Inference of COVID-19 epidemiological distributions from Brazilian hospital data

TL;DR

This study estimates COVID-19 epidemiological distributions in Brazil using a large hospital dataset, revealing significant geographical variation and correlations with socioeconomic factors, aiding pandemic modeling and healthcare planning.

Contribution

It introduces a Bayesian subnational model to estimate epidemiological distributions across Brazil's states, highlighting regional differences and socioeconomic influences.

Findings

Significant variation in symptom-onset-to-death times across states (11.2-17.8 days).

Gamma distribution best fits onset-to-death data; generalized log-normal fits onset-to-hospital-admission.

Epidemiological distributions vary geographically and correlate with poverty, deprivation, and segregation.

Abstract

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.