A follow up report validating long term predictions of the COVID-19 epidemic in the UK using a dynamic causal model.

ABSTRACT:

Background

This paper asks whether Dynamic Causal modelling (DCM) can predict the long-term clinical impact of the COVID-19 epidemic. DCMs are designed to continually assimilate data and modify model parameters, such as transmissibility of the virus, changes in social distancing and vaccine coverage-to accommodate changes in population dynamics and virus behavior. But as a novel way to model epidemics do they produce valid predictions? We presented DCM predictions 12 months ago, which suggested an increase in viral transmission was accompanied by a reduction in pathogenicity. These changes provided plausible reasons why the model underestimated deaths, hospital admissions and acute-post COVID-19 syndrome by 20%. A further 12-month validation exercise could help to assess how useful

SUBMITTER: Bowie C 

PROVIDER: S-EPMC11416950 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature