We apply Bayesian inference methods to a suite of distinct
compartmental models of generalised SEIR type, in which diagnosis
and quarantine are included via extra compartments. We investigate
the evidence for a change in lethality of COVID-19 in late autumn
2020 in the UK, using age-structured, weekly national aggregate data
for cases and mortalities. Models that allow a (step-like or graded)
change in infection fatality rate (IFR) have consistently higher
model evidence than those without. Moreover, they all infer a close
to two-fold increase in IFR. This value lies well above most
previously available estimates. However, the same models
consistently infer that, most probably, the increase in IFR
preceded the time window during which new variant B.1.1.7
became the dominant strain in the UK. Therefore, according to our
models, the caseload and mortality data do not offer unequivocal
evidence for higher lethality of a new variant. We compare
these results for the UK with similar models for Germany and France,
which also show increases in inferred IFR during the same period,
despite the even later arrival of new variants in those
countries. We argue that while the new variant(s) may be one
contributing cause of a large increase in IFR in the UK in autumn
2020, other factors, such as seasonality, or pressure on health
services, are likely to also have contributed.