EMSIG: Uncovering Factors Influencing COVID-19 Vaccination Across Different Subgroups Characterized by Embedding-Based Spatial Information Gain.

ABSTRACT: Background/Objectives: COVID-19 and its variants continue to pose significant threats to public health, with considerable uncertainty surrounding their impact. As of September 2024, the total number of deaths reached 8.8 million worldwide. Vaccination remains the most effective strategy for preventing COVID-19. However, vaccination rates in the Deep South, U.S., are notably lower than the national average due to various factors. Methods: To address this challenge, we developed the Embedding-based Spatial Information Gain (EMSIG) method, an innovative tool using machine learning techniques for subgroup modeling. EMSIG helps identify subgroups where participants share similar perceptions but exhibit high variance in COVID-19 vaccine doses. It introduces spatial information gain

SUBMITTER: Yue Z 

PROVIDER: S-EPMC11599077 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature