ArticlePhilosophical transactions. Series A, Mathematical, physical, and engineering sciences2022
Estimation of age-stratified contact rates during the COVID-19 pandemic using a novel inference algorithm.
Article in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Clustering-based methodology for comparing multi-characteristic epidemiological dynamics with application to COVID-19 epidemiology in Europe.Royal Society open science · 2025Article
- How contact patterns during the COVID-19 pandemic are related to pre-pandemic contact patterns and mobility trends.BMC infectious diseases · 2023Article
- An analytical approach to evaluate the impact of age demographics in a pandemic.Stochastic environmental research and risk assessment : research journal · 2023Article
- Technical challenges of modelling real-life epidemics and examples of overcoming these.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2022Article
- FAIR data pipeline: provenance-driven data management for traceable scientific workflows.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2022Article
- Estimation of age-stratified contact rates during the COVID-19 pandemic using a novel inference algorithm.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2022Article
- An algebraic framework for structured epidemic modelling.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2022Article
- Heterogeneity in the onwards transmission risk between local and imported cases affects practical estimates of the time-dependent reproduction number.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2022Article
- Visualization for epidemiological modelling: challenges, solutions, reflections and recommendations.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Well parameterized epidemiological models including accurate representation of contacts are fundamental to controlling epidemics. However, age-stratified contacts are typically estimated from pre-pandemic/peace-time surveys, even though interventions and public response likely alter contacts. Here, we fit age-stratified models, including re-estimation of relative contact rates between age classes, to public data describing the 2020-2021 COVID-19 outbreak in England. This data includes age-stratified population size, cases, deaths, hospital admissions and results from the Coronavirus Infection Survey (almost 9000 observations in all). Fitting stochastic compartmental models to such detailed data is extremely challenging, especially considering the large number of model parameters being estimated (over 150). An efficient new inference algorithm ABC-MBP combining existing approximate Bayesian computation (ABC) methodology with model-based proposals (MBPs) is applied. Modified contact rates are inferred alongside time-varying reproduction numbers that quantify changes in overall transmission due to pandemic response, and age-stratified proportions of asymptomatic cases, hospitalization rates and deaths. These inferences are robust to a range of assumptions including the values of parameters that cannot be estimated from available data. ABC-MBP is shown to enable reliable joint analysis of complex epidemiological data yielding consistent parametrization of dynamic transmission models that can inform data-driven public health policy and interventions. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.
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