  The recent outbreak of the COVID-19 shocked humanity leading to the death of
millions of people worldwide. To stave off the spread of the virus, the
authorities in the US, employed different strategies including the mask mandate
(MM) order issued by the states' governors. Although most of the previous
studies pointed in the direction that MM can be effective in hindering the
spread of viral infections, the effectiveness of MM in reducing the degree of
exposure to the virus and, consequently, death rates remains indeterminate.
Indeed, the extent to which the degree of exposure to COVID-19 takes part in
the lethality of the virus remains unclear. In the current work, we defined a
parameter called the average death ratio as the monthly average of the ratio of
the number of daily deaths to the total number of daily cases. We utilized
survey data provided by New York Times to quantify people's abidance to the MM
order. Additionally, we implicitly addressed the extent to which people abide
by the MM order that may depend on some parameters like population, income, and
political inclination. Using different machine learning classification
algorithms we investigated how the decrease or increase in death ratio for the
counties in the US West Coast correlates with the input parameters. Our results
showed a promising score as high as 0.94 with algorithms like XGBoost, Random
Forest, and Naive Bayes. To verify the model, the best performing algorithms
were then utilized to analyze other states (Arizona, New Jersey, New York and
Texas) as test cases. The findings show an acceptable trend, further confirming
usability of the chosen features for prediction of similar cases.
