NONNEGATIVE TENSOR COMPLETION FOR DYNAMIC COUNTERFACTUAL PREDICTION ON COVID-19 PANDEMIC

The COVID-19 pandemic has been a worldwide health crisis for the past three years, casting unprecedented challenges for policy makers in different countries and regions. While one country or region can only implement one social mobility restriction policy at a given time, it is of great interest for policy makers to decide whether to elevate or de-elevate the restriction policy from time to time. This article proposes a novel non-negative tensor completion method to predict the potential counterfactual outcomes of multifaceted social mobility restriction policies over time. The proposed method builds upon a low-rank tensor decomposition of the pandemic data, which also explicitly characterizes the ordinal nature of the mobility restriction strength and the smooth trend of the pandemic evolution over time. Its application on the COVID-19 pandemic data reveals some interesting facts regarding the impact of social mobility restriction policy on the spread of the virus. The effectiveness of the proposed method is also supported by its asymptotic estimation consistency and extensive numerical experiments on the synthetic datasets. © 2024 Institute of Mathematical Statistics.