  The paper quantifies the impact of race, poverty, politics, and age on
COVID-19 vaccination rates in counties in the continental US. Both, OLS
regression analysis and Random Forest machine learning algorithms are applied
to quantify factors for county-level vaccination hesitancy. The machine
learning model considers joint effects of variables (race/ethnicity,
partisanship, age, etc.) simultaneously to capture the unique combination of
these factors on the vaccination rate. By implementing a state-of-the-art
Artificial Intelligence Explanations (AIX) algorithm, it is possible to solve
the black box problem with machine learning models and provide answers to the
"how much" question for each measured impact factor in every county. For most
counties, a higher percentage vote for Republicans, a greater African American
population share, and a higher poverty rate lower the vaccination rate. While a
higher Asian population share increases the predicted vaccination rate. The
impact on the vaccination rate from the Hispanic population proportion is
positive in the OLS model, but only positive for counties with a high Hispanic
population (>65%) in the Random Forest model. Both the proportion of seniors
and the one for young people in a county have a significant impact in the OLS
model - positive and negative, respectively. In contrast, the impacts are
ambiguous in the Random Forest model. Because results vary between geographies
and since the AIX algorithm is able to quantify vaccine impacts individually
for each county, this research can be tailored to local communities. An
interactive online mapping dashboard that identifies impact factors for
individual U.S. counties is available at
https://www.cpp.edu/~clange/vacmap.html. It is apparent that the influence of
impact factors is not universally the same across different geographies.
