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

05/13/2020
by   Zhaozhi Qian, et al.
0

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.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

05/13/2020

When to Lift the Lockdown? Global COVID-19 Scenario Planning and Policy Effects using Compartmental Gaussian Processes

The coronavirus disease 2019 (COVID-19) outbreak has led government offi...
06/12/2020

Data-driven Simulation and Optimization for Covid-19 Exit Strategies

The rapid spread of the Coronavirus SARS-2 is a major challenge that led...
07/27/2020

On the robustness of effectiveness estimation of nonpharmaceutical interventions against COVID-19 transmission

There remains much uncertainty about the relative effectiveness of diffe...
04/15/2021

ROC: An Ontology for Country Responses towards COVID-19

The ROC ontology for country responses to COVID-19 provides a model for ...
06/24/2020

Quantifying Policy Responses to a Global Emergency: Insights from the COVID-19 Pandemic

Public policy must confront emergencies that evolve in real time and in ...
06/10/2020

Rethinking Case Fatality Ratios for COVID-19 from a data-driven viewpoint

The case fatality ratio (CFR) for COVID-19 is difficult to estimate. One...
09/14/2020

Disease control as an optimization problem

Traditionally, expert epidemiologists devise policies for disease contro...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.