Inference of COVID-19 epidemiological distributions from Brazilian hospital data
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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 hospitalized with COVID-19 using a large dataset ( N = 21 000 − 157 000) from the Brazilian Sistema de Informação de Vigilância Epidemiológica 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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SciScore for 10.1101/2020.07.15.20154617: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:In the work presented we acknowledge numerous limitations. The database from which distributions have been extracted, though extensive, contains transcription errors, and the degree to which these bias our estimates is largely unknown. Secondly, the PDFs fitted are based on observational hospital data, and therefore should be cautiously interpreted for other settings. Thirdly, though we have fitted PDFs at subnational as well as …
SciScore for 10.1101/2020.07.15.20154617: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:In the work presented we acknowledge numerous limitations. The database from which distributions have been extracted, though extensive, contains transcription errors, and the degree to which these bias our estimates is largely unknown. Secondly, the PDFs fitted are based on observational hospital data, and therefore should be cautiously interpreted for other settings. Thirdly, though we have fitted PDFs at subnational as well as national level, this partition is largely arbitrary and further work is required to understand the likely substantial effect of age, sex, ethnic variation,[33] co-morbidities, and other factors.
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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