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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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Ramos B, Lourenço AB, Monteiro S, Santos R, Cunha MV. Metagenomic profiling of raw wastewater in Portugal highlights microbiota and resistome signatures of public health interest beyond the usual suspects. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 946:174272. [PMID: 38925382 DOI: 10.1016/j.scitotenv.2024.174272] [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: 04/26/2024] [Revised: 06/22/2024] [Accepted: 06/22/2024] [Indexed: 06/28/2024]
Abstract
In response to the rapid emergence and dissemination of antimicrobial resistant bacteria (ARB) and genes (ARGs), integrated surveillance systems are needed to address antimicrobial resistance (AMR) within the One Health Era. Wastewater analyses enable biomarker monitoring at the sewershed level, offering timely insights into pathogen circulation and ARB/ARGs trends originating from different compartments. During two consecutive epidemic waves of the COVID-19 pandemic in Portugal, taxonomic and functional composition of raw urban wastewater from two wastewater treatment plants (WWTPs) representing one million in equivalent population, located in the main urban areas of the country, were profiled by shotgun metagenomics. Hospital wastewater from two central hospitals located in the WWTPs catchment areas were also sequenced. The resistome and virulome were profiled using metagenomic assemblies without taxonomic constraint, and then specifically characterized for ESKAPE pathogens. Urban and hospital wastewater exhibited specific microbiota signatures, Pseudomonadota dominated in the first and Bacteroidota in the latter. Correlation network analyses highlighted 85 (out of top 100) genera co-occurring across samples. The most frequent ARGs were classified in the multidrug, tetracyclines, and Macrolides, Lincosamides, Streptogramins (MLS) classes. Links established between AMR determinants and bacterial hosts evidenced that the diversity and abundance of ARGs is not restricted to ESKAPE, being also highly predominant among emergent enteropathogens, like Aeromonas and Aliarcobacter, or in the iron (II) oxidizer Acidovorax. The Aliarcobacter genus accumulated high abundance of sulphonamides and polymyxins ARGs, while Acinetobacter and Aeromonas hosted the highest abundance of ARGs against beta-lactams. Other bacteria (e.g. Clostridioides, Francisella, Vibrio cholerae) and genes (e.g. vanA-type vancomycin resistance) of public health interest were detected, with targeted monitoring efforts being needed to establish informative baseline data. Altogether, results highlight that wastewater monitoring is a valuable component of pathogen and AMR surveillance in healthy populations, providing a community-representative snapshot of public health trends beyond priority pathogens.
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Affiliation(s)
- Beatriz Ramos
- Pathogen Biology & Global Health Laboratory, Centre for Ecology, Evolution and Environmental Changes (cE3c) & CHANGE - Global Change and Sustainability Institute, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal
| | - Artur B Lourenço
- Pathogen Biology & Global Health Laboratory, Centre for Ecology, Evolution and Environmental Changes (cE3c) & CHANGE - Global Change and Sustainability Institute, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal
| | - Silvia Monteiro
- Laboratório de Águas, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal; Civil Engineering Research and Innovation for Sustainability (CERIS), Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal; Department of Nuclear Sciences and Engineering (DECN), Instituto Superior Técnico, Universidade de Lisboa, Bobadela, Portugal
| | - Ricardo Santos
- Laboratório de Águas, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal; Civil Engineering Research and Innovation for Sustainability (CERIS), Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal; Department of Nuclear Sciences and Engineering (DECN), Instituto Superior Técnico, Universidade de Lisboa, Bobadela, Portugal
| | - Mónica V Cunha
- Pathogen Biology & Global Health Laboratory, Centre for Ecology, Evolution and Environmental Changes (cE3c) & CHANGE - Global Change and Sustainability Institute, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal; Biosystems & Integrative Sciences Institute (BioISI), Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal.
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3
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Li L, Haak L, Carine M, Pagilla KR. Temporal assessment of SARS-CoV-2 detection in wastewater and its epidemiological implications in COVID-19 case dynamics. Heliyon 2024; 10:e29462. [PMID: 38638959 PMCID: PMC11024598 DOI: 10.1016/j.heliyon.2024.e29462] [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: 01/04/2024] [Revised: 04/04/2024] [Accepted: 04/08/2024] [Indexed: 04/20/2024] Open
Abstract
This research evaluated the relationship between daily new Coronavirus Disease 2019 (COVID-19) cases and Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) concentrations in wastewater, followed by effects of differential SARS-CoV-2 shedding loads across various COVID-19 outbreaks. Linear regression analyses were utilized to examine the lead time of the SARS-CoV-2 signal in wastewater relative to new COVID-19 clinical cases. During the Delta wave, no lead time was evident, highlighting limited predictive capability of wastewater monitoring during this phase. However, significant lead times were observed during the Omicron wave, potentially attributed to testing capacity overload and subsequent case reporting delays or changes in shedding patterns. During the Post-Omicron wave (Febuary 23 to May 19, 2022), no lead time was discernible, whereas following the lifting of the COVID-19 state of emergency (May 30, 2022 to May 30, 2023), the correlation coefficient increased and demonstrated the potential of wastewater surveillance as an early warning system. Subsequently, we explored the virus shedding in wastewater through feces, operationalized as the ratio of SARS-CoV-2 concentrations to daily new COVID-19 cases. This ratio varied significantly across the Delta, Omicron, other variants and post-state-emergency phases, with the Kruskal-Wallis H test confirming a significant difference in medians across these stages (P < 0.0001). Despite its promise, wastewater surveillance of COVID-19 disease prevalence presents several challenges, including virus shedding variability, data interpretation complexity, the impact of environmental factors on viral degradation, and the lack of standardized testing procedures. Overall, our findings offer insights into the correlation between COVID-19 cases and wastewater viral concentrations, potential variation in SARS-CoV-2 shedding in wastewater across different pandemic phases, and underscore the promise and limitations of wastewater surveillance as an early warning system for disease prevalence trends.
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Affiliation(s)
- Lin Li
- Department of Civil and Environmental Engineering, University of Nevada Reno, Reno, NV, 89557, USA
| | - Laura Haak
- Department of Civil and Environmental Engineering, University of Nevada Reno, Reno, NV, 89557, USA
| | - Madeline Carine
- Department of Civil and Environmental Engineering, University of Nevada Reno, Reno, NV, 89557, USA
| | - Krishna R. Pagilla
- Department of Civil and Environmental Engineering, University of Nevada Reno, Reno, NV, 89557, USA
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Majumdar R, Taye B, Bjornberg C, Giljork M, Lynch D, Farah F, Abdullah I, Osiecki K, Yousaf I, Luckstein A, Turri W, Sampathkumar P, Moyer AM, Kipp BR, Cattaneo R, Sussman CR, Navaratnarajah CK. From pandemic to endemic: Divergence of COVID-19 positive-tests and hospitalization numbers from SARS-CoV-2 RNA levels in wastewater of Rochester, Minnesota. Heliyon 2024; 10:e27974. [PMID: 38515669 PMCID: PMC10955309 DOI: 10.1016/j.heliyon.2024.e27974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 03/06/2024] [Accepted: 03/08/2024] [Indexed: 03/23/2024] Open
Abstract
Traditionally, public health surveillance relied on individual-level data but recently wastewater-based epidemiology (WBE) for the detection of infectious diseases including COVID-19 became a valuable tool in the public health arsenal. Here, we use WBE to follow the course of the COVID-19 pandemic in Rochester, Minnesota (population 121,395 at the 2020 census), from February 2021 to December 2022. We monitored the impact of SARS-CoV-2 infections on public health by comparing three sets of data: quantitative measurements of viral RNA in wastewater as an unbiased reporter of virus level in the community, positive results of viral RNA or antigen tests from nasal swabs reflecting community reporting, and hospitalization data. From February 2021 to August 2022 viral RNA levels in wastewater were closely correlated with the oscillating course of COVID-19 case and hospitalization numbers. However, from September 2022 cases remained low and hospitalization numbers dropped, whereas viral RNA levels in wastewater continued to oscillate. The low reported cases may reflect virulence reduction combined with abated inclination to report, and the divergence of virus levels in wastewater from reported cases may reflect COVID-19 shifting from pandemic to endemic. WBE, which also detects asymptomatic infections, can provide an early warning of impending cases, and offers crucial insights during pandemic waves and in the transition to the endemic phase.
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Affiliation(s)
| | - Biruhalem Taye
- Department of Molecular Medicine, Mayo Clinic, Rochester, MN, USA
| | | | | | | | | | | | | | - Iris Yousaf
- Department of Molecular Medicine, Mayo Clinic, Rochester, MN, USA
| | | | | | - Priya Sampathkumar
- Division of Infectious Diseases, Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA
| | - Ann M. Moyer
- Division of Laboratory Genetics and Genomics, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Benjamin R. Kipp
- Advanced Diagnostics Laboratory, Mayo Clinic, Rochester, MN, USA
- Division of Laboratory Genetics and Genomics, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Roberto Cattaneo
- Department of Molecular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Caroline R. Sussman
- Division of Nephrology and Hypertension, Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA
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5
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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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6
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Zamarreño JM, Torres-Franco AF, Gonçalves J, Muñoz R, Rodríguez E, Eiros JM, García-Encina P. Wastewater-based epidemiology for COVID-19 using dynamic artificial neural networks. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 917:170367. [PMID: 38278261 DOI: 10.1016/j.scitotenv.2024.170367] [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: 08/31/2023] [Revised: 01/20/2024] [Accepted: 01/20/2024] [Indexed: 01/28/2024]
Abstract
Global efforts in vaccination have led to a decrease in COVID-19 mortality but a high circulation of SARS-CoV-2 is still observed in several countries, resulting in some cases of severe lockdowns. In this sense, wastewater-based epidemiology remains a powerful tool for supporting regional health administrations in assessing risk levels and acting accordingly. In this work, a dynamic artificial neural network (DANN) has been developed for predicting the number of COVID-19 hospitalized patients in hospitals in Valladolid (Spain). This model takes as inputs a wastewater epidemiology indicator for COVID-19 (concentration of RNA from SARS-CoV-2 N1 gene reported from Valladolid Wastewater Treatment Plant), vaccination coverage, and past data of hospitalizations. The model considered both the instantaneous values of these variables and their historical evolution. Two study periods were selected (from May 2021 until September 2022 and from September 2022 to July 2023). During the first period, accurate predictions of hospitalizations (with an overall range between 6 and 171) were favored by the correlation of this indicator with N1 concentrations in wastewater (r = 0.43, p < 0.05), showing accurate forecasting for 1 day ahead and 5 days ahead. The second period's retraining strategy maintained the overall accuracy of the model despite lower hospitalizations. Furthermore, risk levels were assigned to each 1 day ahead prediction during the first and second periods, showing agreement with the level measured and reported by regional health authorities in 95 % and 93 % of cases, respectively. These results evidenced the potential of this novel DANN model for predicting COVID-19 hospitalizations based on SARS-CoV-2 wastewater concentrations at a regional scale. The model architecture herein developed can support regional health authorities in COVID-19 risk management based on wastewater-based epidemiology.
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Affiliation(s)
- Jesús M Zamarreño
- Institute of Sustainable Processes, Dr. Mergelina, s/n, 47011 Valladolid, Spain; Department of System Engineering and Automatic Control, School of Industrial Engineering, Universidad de Valladolid, C/ Dr. Mergelina s/n, 47011 Valladolid, Spain.
| | - Andrés F Torres-Franco
- Institute of Sustainable Processes, Dr. Mergelina, s/n, 47011 Valladolid, Spain; Department of Chemical Engineering and Environmental Technology, School of Industrial Engineering, Universidad de Valladolid, C/ Dr. Mergelina, s/n, 47011 Valladolid, Spain.
| | - José Gonçalves
- Institute of Sustainable Processes, Dr. Mergelina, s/n, 47011 Valladolid, Spain; Department of Chemical Engineering and Environmental Technology, School of Industrial Engineering, Universidad de Valladolid, C/ Dr. Mergelina, s/n, 47011 Valladolid, Spain
| | - Raúl Muñoz
- Institute of Sustainable Processes, Dr. Mergelina, s/n, 47011 Valladolid, Spain; Department of Chemical Engineering and Environmental Technology, School of Industrial Engineering, Universidad de Valladolid, C/ Dr. Mergelina, s/n, 47011 Valladolid, Spain
| | - Elisa Rodríguez
- Institute of Sustainable Processes, Dr. Mergelina, s/n, 47011 Valladolid, Spain; Department of Chemical Engineering and Environmental Technology, School of Industrial Engineering, Universidad de Valladolid, C/ Dr. Mergelina, s/n, 47011 Valladolid, Spain
| | - José María Eiros
- Microbiology Service, Hospital Universitario Río Hortega, Gerencia Regional de Salud, Paseo de Zorrilla 1, 47007 Valladolid, Spain
| | - Pedro García-Encina
- Institute of Sustainable Processes, Dr. Mergelina, s/n, 47011 Valladolid, Spain; Department of Chemical Engineering and Environmental Technology, School of Industrial Engineering, Universidad de Valladolid, C/ Dr. Mergelina, s/n, 47011 Valladolid, Spain
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7
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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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8
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Burnor E, Morin CW, Shirai JH, Zhou NA, Meschke JS. Development of a computational model to inform environmental surveillance sampling plans for Salmonella enterica serovar Typhi in wastewater. PLoS Negl Trop Dis 2024; 18:e0011468. [PMID: 38551999 PMCID: PMC11020695 DOI: 10.1371/journal.pntd.0011468] [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: 06/20/2023] [Revised: 04/16/2024] [Accepted: 02/14/2024] [Indexed: 04/18/2024] Open
Abstract
Typhoid fever-an acute febrile disease caused by infection with the bacterium Salmonella enterica serotype Typhi (S. Typhi)-continues to be a leading cause of global morbidity and mortality, particularly in developing countries with limited access to safe drinking water and adequate sanitation. Environmental surveillance, the process of detecting and enumerating disease-causing agents in wastewater, is a useful tool to monitor the circulation of typhoid fever in endemic regions. The design of environmental surveillance sampling plans and the interpretation of sampling results is complicated by a high degree of uncertainty and variability in factors that affect the final measured pathogens in wastewater samples, such as pathogen travel time through a wastewater network, pathogen dilution, decay and degradation, and laboratory processing methods. Computational models can, to an extent, assist in the design of sampling plans and aid in the evaluation of how different contributing factors affect sampling results. This study presents a computational model combining dynamic and probabilistic modeling techniques to estimate-on a spatial and temporal scale-the approximate probability of detecting S. Typhi within a wastewater system. This model may be utilized to inform environmental surveillance sampling plans and may provide useful insight into selecting appropriate sampling locations and times and interpreting results. A simulated applied modeling scenario is presented to demonstrate the model's functionality for aiding an environmental surveillance study in a typhoid-endemic community.
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Affiliation(s)
- Elisabeth Burnor
- Department of Environmental and Occupational Health Sciences, University of Washington School of Public Health, Seattle, Washington, United States of America
| | - Cory W. Morin
- Department of Environmental and Occupational Health Sciences, University of Washington School of Public Health, Seattle, Washington, United States of America
| | - Jeffry H. Shirai
- Department of Environmental and Occupational Health Sciences, University of Washington School of Public Health, Seattle, Washington, United States of America
| | - Nicolette A. Zhou
- Department of Environmental and Occupational Health Sciences, University of Washington School of Public Health, Seattle, Washington, United States of America
| | - John Scott Meschke
- Department of Environmental and Occupational Health Sciences, University of Washington School of Public Health, Seattle, Washington, United States of America
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9
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Rezaeitavabe F, Rezaie M, Modayil M, Pham T, Ice G, Riefler G, Coschigano KT. Beyond linear regression: Modeling COVID-19 clinical cases with wastewater surveillance of SARS-CoV-2 for the city of Athens and Ohio University campus. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 912:169028. [PMID: 38061656 DOI: 10.1016/j.scitotenv.2023.169028] [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: 05/05/2023] [Revised: 11/20/2023] [Accepted: 11/29/2023] [Indexed: 01/18/2024]
Abstract
Wastewater-based surveillance has emerged as a detection tool for population-wide infectious diseases, including coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Infected individuals shed the virus, which can be detected in wastewater using molecular techniques such as reverse transcription-digital polymerase chain reaction (RT-dPCR). This study examined the association between the number of clinical cases and the concentration of SARS-CoV-2 in wastewater beyond linear regression and for various normalizations of viral loads. Viral loads were measured in a total of 446 wastewater samples during the period from August 2021 to April 2022. These samples were collected from nine different locations, with 220 samples taken from four specific sites within the city of Athens and 226 samples from five sites within Ohio University. The correlation between COVID-19 cases and wastewater viral concentrations, which was estimated using the Pearson correlation coefficient, was statistically significant and ranged from 0.6 to 0.9. In addition, time-lagged cross correlation was applied to identify the lag time between clinical and wastewater data, estimated 4 to 7 days. While we also explored the effect on the correlation coefficients of various normalizations of viral loads accounting for procedural loss or amount of fecal material and of estimated lag times, these alternative specifications did not change our substantive conclusions. Additionally, several linear and non-linear regression models were applied to predict the COVID-19 cases given wastewater data as input. The non-linear approach was found to yield the highest R-squared and Pearson correlation and lowest Mean Absolute Error values between the predicted and actual number of COVID-19 cases for both aggregated OHIO Campus and city data. Our results provide support for previous studies on correlation and time lag and new evidence that non-linear models, approximated with artificial neural networks, should be implemented for WBS of contagious diseases.
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Affiliation(s)
- Fatemeh Rezaeitavabe
- Ohio University, Russ College of Engineering, Department of Civil and Environmental Engineering, Athens, OH 45701, USA
| | - Mehdi Rezaie
- Kansas State University, Department of Physics, Manhattan, KS 66506, USA
| | - Maria Modayil
- Ohio University, Division of Diversity and Inclusion, Athens, OH 45701, USA; Ohio University, College of Health Sciences and Professions, Department of Interdisciplinary Health Studies, Athens, OH 45701, USA
| | - Tuyen Pham
- Ohio University, Voinovich School of Leadership and Public Service, Athens, OH 45701, USA
| | - Gillian Ice
- Ohio University, College of Health Sciences and Professions, Department of Interdisciplinary Health Studies, Athens, OH 45701, USA; Ohio University, Heritage College of Osteopathic Medicine, Department of Social Medicine, Athens, OH 45701, USA
| | - Guy Riefler
- Ohio University, Russ College of Engineering, Department of Civil and Environmental Engineering, Athens, OH 45701, USA
| | - Karen T Coschigano
- Ohio University, Heritage College of Osteopathic Medicine, Department of Biomedical Sciences, Athens, OH 45701, USA.
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10
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Shrestha S, Malla B, Angga MS, Sthapit N, Raya S, Hirai S, Rahmani AF, Thakali O, Haramoto E. Long-term SARS-CoV-2 surveillance in wastewater and estimation of COVID-19 cases: An application of wastewater-based epidemiology. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 896:165270. [PMID: 37400022 DOI: 10.1016/j.scitotenv.2023.165270] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 06/30/2023] [Accepted: 06/30/2023] [Indexed: 07/05/2023]
Abstract
The role of wastewater-based epidemiology (WBE), a powerful tool to complement clinical surveillance, has increased as many grassroots-level facilities, such as municipalities and cities, are actively involved in wastewater monitoring, and the clinical testing of coronavirus disease 2019 (COVID-19) is downscaled widely. This study aimed to conduct long-term wastewater surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Yamanashi Prefecture, Japan, using one-step reverse transcription-quantitative polymerase chain reaction (RT-qPCR) assay and estimate COVID-19 cases using a cubic regression model that is simple to implement. Influent wastewater samples (n = 132) from a wastewater treatment plant were collected normally once weekly between September 2020 and January 2022 and twice weekly between February and August 2022. Viruses in wastewater samples (40 mL) were concentrated by the polyethylene glycol precipitation method, followed by RNA extraction and RT-qPCR. The K-6-fold cross-validation method was used to select the appropriate data type (SARS-CoV-2 RNA concentration and COVID-19 cases) suitable for the final model run. SARS-CoV-2 RNA was successfully detected in 67 % (88 of 132) of the samples tested during the whole surveillance period, 37 % (24 of 65) and 96 % (64 of 67) of the samples collected before and during 2022, respectively, with concentrations ranging from 3.5 to 6.3 log10 copies/L. This study applied a nonnormalized SARS-CoV-2 RNA concentration and nonstandardized data for running the final 14-day (1 to 14 days) offset models to estimate weekly average COVID-19 cases. Comparing the parameters used for a model evaluation, the best model showed that COVID-19 cases lagged 3 days behind the SARS-CoV-2 RNA concentration in wastewater samples during the Omicron variant phase (year 2022). Finally, 3- and 7-day offset models successfully predicted the trend of COVID-19 cases from September 2022 until February 2023, indicating the applicability of WBE as an early warning tool.
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Affiliation(s)
- Sadhana Shrestha
- Interdisciplinary Center for River Basin Environment, 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
| | - Made Sandhyana Angga
- Department of Engineering, 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
| | - Sunayana Raya
- Department of Engineering, 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
| | - Aulia Fajar Rahmani
- Department of Engineering, 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
| | - Eiji Haramoto
- Interdisciplinary Center for River Basin Environment, University of Yamanashi, 4-3-11 Takeda, Kofu, Yamanashi 400-8511, Japan.
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11
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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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12
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Maida CM, Tramuto F, Giammanco GM, Palermo R, Priano W, De Grazia S, Purpari G, La Rosa G, Suffredini E, Lucentini L, Palermo M, Pollina Addario W, Graziano G, Immordino P, Vitale F, Mazzucco W. Wastewater-Based Epidemiology as a Tool to Detect SARS-CoV-2 Circulation at the Community Level: Findings from a One-Year Wastewater Investigation Conducted in Sicily, Italy. Pathogens 2023; 12:748. [PMID: 37375438 PMCID: PMC10305655 DOI: 10.3390/pathogens12060748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 05/18/2023] [Accepted: 05/22/2023] [Indexed: 06/29/2023] Open
Abstract
Wastewater-based epidemiology is a well-established tool for detecting and monitoring the spread of enteric pathogens and the use of illegal drugs in communities in real time. Since only a few studies in Italy have investigated the correlation between SARS-CoV-2 in wastewater and the prevalence of COVID-19 cases from clinical testing, we conducted a one-year wastewater surveillance study in Sicily to correlate the load of SARS-CoV-2 RNA in wastewater and the reported cumulative prevalence of COVID-19 in 14 cities from October 2021 to September 2022. Furthermore, we investigated the role of SARS-CoV-2 variants and subvariants in the increase in the number of SARS-CoV-2 infections. Our findings showed a significant correlation between SARS-CoV-2 RNA load in wastewater and the number of active cases reported by syndromic surveillance in the population. Moreover, the correlation between SARS-CoV-2 in wastewater and the active cases remained high when a lag of 7 or 14 days was considered. Finally, we attributed the epidemic waves observed to the rapid emergence of the Omicron variant and the BA.4 and BA.5 subvariants. We confirmed the effectiveness of wastewater monitoring as a powerful epidemiological proxy for viral variant spread and an efficient complementary method for surveillance.
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Affiliation(s)
- Carmelo Massimo Maida
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties “G. D’Alessandro”, University of Palermo, Via del Vespro 133, 90127 Palermo, Italy
- Clinical Epidemiology Unit, Regional Reference Laboratory of Western Sicily for the Emergence of COVID-19, University Hospital “P. Giaccone”, Via del Vespro 133, 90127 Palermo, Italy
| | - Fabio Tramuto
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties “G. D’Alessandro”, University of Palermo, Via del Vespro 133, 90127 Palermo, Italy
- Clinical Epidemiology Unit, Regional Reference Laboratory of Western Sicily for the Emergence of COVID-19, University Hospital “P. Giaccone”, Via del Vespro 133, 90127 Palermo, Italy
| | - Giovanni Maurizio Giammanco
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties “G. D’Alessandro”, University of Palermo, Via del Vespro 133, 90127 Palermo, Italy
| | - Roberta Palermo
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties “G. D’Alessandro”, University of Palermo, Via del Vespro 133, 90127 Palermo, Italy
| | - Walter Priano
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties “G. D’Alessandro”, University of Palermo, Via del Vespro 133, 90127 Palermo, Italy
| | - Simona De Grazia
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties “G. D’Alessandro”, University of Palermo, Via del Vespro 133, 90127 Palermo, Italy
| | - Giuseppa Purpari
- Istituto Zooprofilattico Sperimentale della Sicilia “A. Mirri”, Via Marinuzzi, 90129 Palermo, Italy;
| | - Giuseppina La Rosa
- Istituto Superiore di Sanità (ISS), Viale Regina Elena 299, 00161 Rome, Italy; (G.L.R.)
| | - Elisabetta Suffredini
- Istituto Superiore di Sanità (ISS), Viale Regina Elena 299, 00161 Rome, Italy; (G.L.R.)
| | - Luca Lucentini
- Istituto Superiore di Sanità (ISS), Viale Regina Elena 299, 00161 Rome, Italy; (G.L.R.)
| | - Mario Palermo
- Regional Health Authority of Sicily, Via Vaccaro 5, 90145 Palermo, Italy
| | | | - Giorgio Graziano
- Clinical Epidemiology Unit, Regional Reference Laboratory of Western Sicily for the Emergence of COVID-19, University Hospital “P. Giaccone”, Via del Vespro 133, 90127 Palermo, Italy
| | - Palmira Immordino
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties “G. D’Alessandro”, University of Palermo, Via del Vespro 133, 90127 Palermo, Italy
| | - Francesco Vitale
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties “G. D’Alessandro”, University of Palermo, Via del Vespro 133, 90127 Palermo, Italy
- Clinical Epidemiology Unit, Regional Reference Laboratory of Western Sicily for the Emergence of COVID-19, University Hospital “P. Giaccone”, Via del Vespro 133, 90127 Palermo, Italy
| | | | - Walter Mazzucco
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties “G. D’Alessandro”, University of Palermo, Via del Vespro 133, 90127 Palermo, Italy
- Clinical Epidemiology Unit, Regional Reference Laboratory of Western Sicily for the Emergence of COVID-19, University Hospital “P. Giaccone”, Via del Vespro 133, 90127 Palermo, Italy
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