  Knowing the infection fatality ratio (IFR) is of crucial importance for
evidence-based epidemic management: for immediate planning; for balancing the
life years saved against the life years lost due to the consequences of
management; and for evaluating the ethical issues associated with the tacit
willingness to pay substantially more for life years lost to the epidemic, than
for those to other diseases. Against this background Verity et al. (2020,
Lancet Infections Diseases) have rapidly assembled case data and used
statistical modelling to infer the IFR for COVID-19. We have attempted an
in-depth statistical review of their approach, to identify to what extent the
data are sufficiently informative about the IFR to play a greater role than the
modelling assumptions, and have tried to identify those assumptions that appear
to play a key role. Given the difficulties with other data sources, we provide
a crude alternative analysis based on the Diamond Princess Cruise ship data and
case data from China, and argue that, given the data problems, modelling of
clinical data to obtain the IFR can only be a stop-gap measure. What is needed
is near direct measurement of epidemic size by PCR and/or antibody testing of
random samples of the at risk population.
