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

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'.

Heterogeneity in the onwards transmission risk between local and imported cases affects practical estimates of the time-dependent reproduction number

“…as opposed to the wider population [24]) or with different characteristics (e.g. different ages [10,[25][26][27][28] or vaccination statuses [29,30]) face different risks of both becoming infected and transmitting the virus. Shortly before the COVID-19 pandemic, the Cori method was extended to account for differences in the source locations of local and imported cases [9], but with an assumption that the expected numbers of onwards transmissions from each local case and each imported case are identical.…”

Incorporating infectious duration‐dependent transmission into Bayesian epidemic models

“…In particular, the model assumes equal susceptibility for all members of the population, which may be unrealistic due to factors such as age or vaccination status. Many approaches to incorporating variable susceptibility exist, including the use of an expanded compartmental structure (Robinson et al., 2022; Xu & Tang, 2021), the adoption of a stratified structure with different population subgroups (Brown et al., 2016; Pooley et al., 2022), or through modifications to the intensity process (Deardon et al., 2010; Lawson & Kim, 2021). While the IDD transmissibility model may theoretically be combined with any of the previously mentioned approaches to incorporate varying susceptibility, the feasibility and implementation may depend on data availability and the disease process of interest.…”

An algebraic framework for structured epidemic modelling

“…A recurring theme in scientific modelling is the importance of stratified models, in which local dynamics are reproduced in multiple strata and strata interact according to a specified scheme. For example, Pooley et al [ 23 ] consider age-stratified models, and Citron et al [ 22 ] compare stratified models defined by a choice of local epidemiological dynamics (SIR, SIS or Ross–Macdonald) and a choice of stratification by location (the flux or simple trip models of metapopulation dynamics). Typed Petri nets offer a general methodology for stratifying models, which contrasts with the by-hand approach taken in [ 22 ].…”