Estimating the number of infections and the impact of non-pharmaceutical interventions on COVID-19 in European countries: technical description update
Estimating the number of infections and the impact of non-pharmaceutical interventions on COVID-19 in European countries: technical description update
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估计欧洲国家的感染人数以及非药物干预措施对 COVID-19 的影响：技术说明更新
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
S. Bhatt
中科院分区：
文献类型：
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作者：
S. Flaxman;Swapnil Mishra;A. Gandy;H. Unwin;H. Coupland;T. Mellan;Harrison Zhu;Tresnia Berah;J. Eaton;Pablo N P Guzman;N. Schmit;L. Callizo;Imperial College COVID;C. Whittaker;P. Winskill;X. Xi;A. Ghani;C. Donnelly;S. Riley;L. Okell;M. Vollmer;N. Ferguson;S. Bhatt
随着新型冠状病毒（SARS-CoV-2）的出现及其在中国以外的传播，欧洲经历了大规模的流行病。为此，许多欧洲国家实施了前所未有的非药物干预措施，包括隔离病例、关闭学校和大学、禁止大规模集会和/或公共活动，以及最近的大规模社交距离，包括地方和国家封锁。
Following the emergence of a novel coronavirus (SARS-CoV-2) and its spread outside of China, Europe has experienced large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, wide-scale social distancing including local and national lockdowns.
In this technical update, we extend a semi-mechanistic Bayesian hierarchical model that infers the impact of these interventions and estimates the number of infections over time. Our methods assume that changes in the reproductive number - a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from temporal data on observed to estimate the number of infections and rate of transmission that occurred several weeks prior, allowing for a probabilistic time lag between infection and death.
In this update we extend our original model [Flaxman, Mishra, Gandy et al 2020, Report #13, Imperial College London] to include (a) population saturation effects, (b) prior uncertainty on the infection fatality ratio, (c) a more balanced prior on intervention effects and (d) partial pooling of the lockdown intervention covariate. We also (e) included another 3 countries (Greece, the Netherlands and Portugal).
The model code is available at this https URL
We are now reporting the results of our updated model online at this https URL
We estimated parameters jointly for all M=14 countries in a single hierarchical model. Inference is performed in the probabilistic programming language Stan using an adaptive Hamiltonian Monte Carlo (HMC) sampler.
影响因子：
5.8
作者：
Carpenter, Bob;Gelman, Andrew;Riddell, Allen
通讯作者：
Riddell, Allen