Inference of COVID-19 epidemiological distributions from Brazilian hospital data.
Inference of COVID-19 epidemiological distributions from Brazilian hospital data.
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DOI：
10.1098/rsif.2020.0596
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发表时间：
2020-11
期刊：
影响因子：
--
通讯作者：
Bhatt S
中科院分区：
文献类型：
--
作者：
Hawryluk I;Mellan TA;Hoeltgebaum H;Mishra S;Schnekenberg RP;Whittaker C;Zhu H;Gandy A;Donnelly CA;Flaxman S;Bhatt S
了解 COVID-19 流行病学分布（例如从患者入院到死亡的时间）与有效的初级和二级护理计划直接相关，而且与大流行的数学模型直接相关。我们使用巴西 Sistema de Informação de Vigilância Epidemiológica da Gripe 数据库中的大型数据集 (N = 21 000 − 157 000) 确定了因 COVID-19 住院的患者的流行病学分布。采用部分合并的联合贝叶斯国家以下模型同时描述巴西 26 个州和一个联邦区，结果显示症状出现到死亡时间的平均值存在显着差异，不同州的范围在 11.2 至 17.8 天之间，巴西的平均值为 15.2 天。我们发现了支持特定概率密度函数选择的有力证据：例如，伽玛分布给出了发病到死亡的最佳拟合以及发病到入院的广义对数正态分布。我们的结果表明，流行病学分布具有相当大的地理差异，并提供了低收入和中等收入环境中这些分布的初步估计。在国家以下各级，发现 COVID-19 结果时间的变化与贫困、剥夺和隔离水平相关，而与平均年龄、财富和城市化程度的相关性较弱。
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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影响因子：
14.6
作者：
Abdollahi, Elaheh;Champredon, David;Moghadas, Seyed M.
通讯作者：
Moghadas, Seyed M.
影响因子：
34.3
作者：
Baqui, Pedro;Bica, Ioana;van der Schaar, Mihaela
通讯作者：
van der Schaar, Mihaela
影响因子：
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作者：
Jombart, Thibaut;van Zandvoort, Kevin;Edmunds, W John
通讯作者：
Edmunds, W John
影响因子：
2.8
作者：
Niquini, Roberta Pereira;Lana, Raquel Martins;Bastos, Leonardo Soares
通讯作者：
Bastos, Leonardo Soares
DOI：
10.1016/s0140-6736(03)13410-1
发表时间：
2003-05-24
期刊：
Lancet (London, England)
影响因子：
--
作者：
Donnelly CA;Ghani AC;Leung GM;Hedley AJ;Fraser C;Riley S;Abu-Raddad LJ;Ho LM;Thach TQ;Chau P;Chan KP;Lam TH;Tse LY;Tsang T;Liu SH;Kong JH;Lau EM;Ferguson NM;Anderson RM
通讯作者：
Anderson RM