This study compared the effectiveness of COVID-19 control policies, including wearing masks, and the vaccine rates through proportional infection rate in 28 states of the United States using the eSIR model. The effective rate of policies was measured by the difference between the predicted daily infection proportion rate using the data before the policy and the actual daily infection proportion rate. The study suggests that both mask and vaccine policy had a significant impact on mitigating the pandemic. We further explored how different social factors influenced the effectiveness of a specific policy through the linear regression model. Out of 9 factors, the population density, number of hospital beds per 1000 people, and percent of the population over 65 are the most substantial factors on mask policy effectiveness, while public health funding per person, percent of immigration have the most significant influence on vaccine policy effectiveness. This study summarized the effectiveness of different policies and factors they associated with. It can be served as a reference for future covid-19 related policy.
翻译:这项研究比较了COVID-19控制政策的有效性,包括戴面罩,以及使用ESIR模式在美国28个州通过按比例的感染率实现的疫苗接种率。有效的政策率是通过使用政策前数据预测的每日感染率与实际每日感染率之间的差别来衡量的。研究表明,口罩和疫苗政策对减轻这一流行病有重大影响。我们进一步探讨了不同的社会因素如何通过线性回归模式影响具体政策的有效性。在9个因素中,人口密度、每1 000人的医院床位数和65岁以上人口的百分率是掩盖政策有效性的最重要因素,而每个移民的公共卫生资金对疫苗政策的有效性影响最大。这项研究总结了不同政策和因素的效力,可以作为今后与19个因素相关的政策的一个参考。