Tracking the emergence of disparities in the subnational spread of COVID-19 in Brazil using an online application for real-time data visualisation: A longitudinal analysis.
Tracking the emergence of disparities in the subnational spread of COVID-19 in Brazil using an online application for real-time data visualisation: A longitudinal analysis.
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DOI：
10.1016/j.lana.2021.100119
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发表时间：
2022-01
期刊：
影响因子：
--
通讯作者：
Brady OJ
中科院分区：
文献类型：
--
作者：
Mee P;Alexander N;Mayaud P;González FJC;Abbott S;Santos AAS;Acosta AL;Parag KV;Pereira RHM;Prete CA Jr;Sabino EC;Faria NR;LSHTM Centre for Mathematical Modelling of Infectious Disease COVID-19 working group;Brady OJ
巴西是受COVID-19疫情影响最严重的国家之一，截至2021年8月，已报告超过2，000万例病例及557，000例死亡。比较不同地区的实时本地COVID-19数据对于了解传播、衡量干预措施的效果和预测疫情进程至关重要，但由于人口规模和结构不同，这往往具有挑战性。我们描述了一个新的应用程序的开发，用于在巴西市政一级实时可视化COVID-19数据。在CLIC-Brazil应用程序中，每日更新的病例和死亡数据被下载，年龄标准化并用于估计有效繁殖数（Rt）。我们展示了此类平台如何执行实时回归分析，以识别与初始传播率和早期繁殖数量相关的因素。我们亦使用生存方法预测COVID-19发病率出现新高峰的可能性。2020年3月初在圣保罗州和里约热内卢州初步传入后，疫情蔓延至北方各州，随后又蔓延至人口密集的沿海地区和中西部。社会发展指标较高的国家经历了COVID-19的提前到来（社会发展指数每增加10%，到达时间减少11.1天[95%CI：8.9，13.2]）。初始流行强度（平均Rt）的差异主要由地理位置和当地发病日期决定。这项研究表明，在地方一级监测、分析和分析流行病学数据的平台可以提供有关疫情动态的有用的实时见解，可用于更好地调整应对当前和未来大流行病的措施。该项目得到了英国医学研究理事会（MRC-UK）-圣保罗研究基金会（FAPESP）CADDE合作奖（MR/S 0195/1和FAPESP 18/14389-0）的支持。
Brazil is one of the countries worst affected by the COVID-19 pandemic with over 20 million cases and 557,000 deaths reported by August 2021. Comparison of real-time local COVID-19 data between areas is essential for understanding transmission, measuring the effects of interventions, and predicting the course of the epidemic, but are often challenging due to different population sizes and structures. We describe the development of a new app for the real-time visualisation of COVID-19 data in Brazil at the municipality level. In the CLIC-Brazil app, daily updates of case and death data are downloaded, age standardised and used to estimate the effective reproduction number (Rt). We show how such platforms can perform real-time regression analyses to identify factors associated with the rate of initial spread and early reproduction number. We also use survival methods to predict the likelihood of occurrence of a new peak of COVID-19 incidence. After an initial introduction in São Paulo and Rio de Janeiro states in early March 2020, the epidemic spread to northern states and then to highly populated coastal regions and the Central-West. Municipalities with higher metrics of social development experienced earlier arrival of COVID-19 (decrease of 11·1 days [95% CI:8.9,13.2] in the time to arrival for each 10% increase in the social development index). Differences in the initial epidemic intensity (mean Rt) were largely driven by geographic location and the date of local onset. This study demonstrates that platforms that monitor, standardise and analyse the epidemiological data at a local level can give useful real-time insights into outbreak dynamics that can be used to better adapt responses to the current and future pandemics. This project was supported by a Medical Research Council UK (MRC-UK) -São Paulo Research Foundation (FAPESP) CADDE partnership award (MR/S0195/1 and FAPESP 18/14389-0)
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影响因子：
56.9
作者：
Castro, Marcia C.;Kim, Sun;Singer, Burton H.
通讯作者：
Singer, Burton H.
影响因子：
5
作者：
Cauchemez, Simon;Boelle, Pierre-Yves;Valleron, Alain-Jacques
通讯作者：
Valleron, Alain-Jacques
影响因子：
3.5
作者：
Eubank, S.;Eckstrand, I;Barrett, C. L.
通讯作者：
Barrett, C. L.
DOI：
10.1126/science.abh2644
发表时间：
2021-05-21
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Faria NR;Mellan TA;Whittaker C;Claro IM;Candido DDS;Mishra S;Crispim MAE;Sales FCS;Hawryluk I;McCrone JT;Hulswit RJG;Franco LAM;Ramundo MS;de Jesus JG;Andrade PS;Coletti TM;Ferreira GM;Silva CAM;Manuli ER;Pereira RHM;Peixoto PS;Kraemer MUG;Gaburo N Jr;Camilo CDC;Hoeltgebaum H;Souza WM;Rocha EC;de Souza LM;de Pinho MC;Araujo LJT;Malta FSV;de Lima AB;Silva JDP;Zauli DAG;Ferreira ACS;Schnekenberg RP;Laydon DJ;Walker PGT;Schlüter HM;Dos Santos ALP;Vidal MS;Del Caro VS;Filho RMF;Dos Santos HM;Aguiar RS;Proença-Modena JL;Nelson B;Hay JA;Monod M;Miscouridou X;Coupland H;Sonabend R;Vollmer M;Gandy A;Prete CA Jr;Nascimento VH;Suchard MA;Bowden TA;Pond SLK;Wu CH;Ratmann O;Ferguson NM;Dye C;Loman NJ;Lemey P;Rambaut A;Fraiji NA;Carvalho MDPSS;Pybus OG;Flaxman S;Bhatt S;Sabino EC
通讯作者：
Sabino EC
影响因子：
3.7
作者：
Dudel, Christian;Riffe, Tim;Myrskyla, Mikko
通讯作者：
Myrskyla, Mikko