Estimation of age-stratified contact rates during the COVID-19 pandemic using a novel inference algorithm

Christopher Pooley, Andrea Wilson, Glenn Marion

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Well parameterised epidemiological models including accurate representation of contacts, are fundamental to controlling epidemics. However, age-stratified contacts are typically estimated from pre-pandemic/peacetime 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 2020-21 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 (MBP) is applied. Modified contact rates are inferred alongside time-varying reproduction numbers that quantify changes in overall transmission due to pandemic response, and agestratified proportions of asymptomatic cases, hospitalisation 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 parametrisation of dynamic transmission models that can inform data-driven public health policy and interventions.
Original languageEnglish
Article number20210298
Pages (from-to)1-18
Number of pages18
JournalPhilosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
Issue number2233
Early online date15 Aug 2022
Publication statusPublished - 3 Oct 2022

Keywords / Materials (for Non-textual outputs)

  • :COVID-19
  • Bayesian inference
  • contact matrix
  • reproduction number
  • ABC
  • MBP


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