Imperial College London
Portrait Picture

Professor Axel Gandy

Faculty of Natural Sciences, Department of Mathematics

Head of Department of Mathematics & Chair in Statistics
 
 
 
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Contact

 

+44 (0)20 7594 8518a.gandy Website

 
 
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Location

 

644Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Hawryluk:2020:10.1098/rsif.2020.0596,
author = {Hawryluk, I and Mellan, TA and Hoeltgebaum, H and Mishra, S and Schnekenberg, RP and Whittaker, C and Zhu, H and Gandy, A and Donnelly, CA and Flaxman, S and Bhatt, S},
doi = {10.1098/rsif.2020.0596},
journal = {Journal of The Royal Society Interface},
pages = {20200596--20200596},
title = {Inference of COVID-19 epidemiological distributions from Brazilian hospital data},
url = {http://dx.doi.org/10.1098/rsif.2020.0596},
volume = {17},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Hawryluk,I
AU  - Mellan,TA
AU  - Hoeltgebaum,H
AU  - Mishra,S
AU  - Schnekenberg,RP
AU  - Whittaker,C
AU  - Zhu,H
AU  - Gandy,A
AU  - Donnelly,CA
AU  - Flaxman,S
AU  - Bhatt,S
DO  - 10.1098/rsif.2020.0596
EP  - 20200596
PY  - 2020///
SN  - 1742-5662
SP  - 20200596
TI  - Inference of COVID-19 epidemiological distributions from Brazilian hospital data
T2  - Journal of The Royal Society Interface
UR  - http://dx.doi.org/10.1098/rsif.2020.0596
UR  - https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2020.0596
UR  - http://hdl.handle.net/10044/1/83815
VL  - 17
ER  -
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