Following the emergence of a novel coronavirus (SARS-CoV-2) and its spread
outside of China, Europe has experienced large epidemics. In response, many
European countries have implemented unprecedented non-pharmaceutical
interventions including case isolation, the closure of schools and
universities, banning of mass gatherings and/or public events, and most
recently, wide-scale social distancing including local and national lockdowns.
In this technical update, we extend a semi-mechanistic Bayesian hierarchical
model that infers the impact of these interventions and estimates the number of
infections over time. Our methods assume that changes in the reproductive
number - a measure of transmission - are an immediate response to these
interventions being implemented rather than broader gradual changes in
behaviour. Our model estimates these changes by calculating backwards from
temporal data on observed to estimate the number of infections and rate of
transmission that occurred several weeks prior, allowing for a probabilistic
time lag between infection and death.
In this update we extend our original model [Flaxman, Mishra, Gandy et al
2020, Report #13, Imperial College London] to include (a) population saturation
effects, (b) prior uncertainty on the infection fatality ratio, (c) a more
balanced prior on intervention effects and (d) partial pooling of the lockdown
intervention covariate. We also (e) included another 3 countries (Greece, the
Netherlands and Portugal).
The model code is available at
https://github.com/ImperialCollegeLondon/covid19model/
We are now reporting the results of our updated model online at
https://mrc-ide.github.io/covid19estimates/
We estimated parameters jointly for all M=14 countries in a single
hierarchical model. Inference is performed in the probabilistic programming
language Stan using an adaptive Hamiltonian Monte Carlo (HMC) sampler