Understanding the Influence of Multiple Factors on the Spread of Omicron Variant Strains via the Multivariate Regression Method

Abstract

The Omicron variant of SARS-CoV-2, emerging in November 2021, has rapidly spread worldwide due to its high transmissibility and ability to evade vaccines. It is still not fully under control, and there is a need to enhance our scientific understanding of the Omicron variant. Investigating the influencing factors and the correlated characteristics of the transmission of the Omicron variant remains an important issue in COVID-19 prevention and control. This study utilized data from various sources to investigate Omicron’s transmission factors. Focusing on populous countries like China, France, and the US, a multiple regression model was optimized through the Gauss-Newton method to reveal links between daily Omicron cases and variables like climate, population, healthcare, and vaccination and etc. Results showed vaccination rates, healthcare facility numbers, and population density as pivotal factors influencing transmission. Higher vaccination rates and more healthcare facilities correlated with lower Omicron transmission, while dense population areas experienced higher spread. These findings hold significance for guiding public health decisions and shaping vaccination strategies amidst the Omicron variant’s ongoing impact.