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The recent outbreak of the COVID-19 led to the death of millions of peopleworldwide. To stave off the spread of the virus, the authorities in the USemployed different strategies, including the mask mandate order issued by thestates' governors. In the current work, we defined a parameter called theaverage death ratio as the monthly average of the number of daily deaths to themonthly average number of daily cases. We utilized survey data to quantifypeople's abidance by the mask mandate order. Additionally, we implicitlyaddressed the extent to which people abide by the mask mandate order that maydepend on some parameters like population, income, and education level. Usingdifferent machine learning classification algorithms, we investigated how thedecrease or increase in death ratio for the counties in the US West Coastcorrelates with the input parameters. The results showed that for most countiesthere, the mask mandate order decreased the death ratio reflecting theeffectiveness of this preventive measure on the West Coast. Additionally, thechanges in the death ratio demonstrated a noticeable correlation with thesocio-economic condition of each county. Moreover, the results showed apromising classification accuracy score as high as around 90%.

Analysis of the Effectiveness of Face-Coverings on the Death Ratio of COVID-19 Using Machine Learning