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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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2
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Hegazy N, Cowan A, D'Aoust PM, Mercier É, Towhid ST, Jia JJ, Wan S, Zhang Z, Kabir MP, Fang W, Graber TE, MacKenzie AE, Guilherme S, Delatolla R. Understanding the dynamic relation between wastewater SARS-CoV-2 signal and clinical metrics throughout the pandemic. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 853:158458. [PMID: 36075428 PMCID: PMC9444583 DOI: 10.1016/j.scitotenv.2022.158458] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 08/26/2022] [Accepted: 08/29/2022] [Indexed: 05/27/2023]
Abstract
Wastewater surveillance (WWS) of SARS-CoV-2 was proven to be a reliable and complementary tool for population-wide monitoring of COVID-19 disease incidence but was not as rigorously explored as an indicator for disease burden throughout the pandemic. Prior to global mass immunization campaigns and during the spread of the wildtype COVID-19 and the Alpha variant of concern (VOC), viral measurement of SARS-CoV-2 in wastewater was a leading indicator for both COVID-19 incidence and disease burden in communities. As the two-dose vaccination rates escalated during the spread of the Delta VOC in Jul. 2021 through Dec. 2021, relations weakened between wastewater signal and community COVID-19 disease incidence and maintained a strong relationship with clinical metrics indicative of disease burden (new hospital admissions, ICU admissions, and deaths). Further, with the onset of the vaccine-resistant Omicron BA.1 VOC in Dec. 2021 through Mar. 2022, wastewater again became a strong indicator of both disease incidence and burden during a period of limited natural immunization (no recent infection), vaccine escape, and waned vaccine effectiveness. Lastly, with the populations regaining enhanced natural and vaccination immunization shortly prior to the onset of the Omicron BA.2 VOC in mid-Mar 2022, wastewater is shown to be a strong indicator for both disease incidence and burden. Hospitalization-to-wastewater ratio is further shown to be a good indicator of VOC virulence when widespread clinical testing is limited. In the future, WWS is expected to show moderate indication of incidence and strong indication of disease burden in the community during future potential seasonal vaccination campaigns.
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Affiliation(s)
- Nada Hegazy
- Department of Civil Engineering, University of Ottawa, Ottawa, Ontario, Canada
| | - Aaron Cowan
- Department of Civil Engineering, University of Ottawa, Ottawa, Ontario, Canada
| | - Patrick M D'Aoust
- Department of Civil Engineering, University of Ottawa, Ottawa, Ontario, Canada
| | - Élisabeth Mercier
- Department of Civil Engineering, University of Ottawa, Ottawa, Ontario, Canada
| | | | - Jian-Jun Jia
- Department of Civil Engineering, University of Ottawa, Ottawa, Ontario, Canada
| | - Shen Wan
- Department of Civil Engineering, University of Ottawa, Ottawa, Ontario, Canada
| | - Zhihao Zhang
- Department of Civil Engineering, University of Ottawa, Ottawa, Ontario, Canada
| | - Md Pervez Kabir
- Department of Civil Engineering, University of Ottawa, Ottawa, Ontario, Canada
| | - Wanting Fang
- Department of Civil Engineering, University of Ottawa, Ottawa, Ontario, Canada
| | - Tyson E Graber
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, Ontario, Canada
| | - Alex E MacKenzie
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, Ontario, Canada
| | - Stéphanie Guilherme
- Department of Civil Engineering, University of Ottawa, Ottawa, Ontario, Canada
| | - Robert Delatolla
- Department of Civil Engineering, University of Ottawa, Ottawa, Ontario, Canada.
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Schumann VF, de Castro Cuadrat RR, Wyler E, Wurmus R, Deter A, Quedenau C, Dohmen J, Faxel M, Borodina T, Blume A, Freimuth J, Meixner M, Grau JH, Liere K, Hackenbeck T, Zietzschmann F, Gnirss R, Böckelmann U, Uyar B, Franke V, Barke N, Altmüller J, Rajewsky N, Landthaler M, Akalin A. SARS-CoV-2 infection dynamics revealed by wastewater sequencing analysis and deconvolution. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 853:158931. [PMID: 36228784 PMCID: PMC9549760 DOI: 10.1016/j.scitotenv.2022.158931] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 08/27/2022] [Accepted: 09/18/2022] [Indexed: 06/16/2023]
Abstract
The use of RNA sequencing from wastewater samples is a valuable way for estimating infection dynamics and circulating lineages of SARS-CoV-2. This approach is independent from testing individuals and can therefore become the key tool to monitor this and potentially other viruses. However, it is equally important to develop easily accessible and scalable tools which can highlight critical changes in infection rates and dynamics over time across different locations given sequencing data from wastewater. Here, we provide an analysis of lineage dynamics in Berlin and New York City using wastewater sequencing and present PiGx SARS-CoV-2, a highly reproducible computational analysis pipeline with comprehensive reports. This end-to-end pipeline includes all steps from raw data to shareable reports, additional taxonomic analysis, deconvolution and geospatial time series analyses. Using simulated datasets (in silico generated and spiked-in samples) we could demonstrate the accuracy of our pipeline calculating proportions of Variants of Concern (VOC) from environmental as well as pre-mixed samples (spiked-in). By applying our pipeline on a dataset of wastewater samples from Berlin between February 2021 and January 2022, we could reconstruct the emergence of B.1.1.7(alpha) in February/March 2021 and the replacement dynamics from B.1.617.2 (delta) to BA.1 and BA.2 (omicron) during the winter of 2021/2022. Using data from very-short-reads generated in an industrial scale setting, we could see even higher accuracy in our deconvolution. Lastly, using a targeted sequencing dataset from New York City (receptor-binding-domain (RBD) only), we could reproduce the results recovering the proportions of the so-called cryptic lineages shown in the original study. Overall our study provides an in-depth analysis reconstructing virus lineage dynamics from wastewater. While applying our tool on a wide range of different datasets (from different types of wastewater sample locations and sequenced with different methods), we show that PiGx SARS-CoV-2 can be used to identify new mutations and detect any emerging new lineages in a highly automated and scalable way. Our approach can support efforts to establish continuous monitoring and early-warning projects for detecting SARS-CoV-2 or any other pathogen.
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Affiliation(s)
- Vic-Fabienne Schumann
- Bioinformatics & Omics Data Science Platform, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine Berlin, Berlin, Germany
| | - Rafael Ricardo de Castro Cuadrat
- Bioinformatics & Omics Data Science Platform, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine Berlin, Berlin, Germany
| | - Emanuel Wyler
- RNA Biology and Posttranscriptional Regulation, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine Berlin, Berlin, Germany
| | - Ricardo Wurmus
- Bioinformatics & Omics Data Science Platform, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine Berlin, Berlin, Germany
| | - Aylina Deter
- RNA Biology and Posttranscriptional Regulation, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine Berlin, Berlin, Germany
| | - Claudia Quedenau
- Genomics Platform, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine Berlin, Berlin, Germany
| | - Jan Dohmen
- Bioinformatics & Omics Data Science Platform, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine Berlin, Berlin, Germany
| | - Miriam Faxel
- Bioinformatics & Omics Data Science Platform, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine Berlin, Berlin, Germany
| | - Tatiana Borodina
- Genomics Platform, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine Berlin, Berlin, Germany
| | - Alexander Blume
- Bioinformatics & Omics Data Science Platform, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine Berlin, Berlin, Germany
| | - Jonas Freimuth
- Bioinformatics & Omics Data Science Platform, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine Berlin, Berlin, Germany
| | | | | | | | | | | | | | | | - Bora Uyar
- Bioinformatics & Omics Data Science Platform, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine Berlin, Berlin, Germany
| | - Vedran Franke
- Bioinformatics & Omics Data Science Platform, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine Berlin, Berlin, Germany
| | - Niclas Barke
- RNA Biology and Posttranscriptional Regulation, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine Berlin, Berlin, Germany
| | - Janine Altmüller
- Genomics Platform, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine Berlin, Berlin, Germany
| | - Nikolaus Rajewsky
- Systems Biology of Gene Regulatory Elements, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine Berlin, Berlin, Germany.
| | - Markus Landthaler
- RNA Biology and Posttranscriptional Regulation, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine Berlin, Berlin, Germany.
| | - Altuna Akalin
- Bioinformatics & Omics Data Science Platform, Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine Berlin, Berlin, Germany.
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Duvallet C, Wu F, McElroy KA, Imakaev M, Endo N, Xiao A, Zhang J, Floyd-O'Sullivan R, Powell MM, Mendola S, Wilson ST, Cruz F, Melman T, Sathyanarayana CL, Olesen SW, Erickson TB, Ghaeli N, Chai P, Alm EJ, Matus M. Nationwide Trends in COVID-19 Cases and SARS-CoV-2 RNA Wastewater Concentrations in the United States. ACS ES&T WATER 2022; 2:1899-1909. [PMID: 36380771 DOI: 10.1101/2021.09.08.21263283] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
Wastewater-based epidemiology has emerged as a promising technology for population-level surveillance of COVID-19. In this study, we present results of a large nationwide SARS-CoV-2 wastewater monitoring system in the United States. We profile 55 locations with at least six months of sampling from April 2020 to May 2021. These locations represent more than 12 million individuals across 19 states. Samples were collected approximately weekly by wastewater treatment utilities as part of a regular wastewater surveillance service and analyzed for SARS-CoV-2 RNA concentrations. SARS-CoV-2 RNA concentrations were normalized to pepper mild mottle virus, an indicator of fecal matter in wastewater. We show that wastewater data reflect temporal and geographic trends in clinical COVID-19 cases and investigate the impact of normalization on correlations with case data within and across locations. We also provide key lessons learned from our broad-scale implementation of wastewater-based epidemiology, which can be used to inform wastewater-based epidemiology approaches for future emerging diseases. This work demonstrates that wastewater surveillance is a feasible approach for nationwide population-level monitoring of COVID-19 disease. With an evolving epidemic and effective vaccines against SARS-CoV-2, wastewater-based epidemiology can serve as a passive surveillance approach for detecting changing dynamics or resurgences of the virus.
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Affiliation(s)
- Claire Duvallet
- Biobot Analytics, Inc., Cambridge, Massachusetts 02139, United States
| | - Fuqing Wu
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
- MIT Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, United States
| | - Kyle A McElroy
- Biobot Analytics, Inc., Cambridge, Massachusetts 02139, United States
| | - Maxim Imakaev
- Biobot Analytics, Inc., Cambridge, Massachusetts 02139, United States
| | - Noriko Endo
- Biobot Analytics, Inc., Cambridge, Massachusetts 02139, United States
| | - Amy Xiao
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
- MIT Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, United States
| | - Jianbo Zhang
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
- MIT Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, United States
| | | | - Morgan M Powell
- Biobot Analytics, Inc., Cambridge, Massachusetts 02139, United States
| | - Samuel Mendola
- Biobot Analytics, Inc., Cambridge, Massachusetts 02139, United States
| | - Shane T Wilson
- Biobot Analytics, Inc., Cambridge, Massachusetts 02139, United States
| | - Francis Cruz
- Biobot Analytics, Inc., Cambridge, Massachusetts 02139, United States
| | - Tamar Melman
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, United States
| | | | - Scott W Olesen
- Biobot Analytics, Inc., Cambridge, Massachusetts 02139, United States
| | - Timothy B Erickson
- Department of Emergency Medicine, Harvard Medical School, Boston, Massachusetts 02115, United States
- Division of Medical Toxicology, Department of Emergency Medicine, Brigham and Women's Hospital, Boston, Massachusetts 02115, United States
- Harvard Humanitarian Initiative, Cambridge, Massachusetts 02138, United States
| | - Newsha Ghaeli
- Biobot Analytics, Inc., Cambridge, Massachusetts 02139, United States
| | - Peter Chai
- Division of Medical Toxicology, Department of Emergency Medicine, Brigham and Women's Hospital, Boston, Massachusetts 02115, United States
- The Fenway Institute, Boston, Massachusetts 02215, United States
- The Koch Institute for Integrated Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139-4307, United States
| | - Eric J Alm
- Biobot Analytics, Inc., Cambridge, Massachusetts 02139, United States
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
- MIT Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, United States
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, United States
| | - Mariana Matus
- Biobot Analytics, Inc., Cambridge, Massachusetts 02139, United States
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5
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Wu F, Lee WL, Chen H, Gu X, Chandra F, Armas F, Xiao A, Leifels M, Rhode SF, Wuertz S, Thompson J, Alm EJ. Making waves: Wastewater surveillance of SARS-CoV-2 in an endemic future. WATER RESEARCH 2022; 219:118535. [PMID: 35605390 PMCID: PMC9062764 DOI: 10.1016/j.watres.2022.118535] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 04/29/2022] [Accepted: 04/30/2022] [Indexed: 05/28/2023]
Abstract
Wastewater-based surveillance (WBS) has been widely used as a public health tool to monitor the emergence and spread of SARS-CoV-2 infections in populations during the COVID-19 pandemic. Coincident with the global vaccination efforts, the world is also enduring new waves of SARS-CoV-2 variants. Reinfections and vaccine breakthroughs suggest an endemic future where SARS-CoV-2 continues to persist in the general population. In this treatise, we aim to explore the future roles of wastewater surveillance. Practically, WBS serves as a relatively affordable and non-invasive tool for mass surveillance of SARS-CoV-2 infection while minimizing privacy concerns, attributes that make it extremely suited for its long-term usage. In an endemic future, the utility of WBS will include 1) monitoring the trend of viral loads of targets in wastewater for quantitative estimate of changes in disease incidence; 2) sampling upstream for pinpointing infections in neighborhoods and at the building level; 3) integrating wastewater and clinical surveillance for cost-efficient population surveillance; and 4) genome sequencing wastewater samples to track circulating and emerging variants in the population. We further discuss the challenges and future developments of WBS to reduce inconsistencies in wastewater data worldwide, improve its epidemiological inference, and advance viral tracking and discovery as a preparation for the next viral pandemic.
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Affiliation(s)
- Fuqing Wu
- Center for Infectious Disease, Department of Epidemiology, Human Genetics, and Environmental Sciences, University of Texas School of Public Health, Houston, TX, USA.
| | - Wei Lin Lee
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
| | - Hongjie Chen
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
| | - Xiaoqiong Gu
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
| | - Franciscus Chandra
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
| | - Federica Armas
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
| | - Amy Xiao
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA; Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Mats Leifels
- Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore
| | | | - Stefan Wuertz
- Campus for Research Excellence and Technological Enterprise (CREATE), Singapore; Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore; School of Civil and Environmental Engineering, Nanyang Technological University, Singapore
| | - Janelle Thompson
- Campus for Research Excellence and Technological Enterprise (CREATE), Singapore; Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore; Asian School of the Environment, Nanyang Technological University, Singapore
| | - Eric J Alm
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore; Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA; Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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6
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Ji Y, Fang Y, Cheng B, Li L, Fang X. Tracheostomy timing and clinical outcomes in ventilated COVID-19 patients: a systematic review and meta-analysis. Crit Care 2022; 26:40. [PMID: 35135597 PMCID: PMC8822732 DOI: 10.1186/s13054-022-03904-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 01/26/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND The association of tracheostomy timing and clinical outcomes in ventilated COVID-19 patients remains controversial. We performed a meta-analysis to evaluate the impact of early tracheostomy compared to late tracheostomy on COVID-19 patients' outcomes. METHODS We searched Medline, Embase, Cochrane, and Scopus database, along with medRxiv, bioRxiv, and Research Square, from December 1, 2019, to August 24, 2021. Early tracheostomy was defined as a tracheostomy conducted 14 days or less after initiation of invasive mechanical ventilation (IMV). Late tracheostomy was any time thereafter. Duration of IMV, duration of ICU stay, and overall mortality were the primary outcomes of the meta-analysis. Pooled odds ratios (OR) or the mean differences (MD) with 95%CIs were calculated using a random-effects model. RESULTS Fourteen studies with a cumulative 2371 tracheostomized COVID-19 patients were included in this review. Early tracheostomy was associated with significant reductions in duration of IMV (2098 patients; MD - 9.08 days, 95% CI - 10.91 to - 7.26 days, p < 0.01) and duration of ICU stay (1224 patients; MD - 9.41 days, 95% CI - 12.36 to - 6.46 days, p < 0.01). Mortality was reported for 2343 patients and was comparable between groups (OR 1.09, 95% CI 0.79-1.51, p = 0.59). CONCLUSIONS The results of this meta-analysis suggest that, compared with late tracheostomy, early tracheostomy in COVID-19 patients was associated with shorter duration of IMV and ICU stay without modifying the mortality rate. These findings may have important implications to improve ICU availability during the COVID-19 pandemic. Trial registration The protocol was registered at INPLASY (INPLASY202180088).
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Affiliation(s)
- Yun Ji
- Department of Surgical Intensive Care Unit, the Second Affiliated Hospital, School of Medicine, Zhejiang University, 88 Jiefang Road, Hangzhou, Zhejiang, China
| | - Yumin Fang
- Department of Intensive Care Unit, Suichang People's Hospital, Lishui, Zhejiang, China
| | - Baoli Cheng
- Department of Anesthesiology and Intensive Care, the First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
| | - Libin Li
- Department of Surgical Intensive Care Unit, the Second Affiliated Hospital, School of Medicine, Zhejiang University, 88 Jiefang Road, Hangzhou, Zhejiang, China
| | - Xiangming Fang
- Department of Anesthesiology and Intensive Care, the First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
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7
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Shah S, Gwee SXW, Ng JQX, Lau N, Koh J, Pang J. Wastewater surveillance to infer COVID-19 transmission: A systematic review. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 804:150060. [PMID: 34798721 PMCID: PMC8423771 DOI: 10.1016/j.scitotenv.2021.150060] [Citation(s) in RCA: 104] [Impact Index Per Article: 52.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 08/27/2021] [Accepted: 08/27/2021] [Indexed: 05/18/2023]
Abstract
Successful detection of SARS-COV-2 in wastewater suggests the potential utility of wastewater-based epidemiology (WBE) for COVID-19 community surveillance. This systematic review aims to assess the performance of wastewater surveillance as early warning system of COVID-19 community transmission. A systematic search was conducted in PubMed, Medline, Embase and the WBE Consortium Registry according to PRISMA guidelines for relevant articles published until 31st July 2021. Relevant data were extracted and summarized. Quality of each paper was assessed using an assessment tool adapted from Bilotta et al.'s tool for environmental science. Of 763 studies identified, 92 studies distributed across 34 countries were shortlisted for qualitative synthesis. A total of 26,197 samples were collected between January 2020 and May 2021 from various locations serving population ranging from 321 to 11,400,000 inhabitants. Overall sample positivity was moderate at 29.2% in all examined settings with the spike (S) gene having maximum rate of positive detections and nucleocapsid (N) gene being the most targeted. Wastewater signals preceded confirmed cases by up to 63 days, with 13 studies reporting sample positivity before the first cases were detected in the community. At least 50 studies reported an association of viral load with community cases. While wastewater surveillance cannot replace large-scale diagnostic testing, it can complement clinical surveillance by providing early signs of potential transmission for more active public health responses. However, more studies using standardized and validated methods are required along with risk analysis and modelling to understand the dynamics of viral outbreaks.
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Affiliation(s)
- Shimoni Shah
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 117549, Singapore; Centre for Infectious Disease Epidemiology and Research, National University of Singapore, Singapore 117549, Singapore.
| | - Sylvia Xiao Wei Gwee
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 117549, Singapore; Centre for Infectious Disease Epidemiology and Research, National University of Singapore, Singapore 117549, Singapore.
| | - Jamie Qiao Xin Ng
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 117549, Singapore; Centre for Infectious Disease Epidemiology and Research, National University of Singapore, Singapore 117549, Singapore.
| | - Nicholas Lau
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 117549, Singapore; Centre for Infectious Disease Epidemiology and Research, National University of Singapore, Singapore 117549, Singapore.
| | - Jiayun Koh
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 117549, Singapore; Centre for Infectious Disease Epidemiology and Research, National University of Singapore, Singapore 117549, Singapore.
| | - Junxiong Pang
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 117549, Singapore; Centre for Infectious Disease Epidemiology and Research, National University of Singapore, Singapore 117549, Singapore.
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8
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Cluzel N, Courbariaux M, Wang S, Moulin L, Wurtzer S, Bertrand I, Laurent K, Monfort P, Gantzer C, Guyader SL, Boni M, Mouchel JM, Maréchal V, Nuel G, Maday Y. A nationwide indicator to smooth and normalize heterogeneous SARS-CoV-2 RNA data in wastewater. ENVIRONMENT INTERNATIONAL 2022; 158:106998. [PMID: 34991258 PMCID: PMC8608586 DOI: 10.1016/j.envint.2021.106998] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 11/20/2021] [Accepted: 11/21/2021] [Indexed: 05/18/2023]
Abstract
Since many infected people experience no or few symptoms, the SARS-CoV-2 epidemic is frequently monitored through massive virus testing of the population, an approach that may be biased and may be difficult to sustain in low-income countries. Since SARS-CoV-2 RNA can be detected in stool samples, quantifying SARS-CoV-2 genome by RT-qPCR in wastewater treatment plants (WWTPs) has been carried out as a complementary tool to monitor virus circulation among human populations. However, measuring SARS-CoV-2 viral load in WWTPs can be affected by many experimental and environmental factors. To circumvent these limits, we propose here a novel indicator, the wastewater indicator (WWI), that partly reduces and corrects the noise associated with the SARS-CoV-2 genome quantification in wastewater (average noise reduction of 19%). All data processing results in an average correlation gain of 18% with the incidence rate. The WWI can take into account the censorship linked to the limit of quantification (LOQ), allows the automatic detection of outliers to be integrated into the smoothing algorithm, estimates the average measurement error committed on the samples and proposes a solution for inter-laboratory normalization in the absence of inter-laboratory assays (ILA). This method has been successfully applied in the context of Obépine, a French national network that has been quantifying SARS-CoV-2 genome in a representative sample of French WWTPs since March 5th 2020. By August 26th, 2021, 168 WWTPs were monitored in the French metropolitan and overseas territories of France. We detail the process of elaboration of this indicator, show that it is strongly correlated to the incidence rate and that the optimal time lag between these two signals is only a few days, making our indicator an efficient complement to the incidence rate. This alternative approach may be especially important to evaluate SARS-CoV-2 dynamics in human populations when the testing rate is low.
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Affiliation(s)
- Nicolas Cluzel
- Sorbonne Université, Maison des Modélisations Ingénieries et Technologies (SUMMIT), 75005 Paris, France.
| | - Marie Courbariaux
- Sorbonne Université, Maison des Modélisations Ingénieries et Technologies (SUMMIT), 75005 Paris, France
| | - Siyun Wang
- Sorbonne Université, Maison des Modélisations Ingénieries et Technologies (SUMMIT), 75005 Paris, France
| | - Laurent Moulin
- Eau de Paris, Département de Recherche, Développement et Qualité de l'Eau, 33 avenue Jean Jaurès, F-94200 Ivry sur Seine, France
| | - Sébastien Wurtzer
- Eau de Paris, Département de Recherche, Développement et Qualité de l'Eau, 33 avenue Jean Jaurès, F-94200 Ivry sur Seine, France
| | | | - Karine Laurent
- Sorbonne Université, Maison des Modélisations Ingénieries et Technologies (SUMMIT), 75005 Paris, France
| | - Patrick Monfort
- HydroSciences Montpellier, UMR 5151, Université de Montpellier, CNRS, IRD, F-34093 Montpellier, France
| | | | - Soizick Le Guyader
- Ifremer, laboratoire de Microbiologie, SG2M/LSEM, BP 21105, 44311 Nantes, France
| | - Mickaël Boni
- Institut de Recherche Biomédicale des Armées, 1 place Valérie André, F-91220 Brétigny-sur-Orge, France
| | - Jean-Marie Mouchel
- Sorbonne Université, CNRS, EPHE, UMR 7619 Metis, e-LTER Zone Atelier Seine, F-75005 Paris, France
| | - Vincent Maréchal
- Sorbonne Université, INSERM, Centre de Recherche Saint-Antoine, F-75012 Paris, France
| | - Grégory Nuel
- Stochastics and Biology Group, Probability and Statistics (LPSM, CNRS 8001), Sorbonne University, Campus Pierre et Marie Curie, 4 Place Jussieu, 75005 Paris, France
| | - Yvon Maday
- Sorbonne Université, CNRS, Université de Paris, Laboratoire Jacques-Louis Lions (LJLL), F-75005 Paris, France; Institut Universaire de France, France.
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