Publisher
Royal Society Publshing
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'
Year
2022
Category
Refereed journal