ArticleEnvironment international2023
A spatio-temporal framework for modelling wastewater concentration during the COVID-19 pandemic.
Article in Environment international, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Spatio-Temporal COVID-19 Modeling: A Global Systematic Review of Data Integration, Equity, and Lessons for Pandemic Preparedness.International journal of environmental research and public health · 2026Pooled it
- Improving Inference from Reported Concentrations in Environmental Surveillance by Modeling the Statistical Features of Digital PCR.ACS ES&T water · 2026Article
- A framework for evaluating implementation, impact, and cost-effectiveness of wastewater and environmental surveillance.Frontiers in public health · 2026Review
- Wastewater Surveillance for Group APathogens (Basel, Switzerland) · 2025Article
- The impact of signal variability on COVID-19 epidemic growth rate estimation from wastewater surveillance data.PloS one · 2025Article
- From infectious diseases to chronic diseases: the paradigm shift of spatial epidemiology in disease prevention and control.Frontiers in public health · 2025Review
- Article
- Article
- Advancing Public Health Surveillance: Integrating Modeling and GIS in the Wastewater-Based Epidemiology of Viruses, a Narrative Review.Pathogens (Basel, Switzerland) · 2024Review
- Online trend estimation and detection of trend deviations in sub-sewershed time series of SARS-CoV-2 RNA measured in wastewater.Scientific reports · 2024Article
- Integrating wastewater and randomised prevalence survey data for national COVID surveillance.Scientific reports · 2024Article
- Wastewater-based surveillance models for COVID-19: A focused review on spatio-temporal models.Heliyon · 2023Review
Corrections and comments
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Authors and funding
16 authors.
Funding
Abstract
The potential utility of wastewater-based epidemiology as an early warning tool has been explored widely across the globe during the current COVID-19 pandemic. Methods to detect the presence of SARS-CoV-2 RNA in wastewater were developed early in the pandemic, and extensive work has been conducted to evaluate the relationship between viral concentration and COVID-19 case numbers at the catchment areas of sewage treatment works (STWs) over time. However, no attempt has been made to develop a model that predicts wastewater concentration at fine spatio-temporal resolutions covering an entire country, a necessary step towards using wastewater monitoring for the early detection of local outbreaks. We consider weekly averages of flow-normalised viral concentration, reported as the number of SARS-CoV-2N1 gene copies per litre (gc/L) of wastewater available at 303 STWs over the period between 1 June 2021 and 30 March 2022. We specify a spatially continuous statistical model that quantifies the relationship between weekly viral concentration and a collection of covariates covering socio-demographics, land cover and virus associated genomic characteristics at STW catchment areas while accounting for spatial and temporal correlation. We evaluate the model's predictive performance at the catchment level through 10-fold cross-validation. We predict the weekly viral concentration at the population-weighted centroid of the 32,844 lower super output areas (LSOAs) in England, then aggregate these LSOA predictions to the Lower Tier Local Authority level (LTLA), a geography that is more relevant to public health policy-making. We also use the model outputs to quantify the probability of local changes of direction (increases or decreases) in viral concentration over short periods (e.g. two consecutive weeks). The proposed statistical framework can predict SARS-CoV-2 viral concentration in wastewater at high spatio-temporal resolution across England. Additionally, the probabilistic quantification of local changes can be used as an early warning tool for public health surveillance.
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