Journal
JOURNAL OF THE ROYAL SOCIETY INTERFACE
Volume 17, Issue 172, Pages -Publisher
ROYAL SOC
DOI: 10.1098/rsif.2020.0596
Keywords
COVID-19; Brazil; symptom-onset-to-death; admission-to-death; model selection
Categories
Funding
- MRC Centre for Global Infectious Disease Analysis - UK Medical Research Council (MRC) [MR/R015600/1]
- MRC Centre for Global Infectious Disease Analysis - UK Foreign, Commonwealth & Development Office (FCDO), under the MRC/FCDO Concordat agreement [MR/R015600/1]
- European Union
- Clarendon Fund
- Merton College, University of Oxford
- EPSRC Centre for Doctoral Training in Modern Statistics and Statistical Machine Learning [EP/S023151/1]
- Department of Mathematics of Imperial College London
- Cervest Limited
- NIHR Health Protection Research Unit in Emerging and Zoonotic Infections
- Academy of Medical Sciences [SBF004/1080]
- Bill & Melinda Gates Foundation [CRR00280]
- Imperial College Healthcare NHS Trust -BRC Funding [RDA02]
- Imperial College COVID-19 Research Fund
- MRC [MC_PC_19012, MR/R015600/1] Funding Source: UKRI
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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 hospitalized with COVID-19 using a large dataset (N = 21 000 - 157 000) from the Brazilian Sistema de InformacAo de Vigilancia Epidemiologica 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 and 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 generalized lognormal 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.
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