面向大流行病复杂模型决策支持的实时校准预测：第一部分
On real-time calibrated prediction for complex model-based decision support in pandemics: Part 1
针对快速演变的大流行病中传统不确定性量化（UQ）方法难以落地的问题，Daniel Williamson 等人提出一种将模型偏差嵌入模型结构、并在模拟中合理同化数据的校准替代方案，可在实时条件下校准模型并生成纳入参数不确定性与模型偏差的疾病轨迹。作者将该方法应用于英格兰的年龄结构 COVID-19 模型，展示了其在 2020 年 3 月封锁前可产出用于政策支持的信息类型。研究指出，英国在 COVID-19 大流行期间并未常规使用 UQ 技术为决策者提供支持。
来源：PLOS Computational Biology · journals.plos.org
by Daniel Williamson, Trevelyan J. McKinley, Xiaoyu Xiong, James M. Salter, Robert Challen, Leon Danon, Ben Youngman, Doug McNeall
Infectious disease models are used to predict the spread and impact of outbreaks of a disease. Like other complex models, they have parameters that need to be calibrated, and structural discrepancies from the reality that they simulate that should be accounted for in calibration and prediction. Whilst Uncertainty Quantification (UQ) techniques have been applied to infectious disease models before, they were not routinely used to inform policymakers in the UK during the COVID-19 pandemic. In this paper, we will argue that during a fast moving pandemic, models and policy are changing on timescales that make traditional UQ methods impractical, if not impossible to implement. We present an alternative formulation to the calibration problem that embeds model discrepancy within the structure of the model, and appropriately assimilates data within the simulation. We then show how UQ can be used to calibrate the model in real-time to produce disease trajectories accounting for parameter uncertainty and model discrepancy. We apply these ideas to an age-structured COVID-19 model for England and demonstrate the types of information it could have produced to feed into policy support prior to the lockdown of March 2020.