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

TL;DR

This study uses machine learning to analyze how face coverings and socio-economic factors influenced COVID-19 death ratios across US counties, highlighting the effectiveness of mask mandates and socio-economic impacts.

Contribution

It introduces a novel parameter called the average death ratio and applies machine learning to quantify mask mandate effectiveness and socio-economic influences on COVID-19 outcomes.

Findings

Mask mandates decreased death ratios in most West Coast counties.

High classification accuracy (~90%) in predicting death ratio changes.

Socio-economic factors significantly correlate with COVID-19 death ratios.

Abstract

The recent outbreak of the COVID-19 led 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 order issued by the states' governors. In the current work, we defined a parameter called the average death ratio as the monthly average of the number of daily deaths to the monthly average number of daily cases. We utilized survey data to quantify people's abidance by the mask mandate order. Additionally, we implicitly addressed the extent to which people abide by the mask mandate order that may depend on some parameters like population, income, and education level. 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. The results showed that for most counties there, the mask mandate order decreased the death ratio reflecting the effectiveness of this preventive measure on the West Coast. Additionally, the changes in the death ratio demonstrated a noticeable correlation with the socio-economic condition of each county. Moreover, the results showed a promising classification accuracy score as high as around 90%.