分析得克萨斯州县级 COVID-19 疫苗接种率:新的林德利回归模型

COVID Pub Date : 2023-12-04 DOI:10.3390/covid3120122
Nicollas S. S. da Costa, Maria do Carmo S. de Lima, G. Cordeiro
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引用次数: 0

摘要

本工作旨在通过具有形状系统成分的广义奇对数-逻辑林德利回归模型研究解释COVID-19疫苗接种率的因素。为了实现这一目标,使用了美国德克萨斯州254个县的疫苗接种率数据集,并进行了模拟,以调查所提出的回归模型中最大似然估计量的准确性。所研究的数学性质提供了有关分布特征的重要信息。诊断分析和偏差残差处理,以检查模型的拟合。该模型在确定县一级COVID-19疫苗接种率的关键变量方面显示出有效性,这有助于改进疫苗接种活动。此外,这些发现证实了先前的研究,并且新的分布是未来在不同数据集上工作的合适替代模型。
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Analyzing County-Level COVID-19 Vaccination Rates in Texas: A New Lindley Regression Model
This work aims to study the factors that explain the COVID-19 vaccination rate through a generalized odd log-logistic Lindley regression model with a shape systematic component. To accomplish this, a dataset of the vaccination rate of 254 counties in the state of Texas, US, was used, and simulations were performed to investigate the accuracy of the maximum likelihood estimators in the proposed regression model. The mathematical properties investigated provide important information about the characteristics of the distribution. Diagnostic analysis and deviance residuals are addressed to examine the fit of the model. The proposed model shows effectiveness in identifying the key variables of COVID-19 vaccination rates at the county level, which can contribute to improving vaccination campaigns. Moreover, the findings corroborate with prior studies, and the new distribution is a suitable alternative model for future works on different datasets.
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