Abstract:
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This study quantified the COVID-19 control policies' effectiveness, including wearing masks, and the vaccine rates through proportional infection rate in 28 states of the United States using the eSIR model. The policy effectiveness rate was measured by the difference between the predicted daily infection proportion rate based on the data before the policy issued date and the actual daily infection proportion rate. The study suggested that both mask and vaccine policy had a significant impact on mitigating the pandemic. We further explored how different social factors influenced policy effectiveness 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. It can be served as a reference for future COVID-19-related policy making.
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