  In the United States, from the start of the COVID-19 pandemic to December 31,
2020, 341,199 deaths and more than 19,663,976 infections were recorded. Recent
literature establishes that communities with poverty-related health problems,
such as obesity, cardiovascular disease, diabetes, and hypertension, are more
susceptible to mortality from SARS-CoV-2 infection. Additionally, controversial
public health policies implemented by the nation's political leaders have
highlighted the socioeconomic inequalities of minorities. Therefore, through
multivariate correlational analysis using machine learning techniques and
structural equations, we measure whether social determinants are associated
with increased infection and death from COVID-19 disease. The PLS least squares
regression analysis allowed identifying a significant impact between social
determinants and COVID-19 disease through a predictive value of R2 = .916,
\b{eta} = .836, p =. 000 (t-value = 66,137) shows that for each unit of
increase in social determinants, COVID-19 disease increases by 91.6%. The
clustering index used for correlational analysis generated a new data set
comprising three groups: C1 Republicans, C2 and C3 Democrats from California,
New York, Texas, and Florida. This analysis made it possible to identify the
poverty variable as the main risk factor related to the high rates of infection
in Republican states and a high positive correlation between the population not
insured with a medical plan and high levels of virus contagion in the states of
group C3. These findings explain the argument that poverty and lack of economic
security put the public or private health system at risk and calamity.
