论文标题
何时以及如何举起锁定？全球COVID-19的场景分析和政策评估使用隔间高斯流程
When and How to Lift the Lockdown? Global COVID-19 Scenario Analysis and Policy Assessment using Compartmental Gaussian Processes
论文作者
论文摘要
2019年冠状病毒病（COVID-19）全球大流行使许多国家采取了前所未有的锁定措施，以减慢疫情的速度。关于政府是否迅速采取了足够的行动以及是否可以尽快取消封锁措施的问题，此后在公共话语中是核心。数据驱动的模型预测COVID-19在不同的锁定政策方案下的死亡人数，对于解决这些问题并以未来的政策方向告知政府至关重要。为此，本文开发了一个贝叶斯模型，用于预测全球环境中共同锁定政策的影响 - 我们将每个国家视为一个独特的数据点，并利用各国各国的政策变化来学习特定国家的政策效应。 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其政策指标。我们的模型结合了SEIR模型（贝叶斯先验）的固体机械基础与灵活的数据驱动建模和基于梯度的机器学习（贝叶斯后期）的优化程序 - 即，整个模型通过随机变量推断对端到端进行了训练。我们将模型与疾病控制中心（CDC）列出的其他模型进行比较，将Covid-19的死亡人数的投影与各种锁定和重新开放策略的场景分析相结合，突显了其对COVID-19的影响。
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.
