March 2024 Nonnegative tensor completion for dynamic counterfactual prediction on COVID-19 pandemic
Yaoming Zhen, Junhui Wang
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Ann. Appl. Stat. 18(1): 224-245 (March 2024). DOI: 10.1214/23-AOAS1787

Abstract

The COVID-19 pandemic has been a worldwide health crisis for the past three years, casting unprecedented challenges for policymakers 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 deelevate the restriction policy from time to time. This article proposes a novel nonnegative 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 to 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.

Funding Statement

This work is supported in part by HK RGC Grants GRF-11304520, GRF-11301521, GRF-11311022, CUHK Startup Grant 4937091, and CUHK Direct Grant 4053588.

Acknowledgments

We thank the Associate Editor and two anonymous referees, whose constructive comments and suggestions have led to significant improvements of the article. We also thank Miss Ruixuan Zhao for her helpful discussion on counterfactual prediction.

Citation

Download Citation

Yaoming Zhen. Junhui Wang. "Nonnegative tensor completion for dynamic counterfactual prediction on COVID-19 pandemic." Ann. Appl. Stat. 18 (1) 224 - 245, March 2024. https://doi.org/10.1214/23-AOAS1787

Information

Received: 1 December 2022; Revised: 1 April 2023; Published: March 2024
First available in Project Euclid: 31 January 2024

Digital Object Identifier: 10.1214/23-AOAS1787

Keywords: Causal inference , imputation , informative missing , latent factor , tensor decomposition

Rights: Copyright © 2024 Institute of Mathematical Statistics

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Vol.18 • No. 1 • March 2024
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