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Schmiege D, Haselhoff T, Thomas A, Kraiselburd I, Meyer F, Moebus S. Small-scale wastewater-based epidemiology (WBE) for infectious diseases and antibiotic resistance: A scoping review. Int J Hyg Environ Health 2024; 259:114379. [PMID: 38626689 DOI: 10.1016/j.ijheh.2024.114379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 03/25/2024] [Accepted: 04/08/2024] [Indexed: 04/18/2024]
Abstract
Wastewater analysis can serve as a source of public health information. In recent years, wastewater-based epidemiology (WBE) has emerged and proven useful for the detection of infectious diseases. However, insights from the wastewater treatment plant do not allow for the small-scale differentiation within the sewer system that is needed to analyze the target population under study in more detail. Small-scale WBE offers several advantages, but there has been no systematic overview of its application. The aim of this scoping review is to provide a comprehensive overview of the current state of knowledge on small-scale WBE for infectious diseases, including methodological considerations for its application. A systematic database search was conducted, considering only peer-reviewed articles. Data analyses included quantitative summary and qualitative narrative synthesis. Of 2130 articles, we included 278, most of which were published since 2020. The studies analyzed wastewater at the building level (n = 203), especially healthcare (n = 110) and educational facilities (n = 80), and at the neighborhood scale (n = 86). The main analytical parameters were viruses (n = 178), notably SARS-CoV-2 (n = 161), and antibiotic resistance (ABR) biomarkers (n = 99), often analyzed by polymerase chain reaction (PCR), with DNA sequencing techniques being less common. In terms of sampling techniques, active sampling dominated. The frequent lack of detailed information on the specification of selection criteria and the characterization of the small-scale sampling sites was identified as a concern. In conclusion, based on the large number of studies, we identified several methodological considerations and overarching strategic aspects for small-scale WBE. An enabling environment for small-scale WBE requires inter- and transdisciplinary knowledge sharing across countries. Promoting the adoption of small-scale WBE will benefit from a common international conceptualization of the approach, including standardized and internationally accepted terminology. In particular, the development of good WBE practices for different aspects of small-scale WBE is warranted. This includes the establishment of guidelines for a comprehensive characterization of the local sewer system and its sub-sewersheds, and transparent reporting to ensure comparability of small-scale WBE results.
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Affiliation(s)
- Dennis Schmiege
- Institute for Urban Public Health (InUPH), University Hospital Essen, University of Duisburg-Essen, 45130, Essen, Germany.
| | - Timo Haselhoff
- Institute for Urban Public Health (InUPH), University Hospital Essen, University of Duisburg-Essen, 45130, Essen, Germany
| | - Alexander Thomas
- Institute for Artificial Intelligence in Medicine (IKIM), University Hospital Essen, University of Duisburg-Essen, 45131, Essen, Germany
| | - Ivana Kraiselburd
- Institute for Artificial Intelligence in Medicine (IKIM), University Hospital Essen, University of Duisburg-Essen, 45131, Essen, Germany
| | - Folker Meyer
- Institute for Artificial Intelligence in Medicine (IKIM), University Hospital Essen, University of Duisburg-Essen, 45131, Essen, Germany
| | - Susanne Moebus
- Institute for Urban Public Health (InUPH), University Hospital Essen, University of Duisburg-Essen, 45130, Essen, Germany
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2
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Baboun J, Beaudry IS, Castro LM, Gutierrez F, Jara A, Rubio B, Verschae J. Identifying outbreaks in sewer networks: An adaptive sampling scheme under network's uncertainty. Proc Natl Acad Sci U S A 2024; 121:e2316616121. [PMID: 38551839 PMCID: PMC10998606 DOI: 10.1073/pnas.2316616121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 02/21/2024] [Indexed: 04/02/2024] Open
Abstract
Motivated by the implementation of a SARS-Cov-2 sewer surveillance system in Chile during the COVID-19 pandemic, we propose a set of mathematical and algorithmic tools that aim to identify the location of an outbreak under uncertainty in the network structure. Given an upper bound on the number of samples we can take on any given day, our framework allows us to detect an unknown infected node by adaptively sampling different network nodes on different days. Crucially, despite the uncertainty of the network, the method allows univocal detection of the infected node, albeit at an extra cost in time. This framework relies on a specific and well-chosen strategy that defines new nodes to test sequentially, with a heuristic that balances the granularity of the information obtained from the samples. We extensively tested our model in real and synthetic networks, showing that the uncertainty of the underlying graph only incurs a limited increase in the number of iterations, indicating that the methodology is applicable in practice.
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Affiliation(s)
- José Baboun
- Facultad de Matemáticas y Facultad de Ingeniería, Institute for Mathematical and Computational Engineering, Pontificia Universidad Católica de Chile, Santiago7820436, Chile
| | - Isabelle S. Beaudry
- Mount Holyoke College, Department of Mathematics and Statistics, South Hadley, MA01075
| | - Luis M. Castro
- Department of Statistics, and MiDaS - Center for the Discovery of Structures in Complex Data, Pontificia Universidad Católica de Chile, Santiago7820436, Chile
| | - Felipe Gutierrez
- Department of Computer Sciences, and MiDaS - Center for the Discovery of Structures in Complex Data, Pontificia Universidad Católica de Chile, Santiago7820436, Chile
| | - Alejandro Jara
- Department of Statistics, and MiDaS - Center for the Discovery of Structures in Complex Data, Pontificia Universidad Católica de Chile, Santiago7820436, Chile
| | - Benjamin Rubio
- Facultad de Matemáticas y Facultad de Ingeniería, Institute for Mathematical and Computational Engineering, Pontificia Universidad Católica de Chile, Santiago7820436, Chile
| | - José Verschae
- Facultad de Matemáticas y Facultad de Ingeniería, Institute for Mathematical and Computational Engineering, Pontificia Universidad Católica de Chile, Santiago7820436, Chile
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Burnor E, Morin CW, Shirai JH, Zhou NA, Meschke JS. Development of a computational model to inform environmental surveillance sampling plans for Salmonella enterica serovar Typhi in wastewater. PLoS Negl Trop Dis 2024; 18:e0011468. [PMID: 38551999 PMCID: PMC11020695 DOI: 10.1371/journal.pntd.0011468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 04/16/2024] [Accepted: 02/14/2024] [Indexed: 04/18/2024] Open
Abstract
Typhoid fever-an acute febrile disease caused by infection with the bacterium Salmonella enterica serotype Typhi (S. Typhi)-continues to be a leading cause of global morbidity and mortality, particularly in developing countries with limited access to safe drinking water and adequate sanitation. Environmental surveillance, the process of detecting and enumerating disease-causing agents in wastewater, is a useful tool to monitor the circulation of typhoid fever in endemic regions. The design of environmental surveillance sampling plans and the interpretation of sampling results is complicated by a high degree of uncertainty and variability in factors that affect the final measured pathogens in wastewater samples, such as pathogen travel time through a wastewater network, pathogen dilution, decay and degradation, and laboratory processing methods. Computational models can, to an extent, assist in the design of sampling plans and aid in the evaluation of how different contributing factors affect sampling results. This study presents a computational model combining dynamic and probabilistic modeling techniques to estimate-on a spatial and temporal scale-the approximate probability of detecting S. Typhi within a wastewater system. This model may be utilized to inform environmental surveillance sampling plans and may provide useful insight into selecting appropriate sampling locations and times and interpreting results. A simulated applied modeling scenario is presented to demonstrate the model's functionality for aiding an environmental surveillance study in a typhoid-endemic community.
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Affiliation(s)
- Elisabeth Burnor
- Department of Environmental and Occupational Health Sciences, University of Washington School of Public Health, Seattle, Washington, United States of America
| | - Cory W. Morin
- Department of Environmental and Occupational Health Sciences, University of Washington School of Public Health, Seattle, Washington, United States of America
| | - Jeffry H. Shirai
- Department of Environmental and Occupational Health Sciences, University of Washington School of Public Health, Seattle, Washington, United States of America
| | - Nicolette A. Zhou
- Department of Environmental and Occupational Health Sciences, University of Washington School of Public Health, Seattle, Washington, United States of America
| | - John Scott Meschke
- Department of Environmental and Occupational Health Sciences, University of Washington School of Public Health, Seattle, Washington, United States of America
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4
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Schmiege D, Kraiselburd I, Haselhoff T, Thomas A, Doerr A, Gosch J, Schoth J, Teichgräber B, Moebus S, Meyer F. Analyzing community wastewater in sub-sewersheds for the small-scale detection of SARS-CoV-2 variants in a German metropolitan area. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 898:165458. [PMID: 37454854 DOI: 10.1016/j.scitotenv.2023.165458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 06/09/2023] [Accepted: 07/08/2023] [Indexed: 07/18/2023]
Abstract
Wastewater surveillance of SARS-CoV-2 proved useful, including for identifying the local appearance of newly identified virus variants. Previous studies focused on wastewater treatment plants (WWTP) with sewersheds of several hundred thousand people or at single building level, representing only a small number of people. Both approaches may prove inadequate for small-scale intra-urban inferences for early detection of emerging or novel virus variants. Our study aims (i) to analyze SARS-CoV-2 single nucleotide variants (SNVs) in wastewater of sub-sewersheds and WWTP using whole genome sequencing in order to (ii) investigate the potential of small-scale detection of novel known SARS-CoV-2 variants of concern (VOC) within a metropolitan wastewater system. We selected three sub-sewershed sampling sites, based on estimated population- and built environment-related indicators, and the inlet of the receiving WWTP in the Ruhr region, Germany. Untreated wastewater was sampled weekly between October and December 2021, with a total of 22 samples collected. SARS-CoV-2 RNA was analyzed by RT-qPCR and whole genome sequencing. For all samples, genome sequences were obtained, while only 13 samples were positive for RT-qPCR. We identified multiple specific SARS-CoV-2 SNVs in the wastewater samples of the sub-sewersheds and the WWTP. Identified SNVs reflected the dominance of VOC Delta at the time of sampling. Interestingly, we could identify an Omicron-specific SNV in one sub-sewershed. A concurrent wastewater study sampling the same WWTP detected the VOC Omicron one week later. Our observations suggest that the small-scale approach may prove particularly useful for the detection and description of spatially confined emerging or existing virus variants circulating in populations. Future studies applying small-scale sampling strategies taking into account the specific features of the wastewater system will be useful to analyze temporal and spatial variance in more detail.
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Affiliation(s)
- Dennis Schmiege
- Institute for Urban Public Health (InUPH), University Hospital Essen, University of Duisburg-Essen, 45130 Essen, Germany.
| | - Ivana Kraiselburd
- Institute for Artificial Intelligence in Medicine (IKIM), University Hospital Essen, University of Duisburg-Essen, 45131 Essen, Germany
| | - Timo Haselhoff
- Institute for Urban Public Health (InUPH), University Hospital Essen, University of Duisburg-Essen, 45130 Essen, Germany
| | - Alexander Thomas
- Institute for Artificial Intelligence in Medicine (IKIM), University Hospital Essen, University of Duisburg-Essen, 45131 Essen, Germany
| | - Adrian Doerr
- Institute for Artificial Intelligence in Medicine (IKIM), University Hospital Essen, University of Duisburg-Essen, 45131 Essen, Germany
| | - Jule Gosch
- Institute for Artificial Intelligence in Medicine (IKIM), University Hospital Essen, University of Duisburg-Essen, 45131 Essen, Germany
| | - Jens Schoth
- Emschergenossenschaft/Lippeverband, Kronprinzenstraße 24, 45128 Essen, Germany
| | | | - Susanne Moebus
- Institute for Urban Public Health (InUPH), University Hospital Essen, University of Duisburg-Essen, 45130 Essen, Germany
| | - Folker Meyer
- Institute for Artificial Intelligence in Medicine (IKIM), University Hospital Essen, University of Duisburg-Essen, 45131 Essen, Germany
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Wang Y, Liu P, VanTassell J, Hilton SP, Guo L, Sablon O, Wolfe M, Freeman L, Rose W, Holt C, Browning M, Bryan M, Waller L, Teunis PFM, Moe CL. When case reporting becomes untenable: Can sewer networks tell us where COVID-19 transmission occurs? WATER RESEARCH 2023; 229:119516. [PMID: 37379453 PMCID: PMC9763902 DOI: 10.1016/j.watres.2022.119516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Revised: 12/11/2022] [Accepted: 12/18/2022] [Indexed: 06/30/2023]
Abstract
Monitoring SARS-CoV-2 in wastewater is a valuable approach to track COVID-19 transmission. Designing wastewater surveillance (WWS) with representative sampling sites and quantifiable results requires knowledge of the sewerage system and virus fate and transport. We developed a multi-level WWS system to track COVID-19 in Atlanta using an adaptive nested sampling strategy. From March 2021 to April 2022, 868 wastewater samples were collected from influent lines to wastewater treatment facilities and upstream community manholes. Variations in SARS-CoV-2 concentrations in influent line samples preceded similar variations in numbers of reported COVID-19 cases in the corresponding catchment areas. Community sites under nested sampling represented mutually-exclusive catchment areas. Community sites with high SARS-CoV-2 detection rates in wastewater covered high COVID-19 incidence areas, and adaptive sampling enabled identification and tracing of COVID-19 hotspots. This study demonstrates how a well-designed WWS provides actionable information including early warning of surges in cases and identification of disease hotspots.
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Affiliation(s)
- Yuke Wang
- Center for Global Safe Water, Sanitation, and Hygiene, Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
| | - Pengbo Liu
- Center for Global Safe Water, Sanitation, and Hygiene, Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
| | - Jamie VanTassell
- Center for Global Safe Water, Sanitation, and Hygiene, Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
| | - Stephen P Hilton
- Center for Global Safe Water, Sanitation, and Hygiene, Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
| | - Lizheng Guo
- Center for Global Safe Water, Sanitation, and Hygiene, Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
| | - Orlando Sablon
- Center for Global Safe Water, Sanitation, and Hygiene, Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
| | - Marlene Wolfe
- Center for Global Safe Water, Sanitation, and Hygiene, Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
| | - Lorenzo Freeman
- City of Atlanta Department of Watershed Management, Atlanta, GA 30303, USA
| | - Wayne Rose
- City of Atlanta Department of Watershed Management, Atlanta, GA 30303, USA
| | - Carl Holt
- City of Atlanta Department of Watershed Management, Atlanta, GA 30303, USA
| | - Mikita Browning
- City of Atlanta Department of Watershed Management, Atlanta, GA 30303, USA
| | - Michael Bryan
- Georgia Department of Public Health, Atlanta, GA 30303, USA
| | - Lance Waller
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
| | - Peter F M Teunis
- Center for Global Safe Water, Sanitation, and Hygiene, Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
| | - Christine L Moe
- Center for Global Safe Water, Sanitation, and Hygiene, Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
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6
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Corpuz MVA, Buonerba A, Zarra T, Hasan SW, Korshin GV, Belgiorno V, Naddeo V. Advances in virus detection methods for wastewater-based epidemiological applications. CASE STUDIES IN CHEMICAL AND ENVIRONMENTAL ENGINEERING 2022; 6:100238. [PMID: 37520925 PMCID: PMC9339091 DOI: 10.1016/j.cscee.2022.100238] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Revised: 07/20/2022] [Accepted: 07/21/2022] [Indexed: 06/08/2023]
Abstract
Wastewater-based epidemiology (WBE) is a powerful tool that has the potential to reveal the extent of an ongoing disease outbreak or to predict an emerging one. Recent studies have shown that SARS-CoV-2 concentration in wastewater may be correlated with the number of COVID-19 cases in the corresponding population. Most of the recent studies and applications of wastewater-based surveillance of SARS-CoV-2 applied the "gold standard" real-time quantitative reverse transcription-polymerase chain reaction (RT-qPCR) detection method. However, this method also has its limitations. The paper aimed to present recent improvements and applications of the PCR-based methods for SARS-CoV-2 monitoring in wastewater. Furthermore, it aimed to review alternative methods utilized and/or proposed for the detection of the virus in wastewater matrices. From the review, it was found that several studies have investigated the use of reverse-transcription digital polymerase reaction (RT-dPCR), which was generally shown to have a lower limit of detection (LOD) over the RT-qPCR. Aside from this, non-PCR-based and non-RNA based methods have also been explored for the detection of SARS-CoV-2 in wastewater, with detailed attention given to the detection of SARS-CoV-2 proteins. The potential methods for protein detection include mass spectrometry, the use of immunosensors, and nanotechnological applications. In addition, the review of recent studies also revealed two types of emerging methods related to the detection of SARS-CoV-2 in wastewater: i) capsid-integrity assays to infer about the infectivity of SARS-CoV-2 present in wastewater, and ii) alternative methods for detection of SARS-CoV-2 variants of concern (VOCs) in wastewater. The recent studies on proposed methods of SARS-CoV-2 detection in wastewater have considered improving this approach in one or more of the following aspects: rapidity, simplicity, cost, sensitivity, and specificity. However, further studies are needed in order to realize the full application of these methods for WBE in the field.
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Affiliation(s)
- Mary Vermi Aizza Corpuz
- Environmental Engineering Program, National Graduate School of Engineering, University of the Philippines, 1101 Diliman, Quezon City, Philippines
| | - Antonio Buonerba
- Department of Chemistry and Biology "Adolfo Zambelli", University of Salerno, Via Giovanni Paolo II-132, 84084, Fisciano, Italy
| | - Tiziano Zarra
- Sanitary Environmental Engineering Division (SEED), Department of Civil Engineering, University of Salerno, Via Giovanni Paolo II-132, 84084, Fisciano, Italy
| | - Shadi W Hasan
- Department of Chemical Engineering, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates
| | - Gregory V Korshin
- Department of Civil and Environmental Engineering, University of Washington, Box 352700, Seattle, WA, 98105-2700, United States
| | - Vincenzo Belgiorno
- Sanitary Environmental Engineering Division (SEED), Department of Civil Engineering, University of Salerno, Via Giovanni Paolo II-132, 84084, Fisciano, Italy
| | - Vincenzo Naddeo
- Sanitary Environmental Engineering Division (SEED), Department of Civil Engineering, University of Salerno, Via Giovanni Paolo II-132, 84084, Fisciano, Italy
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Maidana-Kulesza MN, Poma HR, Sanguino-Jorquera DG, Reyes SI, Del Milagro Said-Adamo M, Mainardi-Remis JM, Gutiérrez-Cacciabue D, Cristóbal HA, Cruz MC, Aparicio González M, Rajal VB. Tracking SARS-CoV-2 in rivers as a tool for epidemiological surveillance. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022. [PMID: 35908692 DOI: 10.1101/2021.06.17.21259122] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
The aim of this work was to evaluate if rivers could be used for SARS-CoV-2 surveillance. Five sampling points from three rivers (AR-1 and AR-2 in Arenales River, MR-1 and MR-2 in Mojotoro River, and CR in La Caldera River) from Salta (Argentina), two of them receiving discharges from wastewater plants (WWTP), were monitored from July to December 2020. Fifteen water samples from each point (75 in total) were collected and characterized physico-chemically and microbiologically and SARS-CoV-2 was quantified by RT-qPCR. Also, two targets linked to human contributions, human polyomavirus (HPyV) and RNase P, were quantified and used to normalize SARS-CoV-2 concentration, which was compared to reported COVID-19 cases. Statistical analyses allowed us to verify the correlation between SARS-CoV-2 and the concentration of fecal indicator bacteria (FIB), as well as to find similarities and differences between sampling points. La Caldera River showed the best water quality; FIBs were within acceptable limits for recreational activities. Mojotoro River's water quality was not affected by the northern WWTP of the city. Instead, Arenales River presented the poorest water quality; at AR-2 was negatively affected by the discharges of the southern WWTP, which contributed to significant increase of fecal contamination. SARS-CoV-2 was found in about half of samples in low concentrations in La Caldera and Mojotoro Rivers, while it was high and persistent in Arenales River. No human tracers were detected in CR, only HPyV was found in MR-1, MR-2 and AR-1, and both were quantified in AR-2. The experimental and normalized viral concentrations strongly correlated with reported COVID-19 cases; thus, Arenales River at AR-2 reflected the epidemiological situation of the city. This is the first study showing the dynamic of SARS-CoV-2 concentration in an urban river highly impacted by wastewater and proved that can be used for SARS-CoV-2 surveillance to support health authorities.
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Affiliation(s)
- María Noel Maidana-Kulesza
- Laboratorio de Aguas y Suelos, Instituto de Investigaciones para la Industria Química (INIQUI), Universidad Nacional de Salta (UNSa) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Av. Bolivia 5150, Salta 4400, Argentina
| | - Hugo Ramiro Poma
- Laboratorio de Aguas y Suelos, Instituto de Investigaciones para la Industria Química (INIQUI), Universidad Nacional de Salta (UNSa) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Av. Bolivia 5150, Salta 4400, Argentina
| | - Diego Gastón Sanguino-Jorquera
- Laboratorio de Aguas y Suelos, Instituto de Investigaciones para la Industria Química (INIQUI), Universidad Nacional de Salta (UNSa) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Av. Bolivia 5150, Salta 4400, Argentina
| | - Sarita Isabel Reyes
- Laboratorio de Aguas y Suelos, Instituto de Investigaciones para la Industria Química (INIQUI), Universidad Nacional de Salta (UNSa) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Av. Bolivia 5150, Salta 4400, Argentina
| | - María Del Milagro Said-Adamo
- Laboratorio de Aguas y Suelos, Instituto de Investigaciones para la Industria Química (INIQUI), Universidad Nacional de Salta (UNSa) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Av. Bolivia 5150, Salta 4400, Argentina; Facultad de Ciencias Naturales, UNSa, Av. Bolivia 5150, Salta 4400, Argentina
| | - Juan Martín Mainardi-Remis
- Laboratorio de Aguas y Suelos, Instituto de Investigaciones para la Industria Química (INIQUI), Universidad Nacional de Salta (UNSa) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Av. Bolivia 5150, Salta 4400, Argentina; Facultad de Ingeniería, UNSa, Av. Bolivia 5150, Salta 4400, Argentina
| | - Dolores Gutiérrez-Cacciabue
- Laboratorio de Aguas y Suelos, Instituto de Investigaciones para la Industria Química (INIQUI), Universidad Nacional de Salta (UNSa) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Av. Bolivia 5150, Salta 4400, Argentina; Facultad de Ingeniería, UNSa, Av. Bolivia 5150, Salta 4400, Argentina
| | - Héctor Antonio Cristóbal
- Laboratorio de Aguas y Suelos, Instituto de Investigaciones para la Industria Química (INIQUI), Universidad Nacional de Salta (UNSa) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Av. Bolivia 5150, Salta 4400, Argentina; Facultad de Ciencias Naturales, UNSa, Av. Bolivia 5150, Salta 4400, Argentina
| | - Mercedes Cecilia Cruz
- Laboratorio de Aguas y Suelos, Instituto de Investigaciones para la Industria Química (INIQUI), Universidad Nacional de Salta (UNSa) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Av. Bolivia 5150, Salta 4400, Argentina
| | - Mónica Aparicio González
- Laboratorio de Aguas y Suelos, Instituto de Investigaciones para la Industria Química (INIQUI), Universidad Nacional de Salta (UNSa) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Av. Bolivia 5150, Salta 4400, Argentina
| | - Verónica Beatriz Rajal
- Laboratorio de Aguas y Suelos, Instituto de Investigaciones para la Industria Química (INIQUI), Universidad Nacional de Salta (UNSa) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Av. Bolivia 5150, Salta 4400, Argentina; Facultad de Ingeniería, UNSa, Av. Bolivia 5150, Salta 4400, Argentina; Singapore Centre for Environmental Life Science Engineering (SCELSE), Nanyang Technological University, Singapore.
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8
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Maidana-Kulesza MN, Poma HR, Sanguino-Jorquera DG, Reyes SI, Del Milagro Said-Adamo M, Mainardi-Remis JM, Gutiérrez-Cacciabue D, Cristóbal HA, Cruz MC, Aparicio González M, Rajal VB. Tracking SARS-CoV-2 in rivers as a tool for epidemiological surveillance. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 848:157707. [PMID: 35908692 PMCID: PMC9334864 DOI: 10.1016/j.scitotenv.2022.157707] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Revised: 07/04/2022] [Accepted: 07/26/2022] [Indexed: 05/22/2023]
Abstract
The aim of this work was to evaluate if rivers could be used for SARS-CoV-2 surveillance. Five sampling points from three rivers (AR-1 and AR-2 in Arenales River, MR-1 and MR-2 in Mojotoro River, and CR in La Caldera River) from Salta (Argentina), two of them receiving discharges from wastewater plants (WWTP), were monitored from July to December 2020. Fifteen water samples from each point (75 in total) were collected and characterized physico-chemically and microbiologically and SARS-CoV-2 was quantified by RT-qPCR. Also, two targets linked to human contributions, human polyomavirus (HPyV) and RNase P, were quantified and used to normalize SARS-CoV-2 concentration, which was compared to reported COVID-19 cases. Statistical analyses allowed us to verify the correlation between SARS-CoV-2 and the concentration of fecal indicator bacteria (FIB), as well as to find similarities and differences between sampling points. La Caldera River showed the best water quality; FIBs were within acceptable limits for recreational activities. Mojotoro River's water quality was not affected by the northern WWTP of the city. Instead, Arenales River presented the poorest water quality; at AR-2 was negatively affected by the discharges of the southern WWTP, which contributed to significant increase of fecal contamination. SARS-CoV-2 was found in about half of samples in low concentrations in La Caldera and Mojotoro Rivers, while it was high and persistent in Arenales River. No human tracers were detected in CR, only HPyV was found in MR-1, MR-2 and AR-1, and both were quantified in AR-2. The experimental and normalized viral concentrations strongly correlated with reported COVID-19 cases; thus, Arenales River at AR-2 reflected the epidemiological situation of the city. This is the first study showing the dynamic of SARS-CoV-2 concentration in an urban river highly impacted by wastewater and proved that can be used for SARS-CoV-2 surveillance to support health authorities.
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Affiliation(s)
- María Noel Maidana-Kulesza
- Laboratorio de Aguas y Suelos, Instituto de Investigaciones para la Industria Química (INIQUI), Universidad Nacional de Salta (UNSa) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Av. Bolivia 5150, Salta 4400, Argentina
| | - Hugo Ramiro Poma
- Laboratorio de Aguas y Suelos, Instituto de Investigaciones para la Industria Química (INIQUI), Universidad Nacional de Salta (UNSa) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Av. Bolivia 5150, Salta 4400, Argentina
| | - Diego Gastón Sanguino-Jorquera
- Laboratorio de Aguas y Suelos, Instituto de Investigaciones para la Industria Química (INIQUI), Universidad Nacional de Salta (UNSa) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Av. Bolivia 5150, Salta 4400, Argentina
| | - Sarita Isabel Reyes
- Laboratorio de Aguas y Suelos, Instituto de Investigaciones para la Industria Química (INIQUI), Universidad Nacional de Salta (UNSa) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Av. Bolivia 5150, Salta 4400, Argentina
| | - María Del Milagro Said-Adamo
- Laboratorio de Aguas y Suelos, Instituto de Investigaciones para la Industria Química (INIQUI), Universidad Nacional de Salta (UNSa) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Av. Bolivia 5150, Salta 4400, Argentina; Facultad de Ciencias Naturales, UNSa, Av. Bolivia 5150, Salta 4400, Argentina
| | - Juan Martín Mainardi-Remis
- Laboratorio de Aguas y Suelos, Instituto de Investigaciones para la Industria Química (INIQUI), Universidad Nacional de Salta (UNSa) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Av. Bolivia 5150, Salta 4400, Argentina; Facultad de Ingeniería, UNSa, Av. Bolivia 5150, Salta 4400, Argentina
| | - Dolores Gutiérrez-Cacciabue
- Laboratorio de Aguas y Suelos, Instituto de Investigaciones para la Industria Química (INIQUI), Universidad Nacional de Salta (UNSa) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Av. Bolivia 5150, Salta 4400, Argentina; Facultad de Ingeniería, UNSa, Av. Bolivia 5150, Salta 4400, Argentina
| | - Héctor Antonio Cristóbal
- Laboratorio de Aguas y Suelos, Instituto de Investigaciones para la Industria Química (INIQUI), Universidad Nacional de Salta (UNSa) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Av. Bolivia 5150, Salta 4400, Argentina; Facultad de Ciencias Naturales, UNSa, Av. Bolivia 5150, Salta 4400, Argentina
| | - Mercedes Cecilia Cruz
- Laboratorio de Aguas y Suelos, Instituto de Investigaciones para la Industria Química (INIQUI), Universidad Nacional de Salta (UNSa) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Av. Bolivia 5150, Salta 4400, Argentina
| | - Mónica Aparicio González
- Laboratorio de Aguas y Suelos, Instituto de Investigaciones para la Industria Química (INIQUI), Universidad Nacional de Salta (UNSa) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Av. Bolivia 5150, Salta 4400, Argentina
| | - Verónica Beatriz Rajal
- Laboratorio de Aguas y Suelos, Instituto de Investigaciones para la Industria Química (INIQUI), Universidad Nacional de Salta (UNSa) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Av. Bolivia 5150, Salta 4400, Argentina; Facultad de Ingeniería, UNSa, Av. Bolivia 5150, Salta 4400, Argentina; Singapore Centre for Environmental Life Science Engineering (SCELSE), Nanyang Technological University, Singapore.
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9
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Kim K, Ban MJ, Kim S, Park MH, Stenstrom MK, Kang JH. Optimal allocation and operation of sewer monitoring sites for wastewater-based disease surveillance: A methodological proposal. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022; 320:115806. [PMID: 35926387 PMCID: PMC9342910 DOI: 10.1016/j.jenvman.2022.115806] [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: 05/17/2022] [Revised: 07/15/2022] [Accepted: 07/18/2022] [Indexed: 06/15/2023]
Abstract
Wastewater-based epidemiology (WBE) is drawing increasing attention as a promising tool for an early warning of emerging infectious diseases such as COVID-19. This study demonstrated the utility of a spatial bisection method (SBM) and a global optimization algorithm (i.e., genetic algorithm, GA), to support better designing and operating a WBE program for disease surveillance and source identification. The performances of SBM and GA were compared in determining the optimal locations of sewer monitoring manholes to minimize the difference among the effective spatial monitoring scales of the selected manholes. While GA was more flexible in determining the spatial resolution of the monitoring areas, SBM allows stepwise selection of optimal sampling manholes with equiareal subcatchments and lowers computational cost. Upon detecting disease outbreaks at a regular sewer monitoring site, additional manholes within the catchment can be selected and monitored to identify source areas with a required spatial resolution. SBM offered an efficient method for rapidly searching for the optimal locations of additional sampling manholes to identify the source areas. This study provides strategic and technical elements of WBE including sampling site selection with required spatial resolution and a source identification method.
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Affiliation(s)
- Keugtae Kim
- Department of Environmental and Energy Engineering, The University of Suwon, 17 Wauan-gil, Bongdam-eup, Hwaseong-si, Gyeonggi-do, 18323, Republic of Korea
| | - Min Jeong Ban
- Department of Civil and Environmental Engineering, Dongguk University-Seoul, 30, Pildong-ro 1gil, Jung-gu, Seoul, 04620, Republic of Korea
| | - Sungpyo Kim
- Department of Environmental Engineering, Korea University-Sejong, 2 511, Sejong-ro, Sejong City, 30019, Republic of Korea
| | - Mi-Hyun Park
- Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Michael K Stenstrom
- Department of Civil and Environmental Engineering, University of California, Los Angeles, CA, 90096, USA
| | - Joo-Hyon Kang
- Department of Civil and Environmental Engineering, Dongguk University-Seoul, 30, Pildong-ro 1gil, Jung-gu, Seoul, 04620, Republic of Korea.
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10
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Ghaffarzadegan N. Effect of mandating vaccination on COVID-19 cases in colleges and universities. Int J Infect Dis 2022; 123:41-45. [PMID: 35985570 PMCID: PMC9381420 DOI: 10.1016/j.ijid.2022.08.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 06/14/2022] [Accepted: 08/07/2022] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND With the introduction of COVID-19 vaccines, many colleges and universities decided to mandate vaccination for all students and employees. The objective of this paper is to empirically investigate the effect of the mandate policy on Fall 2021 COVID-19 cases in institutions of higher education. METHOD We construct a unique dataset of a sample of 94 colleges and universities in the east and southeast regions of the United States, 41 of which required vaccination prior to Fall 2021. A difference-in-differences analysis is conducted, considering vaccine requirement as a policy implemented only in a sub-group of these institutions. We control for several factors, including state-level case per capita and student population. RESULTS Our analysis shows that mandatory vaccination substantially decreased cases in institutions of higher education by 1,473 cases per 100,000 student population (95 CI: 132, 2813). CONCLUSIONS The results suggest that a COVID-19 vaccine requirement is an effective policy in decreasing cases in such institutions, leading to a safer educational experience.
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11
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Mac Mahon J, Criado Monleon AJ, Gill LW, O'Sullivan JJ, Meijer WG. Wastewater-based epidemiology (WBE) for SARS-CoV-2 - A review focussing on the significance of the sewer network using a Dublin city catchment case study. WATER SCIENCE AND TECHNOLOGY : A JOURNAL OF THE INTERNATIONAL ASSOCIATION ON WATER POLLUTION RESEARCH 2022; 86:1402-1425. [PMID: 36178814 DOI: 10.2166/wst.2022.278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Wastewater-based epidemiology (WBE) has been employed by many countries globally since the beginning of the COVID-19 pandemic in order to assess the benefits of this surveillance tool in the context of informing public health measures. WBE has been successfully employed to detect SARS-CoV-2 at wastewater treatment plants for community-wide surveillance, as well as in smaller catchments and institutions for targeted surveillance of COVID-19. In addition, WBE has been successfully used to detect new variants, identify areas of high infection levels, as well as to detect new infection outbreaks. However, due to to the large number of inherent uncertainties in the WBE process, including the inherent intricacies of the sewer network, decay of the virus en route to a monitoring point, levels of recovery from sampling and quantification methods, levels of faecal shedding among the infected population, as well as population normalisation methods, the usefulness of wastewater samples as a means of accurately quantifying SARS-CoV-2 infection levels among a population remains less clear. The current WBE programmes in place globally will help to identify new areas of research aimed at reducing the levels of uncertainty in the WBE process, thus improving WBE as a public health monitoring tool for future pandemics. In the meantime, such programmes can provide valuable comparisons to clinical testing data and other public health metrics, as well being an effective early warning tool for new variants and new infection outbreaks. This review includes a case study of sampled wastewater from the sewer network in Dublin, Ireland, during a peak infection period of COVID-19 in the city, which evaluates the different uncertainties in the WBE process.
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Affiliation(s)
| | | | | | - John J O'Sullivan
- UCD School of Civil Engineering, UCD Dooge Centre for Water Resources Research and UCD Earth Institute, University College Dublin
| | - Wim G Meijer
- UCD School of Biomolecular & Biomedical Science, UCD Earth Institute and UCD Conway Institute, University College Dublin
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12
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Brumfield KD, Leddy M, Usmani M, Cotruvo JA, Tien CT, Dorsey S, Graubics K, Fanelli B, Zhou I, Registe N, Dadlani M, Wimalarante M, Jinasena D, Abayagunawardena R, Withanachchi C, Huq A, Jutla A, Colwell RR. Microbiome Analysis for Wastewater Surveillance during COVID-19. mBio 2022; 13:e0059122. [PMID: 35726918 PMCID: PMC9426581 DOI: 10.1128/mbio.00591-22] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 05/16/2022] [Indexed: 12/18/2022] Open
Abstract
Wastewater surveillance (WS), when coupled with advanced molecular techniques, offers near real-time monitoring of community-wide transmission of SARS-CoV-2 and allows assessing and mitigating COVID-19 outbreaks, by evaluating the total microbial assemblage in a community. Composite wastewater samples (24 h) were collected weekly from a manhole between December 2020 and November 2021 in Maryland, USA. RT-qPCR results showed concentrations of SARS-CoV-2 RNA recovered from wastewater samples reflected incidence of COVID-19 cases. When a drastic increase in COVID-19 was detected in February 2021, samples were selected for microbiome analysis (DNA metagenomics, RNA metatranscriptomics, and targeted SARS-CoV-2 sequencing). Targeted SARS-CoV-2 sequencing allowed for detection of important genetic mutations, such as spike: K417N, D614G, P681H, T716I, S982A, and D1118H, commonly associated with increased cell entry and reinfection. Microbiome analysis (DNA and RNA) provided important insight with respect to human health-related factors, including detection of pathogens and their virulence/antibiotic resistance genes. Specific microbial species comprising the wastewater microbiome correlated with incidence of SARS-CoV-2 RNA, suggesting potential association with SARS-CoV-2 infection. Climatic conditions, namely, temperature, were related to incidence of COVID-19 and detection of SARS-CoV-2 in wastewater, having been monitored as part of an environmental risk score assessment carried out in this study. In summary, the wastewater microbiome provides useful public health information, and hence, a valuable tool to proactively detect and characterize pathogenic agents circulating in a community. In effect, metagenomics of wastewater can serve as an early warning system for communicable diseases, by providing a larger source of information for health departments and public officials. IMPORTANCE Traditionally, testing for COVID-19 is done by detecting SARS-CoV-2 in samples collected from nasal swabs and/or saliva. However, SARS-CoV-2 can also be detected in feces of infected individuals. Therefore, wastewater samples can be used to test all individuals of a community contributing to the sewage collection system, i.e., the infrastructure, such as gravity pipes, manholes, tanks, lift stations, control structures, and force mains, that collects used water from residential and commercial sources and conveys the flow to a wastewater treatment plant. Here, we profile community wastewater collected from a manhole, detect presence of SARS-CoV-2, identify genetic mutations of SARS-CoV-2, and perform COVID-19 risk score assessment of the study area. Using metagenomics analysis, we also detect other microorganisms (bacteria, fungi, protists, and viruses) present in the samples. Results show that by analyzing all microorganisms present in wastewater, pathogens circulating in a community can provide an early warning for contagious diseases.
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Affiliation(s)
- Kyle D. Brumfield
- Maryland Pathogen Research Institute, University of Maryland, College Park, Maryland, USA
- University of Maryland Institute for Advanced Computer Studies, University of Maryland, College Park, Maryland, USA
| | - Menu Leddy
- Essential Environmental and Engineering Systems, Huntington Beach, California, USA
| | - Moiz Usmani
- Geohealth and Hydrology Laboratory, Department of Environmental Engineering Sciences, University of Florida, Gainesville, Florida, USA
| | | | | | - Suzanne Dorsey
- Maryland Department of Environment, Baltimore, Maryland, USA
| | | | | | - Isaac Zhou
- CosmosID Inc., Germantown, Maryland, USA
| | | | | | | | | | | | | | - Anwar Huq
- Maryland Pathogen Research Institute, University of Maryland, College Park, Maryland, USA
| | - Antarpreet Jutla
- Geohealth and Hydrology Laboratory, Department of Environmental Engineering Sciences, University of Florida, Gainesville, Florida, USA
| | - Rita R. Colwell
- Maryland Pathogen Research Institute, University of Maryland, College Park, Maryland, USA
- University of Maryland Institute for Advanced Computer Studies, University of Maryland, College Park, Maryland, USA
- CosmosID Inc., Germantown, Maryland, USA
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13
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Kapoor V, Al-Duroobi H, Phan DC, Palekar RS, Blount B, Rambhia KJ. Wastewater surveillance for SARS-CoV-2 to support return to campus: Methodological considerations and data interpretation. CURRENT OPINION IN ENVIRONMENTAL SCIENCE & HEALTH 2022; 27:100362. [PMID: 35402756 PMCID: PMC8975751 DOI: 10.1016/j.coesh.2022.100362] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
Abstract
The COVID-19 pandemic has been challenging for various institutions such as school systems due to widespread closures. As schools re-open their campuses to in-person education, there is a need for frequent screening and monitoring of the virus to ensure the safety of students and staff and to limit risk to the surrounding community. Wastewater surveillance (WWS) of SARS-CoV-2 is a rapid and economical approach to determine the extent of COVID-19 in the community. The focus of this review is on the emergence of WWS as a tool for safe return to school campuses, taking into account methodological considerations such as site selection, sample collection and processing, SARS-CoV-2 quantification, and data interpretation. Recently published studies on the implementation of COVID-19 WWS on school and college campuses were reviewed. While there are several logistical and technical challenges, WWS can be used to inform decision-making at the school campus and/or building level.
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Affiliation(s)
- Vikram Kapoor
- School of Civil & Environmental Engineering, and Construction Management, University of Texas at San Antonio, San Antonio, TX 78249, USA
| | - Haya Al-Duroobi
- School of Civil & Environmental Engineering, and Construction Management, University of Texas at San Antonio, San Antonio, TX 78249, USA
| | - Duc C Phan
- School of Civil & Environmental Engineering, and Construction Management, University of Texas at San Antonio, San Antonio, TX 78249, USA
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14
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Chau KK, Barker L, Budgell EP, Vihta KD, Sims N, Kasprzyk-Hordern B, Harriss E, Crook DW, Read DS, Walker AS, Stoesser N. Systematic review of wastewater surveillance of antimicrobial resistance in human populations. ENVIRONMENT INTERNATIONAL 2022; 162:107171. [PMID: 35290866 PMCID: PMC8960996 DOI: 10.1016/j.envint.2022.107171] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 01/23/2022] [Accepted: 02/28/2022] [Indexed: 05/05/2023]
Abstract
OBJECTIVES We systematically reviewed studies using wastewater for AMR surveillance in human populations, to determine: (i) evidence of concordance between wastewater-human AMR prevalence estimates, and (ii) methodological approaches which optimised identifying such an association, and which could be recommended as standard. We used Lin's concordance correlation coefficient (CCC) to quantify concordance between AMR prevalence estimates in wastewater and human compartments (where CCC = 1 reflects perfect concordance), and logistic regression to identify study features (e.g. sampling methods) associated with high agreement studies (defined as >70% of within-study wastewater-human AMR prevalence comparisons within ±10%). RESULTS Of 8,867 records and 441 full-text methods reviewed, 33 studies were included. AMR prevalence data was extractable from 24 studies conducting phenotypic-only (n = 7), genotypic-only (n = 1) or combined (n = 16) AMR detection. Overall concordance of wastewater-human AMR prevalence estimates was reasonably high for both phenotypic (CCC = 0.85 [95% CI 0.8-0.89]) and genotypic approaches (CCC = 0.88 (95% CI 0.84-0.9)) despite diverse study designs, bacterial species investigated and phenotypic/genotypic targets. No significant relationships between methodological approaches and high agreement studies were identified using logistic regression; however, this was limited by inconsistent reporting of study features, significant heterogeneity in approaches and limited sample size. Based on a secondary, descriptive synthesis, studies conducting composite sampling of wastewater influent, longitudinal sampling >12 months, and time-/location-matched sampling of wastewater and human compartments generally had higher agreement. CONCLUSION Wastewater-based surveillance of AMR appears promising, with high overall concordance between wastewater and human AMR prevalence estimates in studies irrespective of heterogenous approaches. However, our review suggests future work would benefit from: time-/location-matched sampling of wastewater and human populations, composite sampling of influent, and sampling >12 months for longitudinal studies. Further research and clear and consistent reporting of study methods is required to identify optimal practice.
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Affiliation(s)
- K K Chau
- Nuffield Department of Medicine, John Radcliffe Hospital, Oxford OX3 9DU, United Kingdom.
| | - L Barker
- Nuffield Department of Medicine, John Radcliffe Hospital, Oxford OX3 9DU, United Kingdom.
| | - E P Budgell
- Nuffield Department of Medicine, John Radcliffe Hospital, Oxford OX3 9DU, United Kingdom.
| | - K D Vihta
- Nuffield Department of Medicine, John Radcliffe Hospital, Oxford OX3 9DU, United Kingdom.
| | - N Sims
- Department of Chemistry, Faculty of Science, University of Bath, Bath BA2 7AY, United Kingdom.
| | - B Kasprzyk-Hordern
- Department of Chemistry, Faculty of Science, University of Bath, Bath BA2 7AY, United Kingdom.
| | - E Harriss
- Bodleian Healthcare Libraries, University of Oxford, Oxford OX3 9DU, United Kingdom.
| | - D W Crook
- Nuffield Department of Medicine, John Radcliffe Hospital, Oxford OX3 9DU, United Kingdom; Department of Microbiology/Infectious Diseases, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford OX3 9DU, United Kingdom.
| | - D S Read
- UK Centre for Ecology & Hydrology, Wallingford OX10 8BB, United Kingdom.
| | - A S Walker
- Nuffield Department of Medicine, John Radcliffe Hospital, Oxford OX3 9DU, United Kingdom; NIHR Oxford Biomedical Research Centre, Oxford OX4 2PG, United Kingdom.
| | - N Stoesser
- Nuffield Department of Medicine, John Radcliffe Hospital, Oxford OX3 9DU, United Kingdom; Department of Microbiology/Infectious Diseases, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford OX3 9DU, United Kingdom.
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15
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Domokos E, Sebestyén V, Somogyi V, Trájer AJ, Gerencsér-Berta R, Oláhné Horváth B, Tóth EG, Jakab F, Kemenesi G, Abonyi J. Identification of sampling points for the detection of SARS-CoV-2 in the sewage system. SUSTAINABLE CITIES AND SOCIETY 2022; 76:103422. [PMID: 34729296 PMCID: PMC8554011 DOI: 10.1016/j.scs.2021.103422] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 09/10/2021] [Accepted: 09/30/2021] [Indexed: 06/13/2023]
Abstract
A suitable tool for monitoring the spread of SARS-CoV-2 is to identify potential sampling points in the wastewater collection system that can be used to monitor the distribution of COVID-19 disease affected clusters within a city. The applicability of the developed methodology is presented through the description of the 72,837 population equivalent wastewater collection system of the city of Nagykanizsa, Hungary and the results of the analytical and epidemiological measurements of the wastewater samples. The wastewater sampling was conducted during the 3rd wave of the COVID-19 epidemic. It was found that the overlap between the road system and the wastewater network is high, it is 82 %. It was showed that the proposed methodological approach, using the tools of network science, determines confidently the zones of the wastewater collection system and provides the ideal monitoring points in order to provide the best sampling resolution in urban areas. The strength of the presented approach is that it estimates the network based on publicly available information. It was concluded that the number of zones or sampling points can be chosen based on relevant epidemiological intervention and mitigation strategies. The algorithm allows for continuous effective monitoring of the population infected by SARS-CoV-2 in small-sized cities.
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Affiliation(s)
- Endre Domokos
- Sustainability Solutions Research Lab, University of Pannonia, Egyetem str. 10, Veszprém H-8200, Hungary
| | - Viktor Sebestyén
- Sustainability Solutions Research Lab, University of Pannonia, Egyetem str. 10, Veszprém H-8200, Hungary
- MTA-PE "Lendület" Complex Systems Monitoring Research Group, University of Pannonia, Egyetem str. 10, Veszprém H-8200, Hungary
| | - Viola Somogyi
- Sustainability Solutions Research Lab, University of Pannonia, Egyetem str. 10, Veszprém H-8200, Hungary
| | - Attila János Trájer
- Sustainability Solutions Research Lab, University of Pannonia, Egyetem str. 10, Veszprém H-8200, Hungary
| | - Renáta Gerencsér-Berta
- Soós Ernö Research and Development Center, University of Pannonia, Zrínyi M Str. 18, Nagykanizsa H-8800, Hungary
| | - Borbála Oláhné Horváth
- Soós Ernö Research and Development Center, University of Pannonia, Zrínyi M Str. 18, Nagykanizsa H-8800, Hungary
| | - Endre Gábor Tóth
- National Laboratory of Virology, János Szentágothai Research Centre, University of Pécs, Pécs 7624, Hungary
| | - Ferenc Jakab
- National Laboratory of Virology, János Szentágothai Research Centre, University of Pécs, Pécs 7624, Hungary
| | - Gábor Kemenesi
- National Laboratory of Virology, János Szentágothai Research Centre, University of Pécs, Pécs 7624, Hungary
| | - János Abonyi
- MTA-PE "Lendület" Complex Systems Monitoring Research Group, University of Pannonia, Egyetem str. 10, Veszprém H-8200, Hungary
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16
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Calle E, Martínez D, Brugués-I-Pujolràs R, Farreras M, Saló-Grau J, Pueyo-Ros J, Corominas L. Optimal selection of monitoring sites in cities for SARS-CoV-2 surveillance in sewage networks. ENVIRONMENT INTERNATIONAL 2021; 157:106768. [PMID: 34325220 PMCID: PMC8430229 DOI: 10.1016/j.envint.2021.106768] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 06/25/2021] [Accepted: 07/06/2021] [Indexed: 05/16/2023]
Abstract
Selecting sampling points to monitor traces of SARS-CoV-2 in sewage at the intra-urban scale is no trivial task given the complexity of the networks and the multiple technical, economic and socio-environmental constraints involved. This paper proposes two algorithms for the automatic selection of sampling locations in sewage networks. The first algorithm, is for the optimal selection of a predefined number of sampling locations ensuring maximum coverage of inhabitants and minimum overlapping amongst selected sites (static approach). The second is for establishing a strategy of iterations of sample&analysis to identify patient zero and hot spots of COVID-19 infected inhabitants in cities (dynamic approach). The algorithms are based on graph-theory and are coupled to a greedy optimization algorithm. The usefulness of the algorithms is illustrated in the case study of Girona (NE Iberian Peninsula, 148,504 inhabitants). The results show that the algorithms are able to automatically propose locations for a given number of stations. In the case of Girona, always covering more than 60% of the manholes and with less than 3% of them overlapping amongst stations. Deploying 5, 6 or 7 stations results in more than 80% coverage in manholes and more than 85% of the inhabitants. For the dynamic sensor placement, we demonstrate that assigning infection probabilities to each manhole as a function of the number of inhabitants connected reduces the number of iterations required to detect the zero patient and the hot spot areas.
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Affiliation(s)
- Eusebi Calle
- Institute of Informatics and Applications, Universitat de Girona, Girona, Spain.
| | - David Martínez
- Catalan Institute for Water Research, Emili Grahit 101, 17003 Girona, Spain.
| | | | - Miquel Farreras
- Institute of Informatics and Applications, Universitat de Girona, Girona, Spain.
| | - Joan Saló-Grau
- Institute of Informatics and Applications, Universitat de Girona, Girona, Spain.
| | - Josep Pueyo-Ros
- Catalan Institute for Water Research, Emili Grahit 101, 17003 Girona, Spain.
| | - Lluís Corominas
- Catalan Institute for Water Research, Emili Grahit 101, 17003 Girona, Spain.
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17
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Epidemiological evaluation of sewage surveillance as a tool to detect the presence of COVID-19 cases in a low case load setting. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 786:147469. [PMCID: PMC8087577 DOI: 10.1016/j.scitotenv.2021.147469] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 04/26/2021] [Accepted: 04/27/2021] [Indexed: 05/18/2023]
Abstract
In low prevalence settings the development of sensitive and specific quantitative Reverse Transcription Polymerase Chain Reaction (qRT-PCR) tests to detect SARS-CoV-2 (the virus causing COVID-19) in sewage presents the possibility of using sewage sampling as a diagnostic test for the presence of infected people in the catchment of the sampled sewer. However, the usefulness of such surveillance has not been quantified. In this study in the Australian state of Victoria between August and October 2020 the location of each known SARS-CoV-2-infected person was determined on each day from two days before onset to 55 days after, in 46 metropolitan and rural sewer catchments sampled weekly – a total of 71 positive and 275 negative samples, and 354,155 person-days of location data. These were categorised by time since onset and distance from the sampling site. The odds of detection in sewage were between 5 and 20 times higher where known cases were present, with less effect of distance than time since onset. Using positive qRT-PCR in a sewage sample as a diagnostic test not just for viral RNA in the sample, but for the presence of known infected people in the catchment on the same day, the sensitivity was moderate (31% to 76%) and the specificity high (87% to 94%). The odds of detection were increased with increased numbers of known infected people but decreased with increased distance and time since onset. The probability of detection of the viral subgenome in sewage samples was about 10% when one known infected person was present, and this increased with higher numbers of known infected people and greater proximity to the sampling site. Sewage surveillance can be used to detect people infected with SARS-CoV-2 in the catchment, directing a search for infectious clinical cases and other public health actions. However, detection at least eight weeks after onset may be due to existing cases rather than new ones, and, although not zero, the probability of detecting a single case is low.
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Lee BE, Sikora C, Faulder D, Risling E, Little LA, Qiu Y, Gao T, Bulat R, Craik S, Hrudey SE, Ohinmaa A, Estabrooks CA, Gingras AC, Charlton C, Kim J, Wood H, Robinson A, Kanji JN, Zelyas N, O'Brien SF, Drews S, Pang XL. Early warning and rapid public health response to prevent COVID-19 outbreaks in long-term care facilities (LTCF) by monitoring SARS-CoV-2 RNA in LTCF site-specific sewage samples and assessment of antibodies response in this population: prospective study protocol. BMJ Open 2021; 11:e052282. [PMID: 34417219 PMCID: PMC8382669 DOI: 10.1136/bmjopen-2021-052282] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
INTRODUCTION The COVID-19 pandemic has an excessive impact on residents in long-term care facilities (LTCF), causing high morbidity and mortality. Early detection of presymptomatic and asymptomatic COVID-19 cases supports the timely implementation of effective outbreak control measures but repetitive screening of residents and staff incurs costs and discomfort. Administration of vaccines is key to controlling the pandemic but the robustness and longevity of the antibody response, correlation of neutralising antibodies with commercial antibody assays, and the efficacy of current vaccines for emerging COVID-19 variants require further study. We propose to monitor SARS-CoV-2 in site-specific sewage as an early warning system for COVID-19 in LTCF and to study the immune response of the staff and residents in LTCF to COVID-19 vaccines. METHODS AND ANALYSIS The study includes two parts: (1) detection and quantification of SARS-CoV-2 in LTCF site-specific sewage samples using a molecular assay followed by notification of Public Health within 24 hours as an early warning system for appropriate outbreak investigation and control measures and cost-benefit analyses of the system and (2) testing for SARS-CoV-2 antibodies among staff and residents in LTCF at various time points before and after COVID-19 vaccination using commercial assays and neutralising antibody testing performed at a reference laboratory. ETHICS AND DISSEMINATION Ethics approval was obtained from the University of Alberta Health Research Ethics Board with considerations to minimise risk and discomforts for the participants. Early recognition of a COVID-19 case in an LTCF might prevent further transmission in residents and staff. There was no direct benefit identified to the participants of the immunity study. Anticipated dissemination of information includes a summary report to the immunity study participants, sharing of study data with the scientific community through the Canadian COVID-19 Immunity Task Force, and prompt dissemination of study results in meeting abstracts and manuscripts in peer-reviewed journals.
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Affiliation(s)
- Bonita E Lee
- Department of Pediatrics, University of Alberta, Edmonton, Alberta, Canada
- Women & Children's Health Research Institute, Li Ka Shing Institute of Virology, Edmonton, Alberta, Canada
| | - Christopher Sikora
- Medical Officer of Health (Edmonton Zone), Alberta Health Services, Edmonton, Alberta, Canada
- Department of Medicine, School of Public Health, University of Alberta, Edmonton, Alberta, Canada
| | - Douglas Faulder
- Medical Director, Continuing Care (Edmonton Zone), Alberta Health Services, Edmonton, Alberta, Canada
- Department of Family Medicine, University of Alberta, Edmonton, Alberta, Canada
| | - Eleanor Risling
- Executive Director, Continuing Care (Edmonton Zone), Alberta Health Services, Edmonton, Alberta, Canada
| | - Lorie A Little
- Director, Facility and Supportive Living (Edmonton Zone), Alberta Health Services, Edmonton, Alberta, Canada
| | - Yuanyuan Qiu
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, Alberta, Canada
| | - Tiejun Gao
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, Alberta, Canada
| | - Ross Bulat
- EPCOR Water Services Inc, Edmonton, Alberta, Canada
| | | | - Steve E Hrudey
- Analytical and Environmental Toxicology, University of Alberta, Edmonton, Alberta, Canada
| | - Arto Ohinmaa
- School of Public Health, University of Alberta, Edmonton, Alberta, Canada
| | | | - Anne-Claude Gingras
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Carmen Charlton
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, Alberta, Canada
- Alberta Precision Laboratories, Public Health Laboratory; Li Ka Shing Institute of Virology, Alberta Health Services, Edmonton, Alberta, Canada
| | - John Kim
- National Microbiology Laboratory, Winnipeg, Manitoba, Canada
| | - Heidi Wood
- National Microbiology Laboratory, Winnipeg, Manitoba, Canada
| | | | - Jamil N Kanji
- Public Health Laboratory, Alberta Precision Laboratories, Calgary, Alberta, Canada
- Division of Infectious Diseases, Department of Medicine, University of Calgary Cumming School of Medicine, Calgary, Alberta, Canada
| | - Nathan Zelyas
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, Alberta, Canada
- Alberta Precision Laboratories, Public Health Laboratory, Alberta Health Services, Edmonton, Alberta, Canada
| | - Sheila F O'Brien
- Epidemiology and Surveillance, Canadian Blood Services, Ottawa, Ontario, Canada
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada
| | - Steven Drews
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, Alberta, Canada
- Microbiology, Canadian Blood Services, Edmonton, Alberta, Canada
| | - Xiao-Li Pang
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, Alberta, Canada
- Alberta Precision Laboratories, Public Health Laboratory; Li Ka Shing Institute of Virology, Alberta Health Services, Edmonton, Alberta, Canada
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Mackuľak T, Gál M, Špalková V, Fehér M, Briestenská K, Mikušová M, Tomčíková K, Tamáš M, Butor Škulcová A. Wastewater-Based Epidemiology as an Early Warning System for the Spreading of SARS-CoV-2 and Its Mutations in the Population. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:5629. [PMID: 34070320 PMCID: PMC8197469 DOI: 10.3390/ijerph18115629] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 05/20/2021] [Accepted: 05/21/2021] [Indexed: 12/18/2022]
Abstract
New methodologies based on the principle of "sewage epidemiology" have been successfully applied before in the detection of illegal drugs. The study describes the idea of early detection of a virus, e.g., SARS-CoV-2, in wastewater in order to focus on the area of virus occurrence and supplement the results obtained from clinical examination. By monitoring temporal variation in viral loads in wastewater in combination with other analysis, a virus outbreak can be detected and its spread can be suppressed early. The use of biosensors for virus detection also seems to be an interesting application. Biosensors are highly sensitive, selective, and portable and offer a way for fast analysis. This manuscript provides an overview of the current situation in the area of wastewater analysis, including genetic sequencing regarding viral detection and the technological solution of an early warning system for wastewater monitoring based on biosensors.
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Affiliation(s)
- Tomáš Mackuľak
- Department of Environmental Engineering, Institute of Chemical and Environmental Engineering, Faculty of Chemical and Food Technology, Slovak University of Technology, Radlinského 9, 812 37 Bratislava, Slovakia; (M.F.); (M.T.); (A.B.Š.)
| | - Miroslav Gál
- Department of Inorganic Technology, Faculty of Chemical and Food Technology, Slovak University of Technology, Radlinského 9, 812 37 Bratislava, Slovakia; (M.G.); (V.Š.)
| | - Viera Špalková
- Department of Inorganic Technology, Faculty of Chemical and Food Technology, Slovak University of Technology, Radlinského 9, 812 37 Bratislava, Slovakia; (M.G.); (V.Š.)
- Department of Zoology and Fisheries, Faculty of Agrobiology Food and Natural Resources, Czech University of Life Sciences Prague, Kamýcká 129, 165 21 Prague, Czech Republic
| | - Miroslav Fehér
- Department of Environmental Engineering, Institute of Chemical and Environmental Engineering, Faculty of Chemical and Food Technology, Slovak University of Technology, Radlinského 9, 812 37 Bratislava, Slovakia; (M.F.); (M.T.); (A.B.Š.)
- Department of Inorganic Technology, Faculty of Chemical and Food Technology, Slovak University of Technology, Radlinského 9, 812 37 Bratislava, Slovakia; (M.G.); (V.Š.)
| | - Katarína Briestenská
- Biomedical Research Center of the Slovak Academy of Sciences, Institute of Virology, Dúbravská cesta 9, 845 05 Bratislava, Slovakia; (K.B.); (M.M.); (K.T.)
| | - Miriam Mikušová
- Biomedical Research Center of the Slovak Academy of Sciences, Institute of Virology, Dúbravská cesta 9, 845 05 Bratislava, Slovakia; (K.B.); (M.M.); (K.T.)
| | - Karolína Tomčíková
- Biomedical Research Center of the Slovak Academy of Sciences, Institute of Virology, Dúbravská cesta 9, 845 05 Bratislava, Slovakia; (K.B.); (M.M.); (K.T.)
| | - Michal Tamáš
- Department of Environmental Engineering, Institute of Chemical and Environmental Engineering, Faculty of Chemical and Food Technology, Slovak University of Technology, Radlinského 9, 812 37 Bratislava, Slovakia; (M.F.); (M.T.); (A.B.Š.)
| | - Andrea Butor Škulcová
- Department of Environmental Engineering, Institute of Chemical and Environmental Engineering, Faculty of Chemical and Food Technology, Slovak University of Technology, Radlinského 9, 812 37 Bratislava, Slovakia; (M.F.); (M.T.); (A.B.Š.)
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Nourinejad M, Berman O, Larson RC. Placing sensors in sewer networks: A system to pinpoint new cases of coronavirus. PLoS One 2021; 16:e0248893. [PMID: 33831024 PMCID: PMC8031413 DOI: 10.1371/journal.pone.0248893] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Accepted: 03/05/2021] [Indexed: 01/22/2023] Open
Abstract
We consider a proposed system that would place sensors in a number of wastewater manholes in a community in order to detect genetic remnants of SARS-Cov-2 found in the excreted stool of infected persons. These sensors would continually monitor the manhole's wastewater, and whenever virus remnants are detected, transmit an alert signal. In a recent paper, we described two new algorithms, each sequentially opening and testing successive manholes for genetic remnants, each algorithm homing in on a neighborhood where the infected person or persons are located. This paper extends that work in six important ways: (1) we introduce the concept of in-manhole sensors, as these sensors will reduce the number of manholes requiring on-site testing; (2) we present a realistic tree network depicting the topology of the sewer pipeline network; (3) for simulations, we present a method to create random tree networks exhibiting key attributes of a given community; (4) using the simulations, we empirically demonstrate that the mean and median number of manholes to be opened in a search follows a well-known logarithmic function; (5) we develop procedures for determining the number of sensors to deploy; (6) we formulate the sensor location problem as an integer nonlinear optimization and develop heuristics to solve it. Our sensor-manhole system, to be implemented, would require at least three additional steps in R&D: (a) an accurate, inexpensive and fast SARS-Cov-2 genetic-remnants test that can be done at the manhole; (b) design, test and manufacture of the sensors; (c) in-the-field testing and fine tuning of an implemented system.
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Affiliation(s)
| | - Oded Berman
- Rotman School of Management, University of Toronto, Toronto, Canada
| | - Richard C. Larson
- Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
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Ghaffarzadegan N. Simulation-based what-if analysis for controlling the spread of Covid-19 in universities. PLoS One 2021; 16:e0246323. [PMID: 33524045 PMCID: PMC7850497 DOI: 10.1371/journal.pone.0246323] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Accepted: 01/18/2021] [Indexed: 12/18/2022] Open
Abstract
A simulation model is developed to analyze the spread of covid-19 in universities. The model can be used to conduct a what-if analysis and estimate infection cases under different policies. For proof-of-concept, the model is simulated for a hypothetical university of 25,000 students and 3,000 faculty/staff in a U.S. college town. Simulation results show that early outbreaks are very likely, and there is no silver bullet to avoid them. Instead, a combination of policies should be carefully implemented. The results suggest (almost) full remote university operations from the beginning of the semester. In a less-preferred alternative, if universities decide to have students attend in person, they should encourage remote operations for high-risk individuals, conduct frequent rapid tests, enforce mask use, communicate with students and employees about the risks, and promote social distancing. Universities should be willing to move to remote operations if cases rise. Under this scenario, and considering implementation challenges, many universities are still likely to experience an early outbreak, and the likelihood of having a case of death is worrisome. In the long run, students and faculty react to the risks, and even if universities decide to continue operations, classes are likely to have very low in-person attendance. Overall, our analysis depicts several sources of system complexities, negative unintended consequences of relying on a single policy, non-linear incremental effects, and positive synergies of implementing multiple policies. A simulation platform for a what-if analysis is offered so marginal effectiveness of different policies and different decision-making thresholds for closure can be tested for universities of varying populations.
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Affiliation(s)
- Navid Ghaffarzadegan
- Department of Industrial and Systems Engineering, Virginia Tech, Falls Church, Virginia, United States of America
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