Real-time forecasting of COVID-19-related hospital strain in France using a non-Markovian mechanistic model.

IF 4.3 2区 生物学
Alexander Massey, C. Boennec, C. X. Restrepo-Ortiz, Christophe Blanchet, Samuel Alizon, Mircea T. Sofonea
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引用次数: 0

Abstract

Projects such as the European Covid-19 Forecast Hub publish forecasts on the national level for new deaths, new cases, and hospital admissions, but not direct measurements of hospital strain like critical care bed occupancy at the sub-national level, which is of particular interest to health professionals for planning purposes. We present a sub-national French framework for forecasting hospital strain based on a non-Markovian compartmental model, its associated online visualisation tool and a retrospective evaluation of the real-time forecasts it provided from January to December 2021 by comparing to three baselines derived from standard statistical forecasting methods (a naive model, auto-regression, and an ensemble of exponential smoothing and ARIMA). In terms of median absolute error for forecasting critical care unit occupancy at the two-week horizon, our model only outperformed the naive baseline for 4 out of 14 geographical units and underperformed compared to the ensemble baseline for 5 of them at the 90% confidence level (n = 38). However, for the same level at the 4 week horizon, our model was never statistically outperformed for any unit despite outperforming the baselines 10 times spanning 7 out of 14 geographical units. This implies modest forecasting utility for longer horizons which may justify the application of non-Markovian compartmental models in the context of hospital-strain surveillance for future pandemics.
利用非马尔可夫机理模型实时预测法国与 COVID-19 相关的医院菌株。
欧洲 Covid-19 预测中心等项目发布了国家层面的新增死亡人数、新增病例和入院人数预测,但没有发布次国家层面的重症监护床位占用率等医院负荷的直接测量值,而这正是卫生专业人员在规划时特别感兴趣的。我们介绍了一个基于非马尔可夫分区模型的法国次国家级医院负荷预测框架、其相关的在线可视化工具,以及对其在 2021 年 1 月至 12 月期间提供的实时预测的回顾性评估,并将其与标准统计预测方法得出的三个基线(天真模型、自动回归以及指数平滑和 ARIMA 组合)进行了比较。就两周范围内重症监护病房入住率预测的绝对误差中位数而言,在 90% 置信度水平(n = 38)下,我们的模型仅在 14 个地理单位中的 4 个单位优于天真基线,在其中 5 个单位优于集合基线。然而,在 4 周范围内的相同水平上,尽管我们的模型在 14 个地理单元中的 7 个单元中 10 次优于基线,但从未在统计上优于任何单元。这意味着在更长的时间跨度上,非马尔可夫分区模型的预测效用并不高,这可能证明在未来大流行病的医院菌株监测中应用非马尔可夫分区模型是合理的。
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来源期刊
PLoS Computational Biology
PLoS Computational Biology 生物-生化研究方法
CiteScore
7.10
自引率
4.70%
发文量
820
期刊介绍: PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.
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