Model uncertainty and decision making: Predicting the Impact of COVID-19 Using the CovidSim Epidemiological Code
Model uncertainty and decision making: Predicting the Impact of COVID-19 Using the CovidSim Epidemiological Code
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模型不确定性和决策：使用 CovidSim 流行病学代码预测 COVID-19 的影响
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
P. Coveney
中科院分区：
文献类型：
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作者：
W. Edeling;Arabnejad Hamid;Robert C. Sinclair;D. Suleimenova;Krishnakumar Gopalakrishnan;B. Bosak;D. Groen;Imran Mahmood;D. Crommelin;P. Coveney
自2019年12月以来，严重急性呼吸系统综合征冠状病毒2型（SARS-CoV 2）病毒在全球迅速传播，这种大流行的早期建模工作有助于确定有效的政府干预措施。英国政府部分依赖伦敦帝国理工学院MRC全球传染病分析中心开发的CovidSim模型，以模拟各种非药物干预策略，并指导其政府政策，以寻求应对二零二零年三月至四月期间COVID-19疫情的快速蔓延。CovidSim受到不同来源的不确定性的影响，即输入的参数不确定性、模型结构不确定性（即，缺少流行病学过程）和情景不确定性，这涉及到模型应用条件的不确定性。我们对当前CovidSim代码进行了广泛的参数敏感性分析和不确定性量化。从CovidSim输入的900多个参数中，我们确定了代码输出最敏感的19个参数的关键子集。我们发现，在代码中的不确定性是实质性的，在这个意义上，不完善的知识，在这些输入将被放大到输出，到ca的程度。百分之三百这种不确定性大部分可以追溯到三个参数的敏感性。除此之外，模型可能会显示相对于观察数据的显著偏差，使得输出方差无法以高概率捕获此验证数据。我们的结论是，量化的参数输入的不确定性是不够的，模型结构和场景的不确定性的影响不能忽略验证模型的概率意义上。
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) virus has rapidly spread worldwide since December 2019, and early modelling work of this pandemic has assisted in identifying effective government interventions. The UK government relied in part on the CovidSim model developed by the MRC Centre for Global Infectious Disease Analysis at Imperial College London, to model various non-pharmaceutical intervention strategies, and guide its government policy in seeking to deal with the rapid spread of the COVID-19 pandemic during March and April 2020. CovidSim is subject to different sources of uncertainty, namely parametric uncertainty in the inputs, model structure uncertainty (i.e., missing epidemiological processes) and scenario uncertainty, which relates to uncertainty in the set of conditions under which the model is applied. We have undertaken an extensive parametric sensitivity analysis and uncertainty quantification of the current CovidSim code. From the over 900 parameters that are provided as input to CovidSim, we identified a key subset of 19 parameters to which the code output is most sensitive. We find that the uncertainty in the code is substantial, in the sense that imperfect knowledge in these inputs will be magnified to the outputs, up to the extent of ca. 300%. Most of this uncertainty can be traced back to the sensitivity of three parameters. Compounding this, the model can display significant bias with respect to observed data, such that the output variance does not capture this validation data with high probability. We conclude that quantifying the parametric input uncertainty is not sufficient, and that the effect of model structure and scenario uncertainty cannot be ignored when validating the model in a probabilistic sense.