Department of Agricultural and Biological Engineering, Purdue University, Indiana, 47907, USA.
School of Mechanical Engineering, Purdue University, Indiana, 47907, USA.
Sci Rep. 2021 Nov 4;11(1):21675. doi: 10.1038/s41598-021-01005-y.
The recent outbreak of the COVID-19 led to 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 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, which may depend on some parameters such as 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 the majority of counties, the mask mandate order decreased the death ratio, reflecting the effectiveness of such a 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 90%.
最近爆发的 COVID-19 疫情导致了全球数百万人死亡。为了阻止病毒传播，美国当局采取了不同的策略，包括州长发布的口罩强制令。在当前的工作中，我们定义了一个名为平均死亡率的参数，它是每日死亡人数与每月平均每日病例数的月度平均值。我们利用调查数据来量化人们对口罩强制令的遵守情况。此外，我们还隐含地考虑了人们遵守口罩强制令的程度，这可能取决于人口、收入和教育水平等参数。我们使用不同的机器学习分类算法，研究了美国西海岸各县的死亡率下降或上升与输入参数之间的相关性。结果表明，对于大多数县来说，口罩强制令降低了死亡率，反映了这种预防措施在西海岸的有效性。此外，死亡率的变化与每个县的社会经济状况有明显的相关性。此外，结果显示出高达 90%的有希望的分类准确率。
