English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Knowing COVID-19 epidemiological distributions, such as the time from patientadmission to death, is directly relevant to effective primary and secondarycare planning, and moreover, the mathematical modelling of the pandemicgenerally. We determine epidemiological distributions for patients hospitalisedwith COVID-19 using a large dataset ($N=21{,}000-157{,}000$) from the BrazilianSistema de Informa\c{c}\~ao de Vigil\^ancia Epidemiol\'ogica da Gripe database.A joint Bayesian subnational model with partial pooling is used tosimultaneously describe the 26 states and one federal district of Brazil, andshows 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 of15.2 days for Brazil. We find strong evidence in favour of specific probabilitydensity function choices: for example, the gamma distribution gives the bestfit for onset-to-death and the generalised log-normal foronset-to-hospital-admission. Our results show that epidemiologicaldistributions have considerable geographical variation, and provide the firstestimates of these distributions in a low and middle-income setting. At thesubnational level, variation in COVID-19 outcome timings are found to becorrelated with poverty, deprivation and segregation levels, and weakercorrelation is observed for mean age, wealth and urbanicity.

Inference of COVID-19 epidemiological distributions from Brazilian hospital data