The van der Schaar Lab is delighted to announce that Zhaozhi Qian has received the 2021 CSAR Ph.D. student award administered by the Cambridge Society for the Application of Research.
The purpose of the award is to recognize Ph.D. students from the University of Cambridge who are conducting outstanding research with real world application. Zhaozhi Qian’s award-winning research involved the creation of a machine learning tool developed to guide government decision-making around measures to prevent the spread of COVID-19.
In addition to accurately modeling COVID-19 mortality trends under current policy sets, the model in question is able to adaptively tailor forecasts to show the potential impact of specific policy changes, such as reopening schools or workplaces, implementing mask mandates, or relaxing shelter-in-place requirements. Further details can be found on the lab’s page dedicated to COVID-19 research; the title and abstract of the award-winning paper can be found directly below.
When and How to Lift the Lockdown?
Global COVID-19 Scenario Analysis and Policy Assessment using Compartmental Gaussian Processes
Zhaozhi Qian, Ahmed M. Alaa, Mihaela van der Schaar
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 for informing governments on future policy directions.
To this end, this paper develops a Bayesian model for predicting the effects of COVID-19 containment 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 different “counterfactual” policies within each country, whereas the upper layer is shared across all countries, and learns lower-layer SEIR parameters as a function of country features and 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 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.
For a full list of the van der Schaar Lab’s publications, click here.