A spatio-temporal block aggregation model for latent log Gaussian outcomes: application on modelling wastewater virus concentration in Wales

📅 2026-08-27
📈 Citations: 0
Influential: 0
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🤖 AI Summary
本文提出了一种时空块聚合模型,用于处理废水病毒浓度的潜在对数高斯结果,并通过贝叶斯框架和INLA方法进行推断,应用于威尔士47个集水区的SARS-CoV-2监测。
📝 Abstract
Wastewater-based epidemiology has emerged as a valuable tool for monitoring community-level infectious disease dynamics, providing population-wide signals that complement clinical surveillance. However, wastewater measurements are often observed as aggregated values over irregular spatial units. This work develops an approach to link an underlying spatially continuous processes and an aggregated outcome. We propose a spatio-temporal model for latent log-Gaussian outcomes that provides a coherent framework for inference and prediction, allowing the process to be integrated over arbitrary spatial configurations. This framework can also be used for subsequent analyses, such as linking wastewater signal to health outcomes at administrative areas. We use a Bayesian framework for inference via the linearised integrated nested Laplace approximation (INLA) approach. We apply the proposed methodology to model SARS-CoV-2 N1 gene copies in wastewater across 47 catchment areas in Wales from the beginning of August 2022 to the end of July 2023. The results demonstrate that the model captures spatial and temporal patterns and has good predictive performance. Results also show that estimated viral gene copies are strongly linked to positivity rates from COVID-19 PCR tests at the local authority level. Our findings highlight the importance of explicitly modelling block aggregation when analysing wastewater surveillance data. The proposed framework provides a flexible and principled approach for integrating environmental surveillance data into public health monitoring systems.
Problem

Research questions and friction points this paper is trying to address.

wastewater-based epidemiology
spatio-temporal model
latent log-Gaussian outcomes
block aggregation
public health monitoring
Innovation

Methods, ideas, or system contributions that make the work stand out.

spatio-temporal block aggregation model
latent log-Gaussian outcomes
integrated nested Laplace approximation (INLA)
wastewater-based epidemiology
SARS-CoV-2
S
Stephen Jun Villejo
Faculty of Medicine, Imperial College London, London, W12 0BZ, UK
P
Peter Diggle
Faculty of Health and Medicine, Lancaster University, LA1 4AT, UK
Guangquan Li
Guangquan Li
Senior Lecturer in Statistics, Northumbria University
Bayesian modelsspace-time data analysis
E
Ella White
Faculty of Medicine, Imperial College London, London, W12 0BZ, UK
M
Matthew Wade
UK Health Security Agency E14 4PU UK
Christopher Williams
Christopher Williams
Arthur Andersen Professor of Accounting, University of Michigan
Capital MarketsBankingInternational AccountingAccounting Uncertainty
D
Davey L. Jones
Environment Centre Wales, Bangor University, Bangor, LL57 2UW, UK
A
Alisha Davies
Public Health Wales, Cardiff, CF10 4BZ, UK
Marta Blangiardo
Marta Blangiardo
Professor of Biostatistics, MRC Centre for Environment and Health, Imperial College
Bayesian statisticshierarchical modelsBiostatisticsenvironmental epidemiology