Improving inference in wastewater-based epidemiology by modelling the statistical features of digital PCR DOI Creative Commons
Adrian Lison, Timothy R. Julian, Tanja Stadler

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Окт. 17, 2024

Abstract The growing field of wastewater-based infectious disease surveillance relies on the quantification pathogen concentrations in wastewater using polymerase chain reaction (PCR) techniques. However, existing models for monitoring spread have often been adapted from methods case count data and neglect statistical features PCR In this paper, we seek to overcome widespread simplistic modelling measurements as normally or log-normally distributed by proposing an appropriate model digital (dPCR). Building established theory dPCR, derive approximations coefficient variation probability non-detection propose a hurdle model-based likelihood estimating dPCR measurements. Using simulations real-world data, show that simple likelihoods based normal log-normal distributions are misspecified, affecting estimation infection trends over time. contrast, proposed dPCR-specific accurately distribution measurements, improving epidemiological estimates forecasts even if details laboratory protocol unknown. method has implemented open-source R package “EpiSewer” improve pathogens.

Язык: Английский

Localised wastewater SARS-CoV-2 levels linked to COVID-19 cases: A long-term multisite study in England DOI Creative Commons
Natalia R. Jones, Richard Elson, Matthew J. Wade

и другие.

The Science of The Total Environment, Год журнала: 2025, Номер 962, С. 178455 - 178455

Опубликована: Янв. 1, 2025

Wastewater-based surveillance (WBS) can monitor for the presence of human health pathogens in population. During COVID-19, WBS was widely used to determine wastewater SARS-CoV-2 RNA concentration (concentrations) providing information on community COVID-19 cases (cases). However, studies examining relationship between concentrations and tend be localised or focussed small-scale institutional settings. Few have examined this multiple settings, over long periods, with large sample numbers, nor attempted quantify detail how catchment characteristics affected these. This 18-month study (07/20-12/21) explored correlation quantitative using censored regression. Our analysis >94,000 samples collected from 452 diverse sampling sites (259 Sewage Treatment Works (STW) 193 Sewer Network Sites (SNS)) covering ~65 % English Wastewater were linked ~6 million diagnostically confirmed cases. High coefficients found (STW: median r = 0.66, IQR: 0.57-0.74; SNS: 0.65, 0.54-0.74). The (regression coefficient) variable catchments. Catchment (e.g. size population grab vs automated sampling) had significant but small effects regression coefficients. last six months reduced became highly coincided a shift towards younger cases, vaccinated rapid emergence variant Omicron. programme rapidly introduced at scale during COVID-19. Laboratory methods evolved catchments characteristics. Despite diversity, findings indicate that provides an effective proxy establishing dynamics across wide variety communities. While there is potential predicting concentration, may more smaller scales.

Язык: Английский

Процитировано

2
Analysis Insights to Support the Use of Wastewater and Environmental Surveillance Data for Infectious Diseases and Pandemic Preparedness DOI Creative Commons
Kathleen O’Reilly, Matthew J. Wade, Kata Farkas

и другие.

Epidemics, Год журнала: 2025, Номер unknown, С. 100825 - 100825

Опубликована: Март 1, 2025

Wastewater-based epidemiology is the detection of pathogens from sewage systems and interpretation these data to improve public health. Its use has increased in scope since 2020, when it was demonstrated that SARS-CoV-2 RNA could be successfully extracted wastewater affected populations. In this Perspective we provide an overview recent advances pathogen within wastewater, propose a framework for identifying utility sampling suggest areas where analytics require development. Ensuring both collection analysis are tailored towards key questions at different stages epidemic will inference made. For analyses useful methods determine absence infection, early reliably estimate trajectories prevalence, detect novel variants without reliance on consensus sequences. This research area included many innovations have improved collected optimistic innovation continue future.

Язык: Английский

Процитировано

0
Improving inference in wastewater-based epidemiology by modelling the statistical features of digital PCR DOI Creative Commons
Adrian Lison, Timothy R. Julian, Tanja Stadler

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Окт. 17, 2024

Abstract The growing field of wastewater-based infectious disease surveillance relies on the quantification pathogen concentrations in wastewater using polymerase chain reaction (PCR) techniques. However, existing models for monitoring spread have often been adapted from methods case count data and neglect statistical features PCR In this paper, we seek to overcome widespread simplistic modelling measurements as normally or log-normally distributed by proposing an appropriate model digital (dPCR). Building established theory dPCR, derive approximations coefficient variation probability non-detection propose a hurdle model-based likelihood estimating dPCR measurements. Using simulations real-world data, show that simple likelihoods based normal log-normal distributions are misspecified, affecting estimation infection trends over time. contrast, proposed dPCR-specific accurately distribution measurements, improving epidemiological estimates forecasts even if details laboratory protocol unknown. method has implemented open-source R package “EpiSewer” improve pathogens.

Язык: Английский

Процитировано

1