence was a 24-fold increase of infection counts, which corresponded to an over 9-fold increase in wastewater concentration. Hospitalization burden and wastewater concentration had the strongest correlation (r = 0.95) at 1 week lag. Our study underscores the importance of continued environmental surveillance post-vaccine and provides a proof-of-concept for environmental epidemiology monitoring of infectious disease for future pandemic preparedness.
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17
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Asadi M, Hamilton D, Shomachuk C, Oloye FF, De Lange C, Pu X, Osunla CA, Cantin J, El-Baroudy S, Mejia EM, Gregorchuk B, Becker MG, Mangat C, Brinkmann M, Jones PD, Giesy JP, McPhedran KN. Assessment of rapid wastewater surveillance for determination of communicable disease spread in municipalities. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 901:166541. [PMID: 37625717 DOI: 10.1016/j.scitotenv.2023.166541] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 08/02/2023] [Accepted: 08/22/2023] [Indexed: 08/27/2023]
Abstract
Wastewater surveillance (WS) helps to improve the understanding of the spread of communicable diseases in communities. WS can assist public health decision-makers in the design and implementation of timely mitigation measures. There is an increased need to use reliable, cost-effective, simple, and rapid WS systems, given traditional analytical (or 'gold-standard') programs are instrument/time-intensive, and dependent on highly skilled personnel. This study investigated the application of the portable GeneXpert platform for WS of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), influenza A virus (IAV), influenza B virus (IBV), and respiratory syncytial virus (RSV). The GeneXpert system with the Xpert Xpress-SARS-CoV-2/Flu/RSV test kit uses reverse transcription-quantitative polymerase chain reaction (RT-qPCR) to analyze wastewater samples. From September 2022 through January 2023, wastewater samples were collected from the influents of municipal wastewater treatment plants (MWTPs) of Saskatoon, Prince Albert, and North Battleford in the province of Saskatchewan, Canada. Both raw and concentrated wastewater samples were subjected to the GeneXpert analysis. Results showed that the Saskatoon wastewater viral loads were significantly correlated to Saskatchewan's influenza and COVID-19 clinical cases, with a lead time of 10 days for IAV and a lag time of 4 days for SARS-CoV-2. Additionally, the GeneXpert analysis of the three cities' wastewater samples showed that the raw WS could capture the dynamics of SARS-CoV-2 and IAV due to their correlation with concentrated WS. Interestingly, IBV loads were not detected in any wastewater samples, while the Saskatoon and Prince Albert wastewater samples collected following the 2023 holiday season (end of December and beginning of January) were positive for RSV. This study indicates that the GeneXpert has excellent potential for use in the development of an early warning system for transmissible disease in municipalities and limited-resource communities while simultaneously providing stakeholders with an efficient WS methodology.
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Affiliation(s)
- Mohsen Asadi
- Department of Civil, Geological and Environmental Engineering, College of Engineering, University of Saskatchewan, Saskatoon, SK, Canada; Toxicology Centre, University of Saskatchewan, Saskatoon, SK, Canada
| | - Daniel Hamilton
- Department of Civil, Geological and Environmental Engineering, College of Engineering, University of Saskatchewan, Saskatoon, SK, Canada
| | - Corwyn Shomachuk
- Department of Civil, Geological and Environmental Engineering, College of Engineering, University of Saskatchewan, Saskatoon, SK, Canada
| | - Femi F Oloye
- Toxicology Centre, University of Saskatchewan, Saskatoon, SK, Canada
| | - Chantel De Lange
- Toxicology Centre, University of Saskatchewan, Saskatoon, SK, Canada
| | - Xia Pu
- Toxicology Centre, University of Saskatchewan, Saskatoon, SK, Canada
| | - Charles A Osunla
- Toxicology Centre, University of Saskatchewan, Saskatoon, SK, Canada
| | - Jenna Cantin
- Toxicology Centre, University of Saskatchewan, Saskatoon, SK, Canada
| | - Seba El-Baroudy
- Toxicology Centre, University of Saskatchewan, Saskatoon, SK, Canada
| | - Edgard M Mejia
- JC Wilt Infectious Diseases Research Centre, National Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, Manitoba, Canada
| | - Branden Gregorchuk
- JC Wilt Infectious Diseases Research Centre, National Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, Manitoba, Canada
| | - Michael G Becker
- JC Wilt Infectious Diseases Research Centre, National Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, Manitoba, Canada
| | - Chand Mangat
- Wastewater Surveillance Unit, On-Health Division, National Microbiology Laboratory - Winnipeg, Public Health Agency of Canada, Canada
| | - Markus Brinkmann
- Toxicology Centre, University of Saskatchewan, Saskatoon, SK, Canada; Global Institute for Water Security, University of Saskatchewan, Saskatoon, SK, Canada; School of Environment and Sustainability, University of Saskatchewan, Saskatoon, SK, Canada
| | - Paul D Jones
- Toxicology Centre, University of Saskatchewan, Saskatoon, SK, Canada; School of Environment and Sustainability, University of Saskatchewan, Saskatoon, SK, Canada
| | - John P Giesy
- Toxicology Centre, University of Saskatchewan, Saskatoon, SK, Canada; Department of Veterinary Biomedical Sciences, University of Saskatchewan, Saskatoon, SK, Canada; Department of Environmental Sciences, Baylor University, Waco, TX, USA; Department of Integrative Biology and Center for Integrative Toxicology, Michigan State University, East Lansing, MI, USA
| | - Kerry N McPhedran
- Department of Civil, Geological and Environmental Engineering, College of Engineering, University of Saskatchewan, Saskatoon, SK, Canada; Global Institute for Water Security, University of Saskatchewan, Saskatoon, SK, Canada.
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18
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Williams BB, Newborn A, Karamat A, Zamcho F, Salerno JL, Gillevet PM, Farris D, Wintermeyer SF, Van Aken B. Detection of SARS-CoV-2 RNA in wastewater from dormitory buildings in a university campus: comparison with individual testing results. WATER SCIENCE AND TECHNOLOGY : A JOURNAL OF THE INTERNATIONAL ASSOCIATION ON WATER POLLUTION RESEARCH 2023; 88:2364-2377. [PMID: 37966188 PMCID: wst_2023_348 DOI: 10.2166/wst.2023.348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2023]
Abstract
Wastewater-based epidemiology (WBE) for monitoring COVID-19 has been largely used to detect the spread of the disease at the community level. From February to December 2022, we collected 24-h composite sewage samples from dormitory buildings in George Mason University (Fairfax, Virginia, USA) housing approximately 5,200 resident students. SARS-CoV-2 RNA extraction was achieved using an automated system based on magnetic nanoparticles. Analysis of SARS-CoV-2 RNA was performed using reverse transcription quantitative PCR based on the Centers for Disease Control and Prevention (CDC) N1 and N2 assays. From the 362 samples collected, 86% showed positive detection of SARS-CoV-2 RNA. Wastewater monitoring was able to detect SARS-CoV-2 RNA in 96% of the samples from buildings housing students with COVID-19. Over the period of study, we observed significant correlations between the SARS-CoV-2 concentration (copy number mL-1) in wastewater and the number of positive cases on campus based on individual saliva testing. Although several reports have been published on the wastewater monitoring of COVID-19 in university campuses, our study is one of the very few that provides results that were obtained during the last phase of the pandemic (roughly the year 2022), when the large majority of students were vaccinated and back on campus.
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Affiliation(s)
- Brandi B Williams
- Department of Chemistry & Biochemistry, George Mason University, Fairfax, Virginia, USA E-mail:
| | - Aaron Newborn
- Department of Chemistry & Biochemistry, George Mason University, Fairfax, Virginia, USA
| | - Ayesha Karamat
- Department of Environmental Science and Policy, George Mason University, Fairfax, Virginia, USA
| | - Fanella Zamcho
- Department of Chemistry & Biochemistry, George Mason University, Fairfax, Virginia, USA
| | - Jennifer L Salerno
- Department of Environmental Science and Policy, George Mason University, Fairfax, Virginia, USA
| | | | - David Farris
- Environmental Health and Safety, George Mason University, Fairfax, Virginia, USA
| | | | - Benoit Van Aken
- Department of Chemistry & Biochemistry, George Mason University, Fairfax, Virginia, USA
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19
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Lee J, Acosta N, Waddell BJ, Du K, Xiang K, Van Doorn J, Low K, Bautista MA, McCalder J, Dai X, Lu X, Chekouo T, Pradhan P, Sedaghat N, Papparis C, Buchner Beaudet A, Chen J, Chan L, Vivas L, Westlund P, Bhatnagar S, Stefani S, Visser G, Cabaj J, Bertazzon S, Sarabi S, Achari G, Clark RG, Hrudey SE, Lee BE, Pang X, Webster B, Ghali WA, Buret AG, Williamson T, Southern DA, Meddings J, Frankowski K, Hubert CRJ, Parkins MD. Campus node-based wastewater surveillance enables COVID-19 case localization and confirms lower SARS-CoV-2 burden relative to the surrounding community. WATER RESEARCH 2023; 244:120469. [PMID: 37634459 DOI: 10.1016/j.watres.2023.120469] [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: 03/18/2023] [Revised: 08/06/2023] [Accepted: 08/07/2023] [Indexed: 08/29/2023]
Abstract
Wastewater-based surveillance (WBS) has been established as a powerful tool that can guide health policy at multiple levels of government. However, this approach has not been well assessed at more granular scales, including large work sites such as University campuses. Between August 2021 and April 2022, we explored the occurrence of SARS-CoV-2 RNA in wastewater using qPCR assays from multiple complimentary sewer catchments and residential buildings spanning the University of Calgary's campus and how this compared to levels from the municipal wastewater treatment plant servicing the campus. Real-time contact tracing data was used to evaluate an association between wastewater SARS-CoV-2 burden and clinically confirmed cases and to assess the potential of WBS as a tool for disease monitoring across worksites. Concentrations of wastewater SARS-CoV-2 N1 and N2 RNA varied significantly across six sampling sites - regardless of several normalization strategies - with certain catchments consistently demonstrating values 1-2 orders higher than the others. Relative to clinical cases identified in specific sewersheds, WBS provided one-week leading indicator. Additionally, our comprehensive monitoring strategy enabled an estimation of the total burden of SARS-CoV-2 for the campus per capita, which was significantly lower than the surrounding community (p≤0.001). Allele-specific qPCR assays confirmed that variants across campus were representative of the community at large, and at no time did emerging variants first debut on campus. This study demonstrates how WBS can be efficiently applied to locate hotspots of disease activity at a very granular scale, and predict disease burden across large, complex worksites.
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Affiliation(s)
- Jangwoo Lee
- Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, 3330 Hospital Drive, NW, Calgary, Alberta T2N 2V5, Canada; Department of Biological Sciences, University of Calgary, Calgary, Canada
| | - Nicole Acosta
- Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, 3330 Hospital Drive, NW, Calgary, Alberta T2N 2V5, Canada
| | - Barbara J Waddell
- Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, 3330 Hospital Drive, NW, Calgary, Alberta T2N 2V5, Canada
| | - Kristine Du
- Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, 3330 Hospital Drive, NW, Calgary, Alberta T2N 2V5, Canada
| | - Kevin Xiang
- Department of Biological Sciences, University of Calgary, Calgary, Canada
| | - Jennifer Van Doorn
- Department of Biological Sciences, University of Calgary, Calgary, Canada
| | - Kashtin Low
- Department of Biological Sciences, University of Calgary, Calgary, Canada
| | - Maria A Bautista
- Department of Biological Sciences, University of Calgary, Calgary, Canada
| | - Janine McCalder
- Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, 3330 Hospital Drive, NW, Calgary, Alberta T2N 2V5, Canada; Department of Biological Sciences, University of Calgary, Calgary, Canada
| | - Xiaotian Dai
- Department of Mathematics and Statistics, University of Calgary, Calgary, Canada
| | - Xuewen Lu
- Department of Mathematics and Statistics, University of Calgary, Calgary, Canada
| | - Thierry Chekouo
- Department of Mathematics and Statistics, University of Calgary, Calgary, Canada; Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, USA
| | - Puja Pradhan
- Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, 3330 Hospital Drive, NW, Calgary, Alberta T2N 2V5, Canada; Department of Biological Sciences, University of Calgary, Calgary, Canada
| | - Navid Sedaghat
- Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, 3330 Hospital Drive, NW, Calgary, Alberta T2N 2V5, Canada; Department of Biological Sciences, University of Calgary, Calgary, Canada
| | - Chloe Papparis
- Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, 3330 Hospital Drive, NW, Calgary, Alberta T2N 2V5, Canada; Department of Biological Sciences, University of Calgary, Calgary, Canada
| | - Alexander Buchner Beaudet
- Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, 3330 Hospital Drive, NW, Calgary, Alberta T2N 2V5, Canada
| | - Jianwei Chen
- Department of Biological Sciences, University of Calgary, Calgary, Canada
| | - Leslie Chan
- Department of Biological Sciences, University of Calgary, Calgary, Canada
| | - Laura Vivas
- Department of Biological Sciences, University of Calgary, Calgary, Canada
| | | | - Srijak Bhatnagar
- Department of Biological Sciences, University of Calgary, Calgary, Canada; Faculty of Science and Technology, Athabasca University, Athabasca, Alberta, Canada
| | - September Stefani
- Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, 3330 Hospital Drive, NW, Calgary, Alberta T2N 2V5, Canada
| | - Gail Visser
- Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, 3330 Hospital Drive, NW, Calgary, Alberta T2N 2V5, Canada
| | - Jason Cabaj
- Department of Community Health Sciences, University of Calgary, Calgary, Canada; Department of Medicine, University of Calgary and Alberta Health Services, Calgary, Canada; Provincial Population & Public Health, Alberta Health Services, Calgary, Canada; O'Brien Institute for Public Health, University of Calgary, Calgary, Canada
| | | | - Shahrzad Sarabi
- Department of Geography, University of Calgary, Calgary, Canada
| | - Gopal Achari
- Department of Civil Engineering, University of Calgary, Calgary, Canada
| | - Rhonda G Clark
- Department of Biological Sciences, University of Calgary, Calgary, Canada
| | - Steve E Hrudey
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, Alberta, Canada; Analytical and Environmental Toxicology, University of Alberta, Edmonton, Alberta, Canada
| | - Bonita E Lee
- Department of Pediatrics, University of Alberta, Edmonton, Alberta, Canada; Women & Children's Health Research Institute, Li Ka Shing Institute of Virology, Edmonton, Alberta, Canada
| | - Xiaoli Pang
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, Alberta, Canada; Alberta Precision Laboratories, Public Health Laboratory, Alberta Health Services, Edmonton, Alberta, Canada; Li Ka Shing Institute of Virology, University of Alberta, Edmonton, Alberta, Canada
| | - Brendan Webster
- Occupational Health Staff Wellness, University of Calgary, Calgary, Canada
| | - William Amin Ghali
- Department of Community Health Sciences, University of Calgary, Calgary, Canada; Department of Medicine, University of Calgary and Alberta Health Services, Calgary, Canada; O'Brien Institute for Public Health, University of Calgary, Calgary, Canada; Centre for Health Informatics, University of Calgary, Calgary, Canada
| | - Andre Gerald Buret
- Department of Biological Sciences, University of Calgary, Calgary, Canada
| | - Tyler Williamson
- Department of Community Health Sciences, University of Calgary, Calgary, Canada; O'Brien Institute for Public Health, University of Calgary, Calgary, Canada; Centre for Health Informatics, University of Calgary, Calgary, Canada
| | - Danielle A Southern
- Department of Community Health Sciences, University of Calgary, Calgary, Canada; O'Brien Institute for Public Health, University of Calgary, Calgary, Canada; Centre for Health Informatics, University of Calgary, Calgary, Canada
| | - Jon Meddings
- Department of Medicine, University of Calgary and Alberta Health Services, Calgary, Canada
| | - Kevin Frankowski
- Advancing Canadian Water Assets, University of Calgary, Calgary, Canada
| | - Casey R J Hubert
- Department of Biological Sciences, University of Calgary, Calgary, Canada
| | - Michael D Parkins
- Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, 3330 Hospital Drive, NW, Calgary, Alberta T2N 2V5, Canada; Department of Medicine, University of Calgary and Alberta Health Services, Calgary, Canada; O'Brien Institute for Public Health, University of Calgary, Calgary, Canada.
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20
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Rabe A, Ravuri S, Burnor E, Steele JA, Kantor RS, Choi S, Forman S, Batjiaka R, Jain S, León TM, Vugia DJ, Yu AT. Correlation between wastewater and COVID-19 case incidence rates in major California sewersheds across three variant periods. JOURNAL OF WATER AND HEALTH 2023; 21:1303-1317. [PMID: 37756197 PMCID: wh_2023_173 DOI: 10.2166/wh.2023.173] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/29/2023]
Abstract
Monitoring for COVID-19 through wastewater has been used for adjunctive public health surveillance, with SARS-CoV-2 viral concentrations in wastewater correlating with incident cases in the same sewershed. However, the generalizability of these findings across sewersheds, laboratory methods, and time periods with changing variants and underlying population immunity has not been well described. The California Department of Public Health partnered with six wastewater treatment plants starting in January 2021 to monitor wastewater for SARS-CoV-2, with analyses performed at four laboratories. Using reported PCR-confirmed COVID-19 cases within each sewershed, the relationship between case incidence rates and wastewater concentrations collected over 14 months was evaluated using Spearman's correlation and linear regression. Strong correlations were observed when wastewater concentrations and incidence rates were averaged (10- and 7-day moving window for wastewater and cases, respectively, ρ = 0.73-0.98 for N1 gene target). Correlations remained strong across three time periods with distinct circulating variants and vaccination rates (winter 2020-2021/Alpha, summer 2021/Delta, and winter 2021-2022/Omicron). Linear regression revealed that slopes of associations varied by the dominant variant of concern, sewershed, and laboratory (β = 0.45-1.94). These findings support wastewater surveillance as an adjunctive public health tool to monitor SARS-CoV-2 community trends.
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Affiliation(s)
- Angela Rabe
- California Department of Public Health COVID-19 Detection, Investigation, Surveillance, Clinical, and Outbreak Response, California Department of Public Health, Richmond and Sacramento, CA, USA; These first authors contributed equally to this manuscript. E-mail:
| | - Sindhu Ravuri
- California Department of Public Health COVID-19 Detection, Investigation, Surveillance, Clinical, and Outbreak Response, California Department of Public Health, Richmond and Sacramento, CA, USA; These first authors contributed equally to this manuscript
| | - Elisabeth Burnor
- California Department of Public Health COVID-19 Detection, Investigation, Surveillance, Clinical, and Outbreak Response, California Department of Public Health, Richmond and Sacramento, CA, USA
| | - Joshua A Steele
- Southern California Coastal Water Research Project (SCCWRP), Department of Microbiology, Costa Mesa, CA, USA
| | - Rose S Kantor
- Department of Civil and Environmental Engineering, University of California, Berkeley, CA, USA
| | - Samuel Choi
- Orange County Sanitation District, Fountain Valley, CA, USA
| | - Stanislav Forman
- Zymo Research Corp. Department of Sample Collection and Nucleic Acid Purification, Zymo Research Corp., Irvine, CA, USA
| | - Ryan Batjiaka
- San Francisco Public Utilities Commission, San Francisco, CA, USA
| | - Seema Jain
- California Department of Public Health COVID-19 Detection, Investigation, Surveillance, Clinical, and Outbreak Response, California Department of Public Health, Richmond and Sacramento, CA, USA
| | - Tomás M León
- California Department of Public Health COVID-19 Detection, Investigation, Surveillance, Clinical, and Outbreak Response, California Department of Public Health, Richmond and Sacramento, CA, USA
| | - Duc J Vugia
- California Department of Public Health COVID-19 Detection, Investigation, Surveillance, Clinical, and Outbreak Response, California Department of Public Health, Richmond and Sacramento, CA, USA
| | - Alexander T Yu
- California Department of Public Health COVID-19 Detection, Investigation, Surveillance, Clinical, and Outbreak Response, California Department of Public Health, Richmond and Sacramento, CA, USA
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21
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Dhiyebi HA, Abu Farah J, Ikert H, Srikanthan N, Hayat S, Bragg LM, Qasim A, Payne M, Kaleis L, Paget C, Celmer-Repin D, Folkema A, Drew S, Delatolla R, Giesy JP, Servos MR. Assessment of seasonality and normalization techniques for wastewater-based surveillance in Ontario, Canada. Front Public Health 2023; 11:1186525. [PMID: 37711234 PMCID: PMC10499178 DOI: 10.3389/fpubh.2023.1186525] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 08/04/2023] [Indexed: 09/16/2023] Open
Abstract
Introduction Wastewater-based surveillance is at the forefront of monitoring for community prevalence of COVID-19, however, continued uncertainty exists regarding the use of fecal indicators for normalization of the SARS-CoV-2 virus in wastewater. Using three communities in Ontario, sampled from 2021-2023, the seasonality of a viral fecal indicator (pepper mild mottle virus, PMMoV) and the utility of normalization of data to improve correlations with clinical cases was examined. Methods Wastewater samples from Warden, the Humber Air Management Facility (AMF), and Kitchener were analyzed for SARS-CoV-2, PMMoV, and crAssphage. The seasonality of PMMoV and flow rates were examined and compared by Season-Trend-Loess decomposition analysis. The effects of normalization using PMMoV, crAssphage, and flow rates were analyzed by comparing the correlations to clinical cases by episode date (CBED) during 2021. Results Seasonal analysis demonstrated that PMMoV had similar trends at Humber AMF and Kitchener with peaks in January and April 2022 and low concentrations (troughs) in the summer months. Warden had similar trends but was more sporadic between the peaks and troughs for PMMoV concentrations. Flow demonstrated similar trends but was not correlated to PMMoV concentrations at Humber AMF and was very weak at Kitchener (r = 0.12). Despite the differences among the sewersheds, unnormalized SARS-CoV-2 (raw N1-N2) concentration in wastewater (n = 99-191) was strongly correlated to the CBED in the communities (r = 0.620-0.854) during 2021. Additionally, normalization with PMMoV did not improve the correlations at Warden and significantly reduced the correlations at Humber AMF and Kitchener. Flow normalization (n = 99-191) at Humber AMF and Kitchener and crAssphage normalization (n = 29-57) correlations at all three sites were not significantly different from raw N1-N2 correlations with CBED. Discussion Differences in seasonal trends in viral biomarkers caused by differences in sewershed characteristics (flow, input, etc.) may play a role in determining how effective normalization may be for improving correlations (or not). This study highlights the importance of assessing the influence of viral fecal indicators on normalized SARS-CoV-2 or other viruses of concern. Fecal indicators used to normalize the target of interest may help or hinder establishing trends with clinical outcomes of interest in wastewater-based surveillance and needs to be considered carefully across seasons and sites.
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Affiliation(s)
- Hadi A. Dhiyebi
- Department of Biology, University of Waterloo, Waterloo, ON, Canada
| | - Joud Abu Farah
- Department of Biology, University of Waterloo, Waterloo, ON, Canada
| | - Heather Ikert
- Department of Biology, University of Waterloo, Waterloo, ON, Canada
| | | | - Samina Hayat
- Department of Biology, University of Waterloo, Waterloo, ON, Canada
| | - Leslie M. Bragg
- Department of Biology, University of Waterloo, Waterloo, ON, Canada
| | - Asim Qasim
- Regional Municipality of York, Newmarket, ON, Canada
| | - Mark Payne
- Regional Municipality of York, Newmarket, ON, Canada
| | - Linda Kaleis
- Regional Municipality of York, Newmarket, ON, Canada
| | - Caitlyn Paget
- Regional Municipality of York, Newmarket, ON, Canada
| | | | | | - Stephen Drew
- Regional Municipality of Waterloo, Waterloo, ON, Canada
| | - Robert Delatolla
- Department of Civil Engineering, University of Ottawa, Ottawa, ON, Canada
| | - John P. Giesy
- Department of Biomedical Sciences and Toxicology Centre, University of Saskatchewan, Saskatoon, SK, Canada
- Department of Environmental Science, Baylor University, Waco, TX, United States
| | - Mark R. Servos
- Department of Biology, University of Waterloo, Waterloo, ON, Canada
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22
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Arts PJ, Kelly JD, Midgley CM, Anglin K, Lu S, Abedi GR, Andino R, Bakker KM, Banman B, Boehm AB, Briggs-Hagen M, Brouwer AF, Davidson MC, Eisenberg MC, Garcia-Knight M, Knight S, Peluso MJ, Pineda-Ramirez J, Diaz Sanchez R, Saydah S, Tassetto M, Martin JN, Wigginton KR. Longitudinal and quantitative fecal shedding dynamics of SARS-CoV-2, pepper mild mottle virus, and crAssphage. mSphere 2023; 8:e0013223. [PMID: 37338211 PMCID: PMC10506459 DOI: 10.1128/msphere.00132-23] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 05/03/2023] [Indexed: 06/21/2023] Open
Abstract
Wastewater-based epidemiology (WBE) emerged during the coronavirus disease 2019 (COVID-19) pandemic as a scalable and broadly applicable method for community-level monitoring of infectious disease burden. The lack of high-resolution fecal shedding data for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) limits our ability to link WBE measurements to disease burden. In this study, we present longitudinal, quantitative fecal shedding data for SARS-CoV-2 RNA, as well as for the commonly used fecal indicators pepper mild mottle virus (PMMoV) RNA and crAss-like phage (crAssphage) DNA. The shedding trajectories from 48 SARS-CoV-2-infected individuals suggest a highly individualized, dynamic course of SARS-CoV-2 RNA fecal shedding. Of the individuals that provided at least three stool samples spanning more than 14 days, 77% had one or more samples that tested positive for SARS-CoV-2 RNA. We detected PMMoV RNA in at least one sample from all individuals and in 96% (352/367) of samples overall. CrAssphage DNA was detected in at least one sample from 80% (38/48) of individuals and was detected in 48% (179/371) of all samples. The geometric mean concentrations of PMMoV and crAssphage in stool across all individuals were 8.7 × 104 and 1.4 × 104 gene copies/milligram-dry weight, respectively, and crAssphage shedding was more consistent for individuals than PMMoV shedding. These results provide us with a missing link needed to connect laboratory WBE results with mechanistic models, and this will aid in more accurate estimates of COVID-19 burden in sewersheds. Additionally, the PMMoV and crAssphage data are critical for evaluating their utility as fecal strength normalizing measures and for source-tracking applications. IMPORTANCE This research represents a critical step in the advancement of wastewater monitoring for public health. To date, mechanistic materials balance modeling of wastewater-based epidemiology has relied on SARS-CoV-2 fecal shedding estimates from small-scale clinical reports or meta-analyses of research using a wide range of analytical methodologies. Additionally, previous SARS-CoV-2 fecal shedding data have not contained sufficient methodological information for building accurate materials balance models. Like SARS-CoV-2, fecal shedding of PMMoV and crAssphage has been understudied to date. The data presented here provide externally valid and longitudinal fecal shedding data for SARS-CoV-2, PMMoV, and crAssphage which can be directly applied to WBE models and ultimately increase the utility of WBE.
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Affiliation(s)
- Peter J. Arts
- Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, Michigan, USA
| | - J. Daniel Kelly
- Department of Epidemiology and Biostatistics, University of California, San Francisco, California, USA
- Institute for Global Health Sciences, University of California, San Francisco, California, USA
- Division of Hospital Medicine, UCSF, San Francisco, California, USA
- F.I. Proctor Foundation, University of California, San Francisco, California, USA
| | - Claire M. Midgley
- National Center for Immunizations and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Khamal Anglin
- Department of Epidemiology and Biostatistics, University of California, San Francisco, California, USA
- Institute for Global Health Sciences, University of California, San Francisco, California, USA
| | - Scott Lu
- Department of Epidemiology and Biostatistics, University of California, San Francisco, California, USA
- Institute for Global Health Sciences, University of California, San Francisco, California, USA
| | - Glen R. Abedi
- National Center for Immunizations and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Raul Andino
- Department of Microbiology and Immunology, UCSF, San Francisco, California, USA
| | - Kevin M. Bakker
- Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Bryon Banman
- Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, Michigan, USA
| | - Alexandria B. Boehm
- Department of Civil & Environmental Engineering, Stanford University, Stanford, California, USA
| | - Melissa Briggs-Hagen
- National Center for Immunizations and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Andrew F. Brouwer
- Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, USA
| | | | - Marisa C. Eisenberg
- Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, USA
| | | | - Sterling Knight
- Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, Michigan, USA
| | - Michael J. Peluso
- Division of HIV, Infectious Disease, and Global Medicine, UCSF, San Francisco, California, USA
| | - Jesus Pineda-Ramirez
- Department of Epidemiology and Biostatistics, University of California, San Francisco, California, USA
- Institute for Global Health Sciences, University of California, San Francisco, California, USA
| | - Ruth Diaz Sanchez
- Department of Epidemiology and Biostatistics, University of California, San Francisco, California, USA
- Institute for Global Health Sciences, University of California, San Francisco, California, USA
| | - Sharon Saydah
- National Center for Immunizations and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Michel Tassetto
- Department of Microbiology and Immunology, UCSF, San Francisco, California, USA
| | - Jeffrey N. Martin
- Department of Epidemiology and Biostatistics, University of California, San Francisco, California, USA
| | - Krista R. Wigginton
- Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, Michigan, USA
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23
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Cheng L, Dhiyebi HA, Varia M, Atanas K, Srikanthan N, Hayat S, Ikert H, Fuzzen M, Sing-Judge C, Badlani Y, Zeeb E, Bragg LM, Delatolla R, Giesy JP, Gilliland E, Servos MR. Omicron COVID-19 Case Estimates Based on Previous SARS-CoV-2 Wastewater Load, Regional Municipality of Peel, Ontario, Canada. Emerg Infect Dis 2023; 29:1580-1588. [PMID: 37379513 PMCID: PMC10370834 DOI: 10.3201/eid2908.221580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/30/2023] Open
Abstract
We determined correlations between SARS-CoV-2 load in untreated water and COVID-19 cases and patient hospitalizations before the Omicron variant (September 2020-November 2021) at 2 wastewater treatment plants in the Regional Municipality of Peel, Ontario, Canada. Using pre-Omicron correlations, we estimated incident COVID-19 cases during Omicron outbreaks (November 2021-June 2022). The strongest correlation between wastewater SARS-CoV-2 load and COVID-19 cases occurred 1 day after sampling (r = 0.911). The strongest correlation between wastewater load and COVID-19 patient hospitalizations occurred 4 days after sampling (r = 0.819). At the peak of the Omicron BA.2 outbreak in April 2022, reported COVID-19 cases were underestimated 19-fold because of changes in clinical testing. Wastewater data provided information for local decision-making and are a useful component of COVID-19 surveillance systems.
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24
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Schill R, Nelson KL, Harris-Lovett S, Kantor RS. The dynamic relationship between COVID-19 cases and SARS-CoV-2 wastewater concentrations across time and space: Considerations for model training data sets. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 871:162069. [PMID: 36754324 PMCID: PMC9902279 DOI: 10.1016/j.scitotenv.2023.162069] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 02/01/2023] [Accepted: 02/02/2023] [Indexed: 06/18/2023]
Abstract
During the COVID-19 pandemic, wastewater-based surveillance has been used alongside diagnostic testing to monitor infection rates. With the decline in cases reported to public health departments due to at-home testing, wastewater data may serve as the primary input for epidemiological models, but training these models is not straightforward. We explored factors affecting noise and bias in the ratio between wastewater and case data collected in 26 sewersheds in California from October 2020 to March 2022. The strength of the relationship between wastewater and case data appeared dependent on sampling frequency and population size, but was not increased by wastewater normalization to flow rate or case count normalization to testing rates. Additionally, the lead and lag times between wastewater and case data varied over time and space, and the ratio of log-transformed individual cases to wastewater concentrations changed over time. This ratio decreased between the Epsilon/Alpha and Delta variant surges of COVID-19 and increased during the Omicron BA.1 variant surge, and was also related to the diagnostic testing rate. Based on this analysis, we present a framework of scenarios describing the dynamics of the case to wastewater ratio to aid in data handling decisions for ongoing modeling efforts.
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Affiliation(s)
- Rebecca Schill
- TUM School of Engineering and Design, Technical University of Munich, Germany
| | - Kara L Nelson
- Civil and Environmental Engineering, University of California, Berkeley, CA, USA
| | | | - Rose S Kantor
- Civil and Environmental Engineering, University of California, Berkeley, CA, USA.
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25
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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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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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27
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Acosta N, Bautista MA, Waddell BJ, Du K, McCalder J, Pradhan P, Sedaghat N, Papparis C, Beaudet AB, Chen J, Van Doorn J, Xiang K, Chan L, Vivas L, Low K, Lu X, Lee J, Westlund P, Chekouo T, Dai X, Cabaj J, Bhatnagar S, Ruecker N, Achari G, Clark RG, Pearce C, Harrison JJ, Meddings J, Leal J, Ellison J, Missaghi B, Kanji JN, Larios O, Rennert‐May E, Kim J, Hrudey SE, Lee BE, Pang X, Frankowski K, Conly J, Hubert CRJ, Parkins MD. Surveillance for SARS-CoV-2 and its variants in wastewater of tertiary care hospitals correlates with increasing case burden and outbreaks. J Med Virol 2023; 95:e28442. [PMID: 36579780 PMCID: PMC9880705 DOI: 10.1002/jmv.28442] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 12/20/2022] [Accepted: 12/26/2022] [Indexed: 12/30/2022]
Abstract
Wastewater-based SARS-CoV-2 surveillance enables unbiased and comprehensive monitoring of defined sewersheds. We performed real-time monitoring of hospital wastewater that differentiated Delta and Omicron variants within total SARS-CoV-2-RNA, enabling correlation to COVID-19 cases from three tertiary-care facilities with >2100 inpatient beds in Calgary, Canada. RNA was extracted from hospital wastewater between August/2021 and January/2022, and SARS-CoV-2 quantified using RT-qPCR. Assays targeting R203M and R203K/G204R established the proportional abundance of Delta and Omicron, respectively. Total and variant-specific SARS-CoV-2 in wastewater was compared to data for variant specific COVID-19 hospitalizations, hospital-acquired infections, and outbreaks. Ninety-six percent (188/196) of wastewater samples were SARS-CoV-2 positive. Total SARS-CoV-2 RNA levels in wastewater increased in tandem with total prevalent cases (Delta plus Omicron). Variant-specific assessments showed this increase to be mainly driven by Omicron. Hospital-acquired cases of COVID-19 were associated with large spikes in wastewater SARS-CoV-2 and levels were significantly increased during outbreaks relative to nonoutbreak periods for total SARS-CoV2, Delta and Omicron. SARS-CoV-2 in hospital wastewater was significantly higher during the Omicron-wave irrespective of outbreaks. Wastewater-based monitoring of SARS-CoV-2 and its variants represents a novel tool for passive COVID-19 infection surveillance, case identification, containment, and potentially to mitigate viral spread in hospitals.
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Affiliation(s)
- Nicole Acosta
- Department of Microbiology, Immunology and Infectious DiseasesUniversity of CalgaryCalgaryCanada
| | | | - Barbara J. Waddell
- Department of Microbiology, Immunology and Infectious DiseasesUniversity of CalgaryCalgaryCanada
| | - Kristine Du
- Department of Microbiology, Immunology and Infectious DiseasesUniversity of CalgaryCalgaryCanada
| | - Janine McCalder
- Department of Microbiology, Immunology and Infectious DiseasesUniversity of CalgaryCalgaryCanada,Department of Biological SciencesUniversity of CalgaryCalgaryCanada
| | - Puja Pradhan
- Department of Microbiology, Immunology and Infectious DiseasesUniversity of CalgaryCalgaryCanada,Department of Biological SciencesUniversity of CalgaryCalgaryCanada
| | - Navid Sedaghat
- Department of Microbiology, Immunology and Infectious DiseasesUniversity of CalgaryCalgaryCanada,Department of Biological SciencesUniversity of CalgaryCalgaryCanada
| | - Chloe Papparis
- Department of Microbiology, Immunology and Infectious DiseasesUniversity of CalgaryCalgaryCanada,Department of Biological SciencesUniversity of CalgaryCalgaryCanada
| | | | - Jianwei Chen
- Department of Biological SciencesUniversity of CalgaryCalgaryCanada
| | | | - Kevin Xiang
- Department of Biological SciencesUniversity of CalgaryCalgaryCanada
| | - Leslie Chan
- Department of Biological SciencesUniversity of CalgaryCalgaryCanada
| | - Laura Vivas
- Department of Biological SciencesUniversity of CalgaryCalgaryCanada
| | - Kashtin Low
- Department of Microbiology, Immunology and Infectious DiseasesUniversity of CalgaryCalgaryCanada
| | - Xuewen Lu
- Department of Mathematics and StatisticsUniversity of CalgaryCalgaryCanada
| | - Jangwoo Lee
- Department of Microbiology, Immunology and Infectious DiseasesUniversity of CalgaryCalgaryCanada
| | | | - Thierry Chekouo
- Department of Mathematics and StatisticsUniversity of CalgaryCalgaryCanada,Division of Biostatistics, School of Public HealthUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - Xiaotian Dai
- Department of Mathematics and StatisticsUniversity of CalgaryCalgaryCanada
| | - Jason Cabaj
- Department of Community Health SciencesUniversity of CalgaryCalgaryCanada,Department of MedicineUniversity of Calgary and Alberta Health ServicesCalgaryCanada,Provincial Population & Public HealthAlberta Health ServicesCalgaryCanada,O'Brien Institute for Public HealthUniversity of CalgaryCalgaryCanada
| | - Srijak Bhatnagar
- Faculty of Science and TechnologyAthabasca UniversityAthabascaAlbertaCanada
| | | | - Gopal Achari
- Department of Civil EngineeringUniversity of CalgaryCalgaryCanada
| | - Rhonda G. Clark
- Department of Biological SciencesUniversity of CalgaryCalgaryCanada
| | - Craig Pearce
- Infection Prevention and ControlAlberta Health ServicesCalgaryCanada
| | - Joe J. Harrison
- Department of Biological SciencesUniversity of CalgaryCalgaryCanada,Snyder Institute for Chronic DiseasesUniversity of Calgary and Alberta Health ServicesCalgaryCanada
| | - Jon Meddings
- Department of MedicineUniversity of Calgary and Alberta Health ServicesCalgaryCanada
| | - Jenine Leal
- Department of Microbiology, Immunology and Infectious DiseasesUniversity of CalgaryCalgaryCanada,Department of Community Health SciencesUniversity of CalgaryCalgaryCanada,O'Brien Institute for Public HealthUniversity of CalgaryCalgaryCanada,Infection Prevention and ControlAlberta Health ServicesCalgaryCanada
| | - Jennifer Ellison
- Infection Prevention and ControlAlberta Health ServicesCalgaryCanada
| | - Bayan Missaghi
- Department of MedicineUniversity of Calgary and Alberta Health ServicesCalgaryCanada,Infection Prevention and ControlAlberta Health ServicesCalgaryCanada
| | - Jamil N. Kanji
- Department of MedicineUniversity of Calgary and Alberta Health ServicesCalgaryCanada,Department of Laboratory Medicine and PathologyUniversity of AlbertaEdmontonAlbertaCanada,Alberta Precision Laboratories, Public Health LaboratoryAlberta Health ServicesEdmontonAlbertaCanada,Department of Pathology and Laboratory MedicineUniversity of Calgary and Alberta Health ServicesCalgaryCanada
| | - Oscar Larios
- Department of MedicineUniversity of Calgary and Alberta Health ServicesCalgaryCanada,Infection Prevention and ControlAlberta Health ServicesCalgaryCanada,Alberta Precision Laboratories, Public Health LaboratoryAlberta Health ServicesEdmontonAlbertaCanada
| | - Elissa Rennert‐May
- Department of Microbiology, Immunology and Infectious DiseasesUniversity of CalgaryCalgaryCanada,Department of Community Health SciencesUniversity of CalgaryCalgaryCanada,Department of MedicineUniversity of Calgary and Alberta Health ServicesCalgaryCanada,O'Brien Institute for Public HealthUniversity of CalgaryCalgaryCanada,Snyder Institute for Chronic DiseasesUniversity of Calgary and Alberta Health ServicesCalgaryCanada
| | - Joseph Kim
- Department of MedicineUniversity of Calgary and Alberta Health ServicesCalgaryCanada,Infection Prevention and ControlAlberta Health ServicesCalgaryCanada
| | - Steve E. Hrudey
- Department of Laboratory Medicine and PathologyUniversity of AlbertaEdmontonAlbertaCanada,Department of Analytical and Environmental ToxicologyUniversity of AlbertaEdmontonAlbertaCanada
| | - Bonita E. Lee
- Department of PediatricsUniversity of AlbertaEdmontonAlbertaCanada,Women & Children's Health Research InstituteEdmontonAlbertaCanada,Li Ka Shing Institute of VirologyUniversity of AlbertaEdmontonAlbertaCanada
| | - Xiaoli Pang
- Department of Laboratory Medicine and PathologyUniversity of AlbertaEdmontonAlbertaCanada,Alberta Precision Laboratories, Public Health LaboratoryAlberta Health ServicesEdmontonAlbertaCanada,Li Ka Shing Institute of VirologyUniversity of AlbertaEdmontonAlbertaCanada
| | - Kevin Frankowski
- Advancing Canadian Water AssetsUniversity of CalgaryCalgaryCanada
| | - John Conly
- Department of Microbiology, Immunology and Infectious DiseasesUniversity of CalgaryCalgaryCanada,Department of MedicineUniversity of Calgary and Alberta Health ServicesCalgaryCanada,O'Brien Institute for Public HealthUniversity of CalgaryCalgaryCanada,Infection Prevention and ControlAlberta Health ServicesCalgaryCanada,Snyder Institute for Chronic DiseasesUniversity of Calgary and Alberta Health ServicesCalgaryCanada,Department of Pathology and Laboratory MedicineUniversity of Calgary and Alberta Health ServicesCalgaryCanada
| | | | - Michael D. Parkins
- Department of Microbiology, Immunology and Infectious DiseasesUniversity of CalgaryCalgaryCanada,Department of MedicineUniversity of Calgary and Alberta Health ServicesCalgaryCanada,Snyder Institute for Chronic DiseasesUniversity of Calgary and Alberta Health ServicesCalgaryCanada
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28
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Verani M, Federigi I, Muzio S, Lauretani G, Calà P, Mancuso F, Salvadori R, Valentini C, La Rosa G, Suffredini E, Carducci A. Calibration of Methods for SARS-CoV-2 Environmental Surveillance: A Case Study from Northwest Tuscany. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:16588. [PMID: 36554466 PMCID: PMC9778686 DOI: 10.3390/ijerph192416588] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 12/02/2022] [Accepted: 12/05/2022] [Indexed: 06/17/2023]
Abstract
The current pandemic has provided an opportunity to test wastewater-based epidemiology (WBE) as a complementary method to SARS-CoV-2 monitoring in the community. However, WBE infection estimates can be affected by uncertainty factors, such as heterogeneity in analytical procedure, wastewater volume, and population size. In this paper, raw sewage SARS-CoV-2 samples were collected from four wastewater treatment plants (WWTPs) in Tuscany (Northwest Italy) between February and December 2021. During the surveillance period, viral concentration was based on polyethylene glycol (PEG), but its precipitation method was modified from biphasic separation to centrifugation. Therefore, in parallel, the recovery efficiency of each method was evaluated at lab-scale, using two spiking viruses (human coronavirus 229E and mengovirus vMC0). SARS-CoV-2 genome was found in 80 (46.5%) of the 172 examined samples. Lab-scale experiments revealed that PEG precipitation using centrifugation had the best recovery efficiency (up to 30%). Viral SARS-CoV-2 load obtained from sewage data, adjusted by analytical method and normalized by population of each WWTP, showed a good association with the clinical data in the study area. This study highlights that environmental surveillance data need to be carefully analyzed before their use in the WBE, also considering the sensibility of the analytical methods.
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Affiliation(s)
- Marco Verani
- Laboratory of Hygiene and Environmental Virology, Department of Biology, University of Pisa, Via S. Zeno 35/39, 56127 Pisa, Italy
| | - Ileana Federigi
- Laboratory of Hygiene and Environmental Virology, Department of Biology, University of Pisa, Via S. Zeno 35/39, 56127 Pisa, Italy
| | - Sara Muzio
- Laboratory of Hygiene and Environmental Virology, Department of Biology, University of Pisa, Via S. Zeno 35/39, 56127 Pisa, Italy
| | - Giulia Lauretani
- Laboratory of Hygiene and Environmental Virology, Department of Biology, University of Pisa, Via S. Zeno 35/39, 56127 Pisa, Italy
| | - Piergiuseppe Calà
- Tuscany Region-Health, Department of Prevention Local Health Authority Tuscany Center, Via S. Salvi 12, 50135 Firenze, Italy
| | - Fabrizio Mancuso
- Ingegnerie Toscane-Area R&D, Via Bellatalla 1, 56121 Pisa, Italy
| | | | | | - Giuseppina La Rosa
- Department of Environment and Health, Istituto Superiore di Sanità, Viale Regina Elena 299, 00161 Rome, Italy
| | - Elisabetta Suffredini
- Department of Food Safety, Nutrition and Veterinary Public Health, Istituto Superiore di Sanità, Viale Regina Elena 299, 00161 Rome, Italy
| | - Annalaura Carducci
- Laboratory of Hygiene and Environmental Virology, Department of Biology, University of Pisa, Via S. Zeno 35/39, 56127 Pisa, Italy
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