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Chen C, Kaur G, Adiga A, Espinoza B, Venkatramanan S, Warren A, Lewis B, Crow J, Singh R, Lorentz A, Toney D, Marathe M. Wastewater-based Epidemiology for COVID-19 Surveillance: A Survey. ARXIV 2024:arXiv:2403.15291v1. [PMID: 38562450 PMCID: PMC10984000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
The pandemic of COVID-19 has imposed tremendous pressure on public health systems and social economic ecosystems over the past years. To alleviate its social impact, it is important to proactively track the prevalence of COVID-19 within communities. The traditional way to estimate the disease prevalence is to estimate from reported clinical test data or surveys. However, the coverage of clinical tests is often limited and the tests can be labor-intensive, requires reliable and timely results, and consistent diagnostic and reporting criteria. Recent studies revealed that patients who are diagnosed with COVID-19 often undergo fecal shedding of SARS-CoV-2 virus into wastewater, which makes wastewater-based epidemiology (WBE) for COVID-19 surveillance a promising approach to complement traditional clinical testing. In this paper, we survey the existing literature regarding WBE for COVID-19 surveillance and summarize the current advances in the area. Specifically, we have covered the key aspects of wastewater sampling, sample testing, and presented a comprehensive and organized summary of wastewater data analytical methods. Finally, we provide the open challenges on current wastewater-based COVID-19 surveillance studies, aiming to encourage new ideas to advance the development of effective wastewater-based surveillance systems for general infectious diseases.
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Affiliation(s)
- Chen Chen
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, 22904, United States
| | - Gursharn Kaur
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, 22904, United States
| | - Aniruddha Adiga
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, 22904, United States
| | - Baltazar Espinoza
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, 22904, United States
| | - Srinivasan Venkatramanan
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, 22904, United States
| | - Andrew Warren
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, 22904, United States
| | - Bryan Lewis
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, 22904, United States
| | - Justin Crow
- Virginia Department of Health, Richmond, 23219, United States
| | - Rekha Singh
- Virginia Department of Health, Richmond, 23219, United States
| | - Alexandra Lorentz
- Division of Consolidated Laboratory Services, Department of General Services, Richmond, 23219, United States
| | - Denise Toney
- Division of Consolidated Laboratory Services, Department of General Services, Richmond, 23219, United States
| | - Madhav Marathe
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, 22904, United States
- Department of Computer Science, University of Virginia, Charlottesville, 22904, United States
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Senaratna KYK, Bhatia S, Giek GS, Lim CMB, Gangesh GR, Peng LC, Wong JCC, Ng LC, Gin KYH. Estimating COVID-19 cases on a university campus based on Wastewater Surveillance using machine learning regression models. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 906:167709. [PMID: 37832657 DOI: 10.1016/j.scitotenv.2023.167709] [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: 03/23/2023] [Revised: 08/20/2023] [Accepted: 10/07/2023] [Indexed: 10/15/2023]
Abstract
Wastewater Surveillance (WS) is a crucial tool in the management of COVID-19 pandemic. The surveillance is based on enumerating SARS-CoV-2 RNA concentrations in the community's sewage. In this study, we used WS data to develop a regression model for estimating the number of active COVID-19 cases on a university campus. Eight univariate and multivariate regression model types i.e. Linear Regression (LM), Polynomial Regression (PR), Generalised Additive Model (GAM), Locally Estimated Scatterplot Smoothing Regression (LOESS), K Nearest Neighbours Regression (KNN), Support Vector Regression (SVR), Artificial Neural Networks (ANN) and Random Forest (RF) were developed and compared. We found that the multivariate RF regression model, was the most appropriate for predicting the prevalence of COVID-19 infections at both a campus level and hostel-level. We also found that smoothing the normalised SARS-CoV-2 data and employing multivariate modelling, using student population as a second independent variable, significantly improved the performance of the models. The final RF campus level model showed good accuracy when tested using previously unseen data; correlation coefficient of 0.97 and a mean absolute error (MAE) of 20 %. In summary, our non-intrusive approach has the ability to complement projections based on clinical tests, facilitating timely follow-up and response.
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Affiliation(s)
- Kavindra Yohan Kuhatheva Senaratna
- NUS Environmental Research Institute, National University of Singapore, T-Lab Building, 5A Engineering Drive 1, Singapore 117411, Singapore
| | - Sumedha Bhatia
- NUS Environmental Research Institute, National University of Singapore, T-Lab Building, 5A Engineering Drive 1, Singapore 117411, Singapore
| | - Goh Shin Giek
- Department of Civil & Environmental Engineering, National University of Singapore, Engineering Drive 2, Singapore 117576, Singapore
| | - Chun Min Benjamin Lim
- NUS Environmental Research Institute, National University of Singapore, T-Lab Building, 5A Engineering Drive 1, Singapore 117411, Singapore
| | - G Reuben Gangesh
- NUS Environmental Research Institute, National University of Singapore, T-Lab Building, 5A Engineering Drive 1, Singapore 117411, Singapore
| | - Lim Cheh Peng
- Office of Risk Management and Compliance, National University of Singapore, Singapore 119077, Singapore
| | - Judith Chui Ching Wong
- Environmental Health Institute, National Environment Agency, 11 Biopolis Way, #06-05/08, Singapore 138667, Singapore
| | - Lee Ching Ng
- Environmental Health Institute, National Environment Agency, 11 Biopolis Way, #06-05/08, Singapore 138667, Singapore; School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, Singapore 637551, Singapore
| | - Karina Yew-Hoong Gin
- NUS Environmental Research Institute, National University of Singapore, T-Lab Building, 5A Engineering Drive 1, Singapore 117411, Singapore; Department of Civil & Environmental Engineering, National University of Singapore, Engineering Drive 2, Singapore 117576, Singapore.
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3
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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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Torabi F, Li G, Mole C, Nicholson G, Rowlingson B, Smith CR, Jersakova R, Diggle PJ, Blangiardo M. Wastewater-based surveillance models for COVID-19: A focused review on spatio-temporal models. Heliyon 2023; 9:e21734. [PMID: 38053867 PMCID: PMC10694161 DOI: 10.1016/j.heliyon.2023.e21734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 10/25/2023] [Accepted: 10/26/2023] [Indexed: 12/07/2023] Open
Abstract
The evident shedding of the SARS-CoV-2 RNA particles from infected individuals into the wastewater opened up a tantalizing array of possibilities for prediction of COVID-19 prevalence prior to symptomatic case identification through community testing. Many countries have therefore explored the use of wastewater metrics as a surveillance tool, replacing traditional direct measurement of prevalence with cost-effective approaches based on SARS-CoV-2 RNA concentrations in wastewater samples. Two important aspects in building prediction models are: time over which the prediction occurs and space for which the predicted case numbers is shown. In this review, our main focus was on finding mathematical models which take into the account both the time-varying and spatial nature of wastewater-based metrics into account. We used six main characteristics as our assessment criteria: i) modelling approach; ii) temporal coverage; iii) spatial coverage; iv) sample size; v) wastewater sampling method; and vi) covariates included in the modelling. The majority of studies in the early phases of the pandemic recognized the temporal association of SARS-CoV-2 RNA concentration level in wastewater with the number of COVID-19 cases, ignoring their spatial context. We examined 15 studies up to April 2023, focusing on models considering both temporal and spatial aspects of wastewater metrics. Most early studies correlated temporal SARS-CoV-2 RNA levels with COVID-19 cases but overlooked spatial factors. Linear regression and SEIR models were commonly used (n = 10, 66.6 % of studies), along with machine learning (n = 1, 6.6 %) and Bayesian approaches (n = 1, 6.6 %) in some cases. Three studies employed spatio-temporal modelling approach (n = 3, 20.0 %). We conclude that the development, validation and calibration of further spatio-temporally explicit models should be done in parallel with the advancement of wastewater metrics before the potential of wastewater as a surveillance tool can be fully realised.
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Affiliation(s)
- Fatemeh Torabi
- Turing-RSS Health Data Lab, London, UK
- Population Data Science HDRUK-Wales, Medical School, Swansea University, Wales, UK
| | - Guangquan Li
- Turing-RSS Health Data Lab, London, UK
- Applied Statistics Research Group, Department of Mathematics, Physics and Electrical Engineering, Northumbria University, Newcastle upon Tyne NE1 8ST, UK
| | - Callum Mole
- Turing-RSS Health Data Lab, London, UK
- The Alan Turing Institute, London, UK
| | - George Nicholson
- Turing-RSS Health Data Lab, London, UK
- University of Oxford, Oxford, UK
| | - Barry Rowlingson
- Turing-RSS Health Data Lab, London, UK
- CHICAS, Lancaster Medical School, Lancaster University, England, UK
| | | | - Radka Jersakova
- Turing-RSS Health Data Lab, London, UK
- The Alan Turing Institute, London, UK
| | - Peter J. Diggle
- Turing-RSS Health Data Lab, London, UK
- CHICAS, Lancaster Medical School, Lancaster University, England, UK
| | - Marta Blangiardo
- Turing-RSS Health Data Lab, London, UK
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College, London, UK
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5
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Kuhn KG, Shukla R, Mannell M, Graves GM, Miller AC, Vogel J, Malloy K, Deshpande G, Florea G, Shelton K, Jeffries E, De León KB, Stevenson B. Using Wastewater Surveillance to Monitor Gastrointestinal Pathogen Infections in the State of Oklahoma. Microorganisms 2023; 11:2193. [PMID: 37764037 PMCID: PMC10536226 DOI: 10.3390/microorganisms11092193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 08/23/2023] [Accepted: 08/28/2023] [Indexed: 09/29/2023] Open
Abstract
During the COVID-19 pandemic, wastewater surveillance was widely used to monitor temporal and geographical infection trends. Using this as a foundation, a statewide program for routine wastewater monitoring of gastrointestinal pathogens was established in Oklahoma. The results from 18 months of surveillance showed that wastewater concentrations of Salmonella, Campylobacter, and norovirus exhibit similar seasonal patterns to those observed in reported human cases (F = 4-29, p < 0.05) and that wastewater can serve as an early warning tool for increases in cases, offering between one- and two-weeks lead time. Approximately one third of outbreak alerts in wastewater correlated in time with confirmed outbreaks of Salmonella or Campylobacter and our results further indicated that several outbreaks are likely to go undetected through the traditional surveillance approach currently in place. Better understanding of the true distribution and burden of gastrointestinal infections ultimately facilitates better disease prevention and control and reduces the overall socioeconomic and healthcare related impact of these pathogens. In this respect, wastewater represents a unique opportunity for monitoring infections in real-time, without the need for individual human testing. With increasing demands for sustainable and low-cost disease surveillance, the usefulness of wastewater as a long-term method for tracking infectious disease transmission is likely to become even more pronounced.
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Affiliation(s)
- Katrin Gaardbo Kuhn
- Department of Biostatistics & Epidemiology, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA; (K.M.); (G.D.)
| | - Rishabh Shukla
- School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, OK 73019, USA; (R.S.); (G.M.G.); (A.C.M.); (J.V.); (G.F.)
| | - Mike Mannell
- Acute Diseases Division, Oklahoma State Department of Health, Oklahoma City, OK 73102, USA;
| | - Grant M. Graves
- School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, OK 73019, USA; (R.S.); (G.M.G.); (A.C.M.); (J.V.); (G.F.)
| | - A. Caitlin Miller
- School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, OK 73019, USA; (R.S.); (G.M.G.); (A.C.M.); (J.V.); (G.F.)
| | - Jason Vogel
- School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, OK 73019, USA; (R.S.); (G.M.G.); (A.C.M.); (J.V.); (G.F.)
| | - Kimberly Malloy
- Department of Biostatistics & Epidemiology, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA; (K.M.); (G.D.)
| | - Gargi Deshpande
- Department of Biostatistics & Epidemiology, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA; (K.M.); (G.D.)
| | - Gabriel Florea
- School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, OK 73019, USA; (R.S.); (G.M.G.); (A.C.M.); (J.V.); (G.F.)
- Oklahoma Medical Research Foundation, Oklahoma City, OK 73104, USA
| | - Kristen Shelton
- School of Biological Sciences, University of Oklahoma, Norman, OK 73019, USA; (K.S.); (E.J.); (K.B.D.L.)
| | - Erin Jeffries
- School of Biological Sciences, University of Oklahoma, Norman, OK 73019, USA; (K.S.); (E.J.); (K.B.D.L.)
| | - Kara B. De León
- School of Biological Sciences, University of Oklahoma, Norman, OK 73019, USA; (K.S.); (E.J.); (K.B.D.L.)
| | - Bradley Stevenson
- Department of Earth and Planetary Sciences, Northwestern University, Evanston, IL 60208, USA;
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6
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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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7
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Asadi M, Oloye FF, Xie Y, Cantin J, Challis JK, McPhedran KN, Yusuf W, Champredon D, Xia P, De Lange C, El-Baroudy S, Servos MR, Jones PD, Giesy JP, Brinkmann M. A wastewater-based risk index for SARS-CoV-2 infections among three cities on the Canadian Prairie. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 876:162800. [PMID: 36914129 PMCID: PMC10008033 DOI: 10.1016/j.scitotenv.2023.162800] [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: 09/19/2022] [Revised: 03/06/2023] [Accepted: 03/07/2023] [Indexed: 06/01/2023]
Abstract
Wastewater surveillance (WWS) is useful to better understand the spreading of coronavirus disease 2019 (COVID-19) in communities, which can help design and implement suitable mitigation measures. The main objective of this study was to develop the Wastewater Viral Load Risk Index (WWVLRI) for three Saskatchewan cities to offer a simple metric to interpret WWS. The index was developed by considering relationships between reproduction number, clinical data, daily per capita concentrations of virus particles in wastewater, and weekly viral load change rate. Trends of daily per capita concentrations of SARS-CoV-2 in wastewater for Saskatoon, Prince Albert, and North Battleford were similar during the pandemic, suggesting that per capita viral load can be useful to quantitatively compare wastewater signals among cities and develop an effective and comprehensible WWVLRI. The effective reproduction number (Rt) and the daily per capita efficiency adjusted viral load thresholds of 85 × 106 and 200 × 106 N2 gene counts (gc)/population day (pd) were determined. These values with rates of change were used to categorize the potential for COVID-19 outbreaks and subsequent declines. The weekly average was considered 'low risk' when the per capita viral load was 85 × 106 N2 gc/pd. A 'medium risk' occurs when the per capita copies were between 85 × 106 and 200 × 106 N2 gc/pd. with a rate of change <100 %. The start of an outbreak is indicated by a 'medium-high' risk classification when the week-over-week rate of change was >100 %, and the absolute magnitude of concentrations of viral particles was >85 × 106 N2 gc/pd. Lastly, a 'high risk' occurs when the viral load exceeds 200 × 106 N2 gc/pd. This methodology provides a valuable resource for decision-makers and health authorities, specifically given the limitation of COVID-19 surveillance based on clinical data.
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Affiliation(s)
- Mohsen Asadi
- Department of Civil, Geological and Environmental Engineering, College of Engineering, University of Saskatchewan, Saskatoon, SK, Canada; Toxicology Centre, University of Saskatchewan, Saskatoon, SK, Canada.
| | - Femi F Oloye
- Toxicology Centre, University of Saskatchewan, Saskatoon, SK, Canada.
| | - Yuwei Xie
- Toxicology Centre, University of Saskatchewan, Saskatoon, SK, Canada
| | - Jenna Cantin
- Toxicology Centre, University of Saskatchewan, Saskatoon, SK, Canada
| | | | - Kerry N McPhedran
- Department of Civil, Geological and Environmental Engineering, College of Engineering, University of Saskatchewan, Saskatoon, SK, Canada; Global Institute for Water Security, University of Saskatchewan, Saskatoon, SK, Canada
| | - Warsame Yusuf
- Public Health Risk Division, National Microbiology Laboratory, Public Health Agency of Canada, Guelph, Ontario, Canada
| | - David Champredon
- Public Health Risk Division, National Microbiology Laboratory, Public Health Agency of Canada, Guelph, Ontario, Canada
| | - Pu Xia
- Toxicology Centre, University of Saskatchewan, Saskatoon, SK, Canada
| | - Chantel De Lange
- Toxicology Centre, University of Saskatchewan, Saskatoon, SK, Canada
| | - Seba El-Baroudy
- Toxicology Centre, University of Saskatchewan, Saskatoon, SK, Canada
| | - Mark R Servos
- Department of Biology, University of Waterloo, Waterloo, Ontario, Canada
| | - Paul D Jones
- Toxicology Centre, University of Saskatchewan, Saskatoon, SK, Canada; School of Environment and Sustainability, University of Saskatchewan, Saskatoon, SK, Canada
| | - John P Giesy
- Toxicology Centre, University of Saskatchewan, Saskatoon, SK, Canada; Department of Veterinary Biomedical Sciences, University of Saskatchewan, Saskatoon, SK, Canada; Department of Environmental Sciences, Baylor University, Waco, TX, USA; Department of Integrative Biology and Center for Integrative Toxicology, Michigan State University, East Lansing, MI, USA.
| | - Markus Brinkmann
- Toxicology Centre, University of Saskatchewan, Saskatoon, SK, Canada; Global Institute for Water Security, University of Saskatchewan, Saskatoon, SK, Canada; School of Environment and Sustainability, University of Saskatchewan, Saskatoon, SK, Canada.
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8
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Trigo-Tasende N, Vallejo JA, Rumbo-Feal S, Conde-Pérez K, Vaamonde M, López-Oriona Á, Barbeito I, Nasser-Ali M, Reif R, Rodiño-Janeiro BK, Fernández-Álvarez E, Iglesias-Corrás I, Freire B, Tarrío-Saavedra J, Tomás L, Gallego-García P, Posada D, Bou G, López-de-Ullibarri I, Cao R, Ladra S, Poza M. Wastewater early warning system for SARS-CoV-2 outbreaks and variants in a Coruña, Spain. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023:10.1007/s11356-023-27877-3. [PMID: 37286834 DOI: 10.1007/s11356-023-27877-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 05/19/2023] [Indexed: 06/09/2023]
Abstract
Wastewater-based epidemiology has been widely used as a cost-effective method for tracking the COVID-19 pandemic at the community level. Here we describe COVIDBENS, a wastewater surveillance program running from June 2020 to March 2022 in the wastewater treatment plant of Bens in A Coruña (Spain). The main goal of this work was to provide an effective early warning tool based in wastewater epidemiology to help in decision-making at both the social and public health levels. RT-qPCR procedures and Illumina sequencing were used to weekly monitor the viral load and to detect SARS-CoV-2 mutations in wastewater, respectively. In addition, own statistical models were applied to estimate the real number of infected people and the frequency of each emerging variant circulating in the community, which considerable improved the surveillance strategy. Our analysis detected 6 viral load waves in A Coruña with concentrations between 103 and 106 SARS-CoV-2 RNA copies/L. Our system was able to anticipate community outbreaks during the pandemic with 8-36 days in advance with respect to clinical reports and, to detect the emergence of new SARS-CoV-2 variants in A Coruña such as Alpha (B.1.1.7), Delta (B.1.617.2), and Omicron (B.1.1.529 and BA.2) in wastewater with 42, 30, and 27 days, respectively, before the health system did. Data generated here helped local authorities and health managers to give a faster and more efficient response to the pandemic situation, and also allowed important industrial companies to adapt their production to each situation. The wastewater-based epidemiology program developed in our metropolitan area of A Coruña (Spain) during the SARS-CoV-2 pandemic served as a powerful early warning system combining statistical models with mutations and viral load monitoring in wastewater over time.
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Affiliation(s)
- Noelia Trigo-Tasende
- University of A Coruña (UDC) - Microbiome and Health group (meiGAbiome), Institute of Biomedical Research (INIBIC) - University Hospital of A Coruña (CHUAC) - Interdisciplinary Center for Chemistry and Biology (CICA) - Spanish Network for Infectious Diseases (CIBERINFEC-ISCIII), Campus da Zapateira, 15008, A Coruña, Spain
| | - Juan A Vallejo
- University of A Coruña (UDC) - Microbiome and Health group (meiGAbiome), Institute of Biomedical Research (INIBIC) - University Hospital of A Coruña (CHUAC) - Interdisciplinary Center for Chemistry and Biology (CICA) - Spanish Network for Infectious Diseases (CIBERINFEC-ISCIII), Campus da Zapateira, 15008, A Coruña, Spain
| | - Soraya Rumbo-Feal
- University of A Coruña (UDC) - Microbiome and Health group (meiGAbiome), Institute of Biomedical Research (INIBIC) - University Hospital of A Coruña (CHUAC) - Interdisciplinary Center for Chemistry and Biology (CICA) - Spanish Network for Infectious Diseases (CIBERINFEC-ISCIII), Campus da Zapateira, 15008, A Coruña, Spain
| | - Kelly Conde-Pérez
- University of A Coruña (UDC) - Microbiome and Health group (meiGAbiome), Institute of Biomedical Research (INIBIC) - University Hospital of A Coruña (CHUAC) - Interdisciplinary Center for Chemistry and Biology (CICA) - Spanish Network for Infectious Diseases (CIBERINFEC-ISCIII), Campus da Zapateira, 15008, A Coruña, Spain
| | - Manuel Vaamonde
- Research Group MODES, Research Center for Information and Communication Technologies (CITIC), University of A Coruña (UDC), Campus de Elviña, 15071 , A Coruña, Spain
| | - Ángel López-Oriona
- Research Group MODES, Research Center for Information and Communication Technologies (CITIC), University of A Coruña (UDC), Campus de Elviña, 15071 , A Coruña, Spain
| | - Inés Barbeito
- Research Group MODES, Research Center for Information and Communication Technologies (CITIC), University of A Coruña (UDC), Campus de Elviña, 15071 , A Coruña, Spain
| | - Mohammed Nasser-Ali
- University of A Coruña (UDC) - Microbiome and Health group (meiGAbiome), Institute of Biomedical Research (INIBIC) - University Hospital of A Coruña (CHUAC) - Interdisciplinary Center for Chemistry and Biology (CICA) - Spanish Network for Infectious Diseases (CIBERINFEC-ISCIII), Campus da Zapateira, 15008, A Coruña, Spain
| | - Rubén Reif
- Center for Research in Biological Chemistry and Molecular Materials (CiQUS), University of Santiago de Compostela (USC), 15782, Santiago de Compostela, Spain
| | - Bruno K Rodiño-Janeiro
- BFlow, University of Santiago de Compostela (USC) and Health Research Institute of Santiago de Compostela (IDIS), Campus Vida, 15706, Santiago de Compostela, A Coruña, Spain
| | - Elisa Fernández-Álvarez
- University of A Coruña (UDC), Research Center for Information and Communication Technologies (CITIC), Database Laboratory, Campus de Elviña, 15071, A Coruña, Spain
| | - Iago Iglesias-Corrás
- University of A Coruña (UDC), Research Center for Information and Communication Technologies (CITIC), Database Laboratory, Campus de Elviña, 15071, A Coruña, Spain
| | - Borja Freire
- University of A Coruña (UDC), Research Center for Information and Communication Technologies (CITIC), Database Laboratory, Campus de Elviña, 15071, A Coruña, Spain
| | - Javier Tarrío-Saavedra
- Research Group MODES, Research Center for Information and Communication Technologies (CITIC), University of A Coruña (UDC), Campus de Elviña, 15071 , A Coruña, Spain
| | - Laura Tomás
- CINBIO, Universidade de Vigo, 36310, Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36312, Vigo, Spain
| | - Pilar Gallego-García
- CINBIO, Universidade de Vigo, 36310, Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36312, Vigo, Spain
| | - David Posada
- CINBIO, Universidade de Vigo, 36310, Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36312, Vigo, Spain
- Department of Biochemistry, Genetics, and Immunology, Universidade de Vigo, 36310, Vigo, Spain
| | - Germán Bou
- University of A Coruña (UDC) - Microbiome and Health group (meiGAbiome), Institute of Biomedical Research (INIBIC) - University Hospital of A Coruña (CHUAC) - Interdisciplinary Center for Chemistry and Biology (CICA) - Spanish Network for Infectious Diseases (CIBERINFEC-ISCIII), Campus da Zapateira, 15008, A Coruña, Spain
| | - Ignacio López-de-Ullibarri
- Research Group MODES, Research Center for Information and Communication Technologies (CITIC), University of A Coruña (UDC), Campus de Elviña, 15071 , A Coruña, Spain
| | - Ricardo Cao
- Research Group MODES, Research Center for Information and Communication Technologies (CITIC), University of A Coruña (UDC), Campus de Elviña, 15071 , A Coruña, Spain
| | - Susana Ladra
- University of A Coruña (UDC), Research Center for Information and Communication Technologies (CITIC), Database Laboratory, Campus de Elviña, 15071, A Coruña, Spain
| | - Margarita Poza
- University of A Coruña (UDC) - Microbiome and Health group (meiGAbiome), Institute of Biomedical Research (INIBIC) - University Hospital of A Coruña (CHUAC) - Interdisciplinary Center for Chemistry and Biology (CICA) - Spanish Network for Infectious Diseases (CIBERINFEC-ISCIII), Campus da Zapateira, 15008, A Coruña, Spain.
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9
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Helm B, Geissler M, Mayer R, Schubert S, Oertel R, Dumke R, Dalpke A, El-Armouche A, Renner B, Krebs P. Regional and temporal differences in the relation between SARS-CoV-2 biomarkers in wastewater and estimated infection prevalence - Insights from long-term surveillance. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 857:159358. [PMID: 36240928 PMCID: PMC9554318 DOI: 10.1016/j.scitotenv.2022.159358] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Revised: 10/06/2022] [Accepted: 10/07/2022] [Indexed: 06/16/2023]
Abstract
Wastewater-based epidemiology provides a conceptual framework for the evaluation of the prevalence of public health related biomarkers. In the context of the Coronavirus disease-2019, wastewater monitoring emerged as a complementary tool for epidemic management. In this study, we evaluated data from six wastewater treatment plants in the region of Saxony, Germany. The study period lasted from February to December 2021 and covered the third and fourth regional epidemic waves. We collected 1065 daily composite samples and analyzed SARS-CoV-2 RNA concentrations using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Regression models quantify the relation between RNA concentrations and disease prevalence. We demonstrated that the relation is site and time specific. Median loads per diagnosed case differed by a factor of 3-4 among sites during both waves and were on average 45 % higher during the third wave. In most cases, log-log-transformed data achieved better regression performance than non-transformed data and local calibration outperformed global models for all sites. The inclusion of lag/lead time, discharge and detection probability improved model performance in all cases significantly, but the importance of these components was also site and time specific. In all cases, models with lag/lead time and log-log-transformed data obtained satisfactory goodness-of-fit with adjusted coefficients of determination higher than 0.5. Back-estimation of testing efficiency from wastewater data confirmed state-wide prevalence estimation from individual testing statistics, but revealed pronounced differences throughout the epidemic waves and among the different sites.
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Affiliation(s)
- Björn Helm
- Institute of Urban and Industrial Water Management, Technische Universität Dresden, Helmholtzstrasse 10, 01069 Dresden, Germany.
| | - Michael Geissler
- Institute of Medical Microbiology and Virology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Fetscherstrasse 74, 01307 Dresden, Germany
| | - Robin Mayer
- Institute of Urban and Industrial Water Management, Technische Universität Dresden, Helmholtzstrasse 10, 01069 Dresden, Germany
| | - Sara Schubert
- Institute of Clinical Pharmacology, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Fetscherstrasse 74, 01307 Dresden, Germany; Institute of Hydrobiology, Technische Universität Dresden, Helmholtzstrasse 10, 01069 Dresden, Germany
| | - Reinhard Oertel
- Institute of Clinical Pharmacology, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Fetscherstrasse 74, 01307 Dresden, Germany
| | - Roger Dumke
- Institute of Medical Microbiology and Virology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Fetscherstrasse 74, 01307 Dresden, Germany
| | - Alexander Dalpke
- Institute of Medical Microbiology and Virology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Fetscherstrasse 74, 01307 Dresden, Germany; University Heidelberg, Institute of Medical Microbiology and Hygiene, Heidelberg, Germany
| | - Ali El-Armouche
- Institute of Clinical Pharmacology, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Fetscherstrasse 74, 01307 Dresden, Germany; Institute of Pharmacology and Toxicology, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Fetscherstrasse 74, 01307 Dresden, Germany
| | - Bertold Renner
- Institute of Clinical Pharmacology, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Fetscherstrasse 74, 01307 Dresden, Germany
| | - Peter Krebs
- Institute of Urban and Industrial Water Management, Technische Universität Dresden, Helmholtzstrasse 10, 01069 Dresden, Germany
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10
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Martin M, Goethals P, Newhart K, Rhodes E, Vogel J, Stevenson B. Optimization of sewage sampling for wastewater-based epidemiology through stochastic modeling. JOURNAL OF ENGINEERING AND APPLIED SCIENCE 2023; 70:11. [PMCID: PMC9930068 DOI: 10.1186/s44147-023-00180-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/23/2023]
Abstract
The proliferation of the SARS-CoV-2 global pandemic has brought to attention the need for epidemiological tools that can detect diseases in specific geographical areas through non-contact means. Such methods may protect those potentially infected by facilitating early quarantine policies to prevent the spread of the disease. Sampling of municipal wastewater has been studied as a plausible solution to detect pathogen spread, even from asymptomatic patients. However, many challenges exist in wastewater-based epidemiology such as identifying a representative sample for a population, determining the appropriate sample size, and establishing the right time and place for samples. In this work, a new approach to address these questions is assessed using stochastic modeling to represent wastewater sampling given a particular community of interest. Using estimates for various process parameters, inferences on the population infected are generated with Monte Carlo simulation output. A case study at the University of Oklahoma is examined to calibrate and evaluate the model output. Finally, extensions are provided for more efficient wastewater sampling campaigns in the future. This research provides greater insight into the effects of viral load, the percentage of the population infected, and sampling time on mean SARS-CoV-2 concentration through simulation. In doing so, an earlier warning of infection for a given population may be obtained and aid in reducing the spread of viruses.
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Affiliation(s)
- Max Martin
- grid.419884.80000 0001 2287 2270United States Corps of Cadets, United States Military Academy, West Point, New York, USA
| | - Paul Goethals
- grid.419884.80000 0001 2287 2270Department of Mathematical Sciences, United States Military Academy, West Point, New York, USA
| | - Kathryn Newhart
- grid.419884.80000 0001 2287 2270Department of Geography & Environmental Engineering, United States Military Academy, West Point, New York, USA
| | - Emily Rhodes
- grid.266900.b0000 0004 0447 0018School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, Oklahoma, USA
| | - Jason Vogel
- grid.266900.b0000 0004 0447 0018School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, Oklahoma, USA
| | - Bradley Stevenson
- grid.266900.b0000 0004 0447 0018Microbiology and Plant Biology, University of Oklahoma, Norman, Oklahoma, USA
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11
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Rainey AL, Loeb JC, Robinson SE, Davis P, Liang S, Lednicky JA, Coker ES, Sabo-Attwood T, Bisesi JH, Maurelli AT. Assessment of a mass balance equation for estimating community-level prevalence of COVID-19 using wastewater-based epidemiology in a mid-sized city. Sci Rep 2022; 12:19085. [PMID: 36352013 PMCID: PMC9645338 DOI: 10.1038/s41598-022-21354-6] [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: 04/29/2022] [Accepted: 09/26/2022] [Indexed: 11/11/2022] Open
Abstract
Wastewater-based epidemiology (WBE) has emerged as a valuable epidemiologic tool to detect the presence of pathogens and track disease trends within a community. WBE overcomes some limitations of traditional clinical disease surveillance as it uses pooled samples from the entire community, irrespective of health-seeking behaviors and symptomatic status of infected individuals. WBE has the potential to estimate the number of infections within a community by using a mass balance equation, however, it has yet to be assessed for accuracy. We hypothesized that the mass balance equation-based approach using measured SARS-CoV-2 wastewater concentrations can generate accurate prevalence estimates of COVID-19 within a community. This study encompassed wastewater sampling over a 53-week period during the COVID-19 pandemic in Gainesville, Florida, to assess the ability of the mass balance equation to generate accurate COVID-19 prevalence estimates. The SARS-CoV-2 wastewater concentration showed a significant linear association (Parameter estimate = 39.43, P value < 0.0001) with clinically reported COVID-19 cases. Overall, the mass balance equation produced accurate COVID-19 prevalence estimates with a median absolute error of 1.28%, as compared to the clinical reference group. Therefore, the mass balance equation applied to WBE is an effective tool for generating accurate community-level prevalence estimates of COVID-19 to improve community surveillance.
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Affiliation(s)
- Andrew L. Rainey
- grid.15276.370000 0004 1936 8091Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, FL 32610 USA ,grid.15276.370000 0004 1936 8091Emerging Pathogens Institute, University of Florida, 2055 Mowry Road, PO Box 100009, Gainesville, FL 32610 USA
| | - Julia C. Loeb
- grid.15276.370000 0004 1936 8091Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, FL 32610 USA ,grid.15276.370000 0004 1936 8091Emerging Pathogens Institute, University of Florida, 2055 Mowry Road, PO Box 100009, Gainesville, FL 32610 USA
| | - Sarah E. Robinson
- grid.15276.370000 0004 1936 8091Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, FL 32610 USA ,grid.15276.370000 0004 1936 8091Emerging Pathogens Institute, University of Florida, 2055 Mowry Road, PO Box 100009, Gainesville, FL 32610 USA ,grid.15276.370000 0004 1936 8091Center for Environmental and Human Toxicology, University of Florida, 2187 Mowry Road, PO Box 110885, Gainesville, FL 32611 USA
| | - Paul Davis
- Gainesville Regional Utilities, Gainesville, FL 32614 USA
| | - Song Liang
- grid.15276.370000 0004 1936 8091Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, FL 32610 USA ,grid.15276.370000 0004 1936 8091Emerging Pathogens Institute, University of Florida, 2055 Mowry Road, PO Box 100009, Gainesville, FL 32610 USA
| | - John A. Lednicky
- grid.15276.370000 0004 1936 8091Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, FL 32610 USA ,grid.15276.370000 0004 1936 8091Emerging Pathogens Institute, University of Florida, 2055 Mowry Road, PO Box 100009, Gainesville, FL 32610 USA
| | - Eric S. Coker
- grid.15276.370000 0004 1936 8091Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, FL 32610 USA
| | - Tara Sabo-Attwood
- grid.15276.370000 0004 1936 8091Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, FL 32610 USA ,grid.15276.370000 0004 1936 8091Emerging Pathogens Institute, University of Florida, 2055 Mowry Road, PO Box 100009, Gainesville, FL 32610 USA ,grid.15276.370000 0004 1936 8091Center for Environmental and Human Toxicology, University of Florida, 2187 Mowry Road, PO Box 110885, Gainesville, FL 32611 USA
| | - Joseph H. Bisesi
- grid.15276.370000 0004 1936 8091Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, FL 32610 USA ,grid.15276.370000 0004 1936 8091Emerging Pathogens Institute, University of Florida, 2055 Mowry Road, PO Box 100009, Gainesville, FL 32610 USA ,grid.15276.370000 0004 1936 8091Center for Environmental and Human Toxicology, University of Florida, 2187 Mowry Road, PO Box 110885, Gainesville, FL 32611 USA
| | - Anthony T. Maurelli
- grid.15276.370000 0004 1936 8091Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, FL 32610 USA ,grid.15276.370000 0004 1936 8091Emerging Pathogens Institute, University of Florida, 2055 Mowry Road, PO Box 100009, Gainesville, FL 32610 USA
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Johnson W, Reeves K, Liebig J, Feula A, Butler C, Alkire M, Singh S, Litton S, O'Conor K, Jones K, Ortega N, Shimek T, Witteman J, Bjorkman KK, Mansfeldt C. Effectiveness of building-level sewage surveillance during both community-spread and sporadic-infection phases of SARS-CoV-2 in a university campus population. FEMS MICROBES 2022; 3:xtac024. [PMID: 37332508 PMCID: PMC10117889 DOI: 10.1093/femsmc/xtac024] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 08/27/2022] [Accepted: 09/21/2022] [Indexed: 08/29/2023] Open
Abstract
Pathogen surveillance within wastewater rapidly progressed during the SARS-CoV-2 pandemic and informed public health management. In addition to the successful monitoring of entire sewer catchment basins at the treatment facility scale, subcatchment or building-level monitoring enabled targeted support of resource deployment. However, optimizing the temporal and spatial resolution of these monitoring programs remains complex due to population dynamics and within-sewer physical, chemical, and biological processes. To address these limitations, this study explores the advancement of the building-scale network that monitored the on-campus residential population at the University of Colorado Boulder between August 2020 and May 2021 through a daily SARS-CoV-2 surveillance campaign. During the study period, SARS-CoV-2 infection prevalence transitioned from robust community spread in Fall 2020 to sporadic infections in Spring 2021. Temporally, these distinct phases enabled investigating the effectiveness of resource commitment by exploring subsets of the original daily sampling data. Spatially, select sampling sites were installed along the flow path of the pipe network, enabling the exploration of the conservation of viral concentrations within the wastewater. Infection prevalence and resource commitment for informed action displayed an inverted relationship: higher temporal and spatial resolution surveillance is more imperative during sporadic infection phases than during high prevalence periods. This relationship was reinforced when norovirus (two minor clusters) and influenza (primarily absent) were additionally surveilled at a weekly frequency. Overall, resource commitment should scale to meet the objectives of the monitoring campaign-providing a general prevalence estimate requires fewer resources than an early-warning and targeted-action monitoring framework.
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Affiliation(s)
- William Johnson
- Department of Civil, Environmental, and Architectural Engineering, University of Colorado Boulder, 1111 Engineering Drive, Boulder, CO 80309, United States
- Environmental Engineering Program, University of Colorado Boulder, 4001 Discovery Drive, Boulder, CO 80303, United States
| | - Katelyn Reeves
- Department of Civil, Environmental, and Architectural Engineering, University of Colorado Boulder, 1111 Engineering Drive, Boulder, CO 80309, United States
- Environmental Engineering Program, University of Colorado Boulder, 4001 Discovery Drive, Boulder, CO 80303, United States
| | - Jennifer Liebig
- BioFrontiers Institute, University of Colorado Boulder, 3415 Colorado Avenue, Boulder, CO 80303, United States
| | - Antonio Feula
- BioFrontiers Institute, University of Colorado Boulder, 3415 Colorado Avenue, Boulder, CO 80303, United States
| | - Claire Butler
- Environmental Engineering Program, University of Colorado Boulder, 4001 Discovery Drive, Boulder, CO 80303, United States
| | - Michaela Alkire
- Environmental Engineering Program, University of Colorado Boulder, 4001 Discovery Drive, Boulder, CO 80303, United States
| | - Samiha Singh
- Environmental Engineering Program, University of Colorado Boulder, 4001 Discovery Drive, Boulder, CO 80303, United States
| | - Shelby Litton
- Environmental Engineering Program, University of Colorado Boulder, 4001 Discovery Drive, Boulder, CO 80303, United States
| | - Kerry O'Conor
- Environmental Engineering Program, University of Colorado Boulder, 4001 Discovery Drive, Boulder, CO 80303, United States
| | - Keaton Jones
- Environmental Engineering Program, University of Colorado Boulder, 4001 Discovery Drive, Boulder, CO 80303, United States
| | - Nikolas Ortega
- Environmental Engineering Program, University of Colorado Boulder, 4001 Discovery Drive, Boulder, CO 80303, United States
| | - Trace Shimek
- Environmental Engineering Program, University of Colorado Boulder, 4001 Discovery Drive, Boulder, CO 80303, United States
| | - Julia Witteman
- Environmental Engineering Program, University of Colorado Boulder, 4001 Discovery Drive, Boulder, CO 80303, United States
| | - Kristen K Bjorkman
- BioFrontiers Institute, University of Colorado Boulder, 3415 Colorado Avenue, Boulder, CO 80303, United States
| | - Cresten Mansfeldt
- Department of Civil, Environmental, and Architectural Engineering, University of Colorado Boulder, 1111 Engineering Drive, Boulder, CO 80309, United States
- Environmental Engineering Program, University of Colorado Boulder, 4001 Discovery Drive, Boulder, CO 80303, United States
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13
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14
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Assessing wastewater-based epidemiology for the prediction of SARS-CoV-2 incidence in Catalonia. Sci Rep 2022; 12:15073. [PMID: 36064874 PMCID: PMC9443647 DOI: 10.1038/s41598-022-18518-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 08/12/2022] [Indexed: 11/21/2022] Open
Abstract
While wastewater-based epidemiology has proven a useful tool for epidemiological surveillance during the COVID-19 pandemic, few quantitative models comparing virus concentrations in wastewater samples and cumulative incidence have been established. In this work, a simple mathematical model relating virus concentration and cumulative incidence for full contagion waves was developed. The model was then used for short-term forecasting and compared to a local linear model. Both scenarios were tested using a dataset composed of samples from 32 wastewater treatment plants and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) incidence data covering the corresponding geographical areas during a 7-month period, including two contagion waves. A population-averaged dataset was also developed to model and predict the incidence over the full geography. Overall, the mathematical model based on wastewater data showed a good correlation with cumulative cases and allowed us to anticipate SARS-CoV-2 incidence in one week, which is of special relevance in situations where the epidemiological monitoring system cannot be fully implemented.
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