Year: 2025
Type: article
Abstract: Epidemic prevalence surveys monitor the spread of an infectious disease by regularly testing representative samples of a population for infection. State-of-the-art Bayesian approaches for analysing epidemic survey data were constructed independently and under pressure during the COVID-19 pandemic. In this paper, we compare two existing approaches (one leveraging Bayesian P-splines and the other approximate Gaussian processes) with a novel approach (leveraging a random walk and fit using sequential Monte Carlo) for smoothing and performing inference on epidemic survey data. We use our simpler approach to investigate the impact of survey design and underlying epidemic dynamics on the quality of estimates. We then incorporate these considerations into the existing approaches and compare all three on simulated data and on real-world data from the SARS-CoV-2 REACT-1 prevalence study in England. All three approaches, once appropriate considerations are made, produce similar estimates of infection prevalence; however, estimates of the growth rate and instantaneous reproduction number are more sensitive to underlying assumptions. Interactive notebooks applying all three approaches are also provided alongside recommendations on hyperparameter selection and other practical guidance, with some cases resulting in orders-of-magnitude faster runtime. Author summary Understanding how infections spread in a population is crucial during an epidemic, and largescale surveys that test people for current infection can provide valuable insights. These surveys are resource-intensive and the data they produce can be noisy and hard to interpret. In this study, we investigate how three different statistical approaches (two established and one novel) can explore such data. We found that some common modelling choices, particularly the treatment of observation noise, can meaningfully shape results. Our findings highlight the need for careful, robust methods to help researchers and public health officials make best use of existing data, design more effective surveys, and extract clearer insights from future studies.
Source: PLoS Computational Biology
Language: English
Subfield: Modeling and Simulation
Field: Mathematics
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,043
Funders: UK Research and Innovation, National Institute for Health Research Health Protection Research Unit, Cancer Research UK, National Institute for Health and Care Research, Department of Health and Social Care, University College, Oxford, Imperial College London, University of Oxford, MRC-PHE Centre for Environment and Health, Horizon 2020 Framework Programme, Medical Research Council, Engineering and Physical Sciences Research Council, Horizon 2020
Awards: G1210320885, G1222672577, MRC-PHE Centre for Environment and Health, G3127627966, REACT-GE: Multi-omics to identify biological pathways underlying severity of SARS-CoV-2 infection., G3558608651, G3909006727, REACT Long COVID (REACT-LC), G4108278402, G4132691355, G4360543172, MRC Centre for Environment and Health, G4937900776, G5041174669, G630072835, G6310683849, G6470068279, G7714411780, G7880216529, G7925659073, G8343564501, G8755594907
OpenAlex ID: W4414787888