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Chai X, Liu S, Liu C, Bai J, Meng J, Tian H, Han X, Han G, Xu X, Li Q. Surveillance of SARS-CoV-2 in wastewater by quantitative PCR and digital PCR: a case study in Shijiazhuang city, Hebei province, China. Emerg Microbes Infect 2024; 13:2324502. [PMID: 38465692 DOI: 10.1080/22221751.2024.2324502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Accepted: 02/22/2024] [Indexed: 03/12/2024]
Abstract
In this study, we reported the first long-term monitoring of SARS-CoV-2 in wastewater in Mainland China from November 2021 to October 2023. The city of Shijiazhuang was employed for this case study. We developed a triple reverse transcription droplet digital PCR (RT-ddPCR) method using triple primer-probes for simultaneous detection of the N1 gene, E gene, and Pepper mild mottle virus (PMMoV) to achieve accurate quantification of SARS-CoV-2 RNA in wastewater. Both the RT-ddPCR method and the commercial multiplex reverse transcription quantitative polymerase chain reaction (RT-qPCR) method were implemented for the detection of SARS-CoV-2 in wastewater in Shijiazhuang City over a 24-month period. Results showed that SARS-CoV-2 was detected for the first time in the wastewater of Shijiazhuang City on 10 November 2022. The peak of COVID-19 cases occurred in the middle of December 2022, when the concentration of SARS-CoV-2 in the wastewater was highest. The trend of virus concentration increases and decreases forming a "long-tailed" shape in the COVID-19 outbreak and recession cycle. The results indicated that both multiplex RT-ddPCR and RT-qPCR are effective in detecting SARS-CoV-2 in wastewater, but RT-ddPCR is capable of detecting low concentrations of SARS-CoV-2 in wastewater which is more efficient. The SARS-CoV-2 abundance in wastewater is correlated to clinical data, outlining the public health utility of this work.HighlightsFirst long-term monitoring of SARS-CoV-2 in wastewater in Mainland ChinaCOVID-19 outbreak was tracked in Shijiazhuang City from outbreak to containmentWastewater was monitored simultaneously using RT-ddPCR and RT-qPCR methodsTriple primer-probe RT-ddPCR detects N1 and E genes of SARS-CoV-2 and PMMoV.
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Affiliation(s)
- Xiaoru Chai
- School of Public Health, Hebei Medical University, Shijiazhuang, People's Republic of China
| | - Shiyou Liu
- Hebei Key Laboratory of Pathogens and Epidemiology of Infectious Diseases, Hebei Provincial Center for Disease Control and Prevention, Shijiazhuang, People's Republic of China
| | - Chao Liu
- Shijiazhuang Qiaodong Sewage Treatment Plant, Shijiazhuang, People's Republic of China
| | - Jiaxuan Bai
- School of Public Health, Hebei Medical University, Shijiazhuang, People's Republic of China
| | - Juntao Meng
- School of Public Health, Hebei Medical University, Shijiazhuang, People's Republic of China
| | - Hong Tian
- School of Public Health, Hebei Medical University, Shijiazhuang, People's Republic of China
| | - Xu Han
- Hebei Key Laboratory of Pathogens and Epidemiology of Infectious Diseases, Hebei Provincial Center for Disease Control and Prevention, Shijiazhuang, People's Republic of China
| | - Guangyue Han
- Hebei Key Laboratory of Pathogens and Epidemiology of Infectious Diseases, Hebei Provincial Center for Disease Control and Prevention, Shijiazhuang, People's Republic of China
| | - Xiangdong Xu
- School of Public Health, Hebei Medical University, Shijiazhuang, People's Republic of China
- Hebei Key Laboratory of Environment and Human Health, Shijiazhuang, People's Republic of China
| | - Qi Li
- Hebei Key Laboratory of Pathogens and Epidemiology of Infectious Diseases, Hebei Provincial Center for Disease Control and Prevention, Shijiazhuang, People's Republic of China
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Zhao L, Guzman HP, Xagoraraki I. Comparative analyses of SARS-CoV-2 RNA concentrations in Detroit wastewater quantified with CDC N1, N2, and SC2 assays reveal optimal target for predicting COVID-19 cases. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 945:174140. [PMID: 38906283 DOI: 10.1016/j.scitotenv.2024.174140] [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: 03/17/2024] [Revised: 06/10/2024] [Accepted: 06/17/2024] [Indexed: 06/23/2024]
Abstract
To monitor COVID-19 through wastewater surveillance, global researchers dedicated significant endeavors and resources to develop and implement diverse RT-qPCR or RT-ddPCR assays targeting different genes of SARS-CoV-2. Effective wastewater surveillance hinges on the appropriate selection of the most suitable assay, especially for resource-constrained regions where scant technical and socioeconomic resources restrict the options for testing with multiple assays. Further research is imperative to evaluate the existing assays through comprehensive comparative analyses. Such analyses are crucial for health agencies and wastewater surveillance practitioners in the selection of appropriate methods for monitoring COVID-19. In this study, untreated wastewater samples were collected weekly from the Detroit wastewater treatment plant, Michigan, USA, between January and December 2023. Polyethylene glycol precipitation (PEG) was applied to concentrate the samples followed by RNA extraction and RT-ddPCR. Three assays including N1, N2 (US CDC Real-Time Reverse Transcription PCR Panel for Detection of SARS-CoV-2), and SC2 assay (US CDC Influenza SARS-CoV-2 Multiplex Assay) were implemented to detect SARS-CoV-2 in wastewater. The limit of blank and limit of detection for the three assays were experimentally determined. SARS-CoV-2 RNA concentrations were evaluated and compared through three statistical approaches, including Pearson and Spearman's rank correlations, Dynamic Time Warping, and vector autoregressive models. N1 and N2 demonstrated the highest correlation and most similar time series patterns. Conversely, N2 and SC2 assay demonstrated the lowest correlation and least similar time series patterns. N2 was identified as the optimal target to predict COVID-19 cases. This study presents a rigorous effort in evaluating and comparing SARS-CoV-2 RNA concentrations quantified with N1, N2, and SC2 assays and their interrelations and correlations with clinical cases. This study provides valuable insights into identifying the optimal target for monitoring COVID-19 through wastewater surveillance.
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Affiliation(s)
- Liang Zhao
- Department of Civil and Environmental Engineering, Michigan State University, 1449 Engineering Research Ct., East Lansing, MI 48823, USA
| | - Heidy Peidro Guzman
- Department of Civil and Environmental Engineering, Michigan State University, 1449 Engineering Research Ct., East Lansing, MI 48823, USA
| | - Irene Xagoraraki
- Department of Civil and Environmental Engineering, Michigan State University, 1449 Engineering Research Ct., East Lansing, MI 48823, USA.
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Armenise E, Rustage S, Jackson KJ, Watts G, Hart A. Adjusting for dilution in wastewater using biomarkers: A practical approach. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 366:121596. [PMID: 38991335 DOI: 10.1016/j.jenvman.2024.121596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 06/06/2024] [Accepted: 06/23/2024] [Indexed: 07/13/2024]
Abstract
We developed a biomarker-based approach to quantify in-sewer dilution by measuring wastewater quality parameters (ammoniacal-N, orthophosphate, crAssphage). This approach can enhance the environmental management of wastewater treatment works (WWTW) by optimising their operation and providing cost-effective information on the health and behaviour of populations and their interactions with the environment through wastewater-based epidemiology (WBE). Our method relies on site specific baselines calculated for each biomarker. These baselines reflect the sewer conditions without the influence of rainfall-derived inflow and infiltration (RDII). Ammoniacal-N was the best candidate to use as proxy for dilution. We demonstrated that the dilution calculated using biomarkers correlates well with the dilution indicated by measured flow. In some instances, the biomarkers showed much higher dilution than measured flows. These differences were attributed to the loss of flow volume at wastewater treatment works due to the activation of combined sewer overflows (CSOs) and/or storm tanks. Using flow measured directly at the WWTW could therefore result in underestimation of target analyte loads.
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Affiliation(s)
- E Armenise
- Environment Agency, Horizon House, Deanery Road, Bristol, BS1 5AH, UK.
| | - S Rustage
- Environment Agency, Horizon House, Deanery Road, Bristol, BS1 5AH, UK
| | - K J Jackson
- Environment Agency, Horizon House, Deanery Road, Bristol, BS1 5AH, UK
| | - G Watts
- Environment Agency, Horizon House, Deanery Road, Bristol, BS1 5AH, UK
| | - A Hart
- Environment Agency, Horizon House, Deanery Road, Bristol, BS1 5AH, UK
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Ahmed W, Liu Y, Smith W, Ingall W, Belby M, Bivins A, Bertsch P, Williams DT, Richards K, Simpson S. Leveraging wastewater surveillance to detect viral diseases in livestock settings. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 931:172593. [PMID: 38642765 DOI: 10.1016/j.scitotenv.2024.172593] [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: 02/29/2024] [Revised: 04/14/2024] [Accepted: 04/17/2024] [Indexed: 04/22/2024]
Abstract
Wastewater surveillance has evolved into a powerful tool for monitoring public health-relevant analytes. Recent applications in tracking severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection highlight its potential. Beyond humans, it can be extended to livestock settings where there is increasing demand for livestock products, posing risks of disease emergence. Wastewater surveillance may offer non-invasive, cost-effective means to detect potential outbreaks among animals. This approach aligns with the "One Health" paradigm, emphasizing the interconnectedness of animal, human, and ecosystem health. By monitoring viruses in livestock wastewater, early detection, prevention, and control strategies can be employed, safeguarding both animal and human health, economic stability, and international trade. This integrated "One Health" approach enhances collaboration and a comprehensive understanding of disease dynamics, supporting proactive measures in the Anthropocene era where animal and human diseases are on the rise.
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Affiliation(s)
- Warish Ahmed
- CSIRO Environment, Ecosciences Precinct, 41 Boggo Road, Dutton Park, QLD 4102, Australia.
| | - Yawen Liu
- CSIRO Environment, Ecosciences Precinct, 41 Boggo Road, Dutton Park, QLD 4102, Australia; State Key Laboratory of Marine Environmental Science, College of the Environment & Ecology, Xiamen University, Xiamen 361102, China
| | - Wendy Smith
- CSIRO Environment, Ecosciences Precinct, 41 Boggo Road, Dutton Park, QLD 4102, Australia
| | - Wayne Ingall
- Wide Bay Public Health Unit, 14 Branyan Street, Bundaberg, West Qld 4670, Australia
| | - Michael Belby
- Wide Bay Public Health Unit, 14 Branyan Street, Bundaberg, West Qld 4670, Australia
| | - Aaron Bivins
- Department of Civil & Environmental Engineering, Louisiana State University, Baton Rouge, LA 70803, USA
| | - Paul Bertsch
- CSIRO Environment, Ecosciences Precinct, 41 Boggo Road, Dutton Park, QLD 4102, Australia
| | - David T Williams
- CSIRO Australian Centre for Disease Preparedness, 5 Portarlington Road, Geelong, VIC 3220, Australia
| | - Kirsty Richards
- SunPork Group, 1/6 Eagleview Place, Eagle Farm, QLD 4009, Australia
| | - Stuart Simpson
- CSIRO Environment, Ecosciences Precinct, 41 Boggo Road, Dutton Park, QLD 4102, Australia
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Williams RC, Farkas K, Garcia-Delgado A, Adwan L, Kevill JL, Cross G, Weightman AJ, Jones DL. Simultaneous detection and characterization of common respiratory pathogens in wastewater through genomic sequencing. WATER RESEARCH 2024; 256:121612. [PMID: 38642537 DOI: 10.1016/j.watres.2024.121612] [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: 11/28/2023] [Revised: 04/11/2024] [Accepted: 04/12/2024] [Indexed: 04/22/2024]
Abstract
Genomic surveillance of SARS-CoV-2 has given insight into the evolution and epidemiology of the virus and its variant lineages during the COVID-19 pandemic. Expanding this approach to include a range of respiratory pathogens can better inform public health preparedness for potential outbreaks and epidemics. Here, we simultaneously sequenced 38 pathogens including influenza viruses, coronaviruses and bocaviruses, to examine the abundance and seasonality of respiratory pathogens in urban wastewater. We deployed a targeted bait capture method and short-read sequencing (Illumina Respiratory Virus Oligos Panel; RVOP) on composite wastewater samples from 8 wastewater treatment plants (WWTPs) and one associated hospital site. By combining seasonal sampling with whole genome sequencing, we were able to concurrently detect and characterise a range of common respiratory pathogens, including SARS-CoV-2, adenovirus and parainfluenza virus. We demonstrated that 38 respiratory pathogens can be detected at low abundances year-round, that hospital pathogen diversity is higher in winter vs. summer sampling events, and that significantly more viruses are detected in raw influent compared to treated effluent samples. Finally, we compared detection sensitivity of RT-qPCR vs. next generation sequencing for SARS-CoV-2, enteroviruses, influenza A/B, and respiratory syncytial viruses. We conclude that both should be used in combination; RT-qPCR allowed accurate quantification, whilst genomic sequencing detected pathogens at lower abundance. We demonstrate the valuable role of wastewater genomic surveillance and its contribution to the field of wastewater-based epidemiology, gaining rapid understanding of the seasonal presence and persistence for common respiratory pathogens. By simultaneously monitoring seasonal trends and early warning signs of many viruses circulating in communities, public health agencies can implement targeted prevention and rapid response plans.
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Affiliation(s)
- Rachel C Williams
- School of Environmental and Natural Sciences, Bangor University, Bangor, Gwynedd LL57 2UW, UK.
| | - Kata Farkas
- School of Environmental and Natural Sciences, Bangor University, Bangor, Gwynedd LL57 2UW, UK
| | - Alvaro Garcia-Delgado
- School of Environmental and Natural Sciences, Bangor University, Bangor, Gwynedd LL57 2UW, UK
| | - Latifah Adwan
- School of Environmental and Natural Sciences, Bangor University, Bangor, Gwynedd LL57 2UW, UK
| | - Jessica L Kevill
- School of Environmental and Natural Sciences, Bangor University, Bangor, Gwynedd LL57 2UW, UK
| | - Gareth Cross
- Science Evidence Advice Division, Health and Social Services Group, Welsh Government, Cathays Park, Cardiff, CF10 3NQ, UK
| | - Andrew J Weightman
- School of Biosciences, Cardiff University, Museum Avenue, Cardiff CF10 3AX, UK
| | - Davey L Jones
- School of Environmental and Natural Sciences, Bangor University, Bangor, Gwynedd LL57 2UW, UK; Food Futures Institute, Murdoch University, Murdoch WA 6150, Australia
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Liu Y, Sapoval N, Gallego-García P, Tomás L, Posada D, Treangen TJ, Stadler LB. Crykey: Rapid identification of SARS-CoV-2 cryptic mutations in wastewater. Nat Commun 2024; 15:4545. [PMID: 38806450 PMCID: PMC11133379 DOI: 10.1038/s41467-024-48334-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 04/29/2024] [Indexed: 05/30/2024] Open
Abstract
Wastewater surveillance for SARS-CoV-2 provides early warnings of emerging variants of concerns and can be used to screen for novel cryptic linked-read mutations, which are co-occurring single nucleotide mutations that are rare, or entirely missing, in existing SARS-CoV-2 databases. While previous approaches have focused on specific regions of the SARS-CoV-2 genome, there is a need for computational tools capable of efficiently tracking cryptic mutations across the entire genome and investigating their potential origin. We present Crykey, a tool for rapidly identifying rare linked-read mutations across the genome of SARS-CoV-2. We evaluated the utility of Crykey on over 3,000 wastewater and over 22,000 clinical samples; our findings are three-fold: i) we identify hundreds of cryptic mutations that cover the entire SARS-CoV-2 genome, ii) we track the presence of these cryptic mutations across multiple wastewater treatment plants and over three years of sampling in Houston, and iii) we find a handful of cryptic mutations in wastewater mirror cryptic mutations in clinical samples and investigate their potential to represent real cryptic lineages. In summary, Crykey enables large-scale detection of cryptic mutations in wastewater that represent potential circulating cryptic lineages, serving as a new computational tool for wastewater surveillance of SARS-CoV-2.
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Affiliation(s)
- Yunxi Liu
- Department of Computer Science, Rice University, Houston, TX, 77005, USA
| | - Nicolae Sapoval
- Department of Computer Science, Rice University, Houston, TX, 77005, USA
| | - Pilar Gallego-García
- CINBIO, Universidade de Vigo, 36310, Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
| | - Laura Tomás
- CINBIO, Universidade de Vigo, 36310, Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
| | - David Posada
- CINBIO, Universidade de Vigo, 36310, Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
- Department of Biochemistry, Genetics, and Immunology, Universidade de Vigo, 36310, Vigo, Spain
| | - Todd J Treangen
- Department of Computer Science, Rice University, Houston, TX, 77005, USA.
| | - Lauren B Stadler
- Department of Civil and Environmental Engineering, Rice University, Houston, TX, 77005, USA.
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Starke JC, Bell NS, Martinez CM, Friberg IK, Lawley C, Sriskantharajah V, Hirschberg DL. Measuring SARS-CoV-2 RNA concentrations in neighborhood wastewater. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 926:172021. [PMID: 38552966 DOI: 10.1016/j.scitotenv.2024.172021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 03/22/2024] [Accepted: 03/25/2024] [Indexed: 04/05/2024]
Abstract
Longitudinal wastewater sampling during the COVID-19 pandemic was an important aspect of disease surveillance, adding to a more complete understanding of infection dynamics and providing important data for community public health monitoring and intervention planning. This was largely accomplished by testing SARS-CoV-2 RNA concentrations in samples from municipal wastewater treatment plants (WWTPs). We evaluated the utility of testing for virus levels upstream from WWTP within the residential neighborhoods that feed into the WWTP. We propose that monitoring virus dynamics across residential neighborhoods could reveal important public health-relevant information about community sub-group heterogeneity in virus concentrations. PRINCIPAL RESULTS: Virus concentration patterns display heterogeneity within neighborhoods and between neighborhoods over time. Sewage SARS-CoV-2 RNA concentrations as measured by RT-qPCR also corresponded closely to verified COVID-19 infection counts within individual neighborhoods. More importantly, our data suggest the loss of disease-relevant public health information when sampling occurs only at the level of WWTP instead of upstream in neighborhoods. Spikes in SARS-CoV-2 RNA concentrations in neighborhoods are often masked by dilution from other neighborhoods in the WWTP samples. MAJOR CONCLUSIONS: Wastewater-based epidemiology (WBE) employed at WWTP reliably detects SARS-CoV-2 in a city-sized population but provides less actionable public health information about neighborhoods experiencing greater viral infection and disease. Neighborhood sewershed sampling reveals important population-based information about local virus dynamics and improves opportunities for public health intervention. Longitudinally employed, neighborhood sewershed surveillance may provide a 3-6 day early warning of SARS-CoV-2 infection spikes and, importantly, highly specific information on subpopulations in a community particularly at higher risk at different points in time. Sampling in neighborhoods may thus provide timely and cost-saving information for targeted interventions within communities.
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Affiliation(s)
| | - Nicole S Bell
- RAIN Incubator, Tacoma, WA, USA; Squally Creek, LLC, Tacoma, WA, USA
| | - Chloe Mae Martinez
- RAIN Incubator, Tacoma, WA, USA; University of Washington-Tacoma, Tacoma, WA, USA
| | | | | | | | - David L Hirschberg
- RAIN Incubator, Tacoma, WA, USA; School of Engineering and Technology, University of Washington-Tacoma, Tacoma, WA, USA
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Islam G, Gedge A, Ibrahim R, de Melo T, Lara-Jacobo L, Dlugosz T, Kirkwood AE, Simmons D, Desaulniers JP. The role of catchment population size, data normalization, and chronology of public health interventions on wastewater-based COVID-19 viral trends. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 937:173272. [PMID: 38763190 DOI: 10.1016/j.scitotenv.2024.173272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Revised: 04/28/2024] [Accepted: 05/13/2024] [Indexed: 05/21/2024]
Abstract
The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) pandemic presented the most challenging global crisis in recent times. A pandemic caused by a novel pathogen such as SARS-CoV-2 necessitated the development of innovative techniques for the monitoring and surveillance of COVID-19 infections within communities. Wastewater surveillance (WWS) is recognized as a non-invasive, cost-effective, and valuable epidemiological tool to monitor the prevalence of COVID-19 infections in communities. Seven municipal wastewater sampling sites representing distinct sewershed communities were selected for the surveillance of the SARS-CoV-2 virus in Durham Region, Ontario, Canada over 8 months from March 2021 to October 2021. Viral RNA fragments of SARS-CoV-2 and the normalization target pepper mild mottle virus (PMMoV) were concentrated from wastewater influent using the PEG/NaCl superspeed centrifugation method and quantified using RT-qPCR. Strong significant correlations (Spearman's rs = 0.749 to 0.862, P < 0.001) were observed between SARS-CoV-2 gene copies/mL of wastewater and clinical cases reported in each delineated sewershed by onset date. Although raw wastewater offered higher correlation coefficients with clinical cases by onset date compared to PMMoV normalized data, only one site had a statistically significantly higher Spearman's correlation coefficient value for raw data than normalized data. Implementation of community stay-at-home orders and vaccinations over the course of the study period in 2021 were found to strongly correspond to decreasing SARS-CoV-2 wastewater trends in the wastewater treatment plants and upstream pumping stations.
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Affiliation(s)
- Golam Islam
- Faculty of Science, Ontario Tech University, 2000 Simcoe St N, Oshawa, ON L1G 0C5, Canada.
| | - Ashley Gedge
- Faculty of Science, Ontario Tech University, 2000 Simcoe St N, Oshawa, ON L1G 0C5, Canada
| | - Reeta Ibrahim
- Faculty of Science, Ontario Tech University, 2000 Simcoe St N, Oshawa, ON L1G 0C5, Canada
| | - Tomas de Melo
- Faculty of Science, Ontario Tech University, 2000 Simcoe St N, Oshawa, ON L1G 0C5, Canada
| | - Linda Lara-Jacobo
- Faculty of Science, Ontario Tech University, 2000 Simcoe St N, Oshawa, ON L1G 0C5, Canada
| | - Thomas Dlugosz
- Faculty of Science, Ontario Tech University, 2000 Simcoe St N, Oshawa, ON L1G 0C5, Canada
| | - Andrea E Kirkwood
- Faculty of Science, Ontario Tech University, 2000 Simcoe St N, Oshawa, ON L1G 0C5, Canada
| | - Denina Simmons
- Faculty of Science, Ontario Tech University, 2000 Simcoe St N, Oshawa, ON L1G 0C5, Canada
| | - Jean-Paul Desaulniers
- Faculty of Science, Ontario Tech University, 2000 Simcoe St N, Oshawa, ON L1G 0C5, Canada
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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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Vigil K, D'Souza N, Bazner J, Cedraz FMA, Fisch S, Rose JB, Aw TG. Long-term monitoring of SARS-CoV-2 variants in wastewater using a coordinated workflow of droplet digital PCR and nanopore sequencing. WATER RESEARCH 2024; 254:121338. [PMID: 38430753 DOI: 10.1016/j.watres.2024.121338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 02/12/2024] [Accepted: 02/17/2024] [Indexed: 03/05/2024]
Abstract
Quantitative polymerase chain reaction (PCR) and genome sequencing are important methods for wastewater surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The reverse transcription-droplet digital PCR (RT-ddPCR) is a highly sensitive method for quantifying SARS-CoV-2 RNA in wastewater samples to track the trends of viral activity levels but cannot identify new variants. It also takes time to develop new PCR-based assays targeting variants of interest. Whole genome sequencing (WGS) can be used to monitor known and new SARS-CoV-2 variants, but it is generally not quantitative. Several short-read sequencing techniques can be expensive and might experience delayed turnaround times when outsourced due to inadequate in-house resources. Recently, a portable nanopore sequencing system offers an affordable and real-time method for sequencing SARS-CoV-2 variants in wastewater. This technology has the potential to enable swift response to disease outbreaks without relying on clinical sequencing results. In addressing concerns related to rapid turnaround time and accurate variant analysis, both RT-ddPCR and nanopore sequencing methods were employed to monitor the emergence of SARS-CoV-2 variants in wastewater. This surveillance was conducted at 23 sewer maintenance hole sites and five wastewater treatment plants in Michigan from 2020 to 2022. In 2020, the wastewater samples were dominated by the parental variants (20A, 20C and 20 G), followed by 20I (Alpha, B.1.1.7) in early 2021 and the Delta variant of concern (VOC) in late 2021. For the year 2022, Omicron variants dominated. Nanopore sequencing has the potential to validate suspected variant cases that were initially undetermined by RT-ddPCR assays. The concordance rate between nanopore sequencing and RT-ddPCR assays in identifying SARS-CoV-2 variants to the clade-level was 76.9%. Notably, instances of disagreement between the two methods were most prominent in the identification of the parental and Omicron variants. We also showed that sequencing wastewater samples with SARS-CoV-2 N gene concentrations of >104 GC/100 ml as measured by RT-ddPCR improve genome recovery and coverage depth using MinION device. RT-ddPCR was better at detecting key spike protein mutations A67V, del69-70, K417N, L452R, N501Y, N679K, and R408S (p-value <0.05) as compared to nanopore sequencing. It is suggested that RT-ddPCR and nanopore sequencing should be coordinated in wastewater surveillance where RT-ddPCR can be used as a preliminary quantification method and nanopore sequencing as the confirmatory method for the detection of variants or identification of new variants. The RT-ddPCR and nanopore sequencing methods reported here can be adopted as a reliable in-house analysis of SARS-CoV-2 in wastewater for rapid community level surveillance and public health response.
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Affiliation(s)
- Katie Vigil
- Department of Environmental Health Sciences, School of Public Health and Tropical Medicine, Tulane University, 1440 Canal Street, Suite 2100, New Orleans, LA 70112, United States
| | - Nishita D'Souza
- Department of Fisheries and Wildlife, Michigan State University, East Lansing, Michigan, United States
| | - Julia Bazner
- Department of Fisheries and Wildlife, Michigan State University, East Lansing, Michigan, United States
| | - Fernanda Mac-Allister Cedraz
- Department of Environmental Health Sciences, School of Public Health and Tropical Medicine, Tulane University, 1440 Canal Street, Suite 2100, New Orleans, LA 70112, United States
| | - Samuel Fisch
- Department of Environmental Health Sciences, School of Public Health and Tropical Medicine, Tulane University, 1440 Canal Street, Suite 2100, New Orleans, LA 70112, United States
| | - Joan B Rose
- Department of Fisheries and Wildlife, Michigan State University, East Lansing, Michigan, United States
| | - Tiong Gim Aw
- Department of Environmental Health Sciences, School of Public Health and Tropical Medicine, Tulane University, 1440 Canal Street, Suite 2100, New Orleans, LA 70112, United States.
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11
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Lee CS, Wang M, Nanjappa D, Lu YT, Meliker J, Clouston S, Gobler CJ, Venkatesan AK. Monitoring of over-the-counter (OTC) and COVID-19 treatment drugs complement wastewater surveillance of SARS-CoV-2. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2024; 34:448-456. [PMID: 38052940 PMCID: PMC11222153 DOI: 10.1038/s41370-023-00613-2] [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: 04/24/2023] [Revised: 10/25/2023] [Accepted: 11/13/2023] [Indexed: 12/07/2023]
Abstract
BACKGROUND The application of wastewater-based epidemiology to track the outbreak and prevalence of coronavirus disease (COVID-19) in communities has been tested and validated by several researchers across the globe. However, the RNA-based surveillance has its inherent limitations and uncertainties. OBJECTIVE This study aims to complement the ongoing wastewater surveillance efforts by analyzing other chemical biomarkers in wastewater to help assess community response (hospitalization and treatment) during the pandemic (2020-2021). METHODS Wastewater samples (n = 183) were collected from the largest wastewater treatment facility in Suffolk County, NY, USA and analyzed for COVID-19 treatment drugs (remdesivir, chloroquine, and hydroxychloroquine (HCQ)) and their human metabolites. We additionally monitored 26 pharmaceuticals including common over-the-counter (OTC) drugs. Lastly, we developed a Bayesian model that uses viral RNA, COVID-19 treatment drugs, and pharmaceuticals data to predict the confirmed COVID-19 cases within the catchment area. RESULTS The viral RNA levels in wastewater tracked the actual COVID-19 case numbers well as expected. COVID-19 treatment drugs were detected with varying frequency (9-100%) partly due to their instability in wastewater. We observed a significant correlation (R = 0.30, p < 0.01) between the SARS-CoV-2 genes and desethylhydroxychloroquine (DHCQ, metabolite of HCQ). Remdesivir levels peaked immediately after the Emergency Use Authorization approved by the FDA. Although, 13 out of 26 pharmaceuticals assessed were consistently detected (DF = 100%, n = 111), only acetaminophen was significantly correlated with viral loads, especially when the Omicron variant was dominant. The Bayesian models were capable of reproducing the temporal trend of the confirmed cases. IMPACT In this study, for the first time, we measured COVID-19 treatment and pharmaceutical drugs and their metabolites in wastewater to complement ongoing COVID-19 viral RNA surveillance efforts. Our results highlighted that, although the COVID-19 treatment drugs were not very stable in wastewater, their detection matched with usage trends in the community. Acetaminophen, an OTC drug, was significantly correlated with viral loads and confirmed cases, especially when the Omicron variant was dominant. A Bayesian model was developed which could predict COVID-19 cases more accurately when incorporating other drugs data along with viral RNA levels in wastewater.
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Affiliation(s)
- Cheng-Shiuan Lee
- New York State Center for Clean Water Technology, Stony Brook University, Stony Brook, NY, 11794, USA
- Research Center for Environmental Changes, Academia Sinica, Taipei, 11529, Taiwan
| | - Mian Wang
- New York State Center for Clean Water Technology, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Deepak Nanjappa
- New York State Center for Clean Water Technology, Stony Brook University, Stony Brook, NY, 11794, USA
- School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Yi-Ta Lu
- Department of Human Behavior, Ecology and Culture, Max Planck Institute for Evolutionary Anthropology, 04103, Leipzig, Germany
| | - Jaymie Meliker
- Program in Public Health, Department of Family, Population & Preventive Medicine, Stony Brook University Medical Center, Stony Brook, NY, 11794, USA
| | - Sean Clouston
- Program in Public Health, Department of Family, Population & Preventive Medicine, Stony Brook University Medical Center, Stony Brook, NY, 11794, USA
| | - Christopher J Gobler
- New York State Center for Clean Water Technology, Stony Brook University, Stony Brook, NY, 11794, USA
- School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Arjun K Venkatesan
- New York State Center for Clean Water Technology, Stony Brook University, Stony Brook, NY, 11794, USA.
- Department of Civil and Environmental Engineering, New Jersey Institute of Technology, Newark, NJ, 07102, USA.
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12
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Price M, Tscharke B, Chappell A, Kah M, Sila-Nowicka K, Morris H, Ward D, Trowsdale S. Testing methods to estimate population size for wastewater treatment plants using census data: Implications for wastewater-based epidemiology. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 922:170974. [PMID: 38360313 DOI: 10.1016/j.scitotenv.2024.170974] [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: 11/30/2023] [Revised: 01/31/2024] [Accepted: 02/12/2024] [Indexed: 02/17/2024]
Abstract
In wastewater-based epidemiology (WBE), wastewater loads are commonly reported as a per capita value. Census population counts are often used to obtain a population size to normalise wastewater loads. However, the methods used to calculate the population size of wastewater treatment plants (WWTPs) from census data are rarely reported in the WBE literature. This is problematic because the geographical extents of wastewater catchments and census area units rarely align perfectly with each other and exist at different spatial scales. This complicates efforts to estimate the number of people serviced by WWTPs in these census area units. This study compared four geospatial methods to combine wastewater catchment areas and census area units to calculate the census population size of wastewater treatment plants. These methods were applied nationally to WWTPs across New Zealand. Population estimates varied by up to 73 % between the methods, which could skew comparisons of per capita wastewater loads between sites. Variability in population estimates (relative standard deviation, RSD) was significantly higher in smaller catchments (rs = -0.727, P < .001), highlighting the importance of method selection in smaller sites. Census population estimates were broadly similar to those provided by wastewater operators, but significant variation was observed for some sites (ranging from 42 % lower to 78 % higher, RSD = 262 %). We present a widely applicable method to calculate population size from census, which involves disaggregating census area units by individual properties. The results reinforce the need for transparent reporting to maintain confidence in the comparison of WBE across sites and studies.
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Affiliation(s)
- Mackay Price
- School of Environment, University of Auckland, 23 Symonds Street, Auckland 1010, New Zealand.
| | - Ben Tscharke
- Queensland Alliance for Environmental Health Sciences, University of Queensland, 20 Cornwall Street, Queensland 4102, Australia
| | - Andrew Chappell
- Institute of Environmental Science and Research Ltd., 27 Creyke Road, Christchurch 8041, New Zealand
| | - Melanie Kah
- School of Environment, University of Auckland, 23 Symonds Street, Auckland 1010, New Zealand
| | - Katarzyna Sila-Nowicka
- School of Environment, University of Auckland, 23 Symonds Street, Auckland 1010, New Zealand
| | - Helen Morris
- Institute of Environmental Science and Research Ltd., 27 Creyke Road, Christchurch 8041, New Zealand
| | - Daniel Ward
- Environment Canterbury, 200 Tuam Street, Christchurch 8011, New Zealand
| | - Sam Trowsdale
- School of Environment, University of Auckland, 23 Symonds Street, Auckland 1010, New Zealand
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13
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Saber LB, Kennedy SS, Yang Y, Moore KN, Wang Y, Hilton SP, Chang TY, Liu P, Phillips VL, Akiyama MJ, Moe CL, Spaulding AC. Correlation of SARS-CoV-2 in Wastewater and Individual Testing Results in a Jail, Atlanta, Georgia, USA. Emerg Infect Dis 2024; 30:S21-S27. [PMID: 38561638 PMCID: PMC10986836 DOI: 10.3201/eid3013.230775] [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] [Indexed: 04/04/2024] Open
Abstract
Institution-level wastewater-based surveillance was implemented during the COVID-19 pandemic, including in carceral facilities. We examined the relationship between COVID-19 diagnostic test results of residents in a jail in Atlanta, Georgia, USA (average population ≈2,700), and quantitative reverse transcription PCR signal for SARS-CoV-2 in weekly wastewater samples collected during October 2021‒May 2022. The jail offered residents rapid antigen testing at entry and periodic mass screenings by reverse transcription PCR of self-collected nasal swab specimens. We aggregated individual test data, calculated the Spearman correlation coefficient, and performed logistic regression to examine the relationship between strength of SARS-CoV-2 PCR signal (cycle threshold value) in wastewater and percentage of jail population that tested positive for COVID-19. Of 13,745 nasal specimens collected, 3.9% were COVID-positive (range 0%-29.5% per week). We observed a strong inverse correlation between diagnostic test positivity and cycle threshold value (r = -0.67; p<0.01). Wastewater-based surveillance represents an effective strategy for jailwide surveillance of COVID-19.
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14
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Li Y, Ash K, Alamilla I, Joyner D, Williams DE, McKay PJ, Green B, DeBlander S, North C, Kara-Murdoch F, Swift C, Hazen TC. COVID-19 trends at the University of Tennessee: predictive insights from raw sewage SARS-CoV-2 detection and evaluation and PMMoV as an indicator for human waste. Front Microbiol 2024; 15:1379194. [PMID: 38605711 PMCID: PMC11007199 DOI: 10.3389/fmicb.2024.1379194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 03/19/2024] [Indexed: 04/13/2024] Open
Abstract
Wastewater-based epidemiology (WBE) has become a valuable tool for monitoring the prevalence of SARS-CoV-2 on university campuses. However, concerns about effectiveness of raw sewage as a COVID-19 early warning system still exist, and it's not clear how useful normalization by simultaneous comparison of Pepper Mild Mottle Virus (PMMoV) is in addressing variations resulting from fecal discharge dilution. This study aims to contribute insights into these aspects by conducting an academic-year field trial at the student residences on the University of Tennessee, Knoxville campus, raw sewage. This was done to investigate the correlations between SARS-CoV-2 RNA load, both with and without PMMoV normalization, and various parameters, including active COVID-19 cases, self-isolations, and their combination among all student residents. Significant positive correlations between SARS-CoV-2 RNA load a week prior, during the monitoring week, and the subsequent week with active cases. Despite these correlations, normalization by PMMoV does not enhance these associations. These findings suggest the potential utility of SARS-CoV-2 RNA load as an early warning indicator and provide valuable insights into the application and limitations of WBE for COVID-19 surveillance specifically within the context of raw sewage on university campuses.
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Affiliation(s)
- Ye Li
- Department of Civil and Environmental Sciences, University of Tennessee, Knoxville, TN, United States
| | - Kurt Ash
- Department of Civil and Environmental Sciences, University of Tennessee, Knoxville, TN, United States
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States
| | | | - Dominique Joyner
- Department of Civil and Environmental Sciences, University of Tennessee, Knoxville, TN, United States
| | - Daniel Edward Williams
- Center for Environmental Biotechnology, University of Tennessee, Knoxville, TN, United States
| | - Peter J. McKay
- Battelle Memorial Institute, Columbus, OH, United States
| | - Brianna Green
- Department of Microbiology, University of Tennessee, Knoxville, TN, United States
| | - Sydney DeBlander
- College of Natural Science, Michigan State University, East Lansing, MI, United States
| | - Carman North
- Student Health Center, University of Tennessee, Knoxville, TN, United States
| | - Fadime Kara-Murdoch
- Department of Civil and Environmental Sciences, University of Tennessee, Knoxville, TN, United States
- Battelle Memorial Institute, Columbus, OH, United States
- Center for Environmental Biotechnology, University of Tennessee, Knoxville, TN, United States
| | - Cynthia Swift
- Department of Civil and Environmental Sciences, University of Tennessee, Knoxville, TN, United States
- Center for Environmental Biotechnology, University of Tennessee, Knoxville, TN, United States
| | - Terry C. Hazen
- Department of Civil and Environmental Sciences, University of Tennessee, Knoxville, TN, United States
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States
- Department of Microbiology, University of Tennessee, Knoxville, TN, United States
- Department of Earth and Planetary Sciences, University of Tennessee, Knoxville, TN, United States
- Institute for a Secure and Sustainable Environment, University of Tennessee, Knoxville, TN, United States
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15
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Ryon MG, Langan LM, Brennan C, O'Brien ME, Bain FL, Miller AE, Snow CC, Salinas V, Norman RS, Bojes HK, Brooks BW. Influences of 23 different equations used to calculate gene copies of SARS-CoV-2 during wastewater-based epidemiology. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 917:170345. [PMID: 38272099 DOI: 10.1016/j.scitotenv.2024.170345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Revised: 12/01/2023] [Accepted: 01/19/2024] [Indexed: 01/27/2024]
Abstract
Following the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in late 2019, the use of wastewater-based surveillance (WBS) has increased dramatically along with associated infrastructure globally. However, due to the global nature of its application, and various workflow adaptations (e.g., sample collection, water concentration, RNA extraction kits), numerous methods for back-calculation of gene copies per volume (gc/L) of sewage have also emerged. Many studies have considered the comparability of processing methods (e.g., water concentration, RNA extraction); however, for equations used to calculate gene copies in a wastewater sample and subsequent influences on monitoring viral trends in a community and its association with epidemiological data, less is known. Due to limited information on how many formulas exist for the calculation of SARS-CoV-2 gene copies in wastewater, we initially attempted to quantify how many equations existed in the referred literature. We identified 23 unique equations, which were subsequently applied to an existing wastewater dataset. We observed a range of gene copies based on use of different equations, along with variability of AUC curve values, and results from correlation and regression analyses. Though a number of individual laboratories appear to have independently converged on a similar formula for back-calculation of viral load in wastewater, and share similar relationships with epidemiological data, differential influences of various equations were observed for variation in PCR volumes, RNA extraction volumes, or PCR assay parameters. Such observations highlight challenges when performing comparisons among WBS studies when numerous methodologies and back-calculation methods exist. To facilitate reproducibility among studies, the different gc/L equations were packaged as an R Shiny app, which provides end users the ability to investigate variability within their datasets and support comparisons among studies.
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Affiliation(s)
- Mia G Ryon
- Department of Environmental Science, Baylor University, One Bear Place #97266, Waco, TX 76798, USA; Center for Reservoir and Aquatic Systems Research, Baylor University, One Bear Place #97178, Waco, TX 76798, USA
| | - Laura M Langan
- Department of Environmental Science, Baylor University, One Bear Place #97266, Waco, TX 76798, USA; Center for Reservoir and Aquatic Systems Research, Baylor University, One Bear Place #97178, Waco, TX 76798, USA.
| | - Christopher Brennan
- Department of Entomology, Texas A&M University, TAMU 2475, College Station, TX 77843-2475, USA
| | - Megan E O'Brien
- Department of Environmental Science, Baylor University, One Bear Place #97266, Waco, TX 76798, USA; Center for Reservoir and Aquatic Systems Research, Baylor University, One Bear Place #97178, Waco, TX 76798, USA
| | - Fallon L Bain
- Department of Environmental Science, Baylor University, One Bear Place #97266, Waco, TX 76798, USA; Center for Reservoir and Aquatic Systems Research, Baylor University, One Bear Place #97178, Waco, TX 76798, USA
| | - Aubree E Miller
- Department of Environmental Science, Baylor University, One Bear Place #97266, Waco, TX 76798, USA; Center for Reservoir and Aquatic Systems Research, Baylor University, One Bear Place #97178, Waco, TX 76798, USA
| | - Christine C Snow
- Department of Environmental Science, Baylor University, One Bear Place #97266, Waco, TX 76798, USA; Center for Reservoir and Aquatic Systems Research, Baylor University, One Bear Place #97178, Waco, TX 76798, USA
| | - Victoria Salinas
- Environmental Epidemiology and Disease Registries, Texas Department of State Health Services, Austin, TX 78756, USA
| | - R Sean Norman
- Department of Environmental Health Sciences, Arnold School of Public Health, University of South Carolina, 921 Assembly St., Columbia, SC 28208, USA
| | - Heidi K Bojes
- Environmental Epidemiology and Disease Registries, Texas Department of State Health Services, Austin, TX 78756, USA
| | - Bryan W Brooks
- Department of Environmental Science, Baylor University, One Bear Place #97266, Waco, TX 76798, USA; Center for Reservoir and Aquatic Systems Research, Baylor University, One Bear Place #97178, Waco, TX 76798, USA; Department of Public Health, Baylor University, One Bear Place #97343, Waco, TX 76798, USA.
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16
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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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17
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Usmani M, Brumfield KD, Magers B, Zhou A, Oh C, Mao Y, Brown W, Schmidt A, Wu CY, Shisler JL, Nguyen TH, Huq A, Colwell R, Jutla A. Building Environmental and Sociological Predictive Intelligence to Understand the Seasonal Threat of SARS-CoV-2 in Human Populations. Am J Trop Med Hyg 2024; 110:518-528. [PMID: 38320317 PMCID: PMC10919182 DOI: 10.4269/ajtmh.23-0077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 11/03/2023] [Indexed: 02/08/2024] Open
Abstract
Current modeling practices for environmental and sociological modulated infectious diseases remain inadequate to forecast the risk of outbreak(s) in human populations, partly due to a lack of integration of disciplinary knowledge, limited availability of disease surveillance datasets, and overreliance on compartmental epidemiological modeling methods. Harvesting data knowledge from virus transmission (aerosols) and detection (wastewater) of SARS-CoV-2, a heuristic score-based environmental predictive intelligence system was developed that calculates the risk of COVID-19 in the human population. Seasonal validation of the algorithm was uniquely associated with wastewater surveillance of the virus, providing a lead time of 7-14 days before a county-level outbreak. Using county-scale disease prevalence data from the United States, the algorithm could predict COVID-19 risk with an overall accuracy ranging between 81% and 98%. Similarly, using wastewater surveillance data from Illinois and Maryland, the SARS-CoV-2 detection rate was greater than 80% for 75% of the locations during the same time the risk was predicted to be high. Results suggest the importance of a holistic approach across disciplinary boundaries that can potentially allow anticipatory decision-making policies of saving lives and maximizing the use of available capacity and resources.
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Affiliation(s)
- Moiz Usmani
- GeoHealth and Hydrology Laboratory, Department of Environmental Engineering Sciences, University of Florida, Gainesville, Florida
| | - Kyle D. Brumfield
- Maryland Pathogen Research Institute, University of Maryland, College Park, Maryland
- University of Maryland Institute for Advanced Computer Studies, University of Maryland, College Park, Maryland
| | - Bailey Magers
- GeoHealth and Hydrology Laboratory, Department of Environmental Engineering Sciences, University of Florida, Gainesville, Florida
| | - Aijia Zhou
- Department of Civil and Environmental Engineering, University of Illinois at Urbana–Champaign, Urbana, Illinois
| | - Chamteut Oh
- Department of Environmental Engineering Sciences, University of Florida, Gainesville, Florida
| | - Yuqing Mao
- Department of Civil and Environmental Engineering, University of Illinois at Urbana–Champaign, Urbana, Illinois
| | - William Brown
- Department of Pathobiology, University of Illinois at Urbana–Champaign, Urbana, Illinois
| | - Arthur Schmidt
- Department of Civil and Environmental Engineering, University of Illinois at Urbana–Champaign, Urbana, Illinois
| | - Chang-Yu Wu
- Department of Environmental Engineering Sciences, University of Florida, Gainesville, Florida
- Department of Chemical, Environmental and Materials Engineering, University of Miami, Florida
| | - Joanna L. Shisler
- Department of Microbiology, University of Illinois at Urbana-Champaign, Urbana, Illinois
| | - Thanh H. Nguyen
- Department of Civil and Environmental Engineering, University of Illinois at Urbana–Champaign, Urbana, Illinois
| | - Anwar Huq
- Maryland Pathogen Research Institute, University of Maryland, College Park, Maryland
- University of Maryland Institute for Advanced Computer Studies, University of Maryland, College Park, Maryland
| | - Rita Colwell
- Maryland Pathogen Research Institute, University of Maryland, College Park, Maryland
- University of Maryland Institute for Advanced Computer Studies, University of Maryland, College Park, Maryland
| | - Antarpreet Jutla
- GeoHealth and Hydrology Laboratory, Department of Environmental Engineering Sciences, University of Florida, Gainesville, Florida
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18
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Sharma V, Takamura H, Biyani M, Honda R. Real-Time On-Site Monitoring of Viruses in Wastewater Using Nanotrap ® Particles and RICCA Technologies. BIOSENSORS 2024; 14:115. [PMID: 38534222 DOI: 10.3390/bios14030115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 02/10/2024] [Accepted: 02/17/2024] [Indexed: 03/28/2024]
Abstract
Wastewater-based epidemiology (WBE) is an effective and efficient tool for the early detection of infectious disease outbreaks in a community. However, currently available methods are laborious, costly, and time-consuming due to the low concentration of viruses and the presence of matrix chemicals in wastewater that may interfere with molecular analyses. In the present study, we designed a highly sensitive "Quick Poop (wastewater with fecal waste) Sensor" (termed, QPsor) using a joint approach of Nanotrap microbiome particles and RICCA (RNA Isothermal Co-Assisted and Coupled Amplification). Using QPsor, the WBE study showed a strong correlation with standard PEG concentrations and the qPCR technique. Using a closed format for a paper-based lateral flow assay, we were able to demonstrate the potential of our assay as a real-time, point-of-care test by detecting the heat-inactivated SARS-CoV-2 virus in wastewater at concentrations of 100 copies/mL and within one hour. As a proof-of-concept demonstration, we analyzed the presence of viral RNA of the SARS-CoV-2 virus and PMMoV in raw wastewater samples from wastewater treatment plants on-site and within 60 min. The results show that the QPsor method can be an effective tool for disease outbreak detection by combining an AI-enabled case detection model with real-time on-site viral RNA extraction and amplification, especially in the absence of intensive clinical laboratory facilities. The lab-free, lab-quality test capabilities of QPsor for viral prevalence and transmission in the community can contribute to the efficient management of pandemic situations.
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Affiliation(s)
- Vishnu Sharma
- BioSeeds Corporation, Ishikawa Create Labo-202, Asahidai 2-13, Nomi 923-1211, Ishikawa, Japan
| | - Hitomi Takamura
- Faculty of Geosciences and Civil Engineering, Kanazawa University, Kanazawa 920-1164, Ishikawa, Japan
| | - Manish Biyani
- BioSeeds Corporation, Ishikawa Create Labo-202, Asahidai 2-13, Nomi 923-1211, Ishikawa, Japan
| | - Ryo Honda
- Faculty of Geosciences and Civil Engineering, Kanazawa University, Kanazawa 920-1164, Ishikawa, Japan
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19
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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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20
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Tran DPH, You BC, Liu CW, Chen YN, Wang YF, Chung SN, Lee JJ, You SJ. Identifying spatiotemporal trends of SARS-CoV-2 RNA in wastewater: from the perspective of upstream and downstream wastewater-based epidemiology (WBE). ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:11576-11590. [PMID: 38221556 DOI: 10.1007/s11356-023-31769-x] [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: 01/23/2023] [Accepted: 12/25/2023] [Indexed: 01/16/2024]
Abstract
Recently, many efforts have been made to address the rapid spread of newly identified COVID-19 virus variants. Wastewater-based epidemiology (WBE) is considered a potential early warning tool for identifying the rapid spread of this virus. This study investigated the occurrence of SARS-CoV-2 in eight wastewater treatment plants (WWTPs) and their sewerage systems which serve most of the population in Taoyuan City, Taiwan. Across the entire study period, the wastewater viral concentrations were correlated with the number of COVID-19 cases in each WWTP (Spearman's r = 0.23-0.76). In addition, it is confirmed that several treatment technologies could effectively eliminate the virus RNA from WWTP influent (> 90%). On the other hand, further results revealed that an inverse distance weighted (IDW) interpolation and hotspot model combined with the geographic information system (GIS) method could be applied to analyze the spatiotemporal variations of SARS-CoV-2 in wastewater from the sewer system. In addition, socio-economic factors, namely, population density, land use, and income tax were successfully identified as the potential drivers which substantially affected the onset of the COVID-19 outbreak in Taiwan. Finally, the data obtained from this study can provide a powerful tool in public health decision-making not only in response to the current epidemic situation but also to other epidemic issues in the future.
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Affiliation(s)
- Duyen Phuc-Hanh Tran
- Department of Civil Engineering, Chung Yuan Christian University, Taoyuan City, 320, Taiwan, Republic of China
- Center for Environmental Risk Management, Chung Yuan Christian University, Taoyuan City, 320, Taiwan, Republic of China
| | - Bo-Cheng You
- Center for Environmental Risk Management, Chung Yuan Christian University, Taoyuan City, 320, Taiwan, Republic of China
- Department of Environmental Engineering, Chung Yuan Christian University, Taoyuan City, 320, Taiwan, Republic of China
| | - Chen-Wuing Liu
- Department of Water Resource, Taoyuan City Government, Taoyuan City, 320, Taiwan, Republic of China
| | - Yi-Ning Chen
- Department of Bioscience Technology, Chung Yuan Christian University, Taoyuan City, 320, Taiwan, Republic of China
| | - Ya-Fen Wang
- Center for Environmental Risk Management, Chung Yuan Christian University, Taoyuan City, 320, Taiwan, Republic of China
- Department of Environmental Engineering, Chung Yuan Christian University, Taoyuan City, 320, Taiwan, Republic of China
| | - Shu-Nu Chung
- Department of Water Resource, Taoyuan City Government, Taoyuan City, 320, Taiwan, Republic of China
| | - Jin-Jing Lee
- Department of Water Resource, Taoyuan City Government, Taoyuan City, 320, Taiwan, Republic of China
| | - Sheng-Jie You
- Center for Environmental Risk Management, Chung Yuan Christian University, Taoyuan City, 320, Taiwan, Republic of China.
- Department of Environmental Engineering, Chung Yuan Christian University, Taoyuan City, 320, Taiwan, Republic of China.
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21
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Philo SE, De León KB, Noble RT, Zhou NA, Alghafri R, Bar-Or I, Darling A, D'Souza N, Hachimi O, Kaya D, Kim S, Gaardbo Kuhn K, Layton BA, Mansfeldt C, Oceguera B, Radniecki TS, Ram JL, Saunders LP, Shrestha A, Stadler LB, Steele JA, Stevenson BS, Vogel JR, Bibby K, Boehm AB, Halden RU, Delgado Vela J. Wastewater surveillance for bacterial targets: current challenges and future goals. Appl Environ Microbiol 2024; 90:e0142823. [PMID: 38099657 PMCID: PMC10807411 DOI: 10.1128/aem.01428-23] [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] [Indexed: 01/25/2024] Open
Abstract
Wastewater-based epidemiology (WBE) expanded rapidly in response to the COVID-19 pandemic. As the public health emergency has ended, researchers and practitioners are looking to shift the focus of existing wastewater surveillance programs to other targets, including bacteria. Bacterial targets may pose some unique challenges for WBE applications. To explore the current state of the field, the National Science Foundation-funded Research Coordination Network (RCN) on Wastewater Based Epidemiology for SARS-CoV-2 and Emerging Public Health Threats held a workshop in April 2023 to discuss the challenges and needs for wastewater bacterial surveillance. The targets and methods used in existing programs were diverse, with twelve different targets and nine different methods listed. Discussions during the workshop highlighted the challenges in adapting existing programs and identified research gaps in four key areas: choosing new targets, relating bacterial wastewater data to human disease incidence and prevalence, developing methods, and normalizing results. To help with these challenges and research gaps, the authors identified steps the larger community can take to improve bacteria wastewater surveillance. This includes developing data reporting standards and method optimization and validation for bacterial programs. Additionally, more work is needed to understand shedding patterns for potential bacterial targets to better relate wastewater data to human infections. Wastewater surveillance for bacteria can help provide insight into the underlying prevalence in communities, but much work is needed to establish these methods.IMPORTANCEWastewater surveillance was a useful tool to elucidate the burden and spread of SARS-CoV-2 during the pandemic. Public health officials and researchers are interested in expanding these surveillance programs to include bacterial targets, but many questions remain. The NSF-funded Research Coordination Network for Wastewater Surveillance of SARS-CoV-2 and Emerging Public Health Threats held a workshop to identify barriers and research gaps to implementing bacterial wastewater surveillance programs.
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Affiliation(s)
- Sarah E. Philo
- Department of Civil and Environmental Engineering and Earth Sciences, University of Notre Dame, Notre Dame, Indiana, USA
| | - Kara B. De León
- School of Biological Sciences, University of Oklahoma, Norman, Oklahoma, USA
| | - Rachel T. Noble
- Department of Earth, Marine, and Environmental Sciences, University of North Carolina at Chapel Hill, Institute of Marine Sciences, Morehead City, North Carolina, USA
| | - Nicolette A. Zhou
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington, USA
| | - Rashed Alghafri
- International Center for Forensic Sciences, Dubai Police, Dubai, UAE
| | - Itay Bar-Or
- Israel Ministry of Health, Jerusalem, Israel
| | - Amanda Darling
- Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, Virginia, USA
| | - Nishita D'Souza
- Department of Fisheries and Wildlife, Michigan State University, East Lansing, Michigan, USA
| | - Oumaima Hachimi
- School of Chemical, Biological and Environmental Engineering, Oregon State University, Corvallis, Oregon, USA
| | - Devrim Kaya
- School of Public Health, San Diego State University, San Diego, California, USA
| | - Sooyeol Kim
- Department of Civil and Environmental Engineering, University of California Berkeley, Berkeley, California, USA
| | - Katrin Gaardbo Kuhn
- Department of Biostatistics and Epidemiology, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA
| | | | - Cresten Mansfeldt
- Environmental Engineering Program, University of Colorado Boulder, Boulder, Colorado, USA
| | - Bethany Oceguera
- Department of Civil and Environmental Engineering and Earth Sciences, University of Notre Dame, Notre Dame, Indiana, USA
| | - Tyler S. Radniecki
- School of Chemical, Biological and Environmental Engineering, Oregon State University, Corvallis, Oregon, USA
| | - Jeffrey L. Ram
- Department of Physiology, Wayne State University School of Medicine, Detroit, Michigan, USA
| | | | - Abhilasha Shrestha
- Environmental and Occupational Health Sciences Division, University of Illinois Chicago School of Public Health, Chicago, Illinois, USA
| | - Lauren B. Stadler
- Civil and Environmental Engineering, Rice University, Houston, Texas, USA
| | - Joshua A. Steele
- Department of Microbiology, Southern California Coastal Research Project, Costa Mesa, California, USA
| | | | - Jason R. Vogel
- School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, Oklahoma, USA
| | - Kyle Bibby
- Department of Civil and Environmental Engineering and Earth Sciences, University of Notre Dame, Notre Dame, Indiana, USA
| | - Alexandria B. Boehm
- Department of Civil and Environmental Engineering, Stanford University, Stanford, California, USA
| | - Rolf U. Halden
- School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, Arizona, USA
| | - Jeseth Delgado Vela
- Department of Civil and Environmental Engineering, Duke University, Durham, North Carolina, USA
- Department of Civil and Environmental Engineering, Howard University, Washington, District of Columbia, USA
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22
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Katayama YA, Hayase S, Iwamoto R, Kuroita T, Okuda T, Kitajima M, Masago Y. Simultaneous extraction and detection of DNA and RNA from viruses, prokaryotes, and eukaryotes in wastewater using a modified COPMAN. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 907:167866. [PMID: 37863234 DOI: 10.1016/j.scitotenv.2023.167866] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 10/13/2023] [Accepted: 10/13/2023] [Indexed: 10/22/2023]
Abstract
Wastewater surveillance can offer a comprehensive grasp of infectious disease prevalence and human health because wastewater contains various human-derived microbial pathogens, including viruses, bacteria, and fungi. However, methods capable of simultaneous detection of multiple groups of targets in the automated systems and large-scale surveillance are still under development. Here, we demonstrated the modification, involving the addition of bead-beating, to the existing COPMAN (COagulation and Proteolysis method using MAgnetic beads for detection of Nucleic acids in wastewater) enabled enhanced detection of various microorganisms, including SARS-CoV-2. The modified method, termed bead-beating COPMAN (BB-COPMAN), was evaluated through spike-and-recovery experiments and comparative analysis against three previously reported methods for simultaneous DNA/RNA detection. Our study targeted a range of microorganisms, including enveloped and non-enveloped RNA viruses (SARS-CoV-2, PMMoV), a DNA virus (crAssphage), archaea, gram-negative and gram-positive bacteria (E. coli, Lachnospiraceae), antibiotic resistance gene (ampC), and fungi (Candida albicans). The recovery rates of BB-COPMAN for gram-negative and gram-positive bacteria were 17 and 2.1-fold higher, respectively, compared to the method for DNA/RNA detection. Additionally, BB-COPMAN exhibited the highest extraction efficiency among the tested methods, achieving 1.2-5.7 times more DNA and 1.1-69 times more RNA yield on average. BB-COPMAN allowed the detection of SARS-CoV-2 from all nine samples and PMMoV at concentrations 39-97 times higher than other methods. Moreover, BB-COPMAN detected larger amounts of DNA for four out of six DNA targets than the previously reported DNA/RNA detection method. These results demonstrated that BB-COPMAN enables enhanced detection of multiple targets in a single flow of nucleic acid extraction, making the method well-suited for automated systems. In conclusion, BB-COPMAN is a promising method in wastewater surveillance for assessing the prevalence of wide range of pathogenic microorganisms.
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Affiliation(s)
- Yuka Adachi Katayama
- Shionogi & Co., Ltd., Pharmaceutical Research Center, 1-1, Futaba-cho 3-chome, Toyonaka, Osaka 561-0825, Japan
| | - Shin Hayase
- Shionogi & Co., Ltd., Pharmaceutical Research Center, 1-1, Futaba-cho 3-chome, Toyonaka, Osaka 561-0825, Japan
| | - Ryo Iwamoto
- Shionogi & Co., Ltd., Head Office, 3-1-8 Doshomachi, Chuo-ku, Osaka 541-0045, Japan; AdvanSentinel Inc., 3-1-8 Doshomachi, Chuo-ku, Osaka 541-0045, Japan
| | - Tomohiro Kuroita
- Shionogi & Co., Ltd., Head Office, 3-1-8 Doshomachi, Chuo-ku, Osaka 541-0045, Japan; AdvanSentinel Inc., 3-1-8 Doshomachi, Chuo-ku, Osaka 541-0045, Japan
| | - Tomohiko Okuda
- Shionogi & Co., Ltd., Pharmaceutical Research Center, 1-1, Futaba-cho 3-chome, Toyonaka, Osaka 561-0825, Japan
| | - Masaaki Kitajima
- Division of Environmental Engineering, Faculty of Engineering, Hokkaido University, North 13 West 8, Kita-ku, Sapporo, Hokkaido 060-8628, Japan
| | - Yusaku Masago
- Shionogi & Co., Ltd., Pharmaceutical Research Center, 1-1, Futaba-cho 3-chome, Toyonaka, Osaka 561-0825, Japan.
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23
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Klaassen F, Holm RH, Smith T, Cohen T, Bhatnagar A, Menzies NA. Predictive power of wastewater for nowcasting infectious disease transmission: A retrospective case study of five sewershed areas in Louisville, Kentucky. ENVIRONMENTAL RESEARCH 2024; 240:117395. [PMID: 37838198 PMCID: PMC10863376 DOI: 10.1016/j.envres.2023.117395] [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: 06/15/2023] [Revised: 09/29/2023] [Accepted: 10/11/2023] [Indexed: 10/16/2023]
Abstract
BACKGROUND Epidemiological nowcasting traditionally relies on count surveillance data. The availability and quality of such count data may vary over time, limiting representation of true infections. Wastewater data correlates with traditional surveillance data and may provide additional value for nowcasting disease trends. METHODS We obtained SARS-CoV-2 case, death, wastewater, and serosurvey data for Jefferson County, Kentucky (USA), between August 2020 and March 2021, and parameterized an existing nowcasting model using combinations of these data. We assessed the predictive performance and variability at the sewershed level and compared the effects of adding or replacing wastewater data to case and death reports. FINDINGS Adding wastewater data minimally improved the predictive performance of nowcasts compared to a model fitted to case and death data (Weighted Interval Score (WIS) 0.208 versus 0.223), and reduced the predictive performance compared to a model fitted to deaths data (WIS 0.517 versus 0.500). Adding wastewater data to deaths data improved the nowcasts agreement to estimates from models using cases and deaths data. These findings were consistent across individual sewersheds as well as for models fit to the aggregated total data of 5 sewersheds. Retrospective reconstructions of epidemiological dynamics created using different combinations of data were in general agreement (coverage >75%). INTERPRETATION These findings show wastewater data may be valuable for infectious disease nowcasting when clinical surveillance data are absent, such as early in a pandemic or in low-resource settings where systematic collection of epidemiologic data is difficult.
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Affiliation(s)
- Fayette Klaassen
- Department of Global Health and Population, Harvard TH Chan School of Public Health, Boston, MA, USA.
| | - Rochelle H Holm
- Christina Lee Brown Envirome Institute, School of Medicine, University of Louisville, Louisville, KY, USA.
| | - Ted Smith
- Christina Lee Brown Envirome Institute, School of Medicine, University of Louisville, Louisville, KY, USA.
| | - Ted Cohen
- Department of Epidemiology of Microbial Diseases and Public Health Modeling Unit, Yale School of Public Health, New Haven, CT, USA.
| | - Aruni Bhatnagar
- Christina Lee Brown Envirome Institute, School of Medicine, University of Louisville, Louisville, KY, USA.
| | - Nicolas A Menzies
- Department of Global Health and Population, Harvard TH Chan School of Public Health, Boston, MA, USA; Center for Health Decision Science, Harvard TH Chan School of Public Health, Boston, MA, USA.
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24
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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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25
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Liu AB, Lee D, Jalihal AP, Hanage WP, Springer M. Quantitatively assessing early detection strategies for mitigating COVID-19 and future pandemics. Nat Commun 2023; 14:8479. [PMID: 38123536 PMCID: PMC10733317 DOI: 10.1038/s41467-023-44199-7] [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: 09/19/2023] [Accepted: 12/04/2023] [Indexed: 12/23/2023] Open
Abstract
Researchers and policymakers have proposed systems to detect novel pathogens earlier than existing surveillance systems by monitoring samples from hospital patients, wastewater, and air travel, in order to mitigate future pandemics. How much benefit would such systems offer? We developed, empirically validated, and mathematically characterized a quantitative model that simulates disease spread and detection time for any given disease and detection system. We find that hospital monitoring could have detected COVID-19 in Wuhan 0.4 weeks earlier than it was actually discovered, at 2,300 cases (standard error: 76 cases) compared to 3,400 (standard error: 161 cases). Wastewater monitoring would not have accelerated COVID-19 detection in Wuhan, but provides benefit in smaller catchments and for asymptomatic or long-incubation diseases like polio or HIV/AIDS. Air travel monitoring does not accelerate outbreak detection in most scenarios we evaluated. In sum, early detection systems can substantially mitigate some future pandemics, but would not have changed the course of COVID-19.
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Affiliation(s)
- Andrew Bo Liu
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
| | - Daniel Lee
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - William P Hanage
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Michael Springer
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
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26
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Zhao B, Fujita T, Nihei Y, Yu Z, Chen X, Tanaka H, Ihara M. Tracking community infection dynamics of COVID-19 by monitoring SARS-CoV-2 RNA in wastewater, counting positive reactions by qPCR. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 904:166420. [PMID: 37611711 DOI: 10.1016/j.scitotenv.2023.166420] [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: 11/10/2022] [Revised: 07/18/2023] [Accepted: 08/17/2023] [Indexed: 08/25/2023]
Abstract
Wastewater-based epidemiology has proved useful for monitoring the COVID-19 infection dynamics in communities. However, in regions of low prevalence, low concentrations of SARS-CoV-2 RNA in wastewater make this difficult. Here, we used real-time reverse-transcription PCR (RT-qPCR) to monitor SARS-CoV-2 RNA in wastewater from October 2020 to December 2022 during the third, fourth, fifth, sixth, seventh, and eighth waves of the COVID-19 outbreak in Japan. Viral RNA was below the limit of detection in all samples during the third and fourth waves. However, by counting the number of positive replicates in qPCR of each sample, we found that the positive ratio to all replicates in wastewater was significantly correlated with the number of clinically confirmed cases by the date of symptom onset during the third, fourth, and fifth waves. Time-step analysis indicated that, for 2 days either side of symptom onset, COVID-19 patients excreted in their feces large amounts of virus that wastewater surveillance could detect. We also demonstrated that the viral genome copy number in wastewater, as estimated from the positive ratio of SARSA-CoV-2 RNA, was correlated with the number of clinically confirmed cases. The positive count method is thus useful for tracing COVID-19 dynamics in regions of low prevalence.
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Affiliation(s)
- Bo Zhao
- Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes, Ministry of Education, College of Environment, Hohai University, Nanjing 210098, PR China; Research Center for Environmental Quality Management, Graduate School of Engineering, Kyoto University, 1-2 Yumihama, Otsu, Shiga 520-0811, Japan
| | - Tomonori Fujita
- Research Center for Environmental Quality Management, Graduate School of Engineering, Kyoto University, 1-2 Yumihama, Otsu, Shiga 520-0811, Japan
| | - Yoshiaki Nihei
- Research Center for Environmental Quality Management, Graduate School of Engineering, Kyoto University, 1-2 Yumihama, Otsu, Shiga 520-0811, Japan; Water Agency Inc., 3-25 Higashi-Goken-cho, Shinjuku-ku, Tokyo 162-0813, Japan
| | - Zaizhi Yu
- Research Center for Environmental Quality Management, Graduate School of Engineering, Kyoto University, 1-2 Yumihama, Otsu, Shiga 520-0811, Japan
| | - Xiaohan Chen
- Research Center for Environmental Quality Management, Graduate School of Engineering, Kyoto University, 1-2 Yumihama, Otsu, Shiga 520-0811, Japan
| | - Hiroaki Tanaka
- Research Center for Environmental Quality Management, Graduate School of Engineering, Kyoto University, 1-2 Yumihama, Otsu, Shiga 520-0811, Japan
| | - Masaru Ihara
- Research Center for Environmental Quality Management, Graduate School of Engineering, Kyoto University, 1-2 Yumihama, Otsu, Shiga 520-0811, Japan; Faculty of Agriculture and Marine Science, Kochi University, 200 Monobe-Otsu, Nankoku city, Kochi 783-8502, Japan.
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27
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Xu X, Deng Y, Ding J, Shi X, Zheng X, Wang D, Yang Y, Liu L, Wang C, Li S, Gu H, Poon LLM, Zhang T. Refining detection methods for emerging SARS-CoV-2 mutants in wastewater: A case study on the Omicron variants. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 904:166215. [PMID: 37591380 DOI: 10.1016/j.scitotenv.2023.166215] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 08/07/2023] [Accepted: 08/08/2023] [Indexed: 08/19/2023]
Abstract
COVID-19 is an ongoing public health threat worldwide driven by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Wastewater surveillance has emerged as a complementary tool to clinical surveillance to control the COVID-19 pandemic. With the emergence of new variants of SARS-CoV-2, accumulated mutations that occurred in the SARS-CoV-2 genome raise new challenges for RT-qPCR diagnosis used in wastewater surveillance. There is a pressing need to develop refined methods for modifying primer/probes to better detect these emerging variants in wastewater. Here, we exemplified this process by focusing on the Omicron variants, for which we have developed and validated a modified detection method. We first modified the primers/probe mismatches of three assays commonly used in wastewater surveillance according to in silico analysis results for the mutations of 882 sequences collected during the fifth-wave outbreak in Hong Kong, and then evaluated them alongside the seven original assays. The results showed that five of seven original assays had better sensitivity for detecting Omicron variants, with the limits of detection (LoDs) ranging from 1.53 to 2.76 copies/μL. UCDC-N1 and Charité-E sets had poor performances, having LoDs higher than 10 copies/μL and false-positive/false-negative results in wastewater testing, probably due to the mismatch and demonstrating the need for modification of primer/probe sequences. The modified assays exhibited higher sensitivity and specificity, along with better reproducibility in detecting 81 wastewater samples. In addition, the sequencing results of six wastewater samples by Illumina also validated the presence of mismatches in the primer/probe binding sites of the three assays. This study highlights the importance of re-configuration of the primer-probe sets and refinements for the sequences to ensure the diagnostic effectiveness of RT-qPCR detection.
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Affiliation(s)
- Xiaoqing Xu
- Environmental Microbiome Engineering and Biotechnology Laboratory, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Yu Deng
- Environmental Microbiome Engineering and Biotechnology Laboratory, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Jiahui Ding
- Environmental Microbiome Engineering and Biotechnology Laboratory, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Xianghui Shi
- Environmental Microbiome Engineering and Biotechnology Laboratory, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Xiawan Zheng
- Environmental Microbiome Engineering and Biotechnology Laboratory, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Dou Wang
- Environmental Microbiome Engineering and Biotechnology Laboratory, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Yu Yang
- Environmental Microbiome Engineering and Biotechnology Laboratory, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Lei Liu
- Environmental Microbiome Engineering and Biotechnology Laboratory, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Chunxiao Wang
- Environmental Microbiome Engineering and Biotechnology Laboratory, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Shuxian Li
- Environmental Microbiome Engineering and Biotechnology Laboratory, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Haogao Gu
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Sassoon Road, Hong Kong, China
| | - Leo L M Poon
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Sassoon Road, Hong Kong, China; HKU-Pasteur Research Pole, The University of Hong Kong, Sassoon Road, Hong Kong, China
| | - Tong Zhang
- Environmental Microbiome Engineering and Biotechnology Laboratory, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China.
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Wilhelm A, Schoth J, Meinert-Berning C, Bastian D, Blum H, Elsinga G, Graf A, Heijnen L, Ho J, Kluge M, Krebs S, Stange C, Uchaikina A, Dolny R, Wurzbacher C, Drewes JE, Medema G, Tiehm A, Ciesek S, Teichgräber B, Wintgens T, Weber FA, Widera M. Interlaboratory comparison using inactivated SARS-CoV-2 variants as a feasible tool for quality control in COVID-19 wastewater monitoring. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 903:166540. [PMID: 37634730 DOI: 10.1016/j.scitotenv.2023.166540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 08/11/2023] [Accepted: 08/22/2023] [Indexed: 08/29/2023]
Abstract
Wastewater-based SARS-CoV-2 epidemiology (WBE) has proven as an excellent tool to monitor pandemic dynamics supporting individual testing strategies. WBE can also be used as an early warning system for monitoring the emergence of novel pathogens or viral variants. However, for a timely transmission of results, sophisticated sample logistics and analytics performed in decentralized laboratories close to the sampling sites are required. Since multiple decentralized laboratories commonly use custom in-house workflows for sample purification and PCR-analysis, comparative quality control of the analytical procedures is essential to report reliable and comparable results. In this study, we performed an interlaboratory comparison at laboratories specialized for PCR and high-throughput-sequencing (HTS)-based WBE analysis. Frozen reserve samples from low COVID-19 incidence periods were spiked with different inactivated authentic SARS-CoV-2 variants in graduated concentrations and ratios. Samples were sent to the participating laboratories for analysis using laboratory specific methods and the reported viral genome copy numbers and the detection of viral variants were compared with the expected values. All PCR-laboratories reported SARS-CoV-2 genome copy equivalents (GCE) for all spiked samples with a mean intra- and inter-laboratory variability of 19 % and 104 %, respectively, largely reproducing the spike-in scheme. PCR-based genotyping was, in dependence of the underlying PCR-assay performance, able to predict the relative amount of variant specific substitutions even in samples with low spike-in amount. The identification of variants by HTS, however, required >100 copies/ml wastewater and had limited predictive value when analyzing at a genome coverage below 60 %. This interlaboratory test demonstrates that despite highly heterogeneous isolation and analysis procedures, overall SARS-CoV-2 GCE and mutations were determined accurately. Hence, decentralized SARS-CoV-2 wastewater monitoring is feasible to generate comparable analysis results. However, since not all assays detected the correct variant, prior evaluation of PCR and sequencing workflows as well as sustained quality control such as interlaboratory comparisons are mandatory for correct variant detection.
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Affiliation(s)
- Alexander Wilhelm
- Goethe University Frankfurt, University Hospital, Institute for Medical Virology, Paul-Ehrlich-Str. 40, D-60596 Frankfurt, Germany
| | - Jens Schoth
- Emschergenossenschaft/Lippeverband, Kronprinzenstraße 24, D-45128 Essen, Germany
| | | | - Daniel Bastian
- FiW e.V., Research Institute for Water Management and Climate Future at RWTH Aachen University, Kackertstraße 15-17, D-52056 Aachen, Germany
| | - Helmut Blum
- Laboratory for Functional Genome Analysis, Gene Center, LMU München, Feodor-Lynen-Straße 25, D-81377 Munich, Germany
| | - Goffe Elsinga
- KWR Water Research Institute, Groningenhaven 7, 3433 PE Nieuwegein, the Netherlands
| | - Alexander Graf
- Laboratory for Functional Genome Analysis, Gene Center, LMU München, Feodor-Lynen-Straße 25, D-81377 Munich, Germany
| | - Leo Heijnen
- KWR Water Research Institute, Groningenhaven 7, 3433 PE Nieuwegein, the Netherlands
| | - Johannes Ho
- TZW: DVGW-Technologiezentrum Wasser, Karlsruher Str. 84, 76139 Karlsruhe, Germany
| | - Mariana Kluge
- Chair of Urban Water Systems Engineering, Technical University of Munich, Am Coulombwall 3, D-85748 Garching, Germany
| | - Stefan Krebs
- Laboratory for Functional Genome Analysis, Gene Center, LMU München, Feodor-Lynen-Straße 25, D-81377 Munich, Germany
| | - Claudia Stange
- TZW: DVGW-Technologiezentrum Wasser, Karlsruher Str. 84, 76139 Karlsruhe, Germany
| | - Anna Uchaikina
- Chair of Urban Water Systems Engineering, Technical University of Munich, Am Coulombwall 3, D-85748 Garching, Germany
| | - Regina Dolny
- Institute of Environmental Engineering, RWTH Aachen University, Mies-van-der-Rohe-Strasse 1, D-52074 Aachen, Germany
| | - Christian Wurzbacher
- Chair of Urban Water Systems Engineering, Technical University of Munich, Am Coulombwall 3, D-85748 Garching, Germany
| | - Jörg E Drewes
- Chair of Urban Water Systems Engineering, Technical University of Munich, Am Coulombwall 3, D-85748 Garching, Germany
| | - Gertjan Medema
- KWR Water Research Institute, Groningenhaven 7, 3433 PE Nieuwegein, the Netherlands
| | - Andreas Tiehm
- TZW: DVGW-Technologiezentrum Wasser, Karlsruher Str. 84, 76139 Karlsruhe, Germany
| | - Sandra Ciesek
- Goethe University Frankfurt, University Hospital, Institute for Medical Virology, Paul-Ehrlich-Str. 40, D-60596 Frankfurt, Germany; German Center for Infection Research (DZIF), 38124 Braunschweig, Germany; Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Theodor-Stern-Kai 7, D 60595 Frankfurt am Main, Germany
| | - Burkhard Teichgräber
- Emschergenossenschaft/Lippeverband, Kronprinzenstraße 24, D-45128 Essen, Germany
| | - Thomas Wintgens
- Institute of Environmental Engineering, RWTH Aachen University, Mies-van-der-Rohe-Strasse 1, D-52074 Aachen, Germany
| | - Frank-Andreas Weber
- FiW e.V., Research Institute for Water Management and Climate Future at RWTH Aachen University, Kackertstraße 15-17, D-52056 Aachen, Germany
| | - Marek Widera
- Goethe University Frankfurt, University Hospital, Institute for Medical Virology, Paul-Ehrlich-Str. 40, D-60596 Frankfurt, Germany.
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Tang S, Cao Y. A phenomenological neural network powered by the National Wastewater Surveillance System for estimation of silent COVID-19 infections. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 902:166024. [PMID: 37541490 DOI: 10.1016/j.scitotenv.2023.166024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 07/31/2023] [Accepted: 08/01/2023] [Indexed: 08/06/2023]
Abstract
Although wastewater-based epidemiology (WBE) has emerged as an inexpensive and non-intrusive method in contrast to clinical testing to track public health at community levels, there is a lack of structured interpretative criteria to translate the SARS-CoV-2 concentrations in wastewater to COVID-19 infection cases. The difficulties lie in the uncertainties of the amount of virus shed by an infected individual to wastewater as documented in clinical studies. This situation is even worse considering the existence of a population of silent infections and many other confounding factors. In this research, a quantitative framework of a phenomenological neural network (PNN) was developed to compute silent infections. The PNN was trained using the WBE data from the National Wastewater Surveillance System (NWSS) - a program launched by the CDC of the United States in 2020. It is found that the PNN excelled with superior interpretability and reduced overfitting. A big-data perspective on virus shedding by an infected population revealed more deterministic virus-shedding dynamics compared to the clinical studies perspective on virus shedding by an infected individual. With such characteristics employed as the theoretical basis for the estimation of the silent infections, a ratio of silent to reported infections was found to be 5.7 as the national median during the studied period. The study also noted the influence of temperature, sewershed population, and per-capita flow rates on the computation of silent infections. It is expected that the proposed framework in this work would facilitate public health actions guided by the SARS-CoV-2 concentrations in wastewater. In case of a new wave emergence or a new virus disease outbreak like COVID-19, the PNN powered by the NWSS would outline consolidated and systematic information that would enable rapid deployment of public health actions.
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Affiliation(s)
- Shunyu Tang
- Department of Mathematical and Computer Sciences, Indiana University of Pennsylvania, Indiana, PA 15705, United States of America
| | - Yongtao Cao
- Department of Mathematical and Computer Sciences, Indiana University of Pennsylvania, Indiana, PA 15705, United States of America.
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Wong YHM, Lim JT, Griffiths J, Lee B, Maliki D, Thompson J, Wong M, Chae SR, Teoh YL, Ho ZJM, Lee V, Cook AR, Tay M, Wong JCC, Ng LC. Positive association of SARS-CoV-2 RNA concentrations in wastewater and reported COVID-19 cases in Singapore - A study across three populations. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 902:166446. [PMID: 37604378 DOI: 10.1016/j.scitotenv.2023.166446] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 08/15/2023] [Accepted: 08/18/2023] [Indexed: 08/23/2023]
Abstract
Wastewater testing of SARS-CoV-2 has been adopted globally and has shown to be a useful, non-intrusive surveillance method for monitoring COVID-19 trends. In Singapore, wastewater surveillance has been widely implemented across various sites and has facilitated timely COVID-19 management and response. From April 2020 to February 2022, SARS-CoV-2 RNA concentrations in wastewater monitored across three populations, nationally, in the community, and in High Density Living Environments (HDLEs) were aggregated into indices and compared with reported COVID-19 cases and hospitalisations. Temporal trends and associations of these indices were compared descriptively and quantitatively, using Poisson Generalised Linear Models and Generalised Additive Models. National vaccination rates and vaccine breakthrough infection rates were additionally considered as confounders to shedding. Fitted models quantified the temporal associations between the indices and cases and COVID-related hospitalisations. At the national level, the wastewater index was a leading indicator of COVID-19 cases (p-value <0.001) of one week, and a contemporaneous association with hospitalisations (p-value <0.001) was observed. At finer levels of surveillance, the community index was observed to be contemporaneously associated with COVID-19 cases (p-value <0.001) and had a lagging association of 1-week in HDLEs (p-value <0.001). These temporal differences were attributed to differences in testing routines for different sites during the study period and the timeline of COVID-19 progression in infected persons. Overall, this study demonstrates the utility of wastewater surveillance in understanding underlying COVID-19 transmission and shedding levels, particularly for areas with falling or low case ascertainment. In such settings, wastewater surveillance showed to be a lead indicator of COVID-19 cases. The findings also underscore the potential of wastewater surveillance for monitoring other infectious diseases threats.
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Affiliation(s)
| | - Jue Tao Lim
- Environmental Health Institute, National Environment Agency, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - Jane Griffiths
- Environmental Health Institute, National Environment Agency, Singapore
| | - Benjamin Lee
- Environmental Health Institute, National Environment Agency, Singapore
| | | | - Janelle Thompson
- Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore; Asian School of the Environment, Nanyang Technological University, Singapore
| | - Michelle Wong
- Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore
| | - Sae-Rom Chae
- Ministry of Health, Singapore; National Centre for Infectious Diseases, Singapore
| | - Yee Leong Teoh
- Ministry of Health, Singapore; National Centre for Infectious Diseases, Singapore
| | | | | | - Alex R Cook
- Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore
| | - Martin Tay
- Environmental Health Institute, National Environment Agency, Singapore
| | | | - Lee Ching Ng
- Environmental Health Institute, National Environment Agency, Singapore; School of Biological Sciences, Nanyang Technological University, Singapore
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Oghuan J, Chavarria C, Vanderwal SR, Gitter A, Ojaruega AA, Monserrat C, Bauer CX, Brown EL, Cregeen SJ, Deegan J, Hanson BM, Tisza M, Ocaranza HI, Balliew J, Maresso AW, Rios J, Boerwinkle E, Mena KD, Wu F. Wastewater analysis of Mpox virus in a city with low prevalence of Mpox disease: an environmental surveillance study. LANCET REGIONAL HEALTH. AMERICAS 2023; 28:100639. [PMID: 38076410 PMCID: PMC10701415 DOI: 10.1016/j.lana.2023.100639] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 11/09/2023] [Accepted: 11/13/2023] [Indexed: 02/18/2024]
Abstract
Background Tracking infectious diseases at the community level is challenging due to asymptomatic infections and the logistical complexities of mass surveillance. Wastewater surveillance has emerged as a valuable tool for monitoring infectious disease agents including SARS-CoV-2 and Mpox virus. However, detecting the Mpox virus in wastewater is particularly challenging due to its relatively low prevalence in the community. In this study, we aim to characterize three molecular assays for detecting and tracking the Mpox virus in wastewater from El Paso, Texas, during February and March 2023. Methods In this study, a combined approach utilizing three real-time PCR assays targeting the C22L, F3L, and F8L genes and sequencing was employed to detect and track the Mpox virus in wastewater samples. The samples were collected from four sewersheds in the City of El Paso, Texas, during February and March 2023. Wastewater data was compared with reported clinical case data in the city. Findings Mpox virus DNA was detected in wastewater from all the four sewersheds, whereas only one Mpox case was reported during the sampling period. Positive signals were still observed in multiple sewersheds after the Mpox case was identified. Higher viral concentrations were found in the pellet than in the supernatant of wastewater. Notably, an increasing trend in viral concentration was observed approximately 1-2 weeks before the reporting of the Mpox case. Further sequencing and epidemiological analysis provided supporting evidence for unreported Mpox infections in the city. Interpretation Our analysis suggests that the Mpox cases in the community is underestimated. The findings emphasize the value of wastewater surveillance as a public health tool for monitoring infectious diseases even in low-prevalence areas, and the need for heightened vigilance to mitigate the spread of Mpox disease for safeguarding global health. Funding Center of Infectious Diseases at UTHealth, the University of Texas System, and the Texas Epidemic Public Health Institute. The content of this paper is solely the responsibility of the authors and does not necessarily represent the official views of these funding organizations.
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Affiliation(s)
- Jeremiah Oghuan
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, TX, USA
| | - Carlos Chavarria
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, TX, USA
| | - Scout R. Vanderwal
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, TX, USA
| | - Anna Gitter
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, TX, USA
- Texas Epidemic Public Health Institute (TEPHI), UTHealth Houston, Houston, TX, USA
| | - Akpevwe Amanda Ojaruega
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, TX, USA
| | - Carlos Monserrat
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, TX, USA
| | - Cici X. Bauer
- Texas Epidemic Public Health Institute (TEPHI), UTHealth Houston, Houston, TX, USA
- Department of Biostatistics and Data Science, School of Public Health, University of Texas Health Science Center at Houston, TX, USA
- Center of Spatial-temporal Modeling of Applications in Population Sciences, Houston, TX, USA
| | - Eric L. Brown
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, TX, USA
| | - Sara Javornik Cregeen
- The Alkek Center for Metagenomics and Microbiome Research, Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, USA
| | - Jennifer Deegan
- Texas Epidemic Public Health Institute (TEPHI), UTHealth Houston, Houston, TX, USA
- School of Public Health, University of Texas Health Science Center at Houston, TX, USA
| | - Blake M. Hanson
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, TX, USA
- Texas Epidemic Public Health Institute (TEPHI), UTHealth Houston, Houston, TX, USA
| | - Michael Tisza
- The Alkek Center for Metagenomics and Microbiome Research, Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, USA
| | | | | | - Anthony W. Maresso
- Texas Epidemic Public Health Institute (TEPHI), UTHealth Houston, Houston, TX, USA
- TAILOR Labs, Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Janelle Rios
- Texas Epidemic Public Health Institute (TEPHI), UTHealth Houston, Houston, TX, USA
- School of Public Health, University of Texas Health Science Center at Houston, TX, USA
| | - Eric Boerwinkle
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, TX, USA
- Texas Epidemic Public Health Institute (TEPHI), UTHealth Houston, Houston, TX, USA
| | - Kristina D. Mena
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, TX, USA
- Texas Epidemic Public Health Institute (TEPHI), UTHealth Houston, Houston, TX, USA
| | - Fuqing Wu
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, TX, USA
- Texas Epidemic Public Health Institute (TEPHI), UTHealth Houston, Houston, TX, USA
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Babler KM, Sharkey ME, Amirali A, Boone MM, Comerford S, Currall BB, Grills GS, Laine J, Mason CE, Reding B, Schürer S, Stevenson M, Vidović D, Williams SL, Solo-Gabriele HM. Expanding a Wastewater-Based Surveillance Methodology for DNA Isolation from a Workflow Optimized for SARS-CoV-2 RNA Quantification. J Biomol Tech 2023; 34:3fc1f5fe.dfa8d906. [PMID: 38268997 PMCID: PMC10805363 DOI: 10.7171/3fc1f5fe.dfa8d906] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2024]
Abstract
Wastewater-based surveillance (WBS) is a noninvasive, epidemiological strategy for assessing the spread of COVID-19 in communities. This strategy was based upon wastewater RNA measurements of the viral target, severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). The utility of WBS for assessing the spread of COVID-19 has motivated research to measure targets beyond SARS-CoV-2, including pathogens containing DNA. The objective of this study was to establish the necessary steps for isolating DNA from wastewater by modifying a long-standing RNA-specific extraction workflow optimized for SARS-CoV-2 detection. Modifications were made to the sample concentration process and included an evaluation of bead bashing prior to the extraction of either DNA or RNA. Results showed that bead bashing reduced detection of RNA from wastewater but improved recovery of DNA as assessed by quantitative polymerase chain reaction (qPCR). Bead bashing is therefore not recommended for the quantification of RNA viruses using qPCR. Whereas for Mycobacterium bacterial DNA isolation, bead bashing was necessary for improving qPCR quantification. Overall, we recommend 2 separate workflows, one for RNA viruses that does not include bead bashing and one for other microbes that use bead bashing for DNA isolation. The experimentation done here shows that current-standing WBS program methodologies optimized for SARS-CoV-2 need to be modified and reoptimized to allow for alternative pathogens to be readily detected and monitored, expanding its utility as a tool for public health assessment.
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Affiliation(s)
- Kristina M. Babler
- Department of ChemicalEnvironmental and Materials
EngineeringUniversity of MiamiCoral GablesFlorida33124USA
| | - Mark E. Sharkey
- Department of MedicineUniversity of Miami Miller School
of MedicineMiamiFlorida33136USA
| | - Ayaaz Amirali
- Department of ChemicalEnvironmental and Materials
EngineeringUniversity of MiamiCoral GablesFlorida33124USA
| | - Melinda M. Boone
- Sylvester Comprehensive Cancer CenterUniversity of Miami
Miller School of MedicineMiamiFlorida33136USA
| | - Samuel Comerford
- Department of MedicineUniversity of Miami Miller School
of MedicineMiamiFlorida33136USA
| | - Benjamin B. Currall
- Sylvester Comprehensive Cancer CenterUniversity of Miami
Miller School of MedicineMiamiFlorida33136USA
| | - George S. Grills
- Sylvester Comprehensive Cancer CenterUniversity of Miami
Miller School of MedicineMiamiFlorida33136USA
| | - Jennifer Laine
- Environmental Health and SafetyUniversity of MiamiMiamiFlorida33136USA
| | - Christopher E. Mason
- Department of Physiology and BiophysicsWeill Cornell
MedicineNew YorkNew York10065USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Al-Saud
Institute for Computational BiomedicineWeill Cornell MedicineNew
YorkNew York10065USA
- The WorldQuant Initiative for Quantitative PredictionWeill Cornell MedicineNew YorkNew YorkUSA 10065USA
| | - Brian Reding
- Environmental Health and SafetyUniversity of MiamiMiamiFlorida33136USA
| | - Stephan Schürer
- Sylvester Comprehensive Cancer CenterUniversity of Miami
Miller School of MedicineMiamiFlorida33136USA
- Department of Molecular and Cellular PharmacologyUniversity of Miami Miller School of MedicineMiamiFlorida33136USA
- Institute for Data Science & Computing, University of
MiamiCoral GablesFlorida33146USA
| | - Mario Stevenson
- Department of MedicineUniversity of Miami Miller School
of MedicineMiamiFlorida33136USA
| | - Dušica Vidović
- Department of Molecular and Cellular PharmacologyUniversity of Miami Miller School of MedicineMiamiFlorida33136USA
| | - Sion L. Williams
- Sylvester Comprehensive Cancer CenterUniversity of Miami
Miller School of MedicineMiamiFlorida33136USA
| | - Helena M. Solo-Gabriele
- Department of ChemicalEnvironmental and Materials
EngineeringUniversity of MiamiCoral GablesFlorida33124USA
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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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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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Liu Y, Sapoval N, Gallego-García P, Tomás L, Posada D, Treangen TJ, Stadler LB. Crykey: Rapid Identification of SARS-CoV-2 Cryptic Mutations in Wastewater. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.06.16.23291524. [PMID: 37986916 PMCID: PMC10659477 DOI: 10.1101/2023.06.16.23291524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
We present Crykey, a computational tool for rapidly identifying cryptic mutations of SARS-CoV-2. Specifically, we identify co-occurring single nucleotide mutations on the same sequencing read, called linked-read mutations, that are rare or entirely missing in existing databases, and have the potential to represent novel cryptic lineages found in wastewater. While previous approaches exist for identifying cryptic linked-read mutations from specific regions of the SARS-CoV-2 genome, there is a need for computational tools capable of efficiently tracking cryptic mutations across the entire genome and for tens of thousands of samples and with increased scrutiny, given their potential to represent either artifacts or hidden SARS-CoV-2 lineages. Crykey fills this gap by identifying rare linked-read mutations that pass stringent computational filters to limit the potential for artifacts. We evaluate the utility of Crykey on >3,000 wastewater and >22,000 clinical samples; our findings are three-fold: i) we identify hundreds of cryptic mutations that cover the entire SARS-CoV-2 genome, ii) we track the presence of these cryptic mutations across multiple wastewater treatment plants and over a three years of sampling in Houston, and iii) we find a handful of cryptic mutations in wastewater mirror cryptic mutations in clinical samples and investigate their potential to represent real cryptic lineages. In summary, Crykey enables large-scale detection of cryptic mutations representing potential cryptic lineages in wastewater.
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Affiliation(s)
- Yunxi Liu
- Department of Computer Science, Rice University, Houston, TX, 77005, USA
| | - Nicolae Sapoval
- Department of Computer Science, Rice University, Houston, TX, 77005, USA
| | - Pilar Gallego-García
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO
| | - Laura Tomás
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO
| | - David Posada
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO
- Department of Biochemistry, Genetics, and Immunology, Universidade de Vigo, 36310 Vigo, Spain
| | - Todd J. Treangen
- Department of Computer Science, Rice University, Houston, TX, 77005, USA
| | - Lauren B. Stadler
- Department of Civil and Environmental Engineering, Rice University, Houston, TX, 77005, USA
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Babler K, Sharkey M, Arenas S, Amirali A, Beaver C, Comerford S, Goodman K, Grills G, Holung M, Kobetz E, Laine J, Lamar W, Mason C, Pronty D, Reding B, Schürer S, Schaefer Solle N, Stevenson M, Vidović D, Solo-Gabriele H, Shukla B. Detection of the clinically persistent, pathogenic yeast spp. Candida auris from hospital and municipal wastewater in Miami-Dade County, Florida. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 898:165459. [PMID: 37442462 PMCID: PMC10543605 DOI: 10.1016/j.scitotenv.2023.165459] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Revised: 06/14/2023] [Accepted: 07/08/2023] [Indexed: 07/15/2023]
Abstract
The use of wastewater-based surveillance (WBS) for detecting pathogens within communities has been growing since the beginning of the COVID-19 pandemic with early efforts investigating severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) RNA in wastewater. Recent efforts have shed light on the utilization of WBS for alternative targets, such as fungal pathogens, like Candida auris, in efforts to expand the technology to assess non-viral targets. The objective of this study was to extend workflows developed for SARS-CoV-2 quantification to evaluate whether C. auris can be recovered from wastewater, inclusive of effluent from a wastewater treatment plant (WWTP) and from a hospital with known numbers of patients colonized with C. auris. Measurements of C. auris in wastewater focused on culture-based methods and quantitative PCR (qPCR). Results showed that C. auris can be cultured from wastewater and that levels detected by qPCR were higher in the hospital wastewater compared to the wastewater from the WWTP, suggesting either dilution or degradation of this pathogenic yeast at downstream collection points. The results from this study illustrate that WBS can extend beyond SARS-CoV-2 monitoring to evaluate additional non-viral pathogenic targets and demonstrates that C. auris isolated from wastewater is competent to replicate in vitro using fungal-specific culture media.
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Affiliation(s)
- Kristina Babler
- Department of Chemical, Environmental, and Materials Engineering, University of Miami, Coral Gables, FL 33146, USA
| | - Mark Sharkey
- Department of Medicine, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Sebastian Arenas
- Department of Medicine, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Ayaaz Amirali
- Department of Chemical, Environmental, and Materials Engineering, University of Miami, Coral Gables, FL 33146, USA
| | - Cynthia Beaver
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Samuel Comerford
- Department of Medicine, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Kenneth Goodman
- Department of Medicine, University of Miami Miller School of Medicine, Miami, FL 33136, USA; Institute for Data Science & Computing, University of Miami, Coral Gables, FL, USA
| | - George Grills
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Michelle Holung
- Department of Medicine, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Erin Kobetz
- Department of Medicine, University of Miami Miller School of Medicine, Miami, FL 33136, USA; Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Jennifer Laine
- Environmental Health and Safety, University of Miami, Miami, FL 33136, USA
| | - Walter Lamar
- Division of Occupational Health, Safety & Compliance, University of Miami Health System, Miami, FL 33136, USA
| | - Christopher Mason
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York City, NY 10021, USA; The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY 10021, USA; The WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, New York, NY 10021, USA
| | - Darryl Pronty
- Department of Medicine, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Brian Reding
- Environmental Health and Safety, University of Miami, Miami, FL 33136, USA
| | - Stephan Schürer
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA; Department of Molecular & Cellular Pharmacology, University of Miami Miller School of Medicine, Miami, FL 33136, USA; Institute for Data Science & Computing, University of Miami, Coral Gables, FL, USA
| | - Natasha Schaefer Solle
- Department of Medicine, University of Miami Miller School of Medicine, Miami, FL 33136, USA; Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Mario Stevenson
- Department of Medicine, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Dusica Vidović
- Department of Molecular & Cellular Pharmacology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Helena Solo-Gabriele
- Department of Chemical, Environmental, and Materials Engineering, University of Miami, Coral Gables, FL 33146, USA
| | - Bhavarth Shukla
- Department of Medicine, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
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Bohrerova Z, Brinkman NE, Chakravarti R, Chattopadhyay S, Faith SA, Garland J, Herrin J, Hull N, Jahne M, Kang DW, Keely SP, Lee J, Lemeshow S, Lenhart J, Lytmer E, Malgave D, Miao L, Minard-Smith A, Mou X, Nagarkar M, Quintero A, Savona FDR, Senko J, Slonczewski JL, Spurbeck RR, Sovic MG, Taylor RT, Weavers LK, Weir M. Ohio Coronavirus Wastewater Monitoring Network: Implementation of Statewide Monitoring for Protecting Public Health. JOURNAL OF PUBLIC HEALTH MANAGEMENT AND PRACTICE 2023; 29:845-853. [PMID: 37738597 PMCID: PMC10539008 DOI: 10.1097/phh.0000000000001783] [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] [Indexed: 09/24/2023]
Abstract
CONTEXT Prior to the COVID-19 pandemic, wastewater influent monitoring for tracking disease burden in sewered communities was not performed in Ohio, and this field was only on the periphery of the state academic research community. PROGRAM Because of the urgency of the pandemic and extensive state-level support for this new technology to detect levels of community infection to aid in public health response, the Ohio Water Resources Center established relationships and support of various stakeholders. This enabled Ohio to develop a statewide wastewater SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) monitoring network in 2 months starting in July 2020. IMPLEMENTATION The current Ohio Coronavirus Wastewater Monitoring Network (OCWMN) monitors more than 70 unique locations twice per week, and publicly available data are updated weekly on the public dashboard. EVALUATION This article describes the process and decisions that were made during network initiation, the network progression, and data applications, which can inform ongoing and future pandemic response and wastewater monitoring. DISCUSSION Overall, the OCWMN established wastewater monitoring infrastructure and provided a useful tool for public health professionals responding to the pandemic.
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Affiliation(s)
- Zuzana Bohrerova
- Ohio Water Resources Center (Drs Bohrerova, Lenhart, and Weavers), Civil, Environmental and Geodetic Engineering (Drs Bohrerova, Hull, Lenhart, and Weavers), Infectious Diseases Institute (Drs Faith and Lee and Ms Savona), Sustainability Institute (Dr Hull), Department of Food Science & Technology (Dr Lee), and Center for Applied Plant Sciences (Dr Sovic), The Ohio State University, Columbus, Ohio; Office of Research and Development, US Environmental Protection Agency, Washington, District of Columbia (Drs Brinkman, Garland, Jahne, Keely, and Nagarkar); Departments of Physiology and Pharmacology (Dr Chakravarti) and Medical Microbiology and Immunology (Drs Chattopadhyay and Taylor), University of Toledo College of Medicine and Life Sciences, Toledo, Ohio; LuminUltra Technologies Inc, Hialeah, Florida (Mr Herrin and Dr Quintero); Department of Civil and Environmental Engineering, University of Toledo, Toledo, Ohio (Dr Kang); Divisions of Environmental Health Sciences (Drs Lee and Weir) and Biostatistics (Drs Lemeshow and Malgave and Ms Miao), The Ohio State University College of Public Health, Columbus, Ohio; Department of Biological Sciences, Bowling Green State University, Bowling Green, Ohio (Ms Lytmer); Health Outcomes and Biotechnology Solutions, Battelle Memorial Institute, Columbus, Ohio (Ms Minard-Smith and Dr Spurbeck); Department of Biological Sciences, Kent State University, Kent, Ohio (Dr Mou); Department of Geosciences and Department of Biology, The University of Akron, Akron, Ohio (Dr Senko); and Department of Biology, Kenyon College, Gambier, Ohio (Dr Slonczewski)
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Sanchez Jimenez B, Sterling T, Brown A, Modica B, Gibson K, Collins H, Koch C, Schwarz T, Dye KN. Wastewater surveillance in the COVID-19 post-emergency pandemic period: A promising approach to monitor and predict SARS-CoV-2 surges and evolution. Heliyon 2023; 9:e22356. [PMID: 38045160 PMCID: PMC10689941 DOI: 10.1016/j.heliyon.2023.e22356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 09/17/2023] [Accepted: 11/10/2023] [Indexed: 12/05/2023] Open
Abstract
On May 24, 2023, approximately 3.5 years into the pandemic, the World Health Organization (WHO) declared the end of the COVID-19 global health emergency. However, as there are still ∼3000 COVID-19 deaths per day in May 2023, robust surveillance systems are still warranted to return to normalcy in times of low risk and respond appropriately in times of high risk. The different phases of the pandemic have been defined by infection numbers and variants, both of which have been determined through clinical tests that are subject to many biases. Unfortunately, the end of the COVID-19 emergency threatens to exasperate these biases, thereby warranting alternative tracking methods. We hypothesized that wastewater surveillance could be used as a more accurate and comprehensive method to track SARS-CoV-2 in the post-emergency pandemic period (PEPP). SARS-CoV-2 was quantified and sequenced from wastewater between June 2022 and March 2023 to research the anticipated 2022/23 winter surge. However, in the 2022/23 winter, there was lower-than-expected SARS-CoV-2 circulation, which was hypothesized to be due to diagnostic testing biases but was confirmed by our wastewater analysis, thereby emphasizing the unpredictable nature of SARS-CoV-2 surges while also questioning its winter seasonality. Even in times of low baseline circulation, we found wastewater surveillance to be sensitive enough to detect minor changes in circulation levels ∼30-46 days prior to diagnostic tests, suggesting that wastewater surveillance may be a more appropriate early warning system to prepare for unpredictable surges in the PEPP. Furthermore, sequencing of wastewater detected variants of concern that were positively correlated with clinical samples and also provided a method to identify mutations with a high likelihood of appearing in future variants, necessary for updating vaccines and therapeutics prior to novel variant circulation. Together, these data highlight the effectiveness of wastewater surveillance in the PEPP to limit the global health burden of SARS-CoV-2 due to increases in circulation and/or viral evolution.
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Affiliation(s)
| | - Trinity Sterling
- Department of Health Sciences, Stetson University, DeLand, FL, 32723, USA
| | - Austin Brown
- Department of Health Sciences, Stetson University, DeLand, FL, 32723, USA
| | - Brian Modica
- Department of Health Sciences, Stetson University, DeLand, FL, 32723, USA
| | - Kaylee Gibson
- Department of Health Sciences, Stetson University, DeLand, FL, 32723, USA
| | - Hannah Collins
- Department of Health Sciences, Stetson University, DeLand, FL, 32723, USA
| | - Carolyn Koch
- Department of Health Sciences, Stetson University, DeLand, FL, 32723, USA
| | - Tyler Schwarz
- Department of Health Sciences, Stetson University, DeLand, FL, 32723, USA
| | - Kristine N. Dye
- Department of Health Sciences, Stetson University, DeLand, FL, 32723, USA
- Department of Biology, Stetson University, DeLand, FL, 32723, USA
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Wang H, Churqui MP, Tunovic T, Enache L, Johansson A, Lindh M, Lagging M, Nyström K, Norder H. Measures against COVID-19 affected the spread of human enteric viruses in a Swedish community, as found when monitoring wastewater. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 895:165012. [PMID: 37353026 PMCID: PMC10284612 DOI: 10.1016/j.scitotenv.2023.165012] [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: 02/28/2023] [Revised: 05/25/2023] [Accepted: 06/17/2023] [Indexed: 06/25/2023]
Abstract
The quantification of viral genomes in wastewater reflects the prevalence of viral infections within the community. Knowledge of how the spread of common enteric viruses in the community was affected by the Swedish COVID-19 interventions is limited. To investigate this, the weekly wastewater samples collected for monitoring SARS-CoV-2 throughout the COVID-19 pandemic at the Rya sewage treatment plant in Gothenburg were also analyzed for adenovirus, norovirus GII, astrovirus, and rotavirus. The amount of each viral genome was quantified by real-time-qPCR and compared with the quantity of these viral genomes in wastewater from 2017. The results showed that the winter seasonality of norovirus GII and rotavirus in wastewater observed in 2017 was interrupted shortly after the introduction of the COVID-19 interventions, and they remained at low level throughout the pandemic. The circulation pattern of astrovirus and adenovirus was less affected. When the COVID-19 restrictions were lifted in 2022, a dramatic increase was observed in the amount of norovirus GII, rotavirus, and adenovirus genomes in wastewater. The changes in abundance and seasonality of some viruses identified through wastewater monitoring were consistent with changes in the number of patients diagnosed with these viruses. These findings suggest that moderate intervention to prevent COVID-19 significantly reduced the spread of some enteric viruses in the community. The results show that wastewater monitoring is a valuable tool for detecting the spread and outbreaks of viral infections that may cause gastroenteritis also when people do not seek medical help, such as during the COVID-19 pandemic.
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Affiliation(s)
- Hao Wang
- Institute of Biomedicine, Department of Infectious Diseases, University of Gothenburg, Gothenburg, Sweden; Sahlgrenska University Hospital, Department of Clinical Microbiology, Region Västra Götaland, Gothenburg, Sweden.
| | - Marianela Patzi Churqui
- Institute of Biomedicine, Department of Infectious Diseases, University of Gothenburg, Gothenburg, Sweden
| | - Timur Tunovic
- Institute of Biomedicine, Department of Infectious Diseases, University of Gothenburg, Gothenburg, Sweden
| | | | | | - Magnus Lindh
- Institute of Biomedicine, Department of Infectious Diseases, University of Gothenburg, Gothenburg, Sweden; Sahlgrenska University Hospital, Department of Clinical Microbiology, Region Västra Götaland, Gothenburg, Sweden
| | - Martin Lagging
- Institute of Biomedicine, Department of Infectious Diseases, University of Gothenburg, Gothenburg, Sweden; Sahlgrenska University Hospital, Department of Clinical Microbiology, Region Västra Götaland, Gothenburg, Sweden
| | - Kristina Nyström
- Institute of Biomedicine, Department of Infectious Diseases, University of Gothenburg, Gothenburg, Sweden; Sahlgrenska University Hospital, Department of Clinical Microbiology, Region Västra Götaland, Gothenburg, Sweden
| | - Heléne Norder
- Institute of Biomedicine, Department of Infectious Diseases, University of Gothenburg, Gothenburg, Sweden; Sahlgrenska University Hospital, Department of Clinical Microbiology, Region Västra Götaland, Gothenburg, Sweden
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40
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Liu AB, Lee D, Jalihal AP, Hanage WP, Springer M. Quantitatively assessing early detection strategies for mitigating COVID-19 and future pandemics. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.06.08.23291050. [PMID: 37398047 PMCID: PMC10312821 DOI: 10.1101/2023.06.08.23291050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Researchers and policymakers have proposed systems to detect novel pathogens earlier than existing surveillance systems by monitoring samples from hospital patients, wastewater, and air travel, in order to mitigate future pandemics. How much benefit would such systems offer? We developed, empirically validated, and mathematically characterized a quantitative model that simulates disease spread and detection time for any given disease and detection system. We find that hospital monitoring could have detected COVID-19 in Wuhan 0.4 weeks earlier than it was actually discovered, at 2,300 cases (standard error: 76 cases) compared to 3,400 (standard error: 161 cases). Wastewater monitoring would not have accelerated COVID-19 detection in Wuhan, but provides benefit in smaller catchments and for asymptomatic or long-incubation diseases like polio or HIV/AIDS. Monitoring of air travel provides little benefit in most scenarios we evaluated. In sum, early detection systems can substantially mitigate some future pandemics, but would not have changed the course of COVID-19.
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Affiliation(s)
- Andrew Bo Liu
- Department of Systems Biology, Harvard Medical School; Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School; Boston, MA, USA
| | - Daniel Lee
- Department of Biomedical Informatics, Harvard Medical School; Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard; Cambridge, MA, USA
| | | | - William P. Hanage
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health; Boston, MA, USA
| | - Michael Springer
- Department of Systems Biology, Harvard Medical School; Boston, MA, USA
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41
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Koirala P, Dhakal S, Malla B, Ghimire A, Siddiqui MA, Dawadi P. SARS-CoV-2 Burden in Wastewater and its Elimination Using Disinfection. Microbiol Insights 2023; 16:11786361231201598. [PMID: 37745090 PMCID: PMC10517603 DOI: 10.1177/11786361231201598] [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: 06/09/2023] [Accepted: 08/30/2023] [Indexed: 09/26/2023] Open
Abstract
Background Pathogenic viruses have been abundant and diverse in wastewater, reflecting the pattern of infection in humans. Human feces, urine, and perhaps other washouts that frequently circulate in sewage systems may contaminate wastewater with SARS-CoV-2. It's crucial to effectively disinfect wastewater since poorly handled wastewater could put the population at risk of infection. Aims To emphasize the presence and spread of SARS-CoV-2 in sewage (wastewater) through viral shedding from the patients to detect the virus in the population using wastewater-based epidemiology. Also, to effectively manage the transmission of SARS-CoV-2 and reduce the spread of the virus in the population using disinfectants is highlighted. Methods We evaluated articles from December 2019 to August 2022 that addressed SARS-CoV-2 shedding in wastewater and surveillance through wastewater-based epidemiology. We included the papers on wastewater disinfection for the elimination of SARS-CoV-2. Google Scholar, PubMed, and Research4Life are the three electronic databases from which all of the papers were retrieved. Results It is possible for viral shedding to get into the wastewater. The enumeration of viral RNA from it can be used to monitor virus circulation in the human community. SARS-CoV-2 can be removed from wastewater by using modern disinfection techniques such as sodium hypochlorite, liquid chlorine, chlorine dioxide, peracetic acid, and ultraviolet light. Conclusion SARS-CoV-2 burden estimates at the population level can be obtained via longitudinal examination of wastewater, and SARS-CoV-2 can be removed from the wastewater through disinfection.
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Affiliation(s)
- Prashanna Koirala
- National Animal Breeding and Genetics Research Center, Nepal Agricultural Research Council, Lalitpur, Nepal
| | - Sandesh Dhakal
- Central Department of Microbiology, Tribhuvan University, Kirtipur, Kathmandu, Nepal
| | - Bikram Malla
- Central Department of Microbiology, Tribhuvan University, Kirtipur, Kathmandu, Nepal
| | - Archana Ghimire
- Department of Development Education, School of Education, Kathmandu University, Hattiban, Lalitpur, Nepal
| | - Mohammad Ataullah Siddiqui
- Molecular Biotechnology Unit, Faculty of Science, Nepal Academy of Science and Technology, Khumaltar, Lalitpur, Nepal
| | - Prabin Dawadi
- Central Department of Microbiology, Tribhuvan University, Kirtipur, Kathmandu, Nepal
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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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Rusková M, Bučková M, Puškárová A, Cíchová M, Janská V, Achs A, Šubr Z, Kuchta T, Pangallo D. Comparison of ordinary reverse transcription real-time polymerase chain reaction (qRT-PCR) with a newly developed one-step single-tube nested real-time RT-PCR (OSN-qRT-PCR) for sensitive detection of SARS-CoV-2 in wastewater. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:95579-95589. [PMID: 37553492 PMCID: PMC10482794 DOI: 10.1007/s11356-023-29123-2] [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: 01/26/2023] [Accepted: 07/29/2023] [Indexed: 08/10/2023]
Abstract
Wastewater monitoring has proven to be an important approach to detecting and controlling the development of the SARS-CoV-2 pandemic. Various tests based on reverse transcription real-time PCR (qRT-PCR) have been developed and used for the detection of SARS-CoV-2 in wastewater samples. In this study, we attempted to increase the sensitivity of qRT-PCR by developing a one-step single-tube nested qRT-PCR assay (OSN-qRT-PCR). Two variants were developed, oriented to nucleocapsid phosphoprotein gene (N) and to spike protein gene (S), respectively. The performance of conventional qRT-PCR assays oriented to these genes with two novel OSN-qRT-PCR assays were firstly optimized using wastewater artificially contaminated with two encapsidated RNA mimic systems harboring a portion either N or S gene (ENRM and ESRM, respectively). The assays were coupled to a polyethylene glycol-based RNA precipitation/extraction method and applied to detect SARS-CoV-2 in wastewater samples from four cities in Slovakia. Both novel OSN-qRT-PCR assays demonstrated higher detection rates than the ordinary qRT-PCR counterparts. The virus levels in the analyzed wastewater samples had a high or very high relation with the numbers of clinical cases in the monitored regions. In fact, correlation with a 3-, 4-, or 5-day temporal offset was revealed. The OSN-qRT-PCR assays demonstrated robustness, mainly in samples with low viral loads.
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Affiliation(s)
- Magdaléna Rusková
- Institute of Molecular Biology, Slovak Academy of Sciences, Dúbravská Cesta 21, 845 51, Bratislava, Slovakia
| | - Mária Bučková
- Institute of Molecular Biology, Slovak Academy of Sciences, Dúbravská Cesta 21, 845 51, Bratislava, Slovakia
| | - Andrea Puškárová
- Institute of Molecular Biology, Slovak Academy of Sciences, Dúbravská Cesta 21, 845 51, Bratislava, Slovakia
| | - Marianna Cíchová
- Water Research Institute, Nábrežie Arm. Gen. L. Svobodu 5, 812 49, Bratislava, Slovakia
| | - Veronika Janská
- Water Research Institute, Nábrežie Arm. Gen. L. Svobodu 5, 812 49, Bratislava, Slovakia
| | - Adam Achs
- Biomedical Research Center, Slovak Academy of Sciences, Institute of Virology, Dúbravská Cesta 9, 845 05, Bratislava, Slovakia
| | - Zdeno Šubr
- Biomedical Research Center, Slovak Academy of Sciences, Institute of Virology, Dúbravská Cesta 9, 845 05, Bratislava, Slovakia
| | - Tomáš Kuchta
- Department of Microbiology, Molecular Biology and Biotechnology, Food Research Institute, National Agricultural and Food Centre, Priemyselná 4, 824 75, Bratislava, Slovakia
| | - Domenico Pangallo
- Institute of Molecular Biology, Slovak Academy of Sciences, Dúbravská Cesta 21, 845 51, Bratislava, Slovakia.
- Caravella, s.r.o., Tupolevova 2, 851 01, Bratislava, Slovakia.
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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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Wadi VS, Daou M, Zayed N, AlJabri M, Alsheraifi HH, Aldhaheri SS, Abuoudah M, Alhammadi M, Aldhuhoori M, Lopes A, Alalawi A, Yousef AF, Hasan SW, Alsafar H. Long-term study on wastewater SARS-CoV-2 surveillance across United Arab Emirates. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 887:163785. [PMID: 37149161 PMCID: PMC10156646 DOI: 10.1016/j.scitotenv.2023.163785] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 04/18/2023] [Accepted: 04/23/2023] [Indexed: 05/08/2023]
Abstract
Wastewater-based epidemiology (WBE) demonstrates an efficient tool to monitor and predict SARS-CoV-2 community distribution. Many countries across the world have adopted the technique, however, most of these studies were conducted for a short duration with a limited sampling size. In this study, long-term reliability and quantification of wastewater SARS-CoV-2 surveillance is reported via analyzing 16,858 samples collected from 453 different locations across the United Arab Emirates (UAE) from May 2020 to June 2022. The collected composite samples were first incubated at 60 °C followed by filtration, concentration, and then RNA extraction using commercially available kits. The extracted RNA was then analyzed by one-step RT-qPCR and RT-ddPCR, and the data was compared to the reported clinical cases. The average positivity rate in the wastewater samples was found to be 60.61 % (8.41-96.77 %), however, the positivity rate obtained from the RT-ddPCR was significantly higher than the RT-qPCR suggesting higher sensitivity of RT-ddPCR. Time-lagged correlation analysis indicated an increase in positive cases in the wastewater samples when the clinical positive cases declined suggesting that wastewater data are highly affected by the unreported asymptomatic, pre-symptomatic and recovering individuals. The weekly SARS-CoV-2 viral count in the wastewater samples are positively correlated with the diagnosed new clinical cases throughout the studied period and the studied locations. Viral count in wastewater peaked approximately one to two weeks prior to the peaks appearing in active clinical cases indicating that wastewater viral concentrations are effective in predicting clinical cases. Overall, this study further confirms the long-term sensitivity and robust approach of WBE to detect trends in SARS-CoV-2 spread and helps contribute to pandemic management.
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Affiliation(s)
- Vijay S Wadi
- Center for Membranes and Advanced Water Technology (CMAT), Department of Chemical Engineering, Khalifa University of Science and Technology, PO Box 127788, Abu Dhabi, United Arab Emirates
| | - Mariane Daou
- Department of Biology, Khalifa University of Science and Technology, PO Box 127788, Abu Dhabi, United Arab Emirates
| | - Noora Zayed
- Center for Biotechnology (BTC), Khalifa University of Science and Technology, PO Box 127788, Abu Dhabi, United Arab Emirates
| | - Maryam AlJabri
- Center for Biotechnology (BTC), Khalifa University of Science and Technology, PO Box 127788, Abu Dhabi, United Arab Emirates
| | - Hamad H Alsheraifi
- Center for Biotechnology (BTC), Khalifa University of Science and Technology, PO Box 127788, Abu Dhabi, United Arab Emirates
| | - Saeed S Aldhaheri
- Center for Biotechnology (BTC), Khalifa University of Science and Technology, PO Box 127788, Abu Dhabi, United Arab Emirates
| | - Miral Abuoudah
- Center for Biotechnology (BTC), Khalifa University of Science and Technology, PO Box 127788, Abu Dhabi, United Arab Emirates
| | - Mohammad Alhammadi
- Emirates Bio-Research Center, Ministry of Interior, Abu Dhabi, United Arab Emirates
| | - Malika Aldhuhoori
- Emirates Bio-Research Center, Ministry of Interior, Abu Dhabi, United Arab Emirates
| | - Alvaro Lopes
- Emirates Bio-Research Center, Ministry of Interior, Abu Dhabi, United Arab Emirates
| | - Abdulrahman Alalawi
- Department of Health, Safety and Environment, Department of Energy, Abu Dhabi, United Arab Emirates
| | - Ahmed F Yousef
- Center for Membranes and Advanced Water Technology (CMAT), Department of Chemical Engineering, Khalifa University of Science and Technology, PO Box 127788, Abu Dhabi, United Arab Emirates; Department of Biology, Khalifa University of Science and Technology, PO Box 127788, Abu Dhabi, United Arab Emirates
| | - Shadi W Hasan
- Center for Membranes and Advanced Water Technology (CMAT), Department of Chemical Engineering, Khalifa University of Science and Technology, PO Box 127788, Abu Dhabi, United Arab Emirates
| | - Habiba Alsafar
- Center for Biotechnology (BTC), Khalifa University of Science and Technology, PO Box 127788, Abu Dhabi, United Arab Emirates; Emirates Bio-Research Center, Ministry of Interior, Abu Dhabi, United Arab Emirates; Department of Biomedical Engineering, Khalifa University of Science and Technology, PO Box 127788, Abu Dhabi, United Arab Emirates.
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Kennedy S, Spaulding AC. Four Models of Wastewater-Based Surveillance for SARS-CoV-2 in Jail Settings: How Monitoring Wastewater Complements Individual Screening. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.08.04.23293152. [PMID: 37609187 PMCID: PMC10441506 DOI: 10.1101/2023.08.04.23293152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
Objective To describe four unique models of implementing Wastewater Based Surveillance (WBS) for SARS-CoV-2 in jails of graduated sizes and differing architectural designs. Methods This study summarizes how jails of Cook County (Illinois, average daily population [ADP] 6000), Fulton County (Georgia, ADP 3000, Washington DC (ADP 1600) and Middlesex County (Massachusetts, ADP 875) initiated WBS between 2020 and 2023. Results Positive signal for SARS-CoV-2 via WBS can herald new onset of infection in a previously uninfected housing unit of a jail. Challenges in implementing WBS included political will and realized value, funding, understanding of the building architecture, and the need for granularity in the findings. Conclusions WBS has been effective for detecting outbreaks of SARS-CoV-2 in differing sized jails, both those with dorm-based and cell-based architectural design. Policy implications Given its effectiveness in monitoring SARS-CoV-2, WBS provides a model for population-based surveillance in carceral facilities for future infectious disease outbreaks.
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de Melo T, Islam G, Simmons DBD, Desaulniers JP, Kirkwood AE. An alternative method for monitoring and interpreting influenza A in communities using wastewater surveillance. Front Public Health 2023; 11:1141136. [PMID: 37575124 PMCID: PMC10413874 DOI: 10.3389/fpubh.2023.1141136] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 07/10/2023] [Indexed: 08/15/2023] Open
Abstract
Seasonal influenza is an annual public health challenge that strains healthcare systems, yet population-level prevalence remains under-reported using standard clinical surveillance methods. Wastewater surveillance (WWS) of influenza A can allow for reliable flu surveillance within a community by leveraging existing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) WWS networks regardless of the sample type (primary sludge vs. primary influent) using an RT-qPCR-based viral RNA detection method for both targets. Additionally, current influenza A outbreaks disproportionately affect the pediatric population. In this study, we show the utility of interpreting influenza A WWS data with elementary student absenteeism due to illness to selectively interpret disease spread in the pediatric population. Our results show that the highest statistically significant correlation (Rs = 0.96, p = 0.011) occurred between influenza A WWS data and elementary school absences due to illness. This correlation coefficient is notably higher than the correlations observed between influenza A WWS data and influenza A clinical case data (Rs = 0.79, p = 0.036). This method can be combined with a suite of pathogen data from wastewater to provide a robust system for determining the causative agents of diseases that are strongly symptomatic in children to infer pediatric outbreaks within communities.
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Affiliation(s)
| | - Golam Islam
- Faculty of Science, Ontario Tech University, Oshawa, ON, Canada
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Hayase S, Katayama YA, Hatta T, Iwamoto R, Kuroita T, Ando Y, Okuda T, Kitajima M, Natsume T, Masago Y. Near full-automation of COPMAN using a LabDroid enables high-throughput and sensitive detection of SARS-CoV-2 RNA in wastewater as a leading indicator. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 881:163454. [PMID: 37061063 PMCID: PMC10098305 DOI: 10.1016/j.scitotenv.2023.163454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 04/04/2023] [Accepted: 04/07/2023] [Indexed: 06/01/2023]
Abstract
Wastewater-based epidemiology (WBE) is a promising tool to efficiently monitor COVID-19 prevalence in a community. For WBE community surveillance, automation of the viral RNA detection process is ideal. In the present study, we achieved near full-automation of a previously established method, COPMAN (COagulation and Proteolysis method using MAgnetic beads for detection of Nucleic acids in wastewater), which was then applied to detect SARS-CoV-2 in wastewater for half a year. The automation line employed the Maholo LabDroid and an automated-pipetting device to achieve a high-throughput sample-processing capability of 576 samples per week. SARS-CoV-2 RNA was quantified with the automated COPMAN using samples collected from two wastewater treatment plants in the Sagami River basin in Japan between 1 November 2021 and 24 May 2022, when the numbers of daily reported COVID-19 cases ranged from 0 to 130.3 per 100,000 inhabitants. The automated COPMAN detected SARS-CoV-2 RNA from 81 out of 132 samples at concentrations of up to 2.8 × 105 copies/L. These concentrations showed direct correlations with subsequently reported clinical cases (5-13 days later), as determined by Pearson's and Spearman's cross-correlation analyses. To compare the results, we also conducted testing with the EPISENS-S (Efficient and Practical virus Identification System with ENhanced Sensitivity for Solids, Ando et al., 2022), a previously reported detection method. SARS-CoV-2 RNA detected with EPISENS-S correlated with clinical cases only when using Spearman's method. Our automated COPMAN was shown to be an efficient method for timely and large-scale monitoring of viral RNA, making WBE more feasible for community surveillance.
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Affiliation(s)
- Shin Hayase
- Shionogi & Co., Ltd., Pharmaceutical Research Center, 1-1, Futaba-cho 3-chome, Toyonaka, Osaka 561-0825, Japan
| | - Yuka Adachi Katayama
- Shionogi & Co., Ltd., Pharmaceutical Research Center, 1-1, Futaba-cho 3-chome, Toyonaka, Osaka 561-0825, Japan
| | - Tomohisa Hatta
- Robotic Biology Institute, Inc., 2-5-10, Aomi, Koto-ku, Tokyo 135-0064, Japan
| | - Ryo Iwamoto
- AdvanSentinel Inc., 3-1-8 Doshomachi, Chuo-ku, Osaka 541-0045, Japan
| | - Tomohiro Kuroita
- AdvanSentinel Inc., 3-1-8 Doshomachi, Chuo-ku, Osaka 541-0045, Japan
| | - Yoshinori Ando
- Shionogi & Co., Ltd., Pharmaceutical Research Center, 1-1, Futaba-cho 3-chome, Toyonaka, Osaka 561-0825, Japan
| | - Tomohiko Okuda
- Shionogi & Co., Ltd., Pharmaceutical Research Center, 1-1, Futaba-cho 3-chome, Toyonaka, Osaka 561-0825, Japan
| | - Masaaki Kitajima
- Division of Environmental Engineering, Faculty of Engineering, Hokkaido University, North 13 West 8, Kita-ku, Sapporo, Hokkaido 060-8628, Japan
| | - Tohru Natsume
- Robotic Biology Institute, Inc., 2-5-10, Aomi, Koto-ku, Tokyo 135-0064, Japan
| | - Yusaku Masago
- Shionogi & Co., Ltd., Pharmaceutical Research Center, 1-1, Futaba-cho 3-chome, Toyonaka, Osaka 561-0825, Japan.
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Kadonsky KF, Naughton CC, Susa M, Olson R, Singh GL, Daza-Torres ML, Montesinos-López JC, Garcia YE, Gafurova M, Gushgari A, Cosgrove J, White BJ, Boehm AB, Wolfe MK, Nuño M, Bischel HN. Expansion of wastewater-based disease surveillance to improve health equity in California's Central Valley: sequential shifts in case-to-wastewater and hospitalization-to-wastewater ratios. Front Public Health 2023; 11:1141097. [PMID: 37457240 PMCID: PMC10348812 DOI: 10.3389/fpubh.2023.1141097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 06/08/2023] [Indexed: 07/18/2023] Open
Abstract
Introduction Over a third of the communities (39%) in the Central Valley of California, a richly diverse and important agricultural region, are classified as disadvantaged-with inadequate access to healthcare, lower socio-economic status, and higher exposure to air and water pollution. The majority of racial and ethnic minorities are also at higher risk of COVID-19 infection, hospitalization, and death according to the Centers for Disease Control and Prevention. Healthy Central Valley Together established a wastewater-based disease surveillance (WDS) program that aims to achieve greater health equity in the region through partnership with Central Valley communities and the Sewer Coronavirus Alert Network. WDS offers a cost-effective strategy to monitor trends in SARS-CoV-2 community infection rates. Methods In this study, we evaluated correlations between public health and wastewater data (represented as SARS-CoV-2 target gene copies normalized by pepper mild mottle virus target gene copies) collected for three Central Valley communities over two periods of COVID-19 infection waves between October 2021 and September 2022. Public health data included clinical case counts at county and sewershed scales as well as COVID-19 hospitalization and intensive care unit admissions. Lag-adjusted hospitalization:wastewater ratios were also evaluated as a retrospective metric of disease severity and corollary to hospitalization:case ratios. Results Consistent with other studies, strong correlations were found between wastewater and public health data. However, a significant reduction in case:wastewater ratios was observed for all three communities from the first to the second wave of infections, decreasing from an average of 4.7 ± 1.4 over the first infection wave to 0.8 ± 0.4 over the second. Discussion The decline in case:wastewater ratios was likely due to reduced clinical testing availability and test seeking behavior, highlighting how WDS can fill data gaps associated with under-reporting of cases. Overall, the hospitalization:wastewater ratios remained more stable through the two waves of infections, averaging 0.5 ± 0.3 and 0.3 ± 0.4 over the first and second waves, respectively.
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Affiliation(s)
- Krystin F. Kadonsky
- Department of Civil and Environmental Engineering, University of California, Merced, Merced, CA, United States
| | - Colleen C. Naughton
- Department of Civil and Environmental Engineering, University of California, Merced, Merced, CA, United States
| | - Mirjana Susa
- Department of Public Health Sciences, University of California, Davis, Davis, CA, United States
| | - Rachel Olson
- Department of Civil and Environmental Engineering, University of California, Davis, Davis, CA, United States
| | - Guadalupe L. Singh
- Department of Civil and Environmental Engineering, University of California, Merced, Merced, CA, United States
| | - Maria L. Daza-Torres
- Department of Public Health Sciences, University of California, Davis, Davis, CA, United States
| | | | - Yury Elena Garcia
- Department of Public Health Sciences, University of California, Davis, Davis, CA, United States
| | - Maftuna Gafurova
- Eurofins Environment Testing US, West Sacramento, CA, United States
| | - Adam Gushgari
- Eurofins Environment Testing US, West Sacramento, CA, United States
| | - John Cosgrove
- Eurofins Environment Testing US, West Sacramento, CA, United States
| | | | - Alexandria B. Boehm
- Department of Civil & Environmental Engineering, School of Engineering and Doerr School of Sustainability, Stanford University, Stanford, CA, United States
| | - Marlene K. Wolfe
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, United States
| | - Miriam Nuño
- Department of Public Health Sciences, University of California, Davis, Davis, CA, United States
| | - Heather N. Bischel
- Department of Civil and Environmental Engineering, University of California, Davis, Davis, CA, United States
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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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