Your browser doesn't support javascript.
Generalized Gamma-CUSUM control chart with application of COVID-19 deaths.
Adeoti, Olatunde Adebayo; Adekeye, Kayode Samuel.
  • Adeoti OA; Department of Statistics, School of Physical Sciences, Federal University of Technology Akure, Akure, Nigeria.
  • Adekeye KS; Faculty of Natural Sciences, Department of Mathematical Sciences, Redeemers University Ede, Ede, Nigeria.
PLoS One ; 18(2): e0281360, 2023.
Article in English | MEDLINE | ID: covidwho-2224482
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.
Subject(s)

Full text: Available Collection: International databases Database: MEDLINE Main subject: Pandemics / COVID-19 Type of study: Observational study Limits: Humans Country/Region as subject: Africa Language: English Journal: PLoS One Journal subject: Science / Medicine Year: 2023 Document Type: Article Affiliation country: Journal.pone.0281360

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: International databases Database: MEDLINE Main subject: Pandemics / COVID-19 Type of study: Observational study Limits: Humans Country/Region as subject: Africa Language: English Journal: PLoS One Journal subject: Science / Medicine Year: 2023 Document Type: Article Affiliation country: Journal.pone.0281360