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Delcher C, Quesinberry D, Torabi S, Berry S, Keck JW, Rani A, Subedi B. Wastewater Surveillance for Xylazine in Kentucky. AJPM FOCUS 2024; 3:100203. [PMID: 38883693 PMCID: PMC11180370 DOI: 10.1016/j.focus.2024.100203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/18/2024]
Abstract
Introduction In the U.S., xylazine, the veterinary non-opioid sedative, has emerged as a major threat to people who use illicitly manufactured fentanyl and other drugs. The aim of this study was to compare wastewater detection of xylazine with other public health and safety surveillance data from 2019 to 2023 in Kentucky. Methods Wastewater samples from 5 rest areas, 2 truck weigh stations, and 4 wastewater treatment plants were tested for xylazine. Wastewater xylazine positivity rates were compared with xylazine-positive submission rates from the National Forensic Laboratory Information System and Kentucky's fatal overdoses in 6-month periods (Period 1=January-June; Period 2=July-December). Results Xylazine was detected in 61.6% (424 of 688) of daily wastewater samples from roadway sites/wastewater treatment plants. For roadways, detection increased from 55% (Period 1, 2021) to 94% (Period 1, 2023), and wastewater treatment plants had an overall detection of 25.8% (n=66 samples, Periods 1 and 2, 2022). Increasing roadway positivity corresponded to trends in National Forensic Laboratory Information System xylazine-positive submission rates: from 0.19 per 1,000 submissions (Period 1, 2019) to 2.9 per 1,000 (Period 2, 2022, latest available). No deaths from xylazine were reported publicly in Kentucky, although this study's authors identified 1-4 deaths (true count suppressed) in the overdose surveillance system, which, in back-of-the-envelope comparisons with other states, is far fewer than expected. Conclusions Wastewater signals indicate broad geographic exposure to xylazine in Kentucky, yet health outcomes data suggest otherwise. These findings may inform regional, national, and international efforts to incorporate wastewater-based drug surveillance. Harm-reduction activities along roadways and other suitable locations may be needed.
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Affiliation(s)
- Chris Delcher
- Institute for Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Kentucky, Lexington, Kentucky
| | - Dana Quesinberry
- Department of Health Management & Policy, College of Public Health, University of Kentucky, Lexington, Kentucky
| | - Soroosh Torabi
- Department of Mechanical and Aerospace Engineering, University of Kentucky, Lexington, Kentucky
| | - Scott Berry
- Department of Mechanical and Aerospace Engineering, University of Kentucky, Lexington, Kentucky
- Department of Biomedical Engineering, University of Kentucky, Lexington, Kentucky
| | - James W Keck
- Department of Family and Community Medicine, University of Kentucky, Lexington, Kentucky
| | - Abhya Rani
- Department of Chemistry, Murray State University, Murray, Kentucky
| | - Bikram Subedi
- Department of Chemistry, Murray State University, Murray, Kentucky
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Wright T, Adhikari A. Utilizing a National Wastewater Monitoring Program to Address the U.S. Opioid Epidemic: A Focus on Metro Atlanta, Georgia. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:5282. [PMID: 37047898 PMCID: PMC10093898 DOI: 10.3390/ijerph20075282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 03/23/2023] [Accepted: 03/27/2023] [Indexed: 06/19/2023]
Abstract
The opioid epidemic has continued to be an ongoing public health crisis within Metro Atlanta for the last three decades. However, estimating opioid use and exposure in a large population is almost impossible, and alternative methods are being explored, including wastewater-based epidemiology. Wastewater contains various contaminants that can be monitored to track pathogens, infectious diseases, viruses, opioids, and more. This commentary is focusing on two issues: use of opioid residue data in wastewater as an alternative method for opioid exposure assessment in the community, and the adoption of a streamlined approach that can be utilized by public health officials. Opioid metabolites travel through the sanitary sewer through urine, fecal matter, and improper disposal of opioids to local wastewater treatment plants. Public health officials and researchers within various entities have utilized numerous approaches to reduce the impacts associated with opioid use. National wastewater monitoring programs and wastewater-based epidemiology are approaches that have been utilized globally by researchers and public health officials to combat the opioid epidemic. Currently, public health officials and policy makers within Metro Atlanta are exploring different solutions to reduce opioid use and opioid-related deaths throughout the community. In this commentary, we are proposing a new innovative approach for monitoring opioid use and analyzing trends by utilizing wastewater-based epidemiologic methods, which may help public health officials worldwide manage the opioid epidemic in a large metro area in the future.
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Affiliation(s)
- Tamara Wright
- University College, University of Denver, 2211 South Josephine Street, Denver, CO 80208, USA
| | - Atin Adhikari
- Jiann-Ping Hsu College of Public Health, Georgia Southern University, 501 Forest Drive, Statesboro, GA 30460, USA;
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Singh N, Varshney U. Adaptive interventions for opioid prescription management and consumption monitoring. J Am Med Inform Assoc 2023; 30:511-528. [PMID: 36562638 PMCID: PMC9933075 DOI: 10.1093/jamia/ocac253] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 12/05/2022] [Accepted: 12/15/2022] [Indexed: 12/24/2022] Open
Abstract
OBJECTIVES While opioid addiction, treatment, and recovery are receiving attention, not much has been done on adaptive interventions to prevent opioid use disorder (OUD). To address this, we identify opioid prescription and opioid consumption as promising targets for adaptive interventions and present a design framework. MATERIALS AND METHODS Using the framework, we designed Smart Prescription Management (SPM) and Smart Consumption Monitoring (SCM) interventions. The interventions are evaluated using analytical modeling and secondary data on doctor shopping, opioid overdose, prescription quality, and cost components. RESULTS SPM was most effective (30-90% improvement, for example, prescriptions reduced from 18 to 1.8 per patient) for extensive doctor shopping and reduced overdose events and mortality. Opioid adherence was improved and the likelihood of addiction declined (10-30%) as the response rate to SCM was increased. There is the potential for significant incentives ($2267-$3237) to be offered for addressing severe OUD. DISCUSSION The framework and designed interventions adapt to changing needs and conditions of the patients to become an important part of global efforts in preventing OUD. To the best of our knowledge, this is the first paper on adaptive interventions for preventing OUD by addressing both prescription and consumption. CONCLUSION SPM and SCM improved opioid prescription and consumption while reducing the risk of opioid addiction. These interventions will assist in better prescription decisions and in managing opioid consumption leading to desirable outcomes. The interventions can be extended to other substance use disorders and to study complex scenarios of prescription and nonprescription opioids in clinical studies.
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Affiliation(s)
- Neetu Singh
- Department of Management Information Systems, University of Illinois Springfield, Springfield, Illinois, USA
| | - Upkar Varshney
- Department of Computer Information Systems, Georgia State University, Atlanta, Georgia, USA
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4
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Gitter A, Oghuan J, Godbole AR, Chavarria CA, Monserrat C, Hu T, Wang Y, Maresso AW, Hanson BM, Mena KD, Wu F. Not a waste: Wastewater surveillance to enhance public health. FRONTIERS IN CHEMICAL ENGINEERING 2023. [DOI: 10.3389/fceng.2022.1112876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
Domestic wastewater, when collected and evaluated appropriately, can provide valuable health-related information for a community. As a relatively unbiased and non-invasive approach, wastewater surveillance may complement current practices towards mitigating risks and protecting population health. Spurred by the COVID-19 pandemic, wastewater programs are now widely implemented to monitor viral infection trends in sewersheds and inform public health decision-making. This review summarizes recent developments in wastewater-based epidemiology for detecting and monitoring communicable infectious diseases, dissemination of antimicrobial resistance, and illicit drug consumption. Wastewater surveillance, a quickly advancing Frontier in environmental science, is becoming a new tool to enhance public health, improve disease prevention, and respond to future epidemics and pandemics.
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Sauer J, Stewart K. Geographic information science and the United States opioid overdose crisis: A scoping review of methods, scales, and application areas. Soc Sci Med 2023; 317:115525. [PMID: 36493502 DOI: 10.1016/j.socscimed.2022.115525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 09/23/2022] [Accepted: 11/08/2022] [Indexed: 11/27/2022]
Abstract
BACKGROUND The Opioid Overdose Crisis (OOC) continues to generate morbidity and mortality in the United States, outpacing other prominent accident-related reasons. Multiple disciplines have applied geographic information science (GIScience) to understand geographical patterns in opioid-related health measures. However, there are limited reviews that assess how GIScience has been used. OBJECTIVES This scoping review investigates how GIScience has been used to conduct research on the OOC. Specific sub-objectives involve identifying bibliometric trends, the location and scale of studies, the frequency of use of various GIScience methodologies, and what direction future research can take to address existing gaps. METHODS The review was pre-registered with the Open Science Framework ((https://osf.io/h3mfx/) and followed the PRISMA-ScR guidelines. Scholarly research was gathered from the Web of Science Core Collection, PubMed, IEEE Xplore, ACM Digital Library. Inclusion criteria was defined as having a publication date between January 1999 and August 2021, using GIScience as a central part of the research, and investigating an opioid-related health measure. RESULTS 231 studies met the inclusion criteria. Most studies were published from 2017 onward. While many (41.6%) of studies were conducted using nationwide data, the majority (58.4%) occurred at the sub-national level. California, New York, Ohio, and Appalachia were most frequently studied, while the Midwest, north Rocky Mountains, Alaska, and Hawaii lacked studies. The most common GIScience methodology used was descriptive mapping, and county-level data was the most common unit of analysis across methodologies. CONCLUSIONS Future research of GIScience on the OOC can address gaps by developing use cases for machine learning, conducting analyses at the sub-county level, and applying GIScience to questions involving illicit fentanyl. Research using GIScience is expected to continue to increase, and multidisciplinary research efforts amongst GIScientists, epidemiologists, and other medical professionals can improve the rigor of research.
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Affiliation(s)
- Jeffery Sauer
- Department of Geographical Sciences, University of Maryland at College Park, 4600 River Road, Suite 300, Riverdale, MD, 20737, USA.
| | - Kathleen Stewart
- Department of Geographical Sciences, University of Maryland at College Park, 4600 River Road, Suite 300, Riverdale, MD, 20737, USA.
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Boogaerts T, Ahmed F, Choi PM, Tscharke B, O'Brien J, De Loof H, Gao J, Thai P, Thomas K, Mueller JF, Hall W, Covaci A, van Nuijs ALN. Current and future perspectives for wastewater-based epidemiology as a monitoring tool for pharmaceutical use. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 789:148047. [PMID: 34323839 DOI: 10.1016/j.scitotenv.2021.148047] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 05/21/2021] [Accepted: 05/21/2021] [Indexed: 06/13/2023]
Abstract
The medical and societal consequences of the misuse of pharmaceuticals clearly justify the need for comprehensive drug utilization research (DUR). Wastewater-based epidemiology (WBE) employs the analysis of human metabolic excretion products in wastewater to monitor consumption patterns of xenobiotics at the population level. Recently, WBE has demonstrated its potential to evaluate lifestyle factors such as illicit drug, alcohol and tobacco consumption at the population level, in near real-time and with high spatial and temporal resolution. Up until now there have been fewer WBE studies investigating health biomarkers such as pharmaceuticals. WBE publications monitoring the consumption of pharmaceuticals were systematically reviewed from three databases (PubMed, Web of Science and Google Scholar). 64 publications that reported population-normalised mass loads or defined daily doses of pharmaceuticals were selected. We document that WBE could be employed as a complementary information source for DUR. Interest in using WBE approaches for monitoring pharmaceutical use is growing but more foundation research (e.g. compound-specific uncertainties) is required to link WBE data to routine pharmacoepidemiologic information sources and workflows. WBE offers the possibility of i) estimating consumption of pharmaceuticals through the analysis of human metabolic excretion products in wastewater; ii) monitoring spatial and temporal consumption patterns of pharmaceuticals continuously and in near real-time; and iii) triangulating data with other DUR information sources to assess the impacts of strategies or interventions to reduce inappropriate use of pharmaceuticals.
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Affiliation(s)
- Tim Boogaerts
- Toxicological Centre, University of Antwerp, Belgium, Universiteitsplein 1, 2610 Antwerp, Belgium.
| | - Fahad Ahmed
- Queensland Alliance for Environmental Health Sciences (QAEHS), University of Queensland, 20 Cornwall St, Woolloongabba, QLD 4102, Australia
| | - Phil M Choi
- Queensland Alliance for Environmental Health Sciences (QAEHS), University of Queensland, 20 Cornwall St, Woolloongabba, QLD 4102, Australia; Water Unit, Health Protection Branch, Prevention Division, Queensland Health, GPO Box 48, Brisbane, QLD 4001, Australia
| | - Benjamin Tscharke
- Queensland Alliance for Environmental Health Sciences (QAEHS), University of Queensland, 20 Cornwall St, Woolloongabba, QLD 4102, Australia
| | - Jake O'Brien
- Queensland Alliance for Environmental Health Sciences (QAEHS), University of Queensland, 20 Cornwall St, Woolloongabba, QLD 4102, Australia
| | - Hans De Loof
- Laboratory of Physiopharmacology, University of Antwerp, Universiteitsplein 1, 2610 Wilrijk, Belgium
| | - Jianfa Gao
- College of Chemistry and Environmental Engineering, Shenzhen University, 1066 Xueyuan Avenue, Shenzhen 518060, China
| | - Phong Thai
- Queensland Alliance for Environmental Health Sciences (QAEHS), University of Queensland, 20 Cornwall St, Woolloongabba, QLD 4102, Australia
| | - Kevin Thomas
- Queensland Alliance for Environmental Health Sciences (QAEHS), University of Queensland, 20 Cornwall St, Woolloongabba, QLD 4102, Australia
| | - Jochen F Mueller
- Queensland Alliance for Environmental Health Sciences (QAEHS), University of Queensland, 20 Cornwall St, Woolloongabba, QLD 4102, Australia
| | - Wayne Hall
- Queensland Alliance for Environmental Health Sciences (QAEHS), University of Queensland, 20 Cornwall St, Woolloongabba, QLD 4102, Australia; Centre for Youth Substance Abuse, University of Queensland, 17 Upland Road, Woolloongabba, QLD 4102, Australia
| | - Adrian Covaci
- Toxicological Centre, University of Antwerp, Belgium, Universiteitsplein 1, 2610 Antwerp, Belgium
| | - Alexander L N van Nuijs
- Toxicological Centre, University of Antwerp, Belgium, Universiteitsplein 1, 2610 Antwerp, Belgium.
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Erickson TB, Endo N, Duvallet C, Ghaeli N, Hess K, Alm EJ, Matus M, Chai PR. "Waste Not, Want Not" - Leveraging Sewer Systems and Wastewater-Based Epidemiology for Drug Use Trends and Pharmaceutical Monitoring. J Med Toxicol 2021; 17:397-410. [PMID: 34402038 PMCID: PMC8366482 DOI: 10.1007/s13181-021-00853-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 06/28/2021] [Accepted: 07/09/2021] [Indexed: 12/26/2022] Open
Abstract
During the current global COVID-19 pandemic and opioid epidemic, wastewater-based epidemiology (WBE) has emerged as a powerful tool for monitoring public health trends by analysis of biomarkers including drugs, chemicals, and pathogens. Wastewater surveillance downstream at wastewater treatment plants provides large-scale population and regional-scale aggregation while upstream surveillance monitors locations at the neighborhood level with more precise geographic analysis. WBE can provide insights into dynamic drug consumption trends as well as environmental and toxicological contaminants. Applications of WBE include monitoring policy changes with cannabinoid legalization, tracking emerging illicit drugs, and early warning systems for potent fentanyl analogues along with the resurging wave of stimulants (e.g., methamphetamine, cocaine). Beyond drug consumption, WBE can also be used to monitor pharmaceuticals and their metabolites, including antidepressants and antipsychotics. In this manuscript, we describe the basic tenets and techniques of WBE, review its current application among drugs of abuse, and propose methods to scale and develop both monitoring and early warning systems with respect to measurement of illicit drugs and pharmaceuticals. We propose new frontiers in toxicological research with wastewater surveillance including assessment of medication assisted treatment of opioid use disorder (e.g., buprenorphine, methadone) in the context of other social burdens like COVID-19 disease.
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Affiliation(s)
- Timothy B Erickson
- Department of Emergency Medicine / Division of Toxicology, Brigham & Women's Hospital / Harvard Medical School, 10 Vining St, Boston, MA, 02155, USA.
- Division of Medical Toxicology, Department of Emergency Medicine, Mass General Brigham, Boston, USA.
- Harvard Humanitarian Institute, Cambridge, MA, USA.
| | | | | | | | | | - Eric J Alm
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA, USA
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore, Singapore
- Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Peter R Chai
- Department of Emergency Medicine / Division of Toxicology, Brigham & Women's Hospital / Harvard Medical School, 10 Vining St, Boston, MA, 02155, USA
- Division of Medical Toxicology, Department of Emergency Medicine, Mass General Brigham, Boston, USA
- The Fenway Institute, Boston, MA, USA
- The Koch Institute for Integrated Cancer Research, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
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8
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Boogaerts T, Quireyns M, Covaci A, De Loof H, van Nuijs AL. Analytical method for the simultaneous determination of a broad range of opioids in influent wastewater: Optimization, validation and applicability to monitor consumption patterns. Talanta 2021; 232:122443. [DOI: 10.1016/j.talanta.2021.122443] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 04/13/2021] [Accepted: 04/18/2021] [Indexed: 10/21/2022]
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Marks C, Carrasco-Escobar G, Carrasco-Hernández R, Johnson D, Ciccarone D, Strathdee SA, Smith D, Bórquez A. Methodological approaches for the prediction of opioid use-related epidemics in the United States: a narrative review and cross-disciplinary call to action. Transl Res 2021; 234:88-113. [PMID: 33798764 PMCID: PMC8217194 DOI: 10.1016/j.trsl.2021.03.018] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 03/25/2021] [Accepted: 03/25/2021] [Indexed: 01/01/2023]
Abstract
The opioid crisis in the United States has been defined by waves of drug- and locality-specific Opioid use-Related Epidemics (OREs) of overdose and bloodborne infections, among a range of health harms. The ability to identify localities at risk of such OREs, and better yet, to predict which ones will experience them, holds the potential to mitigate further morbidity and mortality. This narrative review was conducted to identify and describe quantitative approaches aimed at the "risk assessment," "detection" or "prediction" of OREs in the United States. We implemented a PubMed search composed of the: (1) objective (eg, prediction), (2) epidemiologic outcome (eg, outbreak), (3) underlying cause (ie, opioid use), (4) health outcome (eg, overdose, HIV), (5) location (ie, US). In total, 46 studies were included, and the following information extracted: discipline, objective, health outcome, drug/substance type, geographic region/unit of analysis, and data sources. Studies identified relied on clinical, epidemiological, behavioral and drug markets surveillance and applied a range of methods including statistical regression, geospatial analyses, dynamic modeling, phylogenetic analyses and machine learning. Studies for the prediction of overdose mortality at national/state/county and zip code level are rapidly emerging. Geospatial methods are increasingly used to identify hotspots of opioid use and overdose. In the context of infectious disease OREs, routine genetic sequencing of patient samples to identify growing transmission clusters via phylogenetic methods could increase early detection capacity. A coordinated implementation of multiple, complementary approaches would increase our ability to successfully anticipate outbreak risk and respond preemptively. We present a multi-disciplinary framework for the prediction of OREs in the US and reflect on challenges research teams will face in implementing such strategies along with good practices.
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Affiliation(s)
- Charles Marks
- Interdisciplinary Research on Substance Use Joint Doctoral Program at San Diego State University and University of California, San Diego; Division of Infectious Diseases and Global Public Health, University of California, San Diego; School of Social Work, San Diego State University
| | - Gabriel Carrasco-Escobar
- Division of Infectious Diseases and Global Public Health, University of California, San Diego; Health Innovation Laboratory, Institute of Tropical Medicine "Alexander von Humboldt", Universidad Peruana Cayetano Heredia, Lima, Peru
| | | | - Derek Johnson
- Division of Infectious Diseases and Global Public Health, University of California, San Diego
| | - Dan Ciccarone
- Department of Family and Community Medicine, University of California San Francisco
| | - Steffanie A Strathdee
- Division of Infectious Diseases and Global Public Health, University of California, San Diego
| | - Davey Smith
- Division of Infectious Diseases and Global Public Health, University of California, San Diego
| | - Annick Bórquez
- Division of Infectious Diseases and Global Public Health, University of California, San Diego.
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Foppe KS, Kujawinski EB, Duvallet C, Endo N, Erickson TB, Chai PR, Matus M. Analysis of 39 drugs and metabolites, including 8 glucuronide conjugates, in an upstream wastewater network via HPLC-MS/MS. J Chromatogr B Analyt Technol Biomed Life Sci 2021; 1176:122747. [PMID: 34052556 PMCID: PMC8271266 DOI: 10.1016/j.jchromb.2021.122747] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2020] [Revised: 04/17/2021] [Accepted: 04/26/2021] [Indexed: 01/03/2023]
Abstract
Pharmaceutical compounds ingested by humans are metabolized and excreted in urine and feces. These metabolites can be quantified in wastewater networks using wastewater-based epidemiology (WBE) methods. Standard WBE methods focus on samples collected at wastewater treatment plants (WWTPs). However, these methods do not capture more labile classes of metabolites such as glucuronide conjugates, products of the major phase II metabolic pathway for drug elimination. By shifting sample collection more upstream, these unambiguous markers of human exposure are captured before hydrolysis in the wastewater network. In this paper, we present an HPLC-MS/MS method that quantifies 8 glucuronide conjugates in addition to 31 parent and other metabolites of prescription and synthetic opioids, overdose treatment drugs, illicit drugs, and population markers. Calibration curves for all analytes are linear (r2 > 0.98), except THC (r2 = 0.97), and in the targeted range (0.1-1,000 ng mL-1) with lower limits of quantification (S/N = 9) ranging from 0.098 to 48.75 ng mL-1. This method is fast with an injection-to-injection time of 7.5 min. We demonstrate the application of the method to five wastewater samples collected from a manhole in a city in eastern Massachusetts. Collected wastewater samples were filtered and extracted via solid-phase extraction (SPE). The SPE cartridges are eluted and concentrated in the laboratory via nitrogen-drying. The method and case study presented here demonstrate the potential and application of expanding WBE to monitoring labile metabolites in upstream wastewater networks.
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Affiliation(s)
- Katelyn S Foppe
- Biobot Analytics, Inc., 501 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Elizabeth B Kujawinski
- Biobot Analytics, Inc., 501 Massachusetts Avenue, Cambridge, MA 02139, USA; Department of Marine Chemistry and Geochemistry, Woods Hole Oceanographic Institution, Woods Hole, MA 02543, USA
| | - Claire Duvallet
- Biobot Analytics, Inc., 501 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Noriko Endo
- Biobot Analytics, Inc., 501 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Timothy B Erickson
- Division of Medical Toxicology, Department of Medical Toxicology, Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02411, USA; Harvard Humanitarian Institute, Cambridge, MA 02139, USA
| | - Peter R Chai
- Division of Medical Toxicology, Department of Medical Toxicology, Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02411, USA; The Fenway Institute, 1340 Boylston Street, Boston, MA 02215, USA; The Koch Institute for Integrated Cancer Research, Massachusetts Institute of Technology, 500 Main Street, Cambridge, MA 02142, USA; Department of Psychosocial Oncology and Palliative Care, Dana Farber Cancer Institute, 450 Brookline Avenue, Boston, MA 02215, USA
| | - Mariana Matus
- Biobot Analytics, Inc., 501 Massachusetts Avenue, Cambridge, MA 02139, USA.
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11
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Sharara N, Endo N, Duvallet C, Ghaeli N, Matus M, Heussner J, Olesen SW, Alm EJ, Chai PR, Erickson TB. Wastewater network infrastructure in public health: Applications and learnings from the COVID-19 pandemic. PLOS GLOBAL PUBLIC HEALTH 2021; 1:e0000061. [PMID: 34927170 PMCID: PMC8682811 DOI: 10.1371/journal.pgph.0000061] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Accurate estimates of COVID-19 burden of infections in communities can inform public health strategy for the current pandemic. Wastewater based epidemiology (WBE) leverages sewer infrastructure to provide insights on rates of infection by measuring viral concentrations in wastewater. By accessing the sewer network at various junctures, important insights regarding COVID-19 disease activity can be gained. The analysis of sewage at the wastewater treatment plant level enables population-level surveillance of disease trends and virus mutations. At the neighborhood level, WBE can be used to describe trends in infection rates in the community thereby facilitating local efforts at targeted disease mitigation. Finally, at the building level, WBE can suggest the presence of infections and prompt individual testing. In this critical review, we describe the types of data that can be obtained through varying levels of WBE analysis, concrete plans for implementation, and public health actions that can be taken based on WBE surveillance data of infectious diseases, using recent and successful applications of WBE during the COVID-19 pandemic for illustration.
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Affiliation(s)
- Nour Sharara
- Biobot Analytics, Inc., Cambridge, Massachusetts, United States of America
- * E-mail: (TBE); (NS)
| | - Noriko Endo
- Biobot Analytics, Inc., Cambridge, Massachusetts, United States of America
| | - Claire Duvallet
- Biobot Analytics, Inc., Cambridge, Massachusetts, United States of America
| | - Newsha Ghaeli
- Biobot Analytics, Inc., Cambridge, Massachusetts, United States of America
| | - Mariana Matus
- Biobot Analytics, Inc., Cambridge, Massachusetts, United States of America
| | - Jennings Heussner
- Biobot Analytics, Inc., Cambridge, Massachusetts, United States of America
| | - Scott W. Olesen
- Biobot Analytics, Inc., Cambridge, Massachusetts, United States of America
| | - Eric J. Alm
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Singapore-MIT Alliance for Research and Technology, Antimicrobial Resistance Interdisciplinary Research Group, Singapore, Singapore
- Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Peter R. Chai
- Division of Medical Toxicology, Department of Emergency Medicine, Mass General Brigham, Harvard Medical School, Boston, Massachusetts, United States of America
- The Fenway Institute, Boston, Massachusetts, United States of America
- The Koch Institute for Integrated Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Division of Psychosocial Oncology and Palliative Care, Dana Farber Cancer Institute, Boston, Massachusetts, United States of America
| | - Timothy B. Erickson
- Division of Medical Toxicology, Department of Emergency Medicine, Mass General Brigham, Harvard Medical School, Boston, Massachusetts, United States of America
- Harvard Humanitarian Initiative, Cambridge, Massachusetts, United States of America
- * E-mail: (TBE); (NS)
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12
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Shover CL, Falasinnu TO, Dwyer CL, Santos NB, Cunningham NJ, Freedman RB, Vest NA, Humphreys K. Steep increases in fentanyl-related mortality west of the Mississippi River: Recent evidence from county and state surveillance. Drug Alcohol Depend 2020; 216:108314. [PMID: 33038637 PMCID: PMC7521591 DOI: 10.1016/j.drugalcdep.2020.108314] [Citation(s) in RCA: 95] [Impact Index Per Article: 23.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 09/06/2020] [Accepted: 09/17/2020] [Indexed: 12/03/2022]
Abstract
BACKGROUND Overdose deaths from synthetic opioids (e.g., fentanyl) increased 10-fold in the United States from 2013 to 2018, despite such opioids being rare in illicit drug markets west of the Mississippi River. Public health professionals have feared a "fentanyl breakthrough" in western U.S. drug markets could further accelerate overdose mortality. We evaluated the number and nature of western U.S. fentanyl deaths using the most recent data available. METHODS We systematically searched jurisdictions west of the Mississippi River for publicly available data on fentanyl-related deaths since 2018, the most recent Centers for Disease Control and Prevention (CDC) statistics. Using mortality data from 2019 and 2020, we identified changes in fentanyl-related mortality rate and proportion of fatal heroin-, stimulant, and prescription pill overdoses involving fentanyl. RESULTS Seven jurisdictions had publicly available fentanyl death data through December 2019 or later: Arizona; California; Denver County, CO; Harris County, TX; King County, WA; Los Angeles County, CA; and Dallas-Fort Worth, TX (Denton, Johnson, Parker, and Tarrant counties). All reported increased fentanyl deaths over the study period. Their collective contribution to national synthetic narcotics mortality increased 371 % from 2017 to 2019. Available 2020 data shows a 63 % growth in fentanyl-mortality over 2019. Fentanyl-involvement in heroin, stimulant, and prescription pill deaths has substantially grown. DISCUSSION Fentanyl has spread westward, increasing deaths in the short-term and threatening to dramatically worsen the nation's already severe opioid epidemic in the long-term. Increasing the standard dose of naloxone, expanding Medicaid, improving coverage of addiction treatment, and public health educational campaigns should be prioritized.
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Affiliation(s)
- Chelsea L. Shover
- Dept of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, 401 Quarry Rd., Stanford, CA, 94305, USA,Corresponding author at: Stanford University School of Medicine, 1070 Arastradero Rd. Ste 200, Palo Alto, CA, 94304, United States
| | - Titilola O. Falasinnu
- Dept of Health Research and Policy, Stanford University School of Medicine, 150 Governor’s Ln., Stanford, CA, 94305, USA
| | - Candice L. Dwyer
- Dept of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, 401 Quarry Rd., Stanford, CA, 94305, USA,Veterans Affairs Palo Alto Health Care System, 795 Willow Rd., Menlo Park, CA, 94025, USA
| | - Nayelie Benitez Santos
- Dept of Epidemiology, University of California Los Angeles, Los Angeles, CA, 90095, USA.
| | | | | | - Noel A. Vest
- Dept of Anesthesiology, Perioperative, and Pain Medicine, Stanford University School of Medicine, Stanford, CA, 94304, USA
| | - Keith Humphreys
- Dept of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, 401 Quarry Rd., Stanford, CA, 94305, USA; Veterans Affairs Palo Alto Health Care System, 795 Willow Rd., Menlo Park, CA, 94025, USA.
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13
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Thompson JR, Nancharaiah YV, Gu X, Lee WL, Rajal VB, Haines MB, Girones R, Ng LC, Alm EJ, Wuertz S. Making waves: Wastewater surveillance of SARS-CoV-2 for population-based health management. WATER RESEARCH 2020; 184:116181. [PMID: 32707307 PMCID: PMC7357518 DOI: 10.1016/j.watres.2020.116181] [Citation(s) in RCA: 108] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Revised: 07/11/2020] [Accepted: 07/13/2020] [Indexed: 05/18/2023]
Abstract
Worldwide, clinical data remain the gold standard for disease surveillance and tracking. However, such data are limited due to factors such as reporting bias and inability to track asymptomatic disease carriers. Disease agents are excreted in the urine and feces of infected individuals regardless of disease symptom severity. Wastewater surveillance - that is, monitoring disease via human effluent - represents a valuable complement to clinical approaches. Because wastewater is relatively inexpensive and easy to collect and can be monitored at different levels of population aggregation as needed, wastewater surveillance can offer a real-time, cost-effective view of a community's health that is independent of biases associated with case-reporting. For SARS-CoV-2 and other disease-causing agents we envision an aggregate wastewater-monitoring system at the level of a wastewater treatment plant and exploratory or confirmatory monitoring of the sewerage system at the neighborhood scale to identify or confirm clusters of infection or assess impact of control measures where transmission has been established. Implementation will require constructing a framework with collaborating government agencies, public or private utilities, and civil society organizations for appropriate use of data collected from wastewater, identification of an appropriate scale of sample collection and aggregation to balance privacy concerns and risk of stigmatization with public health preservation, and consideration of the social implications of wastewater surveillance.
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Affiliation(s)
- Janelle R Thompson
- Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University (NTU), Singapore; Asian School of the Environment, NTU, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore.
| | - Yarlagadda V Nancharaiah
- Biofouling and Biofilm Processes, Water and Steam Chemistry Division, Chemistry Group, Bhabha Atomic Research Centre, Kalpakkam 603102, India; Homi Bhabha National Institute, BARC Training School Complex, Anushakti Nagar, Trombay, Mumbai 400 094, India
| | - Xiaoqiong Gu
- Campus for Research Excellence and Technological Enterprise (CREATE), Singapore; Singapore-MIT Alliance for Research and Technology, National University of Singapore, Singapore
| | - Wei Lin Lee
- Campus for Research Excellence and Technological Enterprise (CREATE), Singapore; Singapore-MIT Alliance for Research and Technology, National University of Singapore, Singapore
| | - Verónica B Rajal
- Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University (NTU), Singapore; Instituto de Investigaciones para la Industria Química (INIQUI), CONICET, Universidad Nacional de Salta (UNSa), Av. Bolivia 5150, Salta 4400, Argentina; Facultad de Ingeniería, UNSa, Salta, Argentina
| | - Monamie B Haines
- Campus for Research Excellence and Technological Enterprise (CREATE), Singapore; School of Social Sciences, Sociology Division, NTU, Singapore
| | - Rosina Girones
- Section of Microbiology, Virology and Biotechnology, Department of Genetics, Microbiology and Statistics, Faculty of Biology, University of Barcelona, Diagonal, 643, 08028-Barcelona, Spain
| | - Lee Ching Ng
- Environmental Health Institute, National Environment Agency, Singapore
| | - Eric J Alm
- Campus for Research Excellence and Technological Enterprise (CREATE), Singapore; Singapore-MIT Alliance for Research and Technology, National University of Singapore, Singapore; Center for Microbiome Informatics and Therapeutics, Departments of Biological Engineering and Civil and Environmental Engineering, Massachusetts Institute of Technology, United States; Biobot Analytics, Cambridge MA, United States
| | - Stefan Wuertz
- Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University (NTU), Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore; School of Civil and Environmental Engineering, NTU, Singapore.
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14
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Duvallet C, Hayes BD, Erickson TB, Chai PR, Matus M. Mapping Community Opioid Exposure Through Wastewater-Based Epidemiology as a Means to Engage Pharmacies in Harm Reduction Efforts. Prev Chronic Dis 2020; 17:E91. [PMID: 32816660 PMCID: PMC7466868 DOI: 10.5888/pcd17.200053] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Affiliation(s)
| | - Bryan D Hayes
- Department of Pharmacy, Massachusetts General Hospital, Boston, Massachusetts.,Department of Emergency Medicine, Harvard Medical School, Boston, Massachusetts
| | - Timothy B Erickson
- Department of Emergency Medicine, Harvard Medical School, Boston, Massachusetts.,Division of Medical Toxicology, Department of Emergency Medicine, Brigham Health, Boston, Massachusetts.,Harvard Humanitarian Institute, Boston, Massachusetts
| | - Peter R Chai
- Division of Medical Toxicology, Department of Emergency Medicine, Brigham Health, Boston, Massachusetts.,The Fenway Institute, Boston, Massachusetts.,The Koch Institute for Integrated Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Mariana Matus
- Biobot Analytics, Inc, 501 Massachusetts Ave, Cambridge, MA 02139.
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15
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Abstract
Wastewater surveillance represents a complementary approach to clinical surveillance to measure the presence and prevalence of emerging infectious diseases like the novel coronavirus SARS-CoV-2. This innovative data source can improve the precision of epidemiological modeling to understand the penetrance of SARS-CoV-2 in specific vulnerable communities. Here, we tested wastewater collected at a major urban treatment facility in Massachusetts and detected SARS-CoV-2 RNA from the N gene at significant titers (57 to 303 copies per ml of sewage) in the period from 18 to 25 March 2020 using RT-qPCR. We validated detection of SARS-CoV-2 by Sanger sequencing the PCR product from the S gene. Viral titers observed were significantly higher than expected based on clinically confirmed cases in Massachusetts as of 25 March. Our approach is scalable and may be useful in modeling the SARS-CoV-2 pandemic and future outbreaks.IMPORTANCE Wastewater-based surveillance is a promising approach for proactive outbreak monitoring. SARS-CoV-2 is shed in stool early in the clinical course and infects a large asymptomatic population, making it an ideal target for wastewater-based monitoring. In this study, we develop a laboratory protocol to quantify viral titers in raw sewage via qPCR analysis and validate results with sequencing analysis. Our results suggest that the number of positive cases estimated from wastewater viral titers is orders of magnitude greater than the number of confirmed clinical cases and therefore may significantly impact efforts to understand the case fatality rate and progression of disease. These data may help inform decisions surrounding the advancement or scale-back of social distancing and quarantine efforts based on dynamic wastewater catchment-level estimations of prevalence.
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16
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Wu F, Zhang J, Xiao A, Gu X, Lee WL, Armas F, Kauffman K, Hanage W, Matus M, Ghaeli N, Endo N, Duvallet C, Poyet M, Moniz K, Washburne AD, Erickson TB, Chai PR, Thompson J, Alm EJ. SARS-CoV-2 Titers in Wastewater Are Higher than Expected from Clinically Confirmed Cases. mSystems 2020. [PMID: 32694130 DOI: 10.1101/2020.04.%2005.20051540] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/24/2023] Open
Abstract
Wastewater surveillance represents a complementary approach to clinical surveillance to measure the presence and prevalence of emerging infectious diseases like the novel coronavirus SARS-CoV-2. This innovative data source can improve the precision of epidemiological modeling to understand the penetrance of SARS-CoV-2 in specific vulnerable communities. Here, we tested wastewater collected at a major urban treatment facility in Massachusetts and detected SARS-CoV-2 RNA from the N gene at significant titers (57 to 303 copies per ml of sewage) in the period from 18 to 25 March 2020 using RT-qPCR. We validated detection of SARS-CoV-2 by Sanger sequencing the PCR product from the S gene. Viral titers observed were significantly higher than expected based on clinically confirmed cases in Massachusetts as of 25 March. Our approach is scalable and may be useful in modeling the SARS-CoV-2 pandemic and future outbreaks.IMPORTANCE Wastewater-based surveillance is a promising approach for proactive outbreak monitoring. SARS-CoV-2 is shed in stool early in the clinical course and infects a large asymptomatic population, making it an ideal target for wastewater-based monitoring. In this study, we develop a laboratory protocol to quantify viral titers in raw sewage via qPCR analysis and validate results with sequencing analysis. Our results suggest that the number of positive cases estimated from wastewater viral titers is orders of magnitude greater than the number of confirmed clinical cases and therefore may significantly impact efforts to understand the case fatality rate and progression of disease. These data may help inform decisions surrounding the advancement or scale-back of social distancing and quarantine efforts based on dynamic wastewater catchment-level estimations of prevalence.
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Affiliation(s)
- Fuqing Wu
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Jianbo Zhang
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Amy Xiao
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Xiaoqiong Gu
- Singapore-MIT Alliance for Research and Technology, National University of Singapore, Singapore
- Campus for Research Excellence and Technological Enterprise, Singapore
| | - Wei Lin Lee
- Singapore-MIT Alliance for Research and Technology, National University of Singapore, Singapore
- Campus for Research Excellence and Technological Enterprise, Singapore
| | - Federica Armas
- Singapore-MIT Alliance for Research and Technology, National University of Singapore, Singapore
- Campus for Research Excellence and Technological Enterprise, Singapore
| | - Kathryn Kauffman
- University at Buffalo, The State University of New York, Buffalo, New York, USA
| | - William Hanage
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Mariana Matus
- Biobot Analytics, Inc., Cambridge, Massachusetts, USA
| | - Newsha Ghaeli
- Biobot Analytics, Inc., Cambridge, Massachusetts, USA
| | - Noriko Endo
- Biobot Analytics, Inc., Cambridge, Massachusetts, USA
| | | | - Mathilde Poyet
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Katya Moniz
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | | | - Timothy B Erickson
- Division of Medical Toxicology, Department of Emergency Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Harvard Humanitarian Institute, Cambridge, Massachusetts, USA
| | - Peter R Chai
- Division of Medical Toxicology, Department of Emergency Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- The Fenway Institute, Boston, Massachusetts, USA
- The Koch Institute for Integrated Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Janelle Thompson
- Singapore Center for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore
- Asian School of the Environment, Nanyang Technological University, Singapore
- Campus for Research Excellence and Technological Enterprise, Singapore
| | - Eric J Alm
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Singapore-MIT Alliance for Research and Technology, National University of Singapore, Singapore
- Biobot Analytics, Inc., Cambridge, Massachusetts, USA
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Campus for Research Excellence and Technological Enterprise, Singapore
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
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17
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Wu F, Zhang J, Xiao A, Gu X, Lee WL, Armas F, Kauffman K, Hanage W, Matus M, Ghaeli N, Endo N, Duvallet C, Poyet M, Moniz K, Washburne AD, Erickson TB, Chai PR, Thompson J, Alm EJ. SARS-CoV-2 Titers in Wastewater Are Higher than Expected from Clinically Confirmed Cases. mSystems 2020. [PMID: 32694130 DOI: 10.1101/2020.06.15.20117747v2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/10/2023] Open
Abstract
Wastewater surveillance represents a complementary approach to clinical surveillance to measure the presence and prevalence of emerging infectious diseases like the novel coronavirus SARS-CoV-2. This innovative data source can improve the precision of epidemiological modeling to understand the penetrance of SARS-CoV-2 in specific vulnerable communities. Here, we tested wastewater collected at a major urban treatment facility in Massachusetts and detected SARS-CoV-2 RNA from the N gene at significant titers (57 to 303 copies per ml of sewage) in the period from 18 to 25 March 2020 using RT-qPCR. We validated detection of SARS-CoV-2 by Sanger sequencing the PCR product from the S gene. Viral titers observed were significantly higher than expected based on clinically confirmed cases in Massachusetts as of 25 March. Our approach is scalable and may be useful in modeling the SARS-CoV-2 pandemic and future outbreaks.IMPORTANCE Wastewater-based surveillance is a promising approach for proactive outbreak monitoring. SARS-CoV-2 is shed in stool early in the clinical course and infects a large asymptomatic population, making it an ideal target for wastewater-based monitoring. In this study, we develop a laboratory protocol to quantify viral titers in raw sewage via qPCR analysis and validate results with sequencing analysis. Our results suggest that the number of positive cases estimated from wastewater viral titers is orders of magnitude greater than the number of confirmed clinical cases and therefore may significantly impact efforts to understand the case fatality rate and progression of disease. These data may help inform decisions surrounding the advancement or scale-back of social distancing and quarantine efforts based on dynamic wastewater catchment-level estimations of prevalence.
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Affiliation(s)
- Fuqing Wu
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Jianbo Zhang
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Amy Xiao
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Xiaoqiong Gu
- Singapore-MIT Alliance for Research and Technology, National University of Singapore, Singapore
- Campus for Research Excellence and Technological Enterprise, Singapore
| | - Wei Lin Lee
- Singapore-MIT Alliance for Research and Technology, National University of Singapore, Singapore
- Campus for Research Excellence and Technological Enterprise, Singapore
| | - Federica Armas
- Singapore-MIT Alliance for Research and Technology, National University of Singapore, Singapore
- Campus for Research Excellence and Technological Enterprise, Singapore
| | - Kathryn Kauffman
- University at Buffalo, The State University of New York, Buffalo, New York, USA
| | - William Hanage
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Mariana Matus
- Biobot Analytics, Inc., Cambridge, Massachusetts, USA
| | - Newsha Ghaeli
- Biobot Analytics, Inc., Cambridge, Massachusetts, USA
| | - Noriko Endo
- Biobot Analytics, Inc., Cambridge, Massachusetts, USA
| | | | - Mathilde Poyet
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Katya Moniz
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | | | - Timothy B Erickson
- Division of Medical Toxicology, Department of Emergency Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Harvard Humanitarian Institute, Cambridge, Massachusetts, USA
| | - Peter R Chai
- Division of Medical Toxicology, Department of Emergency Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- The Fenway Institute, Boston, Massachusetts, USA
- The Koch Institute for Integrated Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Janelle Thompson
- Singapore Center for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore
- Asian School of the Environment, Nanyang Technological University, Singapore
- Campus for Research Excellence and Technological Enterprise, Singapore
| | - Eric J Alm
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Singapore-MIT Alliance for Research and Technology, National University of Singapore, Singapore
- Biobot Analytics, Inc., Cambridge, Massachusetts, USA
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Campus for Research Excellence and Technological Enterprise, Singapore
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
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18
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Wu F, Zhang J, Xiao A, Gu X, Lee WL, Armas F, Kauffman K, Hanage W, Matus M, Ghaeli N, Endo N, Duvallet C, Poyet M, Moniz K, Washburne AD, Erickson TB, Chai PR, Thompson J, Alm EJ. SARS-CoV-2 Titers in Wastewater Are Higher than Expected from Clinically Confirmed Cases. mSystems 2020. [PMID: 32660974 DOI: 10.1016/j.solener.2019.02.027] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/03/2023] Open
Abstract
Wastewater surveillance represents a complementary approach to clinical surveillance to measure the presence and prevalence of emerging infectious diseases like the novel coronavirus SARS-CoV-2. This innovative data source can improve the precision of epidemiological modeling to understand the penetrance of SARS-CoV-2 in specific vulnerable communities. Here, we tested wastewater collected at a major urban treatment facility in Massachusetts and detected SARS-CoV-2 RNA from the N gene at significant titers (57 to 303 copies per ml of sewage) in the period from 18 to 25 March 2020 using RT-qPCR. We validated detection of SARS-CoV-2 by Sanger sequencing the PCR product from the S gene. Viral titers observed were significantly higher than expected based on clinically confirmed cases in Massachusetts as of 25 March. Our approach is scalable and may be useful in modeling the SARS-CoV-2 pandemic and future outbreaks.IMPORTANCE Wastewater-based surveillance is a promising approach for proactive outbreak monitoring. SARS-CoV-2 is shed in stool early in the clinical course and infects a large asymptomatic population, making it an ideal target for wastewater-based monitoring. In this study, we develop a laboratory protocol to quantify viral titers in raw sewage via qPCR analysis and validate results with sequencing analysis. Our results suggest that the number of positive cases estimated from wastewater viral titers is orders of magnitude greater than the number of confirmed clinical cases and therefore may significantly impact efforts to understand the case fatality rate and progression of disease. These data may help inform decisions surrounding the advancement or scale-back of social distancing and quarantine efforts based on dynamic wastewater catchment-level estimations of prevalence.
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Affiliation(s)
- Fuqing Wu
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Jianbo Zhang
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Amy Xiao
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Xiaoqiong Gu
- Singapore-MIT Alliance for Research and Technology, National University of Singapore, Singapore
- Campus for Research Excellence and Technological Enterprise, Singapore
| | - Wei Lin Lee
- Singapore-MIT Alliance for Research and Technology, National University of Singapore, Singapore
- Campus for Research Excellence and Technological Enterprise, Singapore
| | - Federica Armas
- Singapore-MIT Alliance for Research and Technology, National University of Singapore, Singapore
- Campus for Research Excellence and Technological Enterprise, Singapore
| | - Kathryn Kauffman
- University at Buffalo, The State University of New York, Buffalo, New York, USA
| | - William Hanage
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Mariana Matus
- Biobot Analytics, Inc., Cambridge, Massachusetts, USA
| | - Newsha Ghaeli
- Biobot Analytics, Inc., Cambridge, Massachusetts, USA
| | - Noriko Endo
- Biobot Analytics, Inc., Cambridge, Massachusetts, USA
| | | | - Mathilde Poyet
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Katya Moniz
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | | | - Timothy B Erickson
- Division of Medical Toxicology, Department of Emergency Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Harvard Humanitarian Institute, Cambridge, Massachusetts, USA
| | - Peter R Chai
- Division of Medical Toxicology, Department of Emergency Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- The Fenway Institute, Boston, Massachusetts, USA
- The Koch Institute for Integrated Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Janelle Thompson
- Singapore Center for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore
- Asian School of the Environment, Nanyang Technological University, Singapore
- Campus for Research Excellence and Technological Enterprise, Singapore
| | - Eric J Alm
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Singapore-MIT Alliance for Research and Technology, National University of Singapore, Singapore
- Biobot Analytics, Inc., Cambridge, Massachusetts, USA
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Campus for Research Excellence and Technological Enterprise, Singapore
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
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19
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Wu F, Zhang J, Xiao A, Gu X, Lee WL, Armas F, Kauffman K, Hanage W, Matus M, Ghaeli N, Endo N, Duvallet C, Poyet M, Moniz K, Washburne AD, Erickson TB, Chai PR, Thompson J, Alm EJ. SARS-CoV-2 Titers in Wastewater Are Higher than Expected from Clinically Confirmed Cases. mSystems 2020. [PMID: 32694130 DOI: 10.1101/2020.04.05.20051540] [Citation(s) in RCA: 71] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/12/2023] Open
Abstract
Wastewater surveillance represents a complementary approach to clinical surveillance to measure the presence and prevalence of emerging infectious diseases like the novel coronavirus SARS-CoV-2. This innovative data source can improve the precision of epidemiological modeling to understand the penetrance of SARS-CoV-2 in specific vulnerable communities. Here, we tested wastewater collected at a major urban treatment facility in Massachusetts and detected SARS-CoV-2 RNA from the N gene at significant titers (57 to 303 copies per ml of sewage) in the period from 18 to 25 March 2020 using RT-qPCR. We validated detection of SARS-CoV-2 by Sanger sequencing the PCR product from the S gene. Viral titers observed were significantly higher than expected based on clinically confirmed cases in Massachusetts as of 25 March. Our approach is scalable and may be useful in modeling the SARS-CoV-2 pandemic and future outbreaks.IMPORTANCE Wastewater-based surveillance is a promising approach for proactive outbreak monitoring. SARS-CoV-2 is shed in stool early in the clinical course and infects a large asymptomatic population, making it an ideal target for wastewater-based monitoring. In this study, we develop a laboratory protocol to quantify viral titers in raw sewage via qPCR analysis and validate results with sequencing analysis. Our results suggest that the number of positive cases estimated from wastewater viral titers is orders of magnitude greater than the number of confirmed clinical cases and therefore may significantly impact efforts to understand the case fatality rate and progression of disease. These data may help inform decisions surrounding the advancement or scale-back of social distancing and quarantine efforts based on dynamic wastewater catchment-level estimations of prevalence.
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Affiliation(s)
- Fuqing Wu
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Jianbo Zhang
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Amy Xiao
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Xiaoqiong Gu
- Singapore-MIT Alliance for Research and Technology, National University of Singapore, Singapore
- Campus for Research Excellence and Technological Enterprise, Singapore
| | - Wei Lin Lee
- Singapore-MIT Alliance for Research and Technology, National University of Singapore, Singapore
- Campus for Research Excellence and Technological Enterprise, Singapore
| | - Federica Armas
- Singapore-MIT Alliance for Research and Technology, National University of Singapore, Singapore
- Campus for Research Excellence and Technological Enterprise, Singapore
| | - Kathryn Kauffman
- University at Buffalo, The State University of New York, Buffalo, New York, USA
| | - William Hanage
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Mariana Matus
- Biobot Analytics, Inc., Cambridge, Massachusetts, USA
| | - Newsha Ghaeli
- Biobot Analytics, Inc., Cambridge, Massachusetts, USA
| | - Noriko Endo
- Biobot Analytics, Inc., Cambridge, Massachusetts, USA
| | | | - Mathilde Poyet
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Katya Moniz
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | | | - Timothy B Erickson
- Division of Medical Toxicology, Department of Emergency Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Harvard Humanitarian Institute, Cambridge, Massachusetts, USA
| | - Peter R Chai
- Division of Medical Toxicology, Department of Emergency Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- The Fenway Institute, Boston, Massachusetts, USA
- The Koch Institute for Integrated Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Janelle Thompson
- Singapore Center for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore
- Asian School of the Environment, Nanyang Technological University, Singapore
- Campus for Research Excellence and Technological Enterprise, Singapore
| | - Eric J Alm
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Singapore-MIT Alliance for Research and Technology, National University of Singapore, Singapore
- Biobot Analytics, Inc., Cambridge, Massachusetts, USA
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Campus for Research Excellence and Technological Enterprise, Singapore
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
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20
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Wu F, Zhang J, Xiao A, Gu X, Lee WL, Armas F, Kauffman K, Hanage W, Matus M, Ghaeli N, Endo N, Duvallet C, Poyet M, Moniz K, Washburne AD, Erickson TB, Chai PR, Thompson J, Alm EJ. SARS-CoV-2 Titers in Wastewater Are Higher than Expected from Clinically Confirmed Cases. mSystems 2020. [PMID: 32694130 DOI: 10.1128/2fmsystems.00614-20] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/30/2023] Open
Abstract
Wastewater surveillance represents a complementary approach to clinical surveillance to measure the presence and prevalence of emerging infectious diseases like the novel coronavirus SARS-CoV-2. This innovative data source can improve the precision of epidemiological modeling to understand the penetrance of SARS-CoV-2 in specific vulnerable communities. Here, we tested wastewater collected at a major urban treatment facility in Massachusetts and detected SARS-CoV-2 RNA from the N gene at significant titers (57 to 303 copies per ml of sewage) in the period from 18 to 25 March 2020 using RT-qPCR. We validated detection of SARS-CoV-2 by Sanger sequencing the PCR product from the S gene. Viral titers observed were significantly higher than expected based on clinically confirmed cases in Massachusetts as of 25 March. Our approach is scalable and may be useful in modeling the SARS-CoV-2 pandemic and future outbreaks.IMPORTANCE Wastewater-based surveillance is a promising approach for proactive outbreak monitoring. SARS-CoV-2 is shed in stool early in the clinical course and infects a large asymptomatic population, making it an ideal target for wastewater-based monitoring. In this study, we develop a laboratory protocol to quantify viral titers in raw sewage via qPCR analysis and validate results with sequencing analysis. Our results suggest that the number of positive cases estimated from wastewater viral titers is orders of magnitude greater than the number of confirmed clinical cases and therefore may significantly impact efforts to understand the case fatality rate and progression of disease. These data may help inform decisions surrounding the advancement or scale-back of social distancing and quarantine efforts based on dynamic wastewater catchment-level estimations of prevalence.
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Affiliation(s)
- Fuqing Wu
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Jianbo Zhang
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Amy Xiao
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Xiaoqiong Gu
- Singapore-MIT Alliance for Research and Technology, National University of Singapore, Singapore
- Campus for Research Excellence and Technological Enterprise, Singapore
| | - Wei Lin Lee
- Singapore-MIT Alliance for Research and Technology, National University of Singapore, Singapore
- Campus for Research Excellence and Technological Enterprise, Singapore
| | - Federica Armas
- Singapore-MIT Alliance for Research and Technology, National University of Singapore, Singapore
- Campus for Research Excellence and Technological Enterprise, Singapore
| | - Kathryn Kauffman
- University at Buffalo, The State University of New York, Buffalo, New York, USA
| | - William Hanage
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Mariana Matus
- Biobot Analytics, Inc., Cambridge, Massachusetts, USA
| | - Newsha Ghaeli
- Biobot Analytics, Inc., Cambridge, Massachusetts, USA
| | - Noriko Endo
- Biobot Analytics, Inc., Cambridge, Massachusetts, USA
| | | | - Mathilde Poyet
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Katya Moniz
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | | | - Timothy B Erickson
- Division of Medical Toxicology, Department of Emergency Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Harvard Humanitarian Institute, Cambridge, Massachusetts, USA
| | - Peter R Chai
- Division of Medical Toxicology, Department of Emergency Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- The Fenway Institute, Boston, Massachusetts, USA
- The Koch Institute for Integrated Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Janelle Thompson
- Singapore Center for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore
- Asian School of the Environment, Nanyang Technological University, Singapore
- Campus for Research Excellence and Technological Enterprise, Singapore
| | - Eric J Alm
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Singapore-MIT Alliance for Research and Technology, National University of Singapore, Singapore
- Biobot Analytics, Inc., Cambridge, Massachusetts, USA
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Campus for Research Excellence and Technological Enterprise, Singapore
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
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21
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Wu F, Zhang J, Xiao A, Gu X, Lee WL, Armas F, Kauffman K, Hanage W, Matus M, Ghaeli N, Endo N, Duvallet C, Poyet M, Moniz K, Washburne AD, Erickson TB, Chai PR, Thompson J, Alm EJ. SARS-CoV-2 Titers in Wastewater Are Higher than Expected from Clinically Confirmed Cases. mSystems 2020. [PMID: 32694130 DOI: 10.1101/2020.04.05.200515] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/14/2023] Open
Abstract
Wastewater surveillance represents a complementary approach to clinical surveillance to measure the presence and prevalence of emerging infectious diseases like the novel coronavirus SARS-CoV-2. This innovative data source can improve the precision of epidemiological modeling to understand the penetrance of SARS-CoV-2 in specific vulnerable communities. Here, we tested wastewater collected at a major urban treatment facility in Massachusetts and detected SARS-CoV-2 RNA from the N gene at significant titers (57 to 303 copies per ml of sewage) in the period from 18 to 25 March 2020 using RT-qPCR. We validated detection of SARS-CoV-2 by Sanger sequencing the PCR product from the S gene. Viral titers observed were significantly higher than expected based on clinically confirmed cases in Massachusetts as of 25 March. Our approach is scalable and may be useful in modeling the SARS-CoV-2 pandemic and future outbreaks.IMPORTANCE Wastewater-based surveillance is a promising approach for proactive outbreak monitoring. SARS-CoV-2 is shed in stool early in the clinical course and infects a large asymptomatic population, making it an ideal target for wastewater-based monitoring. In this study, we develop a laboratory protocol to quantify viral titers in raw sewage via qPCR analysis and validate results with sequencing analysis. Our results suggest that the number of positive cases estimated from wastewater viral titers is orders of magnitude greater than the number of confirmed clinical cases and therefore may significantly impact efforts to understand the case fatality rate and progression of disease. These data may help inform decisions surrounding the advancement or scale-back of social distancing and quarantine efforts based on dynamic wastewater catchment-level estimations of prevalence.
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Affiliation(s)
- Fuqing Wu
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Jianbo Zhang
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Amy Xiao
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Xiaoqiong Gu
- Singapore-MIT Alliance for Research and Technology, National University of Singapore, Singapore
- Campus for Research Excellence and Technological Enterprise, Singapore
| | - Wei Lin Lee
- Singapore-MIT Alliance for Research and Technology, National University of Singapore, Singapore
- Campus for Research Excellence and Technological Enterprise, Singapore
| | - Federica Armas
- Singapore-MIT Alliance for Research and Technology, National University of Singapore, Singapore
- Campus for Research Excellence and Technological Enterprise, Singapore
| | - Kathryn Kauffman
- University at Buffalo, The State University of New York, Buffalo, New York, USA
| | - William Hanage
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Mariana Matus
- Biobot Analytics, Inc., Cambridge, Massachusetts, USA
| | - Newsha Ghaeli
- Biobot Analytics, Inc., Cambridge, Massachusetts, USA
| | - Noriko Endo
- Biobot Analytics, Inc., Cambridge, Massachusetts, USA
| | | | - Mathilde Poyet
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Katya Moniz
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | | | - Timothy B Erickson
- Division of Medical Toxicology, Department of Emergency Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Harvard Humanitarian Institute, Cambridge, Massachusetts, USA
| | - Peter R Chai
- Division of Medical Toxicology, Department of Emergency Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- The Fenway Institute, Boston, Massachusetts, USA
- The Koch Institute for Integrated Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Janelle Thompson
- Singapore Center for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore
- Asian School of the Environment, Nanyang Technological University, Singapore
- Campus for Research Excellence and Technological Enterprise, Singapore
| | - Eric J Alm
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Singapore-MIT Alliance for Research and Technology, National University of Singapore, Singapore
- Biobot Analytics, Inc., Cambridge, Massachusetts, USA
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Campus for Research Excellence and Technological Enterprise, Singapore
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
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