  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.
