标题： 何时以及如何解除封锁？ 基于 compartmental Gaussian Processes 的全球 COVID-19 情景分析与政策评估 显示英文标题

标题： When and How to Lift the Lockdown? Global COVID-19 Scenario Analysis and Policy Assessment using Compartmental Gaussian Processes

摘要： 2019冠状病毒病（COVID-19）全球大流行已导致许多国家采取前所未有的封锁措施以减缓疫情传播。关于政府是否行动足够迅速以及封锁措施能否尽快解除的问题已成为公众讨论的核心。 基于数据驱动的模型，预测不同封锁政策情景下的COVID-19死亡人数对于回答这些问题并指导政府未来的政策方向至关重要。 为此，本文开发了一种贝叶斯模型，用于在全球范围内预测COVID-19封锁政策的效果——我们将每个国家视为一个独立的数据点，并利用各国政策的变化来学习特定国家的政策效果。 我们的模型采用两层高斯过程（GP）先验——下层使用分室SEIR（易感、暴露、感染、恢复）模型作为先验均值函数，其中包含“国家和政策特定”的参数，以捕捉各国在“反事实”政策下的死亡曲线，而上层则在所有国家之间共享，并通过国家特征及其政策指标学习下层SEIR参数。 我们的模型结合了SEIR模型的坚实机制基础（贝叶斯先验）与机器学习（贝叶斯后验）灵活的数据驱动建模和基于梯度的优化程序——即整个模型通过随机变分推理端到端训练。 我们将我们模型对COVID-19死亡人数的预测与其他疾病控制中心（CDC）列出的模型进行比较，并针对各种封锁和重新开放策略提供场景分析，突出其对COVID-19死亡人数的影响。 显示英文摘要

摘要： The coronavirus disease 2019 (COVID-19) global pandemic has led many countries to impose unprecedented lockdown measures in order to slow down the outbreak. Questions on whether governments have acted promptly enough, and whether lockdown measures can be lifted soon have since been central in public discourse. Data-driven models that predict COVID-19 fatalities under different lockdown policy scenarios are essential for addressing these questions and informing governments on future policy directions. To this end, this paper develops a Bayesian model for predicting the effects of COVID-19 lockdown policies in a global context -- we treat each country as a distinct data point, and exploit variations of policies across countries to learn country-specific policy effects. Our model utilizes a two-layer Gaussian process (GP) prior -- the lower layer uses a compartmental SEIR (Susceptible, Exposed, Infected, Recovered) model as a prior mean function with "country-and-policy-specific" parameters that capture fatality curves under "counterfactual" policies within each country, whereas the upper layer is shared across all countries, and learns lower-layer SEIR parameters as a function of a country's features and its policy indicators. Our model combines the solid mechanistic foundations of SEIR models (Bayesian priors) with the flexible data-driven modeling and gradient-based optimization routines of machine learning (Bayesian posteriors) -- i.e., the entire model is trained end-to-end via stochastic variational inference. We compare the projections of COVID-19 fatalities by our model with other models listed by the Center for Disease Control (CDC), and provide scenario analyses for various lockdown and reopening strategies highlighting their impact on COVID-19 fatalities.