  Knowing COVID-19 epidemiological distributions, such as the time from patient
admission to death, is directly relevant to effective primary and secondary
care planning, and moreover, the mathematical modelling of the pandemic
generally. We determine epidemiological distributions for patients hospitalised
with COVID-19 using a large dataset ($N=21{,}000-157{,}000$) from the Brazilian
Sistema de Informa\c{c}\~ao de Vigil\^ancia Epidemiol\'ogica da Gripe database.
A joint Bayesian subnational model with partial pooling is used to
simultaneously describe the 26 states and one federal district of Brazil, and
shows significant variation in the mean of the symptom-onset-to-death time,
with ranges between 11.2-17.8 days across the different states, and a mean of
15.2 days for Brazil. We find strong evidence in favour of specific probability
density function choices: for example, the gamma distribution gives the best
fit for onset-to-death and the generalised log-normal for
onset-to-hospital-admission. Our results show that epidemiological
distributions have considerable geographical variation, and provide the first
estimates of these distributions in a low and middle-income setting. At the
subnational level, variation in COVID-19 outcome timings are found to be
correlated with poverty, deprivation and segregation levels, and weaker
correlation is observed for mean age, wealth and urbanicity.
