Sensitivity Analysis of Pandemic Models Can Support Effective Policy Decisions
本文说明敏感性分析方法能识别模型预测中的关键不确定性来源，从而帮助政策制定者优先关注干预相关参数而非流行病学参数，以支持更有效的决策。
The COVID-19 pandemic has required international scientific efforts to address important aspects of the pandemic. Data science and scientific modeling are extensively used to provide assessments and predictions for policy-making purposes. However, resulting communications need to be supported by a proper uncertainty quantification to assess variability in model predictions, by the identification of the key-uncertainty drivers. This information can be provided by statisticians with sensitivity analysis methods. Knowing the drivers of uncertainty supports effective policy-making. Concerning the COVID-19 pandemic diffusion, two recent investigations reveal intervention-related parameters as more important than epidemiological parameters in two different modeling exercises. This result can help prioritize policy decisions.