EpiBeds: Data informed modelling of the COVID-19 hospital burden in England.
EpiBeds: Data informed modelling of the COVID-19 hospital burden in England.
复制标题
DOI：
10.1371/journal.pcbi.1010406
复制
发表时间：
2022-09
影响因子：
4.3
通讯作者：
中科院分区：
文献类型：
--
作者：
COVID-19大流行的第一年给全球医疗系统带来相当大的压力。为了预测当地流行病对英格兰医院能力的影响，我们使用了各种数据流来告知医院进展模型EpiBeds的构建和参数化，该模型与广义流行病模型相结合。在该模型中，个体通过不同的途径进展（例如，可能恢复、死亡或进展到重症监护和恢复或死亡），并且来自部分完整的患者途径行列表的数据用于提供个体在不同医院隔间中花费的平均持续时间的初始估计。然后，我们使用医院占用率和医院死亡率的完整数据拟合EpiBeds，从而能够估计遵循不同临床路径的个人比例，广义流行病的繁殖数量，并对医院床位需求进行短期预测。EpiBeds的构建可以直接适应英国以外的不同患者路径和环境。作为英国应对疫情的一部分，EpiBeds每周向NHS提供英格兰、威尔士、苏格兰和北方爱尔兰国家和地区范围内的医院床位占用率和入院率预测。COVID-19是由SARS-CoV-2引起的疾病，导致需要住院的病例比例很高。再加上全球感染的高负担，这给医疗保健系统带来了巨大的压力。为了使公共卫生系统能够科普高水平的需求，预测模型至关重要。这些模型使公共卫生管理人员能够相应地规划他们的工作量。在这里，我们开发了EpiBeds，它将流行病模型与医院患者流模型相结合。通过将该模型与英格兰的数据进行拟合，EpiBeds已被用于在整个COVID-19大流行期间每周提供住院人数和床位需求的短期预测。在本文中，我们描述了EpiBeds结构背后的动机，模型如何拟合数据，并报告了整个大流行期间关键参数的估计值。然后，我们通过将生成的预测与未来数据点进行比较来评估EpiBeds的性能，发现预测与数据之间存在良好的一致性。
The first year of the COVID-19 pandemic put considerable strain on healthcare systems worldwide. In order to predict the effect of the local epidemic on hospital capacity in England, we used a variety of data streams to inform the construction and parameterisation of a hospital progression model, EpiBeds, which was coupled to a model of the generalised epidemic. In this model, individuals progress through different pathways (e.g. may recover, die, or progress to intensive care and recover or die) and data from a partially complete patient-pathway line-list was used to provide initial estimates of the mean duration that individuals spend in the different hospital compartments. We then fitted EpiBeds using complete data on hospital occupancy and hospital deaths, enabling estimation of the proportion of individuals that follow the different clinical pathways, the reproduction number of the generalised epidemic, and to make short-term predictions of hospital bed demand. The construction of EpiBeds makes it straightforward to adapt to different patient pathways and settings beyond England. As part of the UK response to the pandemic, EpiBeds provided weekly forecasts to the NHS for hospital bed occupancy and admissions in England, Wales, Scotland, and Northern Ireland at national and regional scales. COVID-19, the disease caused by SARS-CoV-2, leads to a high proportion of cases requiring admission to hospital. Coupled with the high burden of infections worldwide, this put substantial pressure on healthcare systems. To enable public health systems to cope with the high levels of demand, forecasting models are vital. These models enable public health managers to plan their workloads accordingly. Here, we developed EpiBeds, which combines an epidemic model with a model for patient flow through hospitals. By fitting this model to data from England, EpiBeds has been used to provide short-term forecasts of hospital admissions and bed demand weekly throughout the COVID-19 pandemic. In this paper, we describe the motivation behind the structure of EpiBeds, how the model is fitted to data, and report the estimates of the key parameters throughout the pandemic. We then evaluate the performance of EpiBeds by comparing generated forecasts to future data points, finding good agreement between the forecasts and data.
登录
查看更多内容
SARS-COV-2血统的估计可传播和影响B.1.1.7在英国。
DOI：
10.1126/science.abg3055
发表时间：
2021-04-09
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Davies NG;Abbott S;Barnard RC;Jarvis CI;Kucharski AJ;Munday JD;Pearson CAB;Russell TW;Tully DC;Washburne AD;Wenseleers T;Gimma A;Waites W;Wong KLM;van Zandvoort K;Silverman JD;CMMID COVID-19 Working Group;COVID-19 Genomics UK (COG-UK) Consortium;Diaz-Ordaz K;Keogh R;Eggo RM;Funk S;Jit M;Atkins KE;Edmunds WJ
通讯作者：
Edmunds WJ
影响因子：
2.8
作者：
Leclerc QJ;Fuller NM;Keogh RH;Diaz-Ordaz K;Sekula R;Semple MG;ISARIC4C Investigators;CMMID COVID-19 Working Group;Atkins KE;Procter SR;Knight GM
通讯作者：
Knight GM
影响因子：
3.6
作者：
Littig, Steven J;Isken, Mark W
通讯作者：
Isken, Mark W
影响因子：
7.7
作者：
Illingworth CJ;Hamilton WL;Warne B;Routledge M;Popay A;Jackson C;Fieldman T;Meredith LW;Houldcroft CJ;Hosmillo M;Jahun AS;Caller LG;Caddy SL;Yakovleva A;Hall G;Khokhar FA;Feltwell T;Pinckert ML;Georgana I;Chaudhry Y;Curran MD;Parmar S;Sparkes D;Rivett L;Jones NK;Sridhar S;Forrest S;Dymond T;Grainger K;Workman C;Ferris M;Gkrania-Klotsas E;Brown NM;Weekes MP;Baker S;Peacock SJ;Goodfellow IG;Gouliouris T;de Angelis D;Török ME
通讯作者：
Török ME
影响因子：
19
作者：
Mizumoto, Kenji;Kagaya, Katsushi;Chowell, Gerardo
通讯作者：
Chowell, Gerardo