1
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Robotto A, Olivero C, Pozzi E, Strumia C, Crasà C, Fedele C, Derosa M, Di Martino M, Latino S, Scorza G, Civra A, Lembo D, Quaglino P, Brizio E, Polato D. Efficient wastewater sample filtration improves the detection of SARS-CoV-2 variants: An extensive analysis based on sequencing parameters. PLoS One 2024; 19:e0304158. [PMID: 38787865 PMCID: PMC11125551 DOI: 10.1371/journal.pone.0304158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 05/06/2024] [Indexed: 05/26/2024] Open
Abstract
During the SARS-CoV-2 pandemic, many countries established wastewater (WW) surveillance to objectively monitor the level of infection within the population. As new variants continue to emerge, it has become clear that WW surveillance is an essential tool for the early detection of variants. The EU Commission published a recommendation suggesting an approach to establish surveillance of SARS-CoV-2 and its variants in WW, besides specifying the methodology for WW concentration and RNA extraction. Therefore, different groups have approached the issue with different strategies, mainly focusing on WW concentration methods, but only a few groups highlighted the importance of prefiltering WW samples and/or purification of RNA samples. Aiming to obtain high-quality sequencing data allowing variants detection, we compared four experimental conditions generated from the treatment of: i) WW samples by WW filtration and ii) the extracted RNA by DNase treatment, purification and concentration of the extracted RNA. To evaluate the best condition, the results were assessed by focusing on several sequencing parameters, as the outcome of SARS-CoV-2 sequencing from WW is crucial for variant detection. Overall, the best sequencing result was obtained by filtering the WW sample. Moreover, the present study provides an overview of some sequencing parameters to consider when optimizing a method for monitoring SARS-CoV-2 variants from WW samples, which can also be applied to any sample preparation methodology.
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Affiliation(s)
- Angelo Robotto
- Environmental Protection Agency of Piedmont (Arpa Piemonte), Torino, Italy
| | - Carlotta Olivero
- Department of Regional Centre of Molecular Biology, Environmental Protection Agency of Piedmont (Arpa Piemonte), La Loggia, Torino, Italy
| | - Elisa Pozzi
- Department of Regional Centre of Molecular Biology, Environmental Protection Agency of Piedmont (Arpa Piemonte), La Loggia, Torino, Italy
| | - Claudia Strumia
- Department of Regional Centre of Molecular Biology, Environmental Protection Agency of Piedmont (Arpa Piemonte), La Loggia, Torino, Italy
| | - Camilla Crasà
- Department of Regional Centre of Molecular Biology, Environmental Protection Agency of Piedmont (Arpa Piemonte), La Loggia, Torino, Italy
| | - Cristina Fedele
- Department of Regional Centre of Molecular Biology, Environmental Protection Agency of Piedmont (Arpa Piemonte), La Loggia, Torino, Italy
| | - Maddalena Derosa
- Department of Regional Centre of Molecular Biology, Environmental Protection Agency of Piedmont (Arpa Piemonte), La Loggia, Torino, Italy
| | - Massimo Di Martino
- Department of Regional Centre of Molecular Biology, Environmental Protection Agency of Piedmont (Arpa Piemonte), La Loggia, Torino, Italy
| | - Stefania Latino
- Department of Regional Centre of Molecular Biology, Environmental Protection Agency of Piedmont (Arpa Piemonte), La Loggia, Torino, Italy
| | - Giada Scorza
- Department of Regional Centre of Molecular Biology, Environmental Protection Agency of Piedmont (Arpa Piemonte), La Loggia, Torino, Italy
| | - Andrea Civra
- Dept. of Clinical and Biological Sciences, University of Turin, Orbassano, Torino, Italy
| | - David Lembo
- Dept. of Clinical and Biological Sciences, University of Turin, Orbassano, Torino, Italy
| | - Paola Quaglino
- Environmental Protection Agency of Piedmont (Arpa Piemonte), Torino, Italy
| | - Enrico Brizio
- Environmental Protection Agency of Piedmont (Arpa Piemonte), Torino, Italy
| | - Denis Polato
- Department of Regional Centre of Molecular Biology, Environmental Protection Agency of Piedmont (Arpa Piemonte), La Loggia, Torino, Italy
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2
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Rashid SA, Rajendiran S, Nazakat R, Mohammad Sham N, Khairul Hasni NA, Anasir MI, Kamel KA, Muhamad Robat R. A scoping review of global SARS-CoV-2 wastewater-based epidemiology in light of COVID-19 pandemic. Heliyon 2024; 10:e30600. [PMID: 38765075 PMCID: PMC11098849 DOI: 10.1016/j.heliyon.2024.e30600] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 04/30/2024] [Accepted: 04/30/2024] [Indexed: 05/21/2024] Open
Abstract
Recently, wastewater-based epidemiology (WBE) research has experienced a strong impetus during the Coronavirus disease 2019 (COVID-19) pandemic. However, a few technical issues related to surveillance strategies, such as standardized procedures ranging from sampling to testing protocols, need to be resolved in preparation for future infectious disease outbreaks. This review highlights the study characteristics, potential use of WBE and overview of methods, as well as methods utilized to detect severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) including its variant in wastewater. A literature search was performed electronically in PubMed and Scopus according to PRISMA guidelines for relevant peer-reviewed articles published between January 2020 and March 2022. The search identified 588 articles, out of which 221 fulfilled the necessary criteria and are discussed in this review. Most global WBE studies were conducted in North America (n = 75, 34 %), followed by Europe (n = 68, 30.8 %), and Asia (n = 43, 19.5 %). The review also showed that most of the application of WBE observed were to correlate SARS-CoV-2 ribonucleic acid (RNA) trends in sewage with epidemiological data (n = 90, 40.7 %). The techniques that were often used globally for sample collection, concentration, preferred matrix recovery control and various sample types were also discussed. Overall, this review provided a framework for researchers specializing in WBE to apply strategic approaches to their research questions in achieving better functional insights. In addition, areas that needed more in-depth analysis, data collection, and ideas for new initiatives were identified.
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Affiliation(s)
- Siti Aishah Rashid
- Environmental Health Research Centre, Institute for Medical Research, National Institutes of Health (NIH), Ministry of Health, Shah Alam, Selangor, Malaysia
| | - Sakshaleni Rajendiran
- Environmental Health Research Centre, Institute for Medical Research, National Institutes of Health (NIH), Ministry of Health, Shah Alam, Selangor, Malaysia
| | - Raheel Nazakat
- Environmental Health Research Centre, Institute for Medical Research, National Institutes of Health (NIH), Ministry of Health, Shah Alam, Selangor, Malaysia
| | - Noraishah Mohammad Sham
- Environmental Health Research Centre, Institute for Medical Research, National Institutes of Health (NIH), Ministry of Health, Shah Alam, Selangor, Malaysia
| | - Nurul Amalina Khairul Hasni
- Environmental Health Research Centre, Institute for Medical Research, National Institutes of Health (NIH), Ministry of Health, Shah Alam, Selangor, Malaysia
| | - Mohd Ishtiaq Anasir
- Infectious Disease Research Centre, Institute for Medical Research, National Institutes of Health (NIH), Ministry of Health, Shah Alam, Selangor, Malaysia
| | - Khayri Azizi Kamel
- Infectious Disease Research Centre, Institute for Medical Research, National Institutes of Health (NIH), Ministry of Health, Shah Alam, Selangor, Malaysia
| | - Rosnawati Muhamad Robat
- Occupational & Environmental Health Unit, Public Health Division, Selangor State Health Department, Ministry of Health Malaysia, Malaysia
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3
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Sthapit N, Malla B, Tandukar S, Thakali O, Sherchand JB, Haramoto E. Evaluating acute gastroenteritis-causing pathogen reduction in wastewater and the applicability of river water for wastewater-based epidemiology in the Kathmandu Valley, Nepal. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 919:170764. [PMID: 38331291 DOI: 10.1016/j.scitotenv.2024.170764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 01/16/2024] [Accepted: 02/04/2024] [Indexed: 02/10/2024]
Abstract
Rapid urbanization and population growth without the implementation of proper waste management are capable of contaminating water sources, which can lead to acute gastroenteritis. This study examined the detection and reduction of five gastroenteritis-causing enteropathogens, Salmonella, Campylobacter coli, Campylobacter jejuni, Clostridium perfringens, and genogroup IV norovirus, and one respiratory pathogen, influenza A virus, in two municipal wastewater treatment plants (WWTP) using an oxidation ditch system (WWTP A; n = 20) and a stabilization pond system (WWTP B; n = 18) in the Kathmandu Valley, Nepal, collected between August 2017 and August 2019. All enteropathogens were detected in wastewater via quantitative PCR. The concentrations of the pathogens ranged from 5.7 to 7.9 log10 copies/L in WWTP A and from 4.9 to 8.1 log10 copies/L in WWTP B. The log10 reduction values of the pathogens ranged from 0.3 to 1.0 in WWTP A and from -0.1 to 0.2 in WWTP B. The association between the pathogen concentrations and the number of clinical cases in the corresponding week could not be evaluated; however, the consistent detection of pathogens in the wastewater despite low number of case reports suggested the use of wastewater-based epidemiology (WBE) for early warning of acute gastroenteritis (AGE) in the Kathmandu Valley. The pathogens were also detected in river water at approximately 7.0 log10 copies/L and exhibited no significant difference in concentration compared to wastewater, suggesting the applicability of river water for WBE of AGE. Insufficient treatment of all pathogens in the wastewater was observed, suggesting the need for full rehabilitation of the treatment plants. However, the influent may be utilized for early detection of AGE-causing pathogens in the city, whereas the river water may serve as an alternative in areas without connection to the WWTPs.
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Affiliation(s)
- Niva Sthapit
- Department of Engineering, University of Yamanashi, 4-3-11 Takeda, Kofu, Yamanashi 400-8511, Japan
| | - Bikash Malla
- Interdisciplinary Center for River Basin Environment, University of Yamanashi, 4-3-11 Takeda, Kofu, Yamanashi 400-8511, Japan
| | - Sarmila Tandukar
- Interdisciplinary Center for River Basin Environment, University of Yamanashi, 4-3-11 Takeda, Kofu, Yamanashi 400-8511, Japan
| | - Ocean Thakali
- Department of Engineering, University of Yamanashi, 4-3-11 Takeda, Kofu, Yamanashi 400-8511, Japan
| | - Jeevan B Sherchand
- Institute of Medicine, Tribhuvan University, Maharajgunj, Kathmandu 1524, Nepal
| | - Eiji Haramoto
- Interdisciplinary Center for River Basin Environment, University of Yamanashi, 4-3-11 Takeda, Kofu, Yamanashi 400-8511, Japan.
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Chen C, Kaur G, Adiga A, Espinoza B, Venkatramanan S, Warren A, Lewis B, Crow J, Singh R, Lorentz A, Toney D, Marathe M. Wastewater-based Epidemiology for COVID-19 Surveillance: A Survey. ARXIV 2024:arXiv:2403.15291v1. [PMID: 38562450 PMCID: PMC10984000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
The pandemic of COVID-19 has imposed tremendous pressure on public health systems and social economic ecosystems over the past years. To alleviate its social impact, it is important to proactively track the prevalence of COVID-19 within communities. The traditional way to estimate the disease prevalence is to estimate from reported clinical test data or surveys. However, the coverage of clinical tests is often limited and the tests can be labor-intensive, requires reliable and timely results, and consistent diagnostic and reporting criteria. Recent studies revealed that patients who are diagnosed with COVID-19 often undergo fecal shedding of SARS-CoV-2 virus into wastewater, which makes wastewater-based epidemiology (WBE) for COVID-19 surveillance a promising approach to complement traditional clinical testing. In this paper, we survey the existing literature regarding WBE for COVID-19 surveillance and summarize the current advances in the area. Specifically, we have covered the key aspects of wastewater sampling, sample testing, and presented a comprehensive and organized summary of wastewater data analytical methods. Finally, we provide the open challenges on current wastewater-based COVID-19 surveillance studies, aiming to encourage new ideas to advance the development of effective wastewater-based surveillance systems for general infectious diseases.
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Affiliation(s)
- Chen Chen
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, 22904, United States
| | - Gursharn Kaur
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, 22904, United States
| | - Aniruddha Adiga
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, 22904, United States
| | - Baltazar Espinoza
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, 22904, United States
| | - Srinivasan Venkatramanan
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, 22904, United States
| | - Andrew Warren
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, 22904, United States
| | - Bryan Lewis
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, 22904, United States
| | - Justin Crow
- Virginia Department of Health, Richmond, 23219, United States
| | - Rekha Singh
- Virginia Department of Health, Richmond, 23219, United States
| | - Alexandra Lorentz
- Division of Consolidated Laboratory Services, Department of General Services, Richmond, 23219, United States
| | - Denise Toney
- Division of Consolidated Laboratory Services, Department of General Services, Richmond, 23219, United States
| | - Madhav Marathe
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, 22904, United States
- Department of Computer Science, University of Virginia, Charlottesville, 22904, United States
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5
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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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6
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Baz Lomba JA, Pires J, Myrmel M, Arnø JK, Madslien EH, Langlete P, Amato E, Hyllestad S. Effectiveness of environmental surveillance of SARS-CoV-2 as an early-warning system: Update of a systematic review during the second year of the pandemic. JOURNAL OF WATER AND HEALTH 2024; 22:197-234. [PMID: 38295081 PMCID: wh_2023_279 DOI: 10.2166/wh.2023.279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/02/2024]
Abstract
The aim of this updated systematic review was to offer an overview of the effectiveness of environmental surveillance (ES) of SARS-CoV-2 as a potential early-warning system (EWS) for COVID-19 and new variants of concerns (VOCs) during the second year of the pandemic. An updated literature search was conducted to evaluate the added value of ES of SARS-CoV-2 for public health decisions. The search for studies published between June 2021 and July 2022 resulted in 1,588 publications, identifying 331 articles for full-text screening. A total of 151 publications met our inclusion criteria for the assessment of the effectiveness of ES as an EWS and early detection of SARS-CoV-2 variants. We identified a further 30 publications among the grey literature. ES confirms its usefulness as an EWS for detecting new waves of SARS-CoV-2 infection with an average lead time of 1-2 weeks for most of the publication. ES could function as an EWS for new VOCs in areas with no registered cases or limited clinical capacity. Challenges in data harmonization and variant detection require standardized approaches and innovations for improved public health decision-making. ES confirms its potential to support public health decision-making and resource allocation in future outbreaks.
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Affiliation(s)
- Jose Antonio Baz Lomba
- Department of Infection Control and Preparedness, Norwegian Institute of Public Health, Oslo, Norway E-mail:
| | - João Pires
- Department of Infection Control and Preparedness, Norwegian Institute of Public Health, Oslo, Norway; ECDC fellowship Programme, Public Health Microbiology path (EUPHEM), European Centre for Disease Prevention and Control (ECDC), Solna, Sweden
| | - Mette Myrmel
- Faculty of Veterinary Medicine, Virology Unit, Norwegian University of Life Science (NMBU), Oslo, Norway
| | - Jorunn Karterud Arnø
- Department of Infection Control and Preparedness, Norwegian Institute of Public Health, Oslo, Norway
| | - Elisabeth Henie Madslien
- Department of Infection Control and Preparedness, Norwegian Institute of Public Health, Oslo, Norway
| | - Petter Langlete
- Department of Infection Control and Preparedness, Norwegian Institute of Public Health, Oslo, Norway
| | - Ettore Amato
- Department of Infection Control and Preparedness, Norwegian Institute of Public Health, Oslo, Norway
| | - Susanne Hyllestad
- Department of Infection Control and Preparedness, Norwegian Institute of Public Health, Oslo, Norway
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Lin T, Karthikeyan S, Satterlund A, Schooley R, Knight R, De Gruttola V, Martin N, Zou J. Optimizing campus-wide COVID-19 test notifications with interpretable wastewater time-series features using machine learning models. Sci Rep 2023; 13:20670. [PMID: 38001346 PMCID: PMC10673837 DOI: 10.1038/s41598-023-47859-2] [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: 04/02/2023] [Accepted: 11/19/2023] [Indexed: 11/26/2023] Open
Abstract
During the COVID-19 pandemic, wastewater surveillance of the SARS CoV-2 virus has been demonstrated to be effective for population surveillance at the county level down to the building level. At the University of California, San Diego, daily high-resolution wastewater surveillance conducted at the building level is being used to identify potential undiagnosed infections and trigger notification of residents and responsive testing, but the optimal determinants for notifications are unknown. To fill this gap, we propose a pipeline for data processing and identifying features of a series of wastewater test results that can predict the presence of COVID-19 in residences associated with the test sites. Using time series of wastewater results and individual testing results during periods of routine asymptomatic testing among UCSD students from 11/2020 to 11/2021, we develop hierarchical classification/decision tree models to select the most informative wastewater features (patterns of results) which predict individual infections. We find that the best predictor of positive individual level tests in residence buildings is whether or not the wastewater samples were positive in at least 3 of the past 7 days. We also demonstrate that the tree models outperform a wide range of other statistical and machine models in predicting the individual COVID-19 infections while preserving interpretability. Results of this study have been used to refine campus-wide guidelines and email notification systems to alert residents of potential infections.
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Affiliation(s)
- Tuo Lin
- Department of Biostatistics, University of Florida, Gainesville, FL, 32608, USA
| | - Smruthi Karthikeyan
- Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA, 91125, USA
| | - Alysson Satterlund
- Student Affairs, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Robert Schooley
- Division of Infectious Diseases and Global Public Health, Department of Medicine, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Rob Knight
- Department of Pediatrics, University of California San Diego, La Jolla, CA, 92093, USA
- Department of Computer Science and Engineering, University of California, San Diego, CA, USA
- Center for Microbiome Innovation, University of California, San Diego, CA, USA
| | - Victor De Gruttola
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Natasha Martin
- Division of Infectious Diseases and Global Public Health, Department of Medicine, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Jingjing Zou
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, CA, 92093, USA.
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8
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Acosta N, Dai X, Bautista MA, Waddell BJ, Lee J, Du K, McCalder J, Pradhan P, Papparis C, Lu X, Chekouo T, Krusina A, Southern D, Williamson T, Clark RG, Patterson RA, Westlund P, Meddings J, Ruecker N, Lammiman C, Duerr C, Achari G, Hrudey SE, Lee BE, Pang X, Frankowski K, Hubert CRJ, Parkins MD. Wastewater-based surveillance can be used to model COVID-19-associated workforce absenteeism. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 900:165172. [PMID: 37379934 PMCID: PMC10292917 DOI: 10.1016/j.scitotenv.2023.165172] [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: 01/21/2023] [Revised: 06/21/2023] [Accepted: 06/25/2023] [Indexed: 06/30/2023]
Abstract
Wastewater-based surveillance (WBS) of infectious diseases is a powerful tool for understanding community COVID-19 disease burden and informing public health policy. The potential of WBS for understanding COVID-19's impact in non-healthcare settings has not been explored to the same degree. Here we examined how SARS-CoV-2 measured from municipal wastewater treatment plants (WWTPs) correlates with workforce absenteeism. SARS-CoV-2 RNA N1 and N2 were quantified three times per week by RT-qPCR in samples collected at three WWTPs servicing Calgary and surrounding areas, Canada (1.4 million residents) between June 2020 and March 2022. Wastewater trends were compared to workforce absenteeism using data from the largest employer in the city (>15,000 staff). Absences were classified as being COVID-19-related, COVID-19-confirmed, and unrelated to COVID-19. Poisson regression was performed to generate a prediction model for COVID-19 absenteeism based on wastewater data. SARS-CoV-2 RNA was detected in 95.5 % (85/89) of weeks assessed. During this period 6592 COVID-19-related absences (1896 confirmed) and 4524 unrelated absences COVID-19 cases were recorded. A generalized linear regression using a Poisson distribution was performed to predict COVID-19-confirmed absences out of the total number of absent employees using wastewater data as a leading indicator (P < 0.0001). The Poisson regression with wastewater as a one-week leading signal has an Akaike information criterion (AIC) of 858, compared to a null model (excluding wastewater predictor) with an AIC of 1895. The likelihood-ratio test comparing the model with wastewater signal with the null model shows statistical significance (P < 0.0001). We also assessed the variation of predictions when the regression model was applied to new data, with the predicted values and corresponding confidence intervals closely tracking actual absenteeism data. Wastewater-based surveillance has the potential to be used by employers to anticipate workforce requirements and optimize human resource allocation in response to trackable respiratory illnesses like COVID-19.
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Affiliation(s)
- Nicole Acosta
- Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, 3330 Hospital Drive NW, Calgary, Alberta T2N 4N1, Canada
| | - Xiaotian Dai
- Department of Mathematics and Statistics, University of Calgary, 2500 University Drive NW, Calgary, Alberta T2N 1N4, Canada
| | - Maria A Bautista
- Department of Biological Sciences, University of Calgary, 2500 University Drive NW, Calgary, Alberta T2N 1N4, Canada
| | - Barbara J Waddell
- Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, 3330 Hospital Drive NW, Calgary, Alberta T2N 4N1, Canada
| | - Jangwoo Lee
- Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, 3330 Hospital Drive NW, Calgary, Alberta T2N 4N1, Canada
| | - Kristine Du
- Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, 3330 Hospital Drive NW, Calgary, Alberta T2N 4N1, Canada
| | - Janine McCalder
- Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, 3330 Hospital Drive NW, Calgary, Alberta T2N 4N1, Canada; Department of Biological Sciences, University of Calgary, 2500 University Drive NW, Calgary, Alberta T2N 1N4, Canada
| | - Puja Pradhan
- Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, 3330 Hospital Drive NW, Calgary, Alberta T2N 4N1, Canada; Department of Biological Sciences, University of Calgary, 2500 University Drive NW, Calgary, Alberta T2N 1N4, Canada
| | - Chloe Papparis
- Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, 3330 Hospital Drive NW, Calgary, Alberta T2N 4N1, Canada; Department of Biological Sciences, University of Calgary, 2500 University Drive NW, Calgary, Alberta T2N 1N4, Canada
| | - Xuewen Lu
- Department of Mathematics and Statistics, University of Calgary, 2500 University Drive NW, Calgary, Alberta T2N 1N4, Canada
| | - Thierry Chekouo
- Department of Mathematics and Statistics, University of Calgary, 2500 University Drive NW, Calgary, Alberta T2N 1N4, Canada; Division of Biostatistics, School of Public Health, University of Minnesota, 420 Delaware St. S.E., Minneapolis, MN 55455, USA
| | - Alexander Krusina
- Department of Community Health Sciences, University of Calgary, 3330 Hospital Drive NW, Calgary, Alberta T2N 4N1, Canada; Department of Medicine, University of Calgary and Alberta Health Services, 3330 Hospital Drive NW, Calgary, Alberta T2N 4N1, Canada
| | - Danielle Southern
- Department of Community Health Sciences, University of Calgary, 3330 Hospital Drive NW, Calgary, Alberta T2N 4N1, Canada; Department of Medicine, University of Calgary and Alberta Health Services, 3330 Hospital Drive NW, Calgary, Alberta T2N 4N1, Canada
| | - Tyler Williamson
- Department of Community Health Sciences, University of Calgary, 3330 Hospital Drive NW, Calgary, Alberta T2N 4N1, Canada; Department of Medicine, University of Calgary and Alberta Health Services, 3330 Hospital Drive NW, Calgary, Alberta T2N 4N1, Canada; O'Brien Institute for Public Health, University of Calgary, 3280 Hospital Dr NW, Calgary, Alberta T2N 4Z6, Canada
| | - Rhonda G Clark
- Department of Biological Sciences, University of Calgary, 2500 University Drive NW, Calgary, Alberta T2N 1N4, Canada
| | - Raymond A Patterson
- Haskayne School of Business, University of Calgary, SH 250, 2500 University Drive NW, Calgary, Alberta T2N 1N4, Canada
| | | | - Jon Meddings
- Department of Medicine, University of Calgary and Alberta Health Services, 3330 Hospital Drive NW, Calgary, Alberta T2N 4N1, Canada
| | - Norma Ruecker
- Water Services, City of Calgary, 625 25 Ave SE, Calgary, Alberta T2G 4k8, Canada
| | - Christopher Lammiman
- Calgary Emergency Management Agency (CEMA), City of Calgary, 673 1 St NE, Calgary, Alberta T2E 6R2, Canada
| | - Coby Duerr
- Calgary Emergency Management Agency (CEMA), City of Calgary, 673 1 St NE, Calgary, Alberta T2E 6R2, Canada
| | - Gopal Achari
- Department of Civil Engineering, University of Calgary, 622 Collegiate Pl NW, T2N 4V8, Canada
| | - Steve E Hrudey
- Department of Laboratory Medicine and Pathology, University of Alberta, 116 St. and 85 Ave, Edmonton, Alberta T6G 2R3, Canada; Analytical and Environmental Toxicology, University of Alberta, 116 St. and 85 Ave, Edmonton, Alberta T6G 2R3, Canada
| | - Bonita E Lee
- Department of Pediatrics, University of Alberta, 116 St. and 85 Ave, Edmonton, Alberta T6G 2R3, Canada; Women & Children's Health Research Institute, 116 St. and 85 Ave, Edmonton, Alberta T6G 2R3, Canada; Li Ka Shing Institute of Virology, University of Alberta, 116 St. and 85 Ave, Edmonton, Alberta T6G 2R3, Canada
| | - Xiaoli Pang
- Department of Laboratory Medicine and Pathology, University of Alberta, 116 St. and 85 Ave, Edmonton, Alberta T6G 2R3, Canada; Li Ka Shing Institute of Virology, University of Alberta, 116 St. and 85 Ave, Edmonton, Alberta T6G 2R3, Canada; Alberta Precision Laboratories, Public Health Laboratory, Alberta Health Services, 116 St. and 85 Ave, Edmonton, Alberta T6G 2R3, Canada
| | - Kevin Frankowski
- Advancing Canadian Water Assets, University of Calgary, 3131 210 Ave SE, Calgary, Alberta T0L 0X0, Canada
| | - Casey R J Hubert
- Department of Biological Sciences, University of Calgary, 2500 University Drive NW, Calgary, Alberta T2N 1N4, Canada
| | - Michael D Parkins
- Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, 3330 Hospital Drive NW, Calgary, Alberta T2N 4N1, Canada; Department of Medicine, University of Calgary and Alberta Health Services, 3330 Hospital Drive NW, Calgary, Alberta T2N 4N1, Canada; Snyder Institute for Chronic Diseases, University of Calgary and Alberta Health Services, 3330 Hospital Drive NW, Calgary, Alberta T2N 4N1, Canada.
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9
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Torabi F, Li G, Mole C, Nicholson G, Rowlingson B, Smith CR, Jersakova R, Diggle PJ, Blangiardo M. Wastewater-based surveillance models for COVID-19: A focused review on spatio-temporal models. Heliyon 2023; 9:e21734. [PMID: 38053867 PMCID: PMC10694161 DOI: 10.1016/j.heliyon.2023.e21734] [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: 07/31/2023] [Revised: 10/25/2023] [Accepted: 10/26/2023] [Indexed: 12/07/2023] Open
Abstract
The evident shedding of the SARS-CoV-2 RNA particles from infected individuals into the wastewater opened up a tantalizing array of possibilities for prediction of COVID-19 prevalence prior to symptomatic case identification through community testing. Many countries have therefore explored the use of wastewater metrics as a surveillance tool, replacing traditional direct measurement of prevalence with cost-effective approaches based on SARS-CoV-2 RNA concentrations in wastewater samples. Two important aspects in building prediction models are: time over which the prediction occurs and space for which the predicted case numbers is shown. In this review, our main focus was on finding mathematical models which take into the account both the time-varying and spatial nature of wastewater-based metrics into account. We used six main characteristics as our assessment criteria: i) modelling approach; ii) temporal coverage; iii) spatial coverage; iv) sample size; v) wastewater sampling method; and vi) covariates included in the modelling. The majority of studies in the early phases of the pandemic recognized the temporal association of SARS-CoV-2 RNA concentration level in wastewater with the number of COVID-19 cases, ignoring their spatial context. We examined 15 studies up to April 2023, focusing on models considering both temporal and spatial aspects of wastewater metrics. Most early studies correlated temporal SARS-CoV-2 RNA levels with COVID-19 cases but overlooked spatial factors. Linear regression and SEIR models were commonly used (n = 10, 66.6 % of studies), along with machine learning (n = 1, 6.6 %) and Bayesian approaches (n = 1, 6.6 %) in some cases. Three studies employed spatio-temporal modelling approach (n = 3, 20.0 %). We conclude that the development, validation and calibration of further spatio-temporally explicit models should be done in parallel with the advancement of wastewater metrics before the potential of wastewater as a surveillance tool can be fully realised.
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Affiliation(s)
- Fatemeh Torabi
- Turing-RSS Health Data Lab, London, UK
- Population Data Science HDRUK-Wales, Medical School, Swansea University, Wales, UK
| | - Guangquan Li
- Turing-RSS Health Data Lab, London, UK
- Applied Statistics Research Group, Department of Mathematics, Physics and Electrical Engineering, Northumbria University, Newcastle upon Tyne NE1 8ST, UK
| | - Callum Mole
- Turing-RSS Health Data Lab, London, UK
- The Alan Turing Institute, London, UK
| | - George Nicholson
- Turing-RSS Health Data Lab, London, UK
- University of Oxford, Oxford, UK
| | - Barry Rowlingson
- Turing-RSS Health Data Lab, London, UK
- CHICAS, Lancaster Medical School, Lancaster University, England, UK
| | | | - Radka Jersakova
- Turing-RSS Health Data Lab, London, UK
- The Alan Turing Institute, London, UK
| | - Peter J. Diggle
- Turing-RSS Health Data Lab, London, UK
- CHICAS, Lancaster Medical School, Lancaster University, England, UK
| | - Marta Blangiardo
- Turing-RSS Health Data Lab, London, UK
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College, London, UK
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10
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Phan T, Brozak S, Pell B, Oghuan J, Gitter A, Hu T, Ribeiro RM, Ke R, Mena KD, Perelson AS, Kuang Y, Wu F. Making waves: Integrating wastewater surveillance with dynamic modeling to track and predict viral outbreaks. WATER RESEARCH 2023; 243:120372. [PMID: 37494742 DOI: 10.1016/j.watres.2023.120372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Revised: 07/10/2023] [Accepted: 07/15/2023] [Indexed: 07/28/2023]
Abstract
Wastewater surveillance has proved to be a valuable tool to track the COVID-19 pandemic. However, most studies using wastewater surveillance data revolve around establishing correlations and lead time relative to reported case data. In this perspective, we advocate for the integration of wastewater surveillance data with dynamic within-host and between-host models to better understand, monitor, and predict viral disease outbreaks. Dynamic models overcome emblematic difficulties of using wastewater surveillance data such as establishing the temporal viral shedding profile. Complementarily, wastewater surveillance data bypasses the issues of time lag and underreporting in clinical case report data, thus enhancing the utility and applicability of dynamic models. The integration of wastewater surveillance data with dynamic models can enhance real-time tracking and prevalence estimation, forecast viral transmission and intervention effectiveness, and most importantly, provide a mechanistic understanding of infectious disease dynamics and the driving factors. Dynamic modeling of wastewater surveillance data will advance the development of a predictive and responsive monitoring system to improve pandemic preparedness and population health.
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Affiliation(s)
- Tin Phan
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, NM 87544, USA
| | - Samantha Brozak
- School of Mathematical and Statistical Sciences, Arizona State University, AZ 85281, USA
| | - Bruce Pell
- Department of Mathematics and Computer Science, Lawrence Technological University, MI 48075, USA
| | - Jeremiah Oghuan
- School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Anna Gitter
- School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Tao Hu
- Department of Geography, Oklahoma State University, Stillwater, OK 74078, USA
| | - Ruy M Ribeiro
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, NM 87544, USA
| | - Ruian Ke
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, NM 87544, USA
| | - Kristina D Mena
- School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA; Texas Epidemic Public Health Institute, Houston, TX 77030, USA
| | - Alan S Perelson
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, NM 87544, USA; Santa Fe Institute, Santa Fe, NM 87501, USA
| | - Yang Kuang
- School of Mathematical and Statistical Sciences, Arizona State University, AZ 85281, USA
| | - Fuqing Wu
- School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA; Texas Epidemic Public Health Institute, Houston, TX 77030, USA.
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11
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Mattei M, Pintó RM, Guix S, Bosch A, Arenas A. Analysis of SARS-CoV-2 in wastewater for prevalence estimation and investigating clinical diagnostic test biases. WATER RESEARCH 2023; 242:120223. [PMID: 37354838 PMCID: PMC10265495 DOI: 10.1016/j.watres.2023.120223] [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: 03/10/2023] [Revised: 05/10/2023] [Accepted: 06/12/2023] [Indexed: 06/26/2023]
Abstract
Here we analyze SARS-CoV-2 genome copies in Catalonia's wastewater during the Omicron peak and develop a mathematical model to estimate the number of infections and the temporal relationship between reported and unreported cases. 1-liter samples from 16 wastewater treatment plants were collected and used in a compartmental epidemiological model. The average correlation between genome copies and reported cases was 0.85, with an average delay of 8.8 days. The model estimated that 53% of the population was infected, compared to the 19% reported cases. The under-reporting was highest in November and December 2021. The maximum genome copies shed in feces by an infected individual was estimated to range from 1.4×108 gc/g to 4.4×108 gc/g. Our framework demonstrates the potential of wastewater data as a leading indicator for daily new infections, particularly in contexts with low detection rates. It also serves as a complementary tool for prevalence estimation and offers a general approach for integrating wastewater data into compartmental models.
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Affiliation(s)
- Mattia Mattei
- Departament d'Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, 43007 Tarragona, Spain.
| | - Rosa M Pintó
- Enteric Virus Laboratory, School of Biology, University of Barcelona, 08028, Barcelona, Spain
| | - Susana Guix
- Enteric Virus Laboratory, School of Biology, University of Barcelona, 08028, Barcelona, Spain
| | - Albert Bosch
- Enteric Virus Laboratory, School of Biology, University of Barcelona, 08028, Barcelona, Spain
| | - Alex Arenas
- Departament d'Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, 43007 Tarragona, Spain; Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA, 99354, USA.
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12
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Ciannella S, González-Fernández C, Gomez-Pastora J. Recent progress on wastewater-based epidemiology for COVID-19 surveillance: A systematic review of analytical procedures and epidemiological modeling. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 878:162953. [PMID: 36948304 PMCID: PMC10028212 DOI: 10.1016/j.scitotenv.2023.162953] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 03/13/2023] [Accepted: 03/15/2023] [Indexed: 05/13/2023]
Abstract
On March 11, 2020, the World Health Organization declared the coronavirus disease 2019 (COVID-19), whose causative agent is the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), a pandemic. This virus is predominantly transmitted via respiratory droplets and shed via sputum, saliva, urine, and stool. Wastewater-based epidemiology (WBE) has been able to monitor the circulation of viral pathogens in the population. This tool demands both in-lab and computational work to be meaningful for, among other purposes, the prediction of outbreaks. In this context, we present a systematic review that organizes and discusses laboratory procedures for SARS-CoV-2 RNA quantification from a wastewater matrix, along with modeling techniques applied to the development of WBE for COVID-19 surveillance. The goal of this review is to present the current panorama of WBE operational aspects as well as to identify current challenges related to it. Our review was conducted in a reproducible manner by following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for systematic reviews. We identified a lack of standardization in wastewater analytical procedures. Regardless, the reverse transcription-quantitative polymerase chain reaction (RT-qPCR) approach was the most reported technique employed to detect and quantify viral RNA in wastewater samples. As a more convenient sample matrix, we suggest the solid portion of wastewater to be considered in future investigations due to its higher viral load compared to the liquid fraction. Regarding the epidemiological modeling, the data-driven approach was consistently used for the prediction of variables associated with outbreaks. Future efforts should also be directed toward the development of rapid, more economical, portable, and accurate detection devices.
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Affiliation(s)
- Stefano Ciannella
- Department of Chemical Engineering, Texas Tech University, Lubbock 79409, TX, USA.
| | - Cristina González-Fernández
- Department of Chemical Engineering, Texas Tech University, Lubbock 79409, TX, USA; Departamento de Ingenierías Química y Biomolecular, Universidad de Cantabria, Avda. Los Castros, s/n, 39005 Santander, Spain.
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13
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Belmonte-Lopes R, Barquilha CER, Kozak C, Barcellos DS, Leite BZ, da Costa FJOG, Martins WL, Oliveira PE, Pereira EHRA, Filho CRM, de Souza EM, Possetti GRC, Vicente VA, Etchepare RG. 20-Month monitoring of SARS-CoV-2 in wastewater of Curitiba, in Southern Brazil. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023:10.1007/s11356-023-27926-x. [PMID: 37243767 DOI: 10.1007/s11356-023-27926-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 05/22/2023] [Indexed: 05/29/2023]
Abstract
The COVID-19 pandemic resulted in the collapse of healthcare systems and led to the development and application of several approaches of wastewater-based epidemiology to monitor infected populations. The main objective of this study was to carry out a SARS-CoV-2 wastewater based surveillance in Curitiba, Southern Brazil Sewage samples were collected weekly for 20 months at the entrance of five treatment plants representing the entire city and quantified by qPCR using the N1 marker. The viral loads were correlated with epidemiological data. The correlation by sampling points showed that the relationship between the viral loads and the number of reported cases was best described by a cross-correlation function, indicating a lag between 7 and 14 days amidst the variables, whereas the data for the entire city presented a higher correlation (0.84) with the number of positive tests at lag 0 (sampling day). The results also suggest that the Omicron VOC resulted in higher titers than the Delta VOC. Overall, our results showed that the approach used was robust as an early warning system, even with the use of different epidemiological indicators or changes in the virus variants in circulation. Therefore, it can contribute to public decision-makers and health interventions, especially in vulnerable and low-income regions with limited clinical testing capacity. Looking toward the future, this approach will contribute to a new look at environmental sanitation and should even induce an increase in sewage coverage rates in emerging countries.
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Affiliation(s)
- Ricardo Belmonte-Lopes
- Graduate Program On Pathology, Parasitology, and Microbiology, Federal University of Paraná, 100 Coronel Francisco Heráclito Dos Santos Avenue, Curitiba, PR, 81530-000, Brazil
- Basic Pathology Department, Biological Sciences Sector, Microbiological Collections of Paraná Network, Room 135/136. 100 Coronel Francisco Heráclito Dos Santos Avenue, Curitiba, PR, 81530-000, Brazil
- Basic Pathology Department, Federal University of Paraná, 100 Coronel Francisco Heráclito Dos Santos Avenue, Curitiba, PR, 81530-000, Brazil
| | - Carlos E R Barquilha
- Graduate Program On Water Resources and Environmental Engineering, Hydraulics and Sanitation Department, Federal University of Paraná, 100 Coronel Francisco Heráclito Dos Santos Avenue, Curitiba, PR, 81530-000, Brazil
- Hydraulics and Sanitation Department, Federal University of Paraná, 100 Coronel Francisco Heráclito Dos Santos Avenue, Curitiba, PR, 81530-000, Brazil
| | - Caroline Kozak
- Environment Department, Maringa State University, SESI Block, 1800 Ângelo Moreira da Fonseca AvenueRoom 15, Parque Danielle, Umuarama, PR, 87506-370, Brazil
| | - Demian S Barcellos
- Hydraulics and Sanitation Department, Federal University of Paraná, 100 Coronel Francisco Heráclito Dos Santos Avenue, Curitiba, PR, 81530-000, Brazil
| | - Bárbara Z Leite
- Research and Innovation Management, Paraná Sanitation Company (SANEPAR), 1376 Eng. Rebouças St, Rebouças, Curitiba, PR, 80215-900, Brazil
| | - Fernanda J O Gomes da Costa
- Research and Innovation Management, Paraná Sanitation Company (SANEPAR), 1376 Eng. Rebouças St, Rebouças, Curitiba, PR, 80215-900, Brazil
| | - William L Martins
- Basic Pathology Department, Federal University of Paraná, 100 Coronel Francisco Heráclito Dos Santos Avenue, Curitiba, PR, 81530-000, Brazil
| | - Pâmela E Oliveira
- Hydraulics and Sanitation Department, Federal University of Paraná, 100 Coronel Francisco Heráclito Dos Santos Avenue, Curitiba, PR, 81530-000, Brazil
| | - Edy H R A Pereira
- Hydraulics and Sanitation Department, Federal University of Paraná, 100 Coronel Francisco Heráclito Dos Santos Avenue, Curitiba, PR, 81530-000, Brazil
| | - Cesar R Mota Filho
- Sanitary and Environmental Engineering Department, Federal University of Minas Gerais (UFMG), 6627 Antonio Carlos Avenue, Block 1, Room 4529, Belo Horizonte, MG, 31270-901, Brazil
| | - Emanuel M de Souza
- Biochemistry and Molecular Biology Department, Federal University of Paraná, 100 Coronel Francisco Heráclito Dos Santos Avenue, Curitiba, PR, 81530-000, Brazil
| | - Gustavo R C Possetti
- Research and Innovation Management, Paraná Sanitation Company (SANEPAR), 1376 Eng. Rebouças St, Rebouças, Curitiba, PR, 80215-900, Brazil
| | - Vania A Vicente
- Basic Pathology Department, Biological Sciences Sector, Microbiological Collections of Paraná Network, Room 135/136. 100 Coronel Francisco Heráclito Dos Santos Avenue, Curitiba, PR, 81530-000, Brazil
- Basic Pathology Department, Federal University of Paraná, 100 Coronel Francisco Heráclito Dos Santos Avenue, Curitiba, PR, 81530-000, Brazil
| | - Ramiro G Etchepare
- Hydraulics and Sanitation Department, Federal University of Paraná, 100 Coronel Francisco Heráclito Dos Santos Avenue, Curitiba, PR, 81530-000, Brazil.
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14
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Polcz P, Tornai K, Juhász J, Cserey G, Surján G, Pándics T, Róka E, Vargha M, Reguly IZ, Csikász-Nagy A, Pongor S, Szederkényi G. Wastewater-based modeling, reconstruction, and prediction for COVID-19 outbreaks in Hungary caused by highly immune evasive variants. WATER RESEARCH 2023; 241:120098. [PMID: 37295226 DOI: 10.1016/j.watres.2023.120098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 05/16/2023] [Accepted: 05/17/2023] [Indexed: 06/12/2023]
Abstract
(MOTIVATION) Wastewater-based epidemiology (WBE) has emerged as a promising approach for monitoring the COVID-19 pandemic, since the measurement process is cost-effective and is exposed to fewer potential errors compared to other indicators like hospitalization data or the number of detected cases. Consequently, WBE was gradually becoming a key tool for epidemic surveillance and often the most reliable data source, as the intensity of clinical testing for COVID-19 drastically decreased by the third year of the pandemic. Recent results suggests that the model-based fusion of wastewater measurements with clinical data and other indicators is essential in future epidemic surveillance. (METHOD) In this work, we developed a wastewater-based compartmental epidemic model with a two-phase vaccination dynamics and immune evasion. We proposed a multi-step optimization-based data assimilation method for epidemic state reconstruction, parameter estimation, and prediction. The computations make use of the measured viral load in wastewater, the available clinical data (hospital occupancy, delivered vaccine doses, and deaths), the stringency index of the official social distancing rules, and other measures. The current state assessment and the estimation of the current transmission rate and immunity loss allow a plausible prediction of the future progression of the pandemic. (RESULTS) Qualitative and quantitative evaluations revealed that the contribution of wastewater data in our computational epidemiological framework makes predictions more reliable. Predictions suggest that at least half of the Hungarian population has lost immunity during the epidemic outbreak caused by the BA.1 and BA.2 subvariants of Omicron in the first half of 2022. We obtained a similar result for the outbreaks caused by the subvariant BA.5 in the second half of 2022. (APPLICABILITY) The proposed approach has been used to support COVID management in Hungary and could be customized for other countries as well.
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Affiliation(s)
- Péter Polcz
- National Laboratory for Health Security, Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter utca 85, Budapest, H-1083, Hungary.
| | - Kálmán Tornai
- National Laboratory for Health Security, Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter utca 85, Budapest, H-1083, Hungary
| | - János Juhász
- National Laboratory for Health Security, Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter utca 85, Budapest, H-1083, Hungary; Institute of Medical Microbiology, Semmelweis University, Üllői út 26, Budapest, H-1085, Hungary
| | - György Cserey
- National Laboratory for Health Security, Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter utca 85, Budapest, H-1083, Hungary
| | - György Surján
- Department of Public Health Laboratory, National Public Health Centre, Albert Flórián út 2-6, Budapest, H-1097, Hungary; Department of Digital Health Sciences, Semmelweis University, Üllői út 26, Budapest, H-1085, Hungary
| | - Tamás Pándics
- Department of Public Health Laboratory, National Public Health Centre, Albert Flórián út 2-6, Budapest, H-1097, Hungary; Department of Public Health Sciences, Faculty of Health Sciences, Semmelweis University, Vas utca 17, Budapest, H-1088, Hungary
| | - Eszter Róka
- Department of Public Health Laboratory, National Public Health Centre, Albert Flórián út 2-6, Budapest, H-1097, Hungary
| | - Márta Vargha
- Department of Public Health Laboratory, National Public Health Centre, Albert Flórián út 2-6, Budapest, H-1097, Hungary
| | - István Z Reguly
- National Laboratory for Health Security, Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter utca 85, Budapest, H-1083, Hungary
| | - Attila Csikász-Nagy
- National Laboratory for Health Security, Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter utca 85, Budapest, H-1083, Hungary
| | - Sándor Pongor
- National Laboratory for Health Security, Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter utca 85, Budapest, H-1083, Hungary
| | - Gábor Szederkényi
- National Laboratory for Health Security, Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter utca 85, Budapest, H-1083, Hungary
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15
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Phan T, Brozak S, Pell B, Gitter A, Xiao A, Mena KD, Kuang Y, Wu F. A simple SEIR-V model to estimate COVID-19 prevalence and predict SARS-CoV-2 transmission using wastewater-based surveillance data. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 857:159326. [PMID: 36220466 PMCID: PMC9547654 DOI: 10.1016/j.scitotenv.2022.159326] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 09/15/2022] [Accepted: 10/05/2022] [Indexed: 06/12/2023]
Abstract
Wastewater-based surveillance (WBS) has been widely used as a public health tool to monitor SARS-CoV-2 transmission. However, epidemiological inference from WBS data remains understudied and limits its application. In this study, we have established a quantitative framework to estimate COVID-19 prevalence and predict SARS-CoV-2 transmission through integrating WBS data into an SEIR-V model. We conceptually divide the individual-level viral shedding course into exposed, infectious, and recovery phases as an analogy to the compartments in a population-level SEIR model. We demonstrated that the effect of temperature on viral losses in the sewer can be straightforwardly incorporated in our framework. Using WBS data from the second wave of the pandemic (Oct 02, 2020-Jan 25, 2021) in the Greater Boston area, we showed that the SEIR-V model successfully recapitulates the temporal dynamics of viral load in wastewater and predicts the true number of cases peaked earlier and higher than the number of reported cases by 6-16 days and 8.3-10.2 folds (R = 0.93). This work showcases a simple yet effective method to bridge WBS and quantitative epidemiological modeling to estimate the prevalence and transmission of SARS-CoV-2 in the sewershed, which could facilitate the application of wastewater surveillance of infectious diseases for epidemiological inference and inform public health actions.
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Affiliation(s)
- Tin Phan
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, NM, USA
| | - Samantha Brozak
- School of Mathematical and Statistical Sciences, Arizona State University, AZ, USA
| | - Bruce Pell
- Department of Mathematics and Computer Science, Lawrence Technological University, MI, USA
| | - Anna Gitter
- The University of Texas Health Science Center at Houston, School of Public Health, Houston, TX, USA 77030
| | - Amy Xiao
- Center for Microbiome Informatics and Therapeutics; Department of Biological Engineering, Massachusetts Institute of Technology
| | - Kristina D Mena
- The University of Texas Health Science Center at Houston, School of Public Health, Houston, TX, USA 77030
| | - Yang Kuang
- School of Mathematical and Statistical Sciences, Arizona State University, AZ, USA.
| | - Fuqing Wu
- The University of Texas Health Science Center at Houston, School of Public Health, Houston, TX, USA 77030.
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16
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Hopkins L, Persse D, Caton K, Ensor K, Schneider R, McCall C, Stadler LB. Citywide wastewater SARS-CoV-2 levels strongly correlated with multiple disease surveillance indicators and outcomes over three COVID-19 waves. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 855:158967. [PMID: 36162580 PMCID: PMC9507781 DOI: 10.1016/j.scitotenv.2022.158967] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 09/16/2022] [Accepted: 09/19/2022] [Indexed: 06/16/2023]
Abstract
Public health surveillance systems for COVID-19 are multifaceted and include multiple indicators reflective of different aspects of the burden and spread of the disease in a community. With the emergence of wastewater disease surveillance as a powerful tool to track infection dynamics of SARS-CoV-2, there is a need to integrate and validate wastewater information with existing disease surveillance systems and demonstrate how it can be used as a routine surveillance tool. A first step toward integration is showing how it relates to other disease surveillance indicators and outcomes, such as case positivity rates, syndromic surveillance data, and hospital bed use rates. Here, we present an 86-week long surveillance study that covers three major COVID-19 surges. City-wide SARS-CoV-2 RNA viral loads in wastewater were measured across 39 wastewater treatment plants and compared to other disease metrics for the city of Houston, TX. We show that wastewater levels are strongly correlated with positivity rate, syndromic surveillance rates of COVID-19 visits, and COVID-19-related general bed use rates at hospitals. We show that the relative timing of wastewater relative to each indicator shifted across the pandemic, likely due to a multitude of factors including testing availability, health-seeking behavior, and changes in viral variants. Next, we show that individual WWTPs led city-wide changes in SARS-CoV-2 viral loads, indicating a distributed monitoring system could be used to enhance the early-warning capability of a wastewater monitoring system. Finally, we describe how the results were used in real-time to inform public health response and resource allocation.
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Affiliation(s)
- Loren Hopkins
- Houston Health Department, 8000 N. Stadium Dr., Houston, TX, United States of America; Department of Statistics, Rice University, 6100 Main Street MS 138, Houston, TX, United States of America
| | - David Persse
- Houston Health Department, 8000 N. Stadium Dr., Houston, TX, United States of America; Department of Medicine and Surgery, Baylor College of Medicine, Houston, TX, United States of America; City of Houston Emergency Medical Services, Houston, TX, United States of America
| | - Kelsey Caton
- Houston Health Department, 8000 N. Stadium Dr., Houston, TX, United States of America
| | - Katherine Ensor
- Department of Statistics, Rice University, 6100 Main Street MS 138, Houston, TX, United States of America
| | - Rebecca Schneider
- Houston Health Department, 8000 N. Stadium Dr., Houston, TX, United States of America
| | - Camille McCall
- Department of Civil and Environmental Engineering, Rice University, 6100 Main Street MS-519, Houston, TX, United States of America
| | - Lauren B Stadler
- Department of Civil and Environmental Engineering, Rice University, 6100 Main Street MS-519, Houston, TX, United States of America.
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17
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Sridhar J, Parit R, Boopalakrishnan G, Rexliene MJ, Praveen R, Viswananathan B. Importance of wastewater-based epidemiology for detecting and monitoring SARS-CoV-2. CASE STUDIES IN CHEMICAL AND ENVIRONMENTAL ENGINEERING 2022; 6:100241. [PMID: 37520919 PMCID: PMC9341170 DOI: 10.1016/j.cscee.2022.100241] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 07/26/2022] [Accepted: 07/27/2022] [Indexed: 08/01/2023]
Abstract
Coronavirus disease caused by the SARS-CoV-2 virus has emerged as a global challenge in terms of health and disease monitoring. COVID-19 infection is mainly spread through the SARS-CoV-2 infection leading to the development of mild to severe clinical manifestations. The virus binds to its cognate receptor ACE2 which is widely expressed among different tissues in the body. Notably, SARS-CoV-2 shedding in the fecal samples has been reported through the screening of sewage water across various countries. Wastewater screening for the presence of SARS-CoV-2 provides an alternative method to monitor infection threat, variant identification, and clinical evaluation to restrict the virus progression. Multiple cohort studies have reported the application of wastewater treatment approaches and epidemiological significance in terms of virus monitoring. Thus, the manuscript outlines consolidated and systematic information regarding the application of wastewater-based epidemiology in terms of monitoring and managing a viral disease outbreak like COVID-19.
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Affiliation(s)
- Jayavel Sridhar
- Department of Biotechnology (DDE), Madurai Kamaraj University, Madurai, 625021, Tamilnadu, India
| | - Rahul Parit
- Department of Biotechnology (DDE), Madurai Kamaraj University, Madurai, 625021, Tamilnadu, India
| | | | - M Johni Rexliene
- Department of Biotechnology (DDE), Madurai Kamaraj University, Madurai, 625021, Tamilnadu, India
| | - Rajkumar Praveen
- Department of Biotechnology (DDE), Madurai Kamaraj University, Madurai, 625021, Tamilnadu, India
| | - Balaji Viswananathan
- Department of Biotechnology (DDE), Madurai Kamaraj University, Madurai, 625021, Tamilnadu, India
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18
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Kilaru P, Hill D, Anderson K, Collins MB, Green H, Kmush BL, Larsen DA. Wastewater Surveillance for Infectious Disease: A Systematic Review. Am J Epidemiol 2022; 192:305-322. [PMID: 36227259 PMCID: PMC9620728 DOI: 10.1093/aje/kwac175] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 08/25/2022] [Accepted: 10/05/2022] [Indexed: 02/07/2023] Open
Abstract
Wastewater surveillance for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been shown to be a valuable source of information regarding SARS-CoV-2 transmission and coronavirus disease 2019 (COVID-19) cases. Although the method has been used for several decades to track other infectious diseases, there has not been a comprehensive review outlining all of the pathogens that have been surveilled through wastewater. Herein we identify the infectious diseases that have been previously studied via wastewater surveillance prior to the COVID-19 pandemic. Infectious diseases and pathogens were identified in 100 studies of wastewater surveillance across 38 countries, as were themes of how wastewater surveillance and other measures of disease transmission were linked. Twenty-five separate pathogen families were identified in the included studies, with the majority of studies examining pathogens from the family Picornaviridae, including polio and nonpolio enteroviruses. Most studies of wastewater surveillance did not link what was found in the wastewater to other measures of disease transmission. Among those studies that did, the value reported varied by study. Wastewater surveillance should be considered as a potential public health tool for many infectious diseases. Wastewater surveillance studies can be improved by incorporating other measures of disease transmission at the population-level including disease incidence and hospitalizations.
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Affiliation(s)
- Pruthvi Kilaru
- Department of Public Health, Syracuse University, Syracuse, New York, United States,Des Moines University College of Osteopathic Medicine, Des Moines, Iowa, United States
| | - Dustin Hill
- Department of Public Health, Syracuse University, Syracuse, New York, United States,Graduate Program in Environmental Science, State University of New York College of Environmental Science and Forestry, Syracuse, New York, United States
| | - Kathryn Anderson
- Department of Medicine, State University of New York Upstate Medical University, Syracuse, New York, United States
| | - Mary B Collins
- Department of Environmental Studies, State University of New York College of Environmental Science, Syracuse, New York, United States
| | - Hyatt Green
- Department of Environmental Biology, State University of New York College of Environmental Science, Syracuse, New York, United States
| | - Brittany L Kmush
- Department of Public Health, Syracuse University, Syracuse, New York, United States
| | - David A Larsen
- Correspondence to Dr. Dave Larsen, Department of Public Health, Syracuse University, 430C White Hall, Syracuse, NY 13244 ()
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19
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Reynolds LJ, Gonzalez G, Sala-Comorera L, Martin NA, Byrne A, Fennema S, Holohan N, Kuntamukkula SR, Sarwar N, Nolan TM, Stephens JH, Whitty M, Bennett C, Luu Q, Morley U, Yandle Z, Dean J, Joyce E, O'Sullivan JJ, Cuddihy JM, McIntyre AM, Robinson EP, Dahly D, Fletcher NF, Carr M, De Gascun C, Meijer WG. SARS-CoV-2 variant trends in Ireland: Wastewater-based epidemiology and clinical surveillance. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 838:155828. [PMID: 35588817 PMCID: PMC9110007 DOI: 10.1016/j.scitotenv.2022.155828] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 05/05/2022] [Accepted: 05/06/2022] [Indexed: 05/21/2023]
Abstract
SARS-CoV-2 RNA quantification in wastewater is an important tool for monitoring the prevalence of COVID-19 disease on a community scale which complements case-based surveillance systems. As novel variants of concern (VOCs) emerge there is also a need to identify the primary circulating variants in a community, accomplished to date by sequencing clinical samples. Quantifying variants in wastewater offers a cost-effective means to augment these sequencing efforts. In this study, SARS-CoV-2 N1 RNA concentrations and daily loadings were determined and compared to case-based data collected as part of a national surveillance programme to determine the validity of wastewater surveillance to monitor infection spread in the greater Dublin area. Further, sequencing of clinical samples was conducted to determine the primary SARS-CoV-2 lineages circulating in Dublin. Finally, digital PCR was employed to determine whether SARS-CoV-2 VOCs, Alpha and Delta, were quantifiable from wastewater. No lead or lag time was observed between SARS-CoV-2 wastewater and case-based data and SARS-CoV-2 trends in Dublin wastewater significantly correlated with the notification of confirmed cases through case-based surveillance preceding collection with a 5-day average. This demonstrates that viral RNA in Dublin's wastewater mirrors the spread of infection in the community. Clinical sequence data demonstrated that increased COVID-19 cases during Ireland's third wave coincided with the introduction of the Alpha variant, while the fourth wave coincided with increased prevalence of the Delta variant. Interestingly, the Alpha variant was detected in Dublin wastewater prior to the first genome being sequenced from clinical samples, while the Delta variant was identified at the same time in clinical and wastewater samples. This work demonstrates the validity of wastewater surveillance for monitoring SARS-CoV-2 infections and also highlights its effectiveness in identifying circulating variants which may prove useful when sequencing capacity is limited.
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Affiliation(s)
- Liam J Reynolds
- UCD School of Biomolecular and Biomedical Science, UCD Earth Institute, UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - Gabriel Gonzalez
- National Virus Reference Laboratory (NVRL), School of Medicine, University College Dublin, Belfield, Dublin, Ireland; International Collaboration Unit, Research Center for Zoonosis Control, Hokkaido University, N20 W10 Kita-ku, Sapporo 001-0020, Japan
| | - Laura Sala-Comorera
- UCD School of Biomolecular and Biomedical Science, UCD Earth Institute, UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - Niamh A Martin
- UCD School of Biomolecular and Biomedical Science, UCD Earth Institute, UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - Alannah Byrne
- UCD School of Biomolecular and Biomedical Science, UCD Earth Institute, UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - Sanne Fennema
- UCD School of Biomolecular and Biomedical Science, UCD Earth Institute, UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - Niamh Holohan
- UCD School of Biomolecular and Biomedical Science, UCD Earth Institute, UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - Sailusha Ratnam Kuntamukkula
- UCD School of Biomolecular and Biomedical Science, UCD Earth Institute, UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - Natasha Sarwar
- UCD School of Biomolecular and Biomedical Science, UCD Earth Institute, UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - Tristan M Nolan
- UCD School of Biomolecular and Biomedical Science, UCD Earth Institute, UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - Jayne H Stephens
- UCD School of Biomolecular and Biomedical Science, UCD Earth Institute, UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - Megan Whitty
- UCD School of Biomolecular and Biomedical Science, UCD Earth Institute, UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - Charlene Bennett
- National Virus Reference Laboratory (NVRL), School of Medicine, University College Dublin, Belfield, Dublin, Ireland
| | - Quynh Luu
- National Virus Reference Laboratory (NVRL), School of Medicine, University College Dublin, Belfield, Dublin, Ireland
| | - Ursula Morley
- National Virus Reference Laboratory (NVRL), School of Medicine, University College Dublin, Belfield, Dublin, Ireland
| | - Zoe Yandle
- National Virus Reference Laboratory (NVRL), School of Medicine, University College Dublin, Belfield, Dublin, Ireland
| | - Jonathan Dean
- National Virus Reference Laboratory (NVRL), School of Medicine, University College Dublin, Belfield, Dublin, Ireland
| | - Eadaoin Joyce
- Irish Water, Colvill House, 24-26 Talbot Street, Dublin 1, Ireland
| | - John J O'Sullivan
- UCD School of Civil Engineering, UCD Dooge Centre for Water Resources Research and UCD Earth Institute, University College Dublin, Dublin 4, Ireland
| | - John M Cuddihy
- HSE - Health Protection Surveillance Centre, Dublin, Ireland
| | | | - Eve P Robinson
- HSE - Health Protection Surveillance Centre, Dublin, Ireland
| | - Darren Dahly
- Health Research Board Clinical Research Facility, University College Cork, Cork, Ireland
| | - Nicola F Fletcher
- UCD School of Veterinary Medicine and UCD Conway Institute, University College Dublin, Belfield, Dublin, Ireland
| | - Michael Carr
- National Virus Reference Laboratory (NVRL), School of Medicine, University College Dublin, Belfield, Dublin, Ireland; International Collaboration Unit, Research Center for Zoonosis Control, Hokkaido University, N20 W10 Kita-ku, Sapporo 001-0020, Japan
| | - Cillian De Gascun
- National Virus Reference Laboratory (NVRL), School of Medicine, University College Dublin, Belfield, Dublin, Ireland
| | - Wim G Meijer
- UCD School of Biomolecular and Biomedical Science, UCD Earth Institute, UCD Conway Institute, University College Dublin, Dublin, Ireland.
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20
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Xiao A, Wu F, Bushman M, Zhang J, Imakaev M, Chai PR, Duvallet C, Endo N, Erickson TB, Armas F, Arnold B, Chen H, Chandra F, Ghaeli N, Gu X, Hanage WP, Lee WL, Matus M, McElroy KA, Moniz K, Rhode SF, Thompson J, Alm EJ. Metrics to relate COVID-19 wastewater data to clinical testing dynamics. WATER RESEARCH 2022; 212:118070. [PMID: 35101695 PMCID: PMC8758950 DOI: 10.1016/j.watres.2022.118070] [Citation(s) in RCA: 47] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2021] [Revised: 11/29/2021] [Accepted: 01/11/2022] [Indexed: 05/02/2023]
Abstract
Wastewater surveillance has emerged as a useful tool in the public health response to the COVID-19 pandemic. While wastewater surveillance has been applied at various scales to monitor population-level COVID-19 dynamics, there is a need for quantitative metrics to interpret wastewater data in the context of public health trends. 24-hour composite wastewater samples were collected from March 2020 through May 2021 from a Massachusetts wastewater treatment plant and SARS-CoV-2 RNA concentrations were measured using RT-qPCR. The relationship between wastewater copy numbers of SARS-CoV-2 gene fragments and COVID-19 clinical cases and deaths varies over time. We demonstrate the utility of three new metrics to monitor changes in COVID-19 epidemiology: (1) the ratio between wastewater copy numbers of SARS-CoV-2 gene fragments and clinical cases (WC ratio), (2) the time lag between wastewater and clinical reporting, and (3) a transfer function between the wastewater and clinical case curves. The WC ratio increases after key events, providing insight into the balance between disease spread and public health response. Time lag and transfer function analysis showed that wastewater data preceded clinically reported cases in the first wave of the pandemic but did not serve as a leading indicator in the second wave, likely due to increased testing capacity, which allows for more timely case detection and reporting. These three metrics could help further integrate wastewater surveillance into the public health response to the COVID-19 pandemic and future pandemics.
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Affiliation(s)
- Amy Xiao
- Department of Biological Engineering, Massachusetts Institute of Technology USA; Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology USA
| | - Fuqing Wu
- Department of Biological Engineering, Massachusetts Institute of Technology USA; Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology USA
| | - Mary Bushman
- Harvard T.H. Chan School of Public Health, Harvard University USA
| | - Jianbo Zhang
- Department of Biological Engineering, Massachusetts Institute of Technology USA; Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology USA
| | | | - Peter R Chai
- Division of Medical Toxicology, Department of Emergency Medicine, Brigham and Women's Hospital, Harvard Medical School USA; The Fenway Institute, Fenway Health, Boston, MA USA; The Koch Institute for Integrated Cancer Research, Massachusetts Institute of Technology USA; Department of Psychosocial Oncology and Palliative Care, Dana Farber Cancer Institute USA
| | | | | | - Timothy B Erickson
- Division of Medical Toxicology, Department of Emergency Medicine, Brigham and Women's Hospital, Harvard Medical School USA; Harvard Humanitarian Initiative, Harvard University USA
| | - Federica Armas
- Singapore-MIT Alliance for Research and Technology, Antimicrobial Resistance Interdisciplinary Research Group, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
| | - Brian Arnold
- Department of Computer Science, Princeton University USA; Center for Statistics and Machine Learning, Princeton University USA
| | - Hongjie Chen
- Singapore-MIT Alliance for Research and Technology, Antimicrobial Resistance Interdisciplinary Research Group, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
| | - Franciscus Chandra
- Singapore-MIT Alliance for Research and Technology, Antimicrobial Resistance Interdisciplinary Research Group, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
| | | | - Xiaoqiong Gu
- Singapore-MIT Alliance for Research and Technology, Antimicrobial Resistance Interdisciplinary Research Group, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
| | - William P Hanage
- Harvard T.H. Chan School of Public Health, Harvard University USA
| | - Wei Lin Lee
- Singapore-MIT Alliance for Research and Technology, Antimicrobial Resistance Interdisciplinary Research Group, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
| | | | | | - Katya Moniz
- Department of Biological Engineering, Massachusetts Institute of Technology USA; Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology USA
| | | | - Janelle Thompson
- Campus for Research Excellence and Technological Enterprise (CREATE), Singapore; Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore; Asian School of the Environment, Nanyang Technological University, Singapore
| | - Eric J Alm
- Department of Biological Engineering, Massachusetts Institute of Technology USA; Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology USA; Singapore-MIT Alliance for Research and Technology, Antimicrobial Resistance Interdisciplinary Research Group, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore; Broad Institute of MIT and Harvard, Cambridge, MA USA.
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21
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Mahmoudi T, Naghdi T, Morales-Narváez E, Golmohammadi H. Toward smart diagnosis of pandemic infectious diseases using wastewater-based epidemiology. Trends Analyt Chem 2022; 153:116635. [PMID: 35440833 PMCID: PMC9010328 DOI: 10.1016/j.trac.2022.116635] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 03/21/2022] [Accepted: 04/07/2022] [Indexed: 12/12/2022]
Abstract
COVID-19 outbreak revealed fundamental weaknesses of current diagnostic systems, particularly in prediction and subsequently prevention of pandemic infectious diseases (PIDs). Among PIDs detection methods, wastewater-based epidemiology (WBE) has been demonstrated to be a favorable mean for estimation of community-wide health. Besides, by going beyond purely sensing usages of WBE, it can be efficiently exploited in Healthcare 4.0/5.0 for surveillance, monitoring, control, and above all prediction and prevention, thereby, resulting in smart sensing and management of potential outbreaks/epidemics/pandemics. Herein, an overview of WBE sensors for PIDs is presented. The philosophy behind the smart diagnosis of PIDs using WBE with the help of digital technologies is then discussed, as well as their characteristics to be met. Analytical techniques that are pushing the frontiers of smart sensing and have a high potential to be used in the smart diagnosis of PIDs via WBE are surveyed. In this context, we underscore key challenges ahead and provide recommendations for implementing and moving faster toward smart diagnostics.
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22
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Cluzel N, Courbariaux M, Wang S, Moulin L, Wurtzer S, Bertrand I, Laurent K, Monfort P, Gantzer C, Guyader SL, Boni M, Mouchel JM, Maréchal V, Nuel G, Maday Y. A nationwide indicator to smooth and normalize heterogeneous SARS-CoV-2 RNA data in wastewater. ENVIRONMENT INTERNATIONAL 2022; 158:106998. [PMID: 34991258 PMCID: PMC8608586 DOI: 10.1016/j.envint.2021.106998] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 11/20/2021] [Accepted: 11/21/2021] [Indexed: 05/18/2023]
Abstract
Since many infected people experience no or few symptoms, the SARS-CoV-2 epidemic is frequently monitored through massive virus testing of the population, an approach that may be biased and may be difficult to sustain in low-income countries. Since SARS-CoV-2 RNA can be detected in stool samples, quantifying SARS-CoV-2 genome by RT-qPCR in wastewater treatment plants (WWTPs) has been carried out as a complementary tool to monitor virus circulation among human populations. However, measuring SARS-CoV-2 viral load in WWTPs can be affected by many experimental and environmental factors. To circumvent these limits, we propose here a novel indicator, the wastewater indicator (WWI), that partly reduces and corrects the noise associated with the SARS-CoV-2 genome quantification in wastewater (average noise reduction of 19%). All data processing results in an average correlation gain of 18% with the incidence rate. The WWI can take into account the censorship linked to the limit of quantification (LOQ), allows the automatic detection of outliers to be integrated into the smoothing algorithm, estimates the average measurement error committed on the samples and proposes a solution for inter-laboratory normalization in the absence of inter-laboratory assays (ILA). This method has been successfully applied in the context of Obépine, a French national network that has been quantifying SARS-CoV-2 genome in a representative sample of French WWTPs since March 5th 2020. By August 26th, 2021, 168 WWTPs were monitored in the French metropolitan and overseas territories of France. We detail the process of elaboration of this indicator, show that it is strongly correlated to the incidence rate and that the optimal time lag between these two signals is only a few days, making our indicator an efficient complement to the incidence rate. This alternative approach may be especially important to evaluate SARS-CoV-2 dynamics in human populations when the testing rate is low.
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Affiliation(s)
- Nicolas Cluzel
- Sorbonne Université, Maison des Modélisations Ingénieries et Technologies (SUMMIT), 75005 Paris, France.
| | - Marie Courbariaux
- Sorbonne Université, Maison des Modélisations Ingénieries et Technologies (SUMMIT), 75005 Paris, France
| | - Siyun Wang
- Sorbonne Université, Maison des Modélisations Ingénieries et Technologies (SUMMIT), 75005 Paris, France
| | - Laurent Moulin
- Eau de Paris, Département de Recherche, Développement et Qualité de l'Eau, 33 avenue Jean Jaurès, F-94200 Ivry sur Seine, France
| | - Sébastien Wurtzer
- Eau de Paris, Département de Recherche, Développement et Qualité de l'Eau, 33 avenue Jean Jaurès, F-94200 Ivry sur Seine, France
| | | | - Karine Laurent
- Sorbonne Université, Maison des Modélisations Ingénieries et Technologies (SUMMIT), 75005 Paris, France
| | - Patrick Monfort
- HydroSciences Montpellier, UMR 5151, Université de Montpellier, CNRS, IRD, F-34093 Montpellier, France
| | | | - Soizick Le Guyader
- Ifremer, laboratoire de Microbiologie, SG2M/LSEM, BP 21105, 44311 Nantes, France
| | - Mickaël Boni
- Institut de Recherche Biomédicale des Armées, 1 place Valérie André, F-91220 Brétigny-sur-Orge, France
| | - Jean-Marie Mouchel
- Sorbonne Université, CNRS, EPHE, UMR 7619 Metis, e-LTER Zone Atelier Seine, F-75005 Paris, France
| | - Vincent Maréchal
- Sorbonne Université, INSERM, Centre de Recherche Saint-Antoine, F-75012 Paris, France
| | - Grégory Nuel
- Stochastics and Biology Group, Probability and Statistics (LPSM, CNRS 8001), Sorbonne University, Campus Pierre et Marie Curie, 4 Place Jussieu, 75005 Paris, France
| | - Yvon Maday
- Sorbonne Université, CNRS, Université de Paris, Laboratoire Jacques-Louis Lions (LJLL), F-75005 Paris, France; Institut Universaire de France, France.
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