An Analysis of Inoculation Rates Utilizing Statistical Learning to Validate the Significance of Predictors

Abstract

In a previous study, data analyzed from measuring cell phone signal movement showed that the most predictive factors of social distancing in response to the SARS-CoV-2 (COVID-19) pandemic were income and population. While a great deal of factors could be investigated to determine what the most predictive factors of inoculation rates are, this study is a continuation of the previous work by Smith, Boquet, and Chin (2020) and investigates if there is a significant difference between inoculation rates when separated by median income and population. Data is drawn from the State of Florida counties to remain consistent with the preceding work. The twosample t-test performed revealed that there was a significant difference between the inoculation rates of counties with a high population when compared to those with a low population. A similar result was found for the inoculation rates of counties with high a median income when compared to those with a low median income. These results demonstrated that median county income and county population had impacts on both inoculation rates and social distancing.