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Watson LM, Plank MJ, Armstrong BA, Chapman JR, Hewitt J, Morris H, Orsi A, Bunce M, Donnelly CA, Steyn N. Jointly estimating epidemiological dynamics of Covid-19 from case and wastewater data in Aotearoa New Zealand. COMMUNICATIONS MEDICINE 2024; 4:143. [PMID: 39009723 PMCID: PMC11250817 DOI: 10.1038/s43856-024-00570-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 07/04/2024] [Indexed: 07/17/2024] Open
Abstract
BACKGROUND Timely and informed public health responses to infectious diseases such as COVID-19 necessitate reliable information about infection dynamics. The case ascertainment rate (CAR), the proportion of infections that are reported as cases, is typically much less than one and varies with testing practices and behaviours, making reported cases unreliable as the sole source of data. The concentration of viral RNA in wastewater samples provides an alternate measure of infection prevalence that is not affected by clinical testing, healthcare-seeking behaviour or access to care. METHODS We construct a state-space model with observed data of levels of SARS-CoV-2 in wastewater and reported case incidence and estimate the hidden states of the effective reproduction number, R, and CAR using sequential Monte Carlo methods. RESULTS We analyse data from 1 January 2022 to 31 March 2023 from Aotearoa New Zealand. Our model estimates that R peaks at 2.76 (95% CrI 2.20, 3.83) around 18 February 2022 and the CAR peaks around 12 March 2022. We calculate that New Zealand's second Omicron wave in July 2022 is similar in size to the first, despite fewer reported cases. We estimate that the CAR in the BA.5 Omicron wave in July 2022 is approximately 50% lower than in the BA.1/BA.2 Omicron wave in March 2022. CONCLUSIONS Estimating R, CAR, and cumulative number of infections provides useful information for planning public health responses and understanding the state of immunity in the population. This model is a useful disease surveillance tool, improving situational awareness of infectious disease dynamics in real-time.
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Affiliation(s)
- Leighton M Watson
- School of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand.
| | - Michael J Plank
- School of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand
| | | | - Joanne R Chapman
- Institute of Environmental Science and Research Ltd, Porirua, New Zealand
| | - Joanne Hewitt
- Institute of Environmental Science and Research Ltd, Porirua, New Zealand
| | - Helen Morris
- Institute of Environmental Science and Research Ltd, Porirua, New Zealand
| | - Alvaro Orsi
- Institute of Environmental Science and Research Ltd, Porirua, New Zealand
| | - Michael Bunce
- Institute of Environmental Science and Research Ltd, Porirua, New Zealand
| | - Christl A Donnelly
- Department of Statistics, University of Oxford, Oxford, United Kingdom
- Pandemic Sciences Institute, University of Oxford, Oxford, United Kingdom
| | - Nicholas Steyn
- Department of Statistics, University of Oxford, Oxford, United Kingdom
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2
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van der Drift AMR, Haver A, Kloosterman A, van der Beek RFHJ, Nagelkerke E, Eggink D, Laros JFJ, Nrs C, van Dissel JT, de Roda Husman AM, Lodder WJ. Long-term wastewater monitoring of SARS-CoV-2 viral loads and variants at the major international passenger hub Amsterdam Schiphol Airport: A valuable addition to COVID-19 surveillance. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 937:173535. [PMID: 38802021 DOI: 10.1016/j.scitotenv.2024.173535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 05/07/2024] [Accepted: 05/24/2024] [Indexed: 05/29/2024]
Abstract
Wastewater-based epidemiological surveillance at municipal wastewater treatment plants has proven to play an important role in COVID-19 surveillance. Considering international passenger hubs contribute extensively to global transmission of viruses, wastewater surveillance at this type of location may be of added value as well. The aim of this study is to explore the potential of long-term wastewater surveillance at a large passenger hub as an additional tool for public health surveillance during different stages of a pandemic. Here, we present an analysis of SARS-CoV-2 viral loads in airport wastewater by reverse-transcription quantitative polymerase chain reaction (RT-qPCR) from the beginning of the COVID-19 pandemic in Feb 2020, and an analysis of SARS-CoV-2 variants by whole-genome next-generation sequencing from Sep 2020, both until Sep 2022, in the Netherlands. Results are contextualized using (inter)national measures and data sources such as passenger numbers, clinical surveillance data and national wastewater surveillance data. Our findings show that wastewater surveillance was possible throughout the study period, irrespective of measures, as viral loads were detected and quantified in 98.6 % (273/277) of samples. Emergence of SARS-CoV-2 variants, identified in 91.0 % (161/177) of sequenced samples, coincided with increases in viral loads. Furthermore, trends in viral load and variant detection in airport wastewater closely followed, and in some cases preceded, trends in national daily average viral load in wastewater and variants detected in clinical surveillance. Wastewater-based epidemiology at a large international airport is a valuable addition to classical COVID-19 surveillance and the developed expertise can be applied in pandemic preparedness plans for other (emerging) pathogens in the future.
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Affiliation(s)
- Anne-Merel R van der Drift
- Centre for Infectious Disease Control (CIb), National Institute for Public Health and the Environment (RIVM), Antonie van Leeuwenhoeklaan 9, 3721 MA Bilthoven, the Netherlands; Institute for Risk Assessment Science (IRAS), Utrecht University (UU), Yalelaan 2, 3584 CM Utrecht, the Netherlands
| | - Auke Haver
- Centre for Infectious Disease Control (CIb), National Institute for Public Health and the Environment (RIVM), Antonie van Leeuwenhoeklaan 9, 3721 MA Bilthoven, the Netherlands
| | - Astrid Kloosterman
- Centre for Environmental Safety and Security (M&V), National Institute for Public Health and the Environment (RIVM), Antonie van Leeuwenhoeklaan 9, 3721, MA, Bilthoven, the Netherlands
| | - Rudolf F H J van der Beek
- Centre for Infectious Disease Control (CIb), National Institute for Public Health and the Environment (RIVM), Antonie van Leeuwenhoeklaan 9, 3721 MA Bilthoven, the Netherlands
| | - Erwin Nagelkerke
- Centre for Infectious Disease Control (CIb), National Institute for Public Health and the Environment (RIVM), Antonie van Leeuwenhoeklaan 9, 3721 MA Bilthoven, the Netherlands
| | - Dirk Eggink
- Centre for Infectious Disease Control (CIb), National Institute for Public Health and the Environment (RIVM), Antonie van Leeuwenhoeklaan 9, 3721 MA Bilthoven, the Netherlands; Amsterdam UMC Location University of Amsterdam, Department of Medical Microbiology and Infection prevention, Laboratory of Applied Evolutionary Biology, 1105 AZ Amsterdam, the Netherlands
| | - Jeroen F J Laros
- Department of Human Genetics (HG), Leiden University Medical Center (LUMC); Einthovenweg 20, 2333 ZC Leiden, the Netherlands; Department of BioInformatics and computational services (BIR), National Institute for Public Health and the Environment (RIVM), Antonie van Leeuwenhoeklaan 9, 3721, MA, Bilthoven, the Netherlands
| | - Consortium Nrs
- Centre for Infectious Disease Control (CIb), National Institute for Public Health and the Environment (RIVM), Antonie van Leeuwenhoeklaan 9, 3721 MA Bilthoven, the Netherlands
| | - Jaap T van Dissel
- Centre for Infectious Disease Control (CIb), National Institute for Public Health and the Environment (RIVM), Antonie van Leeuwenhoeklaan 9, 3721 MA Bilthoven, the Netherlands; Department of Infectious Diseases, Leiden University Medical Center (LUMC); Albinusdreef 2, 2333, ZA, Leiden, the Netherlands
| | - Ana Maria de Roda Husman
- Centre for Infectious Disease Control (CIb), National Institute for Public Health and the Environment (RIVM), Antonie van Leeuwenhoeklaan 9, 3721 MA Bilthoven, the Netherlands; Institute for Risk Assessment Science (IRAS), Utrecht University (UU), Yalelaan 2, 3584 CM Utrecht, the Netherlands
| | - Willemijn J Lodder
- Centre for Infectious Disease Control (CIb), National Institute for Public Health and the Environment (RIVM), Antonie van Leeuwenhoeklaan 9, 3721 MA Bilthoven, the Netherlands.
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3
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Leisman KP, Owen C, Warns MM, Tiwari A, Bian GZ, Owens SM, Catlett C, Shrestha A, Poretsky R, Packman AI, Mangan NM. A modeling pipeline to relate municipal wastewater surveillance and regional public health data. WATER RESEARCH 2024; 252:121178. [PMID: 38309063 DOI: 10.1016/j.watres.2024.121178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 12/18/2023] [Accepted: 01/22/2024] [Indexed: 02/05/2024]
Abstract
As COVID-19 becomes endemic, public health departments benefit from improved passive indicators, which are independent of voluntary testing data, to estimate the prevalence of COVID-19 in local communities. Quantification of SARS-CoV-2 RNA from wastewater has the potential to be a powerful passive indicator. However, connecting measured SARS-CoV-2 RNA to community prevalence is challenging due to the high noise typical of environmental samples. We have developed a generalized pipeline using in- and out-of-sample model selection to test the ability of different correction models to reduce the variance in wastewater measurements and applied it to data collected from treatment plants in the Chicago area. We built and compared a set of multi-linear regression models, which incorporate pepper mild mottle virus (PMMoV) as a population biomarker, Bovine coronavirus (BCoV) as a recovery control, and wastewater system flow rate into a corrected estimate for SARS-CoV-2 RNA concentration. For our data, models with BCoV performed better than those with PMMoV, but the pipeline should be used to reevaluate any new data set as the sources of variance may change across locations, lab methods, and disease states. Using our best-fit model, we investigated the utility of RNA measurements in wastewater as a leading indicator of COVID-19 trends. We did this in a rolling manner for corrected wastewater data and for other prevalence indicators and statistically compared the temporal relationship between new increases in the wastewater data and those in other prevalence indicators. We found that wastewater trends often lead other COVID-19 indicators in predicting new surges.
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Affiliation(s)
- Katelyn Plaisier Leisman
- Department of Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, IL, USA
| | - Christopher Owen
- Department of Biological Sciences, University of Illinois Chicago, Chicago, IL, USA
| | - Maria M Warns
- Department of Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, IL, USA
| | - Anuj Tiwari
- Discovery Partners Institute, University of Illinois Chicago, Chicago, IL, USA
| | - George Zhixin Bian
- Department of Computer Science, Northwestern University, Evanston, IL, USA
| | - Sarah M Owens
- Biosciences, Argonne National Laboratory, Lemont, IL, USA
| | - Charlie Catlett
- Discovery Partners Institute, University of Illinois Chicago, Chicago, IL, USA; Computing, Environment, and Life Sciences, Argonne National Laboratory, Lemont, IL, USA
| | - Abhilasha Shrestha
- Division of Environmental and Occupational Health Sciences, School of Public Health, University of Illinois Chicago, Chicago, IL, USA
| | - Rachel Poretsky
- Department of Biological Sciences, University of Illinois Chicago, Chicago, IL, USA
| | - Aaron I Packman
- Center for Water Research, Northwestern University, Evanston, IL, USA; Department of Civil and Environmental Engineering, Northwestern University, Evanston, IL, USA
| | - Niall M Mangan
- Department of Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, IL, USA; Center for Water Research, Northwestern University, Evanston, IL, USA.
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4
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Ensor KB, Schedler JC, Sun T, Schneider R, Mulenga A, Wu J, Stadler LB, Hopkins L. Online trend estimation and detection of trend deviations in sub-sewershed time series of SARS-CoV-2 RNA measured in wastewater. Sci Rep 2024; 14:5575. [PMID: 38448481 PMCID: PMC10918082 DOI: 10.1038/s41598-024-56175-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 03/03/2024] [Indexed: 03/08/2024] Open
Abstract
Wastewater surveillance has proven a cost-effective key public health tool to understand a wide range of community health diseases and has been a strong source of information on community levels and spread for health departments throughout the SARS- CoV-2 pandemic. Studies spanning the globe demonstrate the strong association between virus levels observed in wastewater and quality clinical case information of the population served by the sewershed. Few of these studies incorporate the temporal dependence present in sampling over time, which can lead to estimation issues which in turn impact conclusions. We contribute to the literature for this important public health science by putting forward time series methods coupled with statistical process control that (1) capture the evolving trend of a disease in the population; (2) separate the uncertainty in the population disease trend from the uncertainty due to sampling and measurement; and (3) support comparison of sub-sewershed population disease dynamics with those of the population represented by the larger downstream treatment plant. Our statistical methods incorporate the fact that measurements are over time, ensuring correct statistical conclusions. We provide a retrospective example of how sub-sewersheds virus levels compare to the upstream wastewater treatment plant virus levels. An on-line algorithm supports real-time statistical assessment of deviations of virus level in a population represented by a sub-sewershed to the virus level in the corresponding larger downstream wastewater treatment plant. This information supports public health decisions by spotlighting segments of the population where outbreaks may be occurring.
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Affiliation(s)
- Katherine B Ensor
- Department of Statistics, Rice University, 6100 Main St., Houston, TX, 77005, USA.
| | - Julia C Schedler
- Department of Statistics, Rice University, 6100 Main St., Houston, TX, 77005, USA
| | - Thomas Sun
- Department of Statistics, Rice University, 6100 Main St., Houston, TX, 77005, USA
| | - Rebecca Schneider
- Houston Health Department, 8000 N. Stadium Dr., Houston, TX, 77054, USA
| | - Anthony Mulenga
- Houston Health Department, 8000 N. Stadium Dr., Houston, TX, 77054, USA
| | - Jingjing Wu
- Department of Civil and Environment Engineering, Rice University, 6100 Main St, Houston, TX, 77005, USA
| | - Lauren B Stadler
- Department of Civil and Environment Engineering, Rice University, 6100 Main St, Houston, TX, 77005, USA
| | - Loren Hopkins
- Houston Health Department and Department of Statistics, Rice University, 6100 Main St., Houston, TX, 77005, USA
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5
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Li G, Diggle P, Blangiardo M. Integrating wastewater and randomised prevalence survey data for national COVID surveillance. Sci Rep 2024; 14:5124. [PMID: 38429366 PMCID: PMC10907376 DOI: 10.1038/s41598-024-55752-9] [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: 10/09/2023] [Accepted: 02/27/2024] [Indexed: 03/03/2024] Open
Abstract
During the COVID-19 pandemic, studies in a number of countries have shown how wastewater can be used as an efficient surveillance tool to detect outbreaks at much lower cost than traditional prevalence surveys. In this study, we consider the utilisation of wastewater data in the post-pandemic setting, in which collection of health data via national randomised prevalence surveys will likely be run at a reduced scale; hence an affordable ongoing surveillance system will need to combine sparse prevalence data with non-traditional disease metrics such as wastewater measurements in order to estimate disease progression in a cost-effective manner. Here, we use data collected during the pandemic to model the dynamic relationship between spatially granular wastewater viral load and disease prevalence. We then use this relationship to nowcast local disease prevalence under the scenario that (i) spatially granular wastewater data continue to be collected; (ii) direct measurements of prevalence are only available at a coarser spatial resolution, for example at national or regional scale. The results from our cross-validation study demonstrate the added value of wastewater data in improving nowcast accuracy and reducing nowcast uncertainty. Our results also highlight the importance of incorporating prevalence data at a coarser spatial scale when nowcasting prevalence at fine spatial resolution, calling for the need to maintain some form of reduced-scale national prevalence surveys in non-epidemic periods. The model framework is disease-agnostic and could therefore be adapted to different diseases and incorporated into a multiplex surveillance system for early detection of emerging local outbreaks.
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Affiliation(s)
- Guangquan Li
- Applied Statistics Research Group, Department of Mathematics, Physics and Electrical Engineering, Northumbria University, Newcastle upon Tyne, NE1 8ST, UK.
- Turing-RSS Health Data Lab, London, UK.
| | - Peter Diggle
- Lancaster University, Lancaster, LA1 4YW, UK
- Turing-RSS Health Data Lab, London, UK
| | - Marta Blangiardo
- MRC Centre for Environment and Health, Imperial College London, St Mary's Campus, Norfolk Place, London, W2 1PG, UK
- Turing-RSS Health Data Lab, London, UK
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6
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Reeves A, Shaikh WA, Chakraborty S, Chaudhuri P, Biswas JK, Maity JP. Potential transmission of SARS-CoV-2 through microplastics in sewage: A wastewater-based epidemiological review. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 334:122171. [PMID: 37437759 DOI: 10.1016/j.envpol.2023.122171] [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: 04/27/2023] [Revised: 06/24/2023] [Accepted: 07/08/2023] [Indexed: 07/14/2023]
Abstract
In light of the current COVID-19 pandemic caused by the virus SARS-CoV-2, there is an urgent need to identify and investigate the various pathways of transmission. In addition to contact and aerosol transmission of the virus, this review investigated the possibility of its transmission via microplastics found in sewage. Wastewater-based epidemiological studies on the virus have confirmed its presence and persistence in both influent sewage as well as treated ones. The hypothesis behind the study is that the huge amount of microplastics, especially Polyvinyl Chloride and Polyethylene particles released into the open waters from sewage can become a good substrate and vector for microbes, especially Polyvinyl Chloride and Polyethylene particles, imparting stability to microbes and aiding the "plastisphere" formation. A bibliometric analysis highlights the negligence of research toward plastispheres and their presence in sewage. The ubiquity of microplastics and their release along with the virus into the open waters increases the risk of viral plastispheres. These plastispheres may be ingested by aquatic organisms facilitating reverse zoonosis and the commercial organisms already reported with accumulating microplastics through the food chain poses a risk to human populations as well. Reliance of high population density areas on open waters served by untreated sewage in economically less developed countries might bring back viral transmission.
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Affiliation(s)
- Arijit Reeves
- Department of Environmental Science, University of Calcutta, Kolkata, West Bengal, 700019, India
| | - Wasim Akram Shaikh
- Department of Basic Sciences, School of Science and Technology, The Neotia University, Sarisha, South 24 Parganas, West Bengal, 743368, India
| | - Sukalyan Chakraborty
- Department of Civil and Environmental Engineering, Birla Institute of Technology, Mesra, Jharkhand, 835215, India.
| | - Punarbasu Chaudhuri
- Department of Environmental Science, University of Calcutta, Kolkata, West Bengal, 700019, India
| | - Jayanta Kumar Biswas
- Enviromicrobiology, Ecotoxicology and Ecotechnology Research Laboratory (3E-MicroToxTech Lab), Department of Ecological Studies, University of Kalyani, Kalyani, Nadia, 741235, West Bengal, India; International Centre for Ecological Engineering, University of Kalyani, Kalyani, 741235, West Bengal, India
| | - Jyoti Prakash Maity
- Environmental Science Laboratory, Department of Chemistry, School of Applied Sciences, KIIT Deemed to be University, Bhubaneswar, Odisha, 751024, India.
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