This study proposes new statistical tools to analyze the counts of the daily coronavirus cases and deaths. Since the daily new deaths exhibit highly over-dispersion, we introduce a new two-parameter discrete distribution, called discrete generalized Lindley, which enables us to model all kinds of dispersion such as under-, equi-, and over-dispersion. Additionally, we introduce a new count regression model based on the proposed distribution to investigate the effects of the important risk factors on the counts of deaths for OECD countries. Three data sets are analyzed with proposed models and competitive models. Empirical findings show that air pollution, the proportion of obesity, and smokers in a population do not affect the counts of deaths for OECD countries. The interesting empirical result is that the countries with having higher alcohol consumption have lower counts of deaths.

这项研究提出了新的统计工具来分析每日冠状病毒病例和死亡人数。由于每日新死亡人数表现出高度过度分散，我们引入了一种新的两参数离散分布，称为离散广义 Lindley，这使我们能够对各种色散进行建模，例如欠色散、等色散和过度色散。此外，我们基于提议的分布引入了一个新的计数回归模型，以调查重要风险因素对经合组织国家死亡人数的影响。用提出的模型和竞争模型分析了三个数据集。实证结果表明，空气污染、肥胖比例和人口中的吸烟者不会影响经合组织国家的死亡人数。有趣的实证结果是，饮酒量较高的国家的死亡人数较低。