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基于 COVID-19 死亡数据的广义伽玛-CUSUM 控制图。

Generalized Gamma-CUSUM control chart with application of COVID-19 deaths.

机构信息

Department of Statistics, School of Physical Sciences, Federal University of Technology Akure, Akure, Nigeria.

Faculty of Natural Sciences, Department of Mathematical Sciences, Redeemers University Ede, Ede, Nigeria.

出版信息

PLoS One. 2023 Feb 2;18(2):e0281360. doi: 10.1371/journal.pone.0281360. eCollection 2023.

Abstract

The increase in the number of infections and the worrisome state of mortality linked to the COVID-19 pandemic demand an optimal statistical model and efficient monitoring scheme to analyze the deaths. This paper aims to model the COVID-19 mortality in Nigeria using four non-normal distributions grouped under the generalized gamma distribution, by specifying the best-fit distribution to model the number of deaths linked to the COVID-19 pandemic. In addition, a control chart to monitor the COVID-19 deaths based on the best-fit distribution is proposed. The performance of the proposed Gamma-CUSUM chart as a monitoring scheme was compared with the standard normal-CUSUM chart. The results revealed that the Gamma-CUSUM chart first signals a change in the number of deaths on day 68 while there was no change in the number of deaths for the standard normal-CUSUM chart. Also, the exact point of change was visible on the Gamma-CUSUM chart which was impossible on a standard normal-CUSUM control chart.

摘要

新冠疫情感染人数的增加和令人担忧的死亡率,要求我们采用最优的统计模型和高效的监测方案来分析死亡人数。本文旨在使用广义伽马分布下的四个非正态分布来对尼日利亚的新冠死亡率进行建模,通过指定最适合的分布来模拟与新冠疫情相关的死亡人数。此外,还提出了一种基于最佳拟合分布的新冠死亡监测控制图。将提出的 Gamma-CUSUM 图作为监测方案的性能与标准正态-CUSUM 图进行了比较。结果表明,Gamma-CUSUM 图在第 68 天首次发出死亡人数变化的信号,而标准正态-CUSUM 图的死亡人数没有变化。此外,Gamma-CUSUM 图上可以清楚地看到变化的确切点,而标准正态-CUSUM 控制图则不可能看到。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d179/9894481/a42b54de1b0d/pone.0281360.g001.jpg

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