Unveiling Social Vulnerability: A Variational Inference Framework for Regularized Multivariate Regression

Music City Center 

In this work, we develop a novel variational inference framework for a regularized multivariate regression model that integrates latent clustering with advanced low-rank regression techniques. We demonstrate the utility of our method through simulation studies and an application to county-level COVID-19 outcomes, the Social Vulnerability Index (SVI), and non-pharmaceutical interventions (NPIs) in Florida. Our experiments show that the proposed framework not only enhances model flexibility and computational scalability but also offers valuable insights for targeted interventions, particularly in identifying vulnerable groups.

Low-Rank Regression

Variational Inference

Social Vulnerability 

Section on Statistical Computing