Statistical modeling for COVID 19 infected patient's data in Kingdom of Saudi Arabia

Ramy Aldallal; Ahmed M Gemeay; Eslam Hussam; Mutua Kilai

doi:10.1371/journal.pone.0276688

Statistical modeling for COVID 19 infected patient's data in Kingdom of Saudi Arabia

PLoS One. 2022 Oct 28;17(10):e0276688. doi: 10.1371/journal.pone.0276688. eCollection 2022.

Authors

Ramy Aldallal¹, Ahmed M Gemeay², Eslam Hussam³, Mutua Kilai⁴

Affiliations

¹ Department of Accounting, College of Business Administration in Hawtat bani Tamim, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia.
² Department of Mathematics, Faculty of Science, Tanta University, Tanta, Egypt.
³ Department of Mathematics, Helwan University Faculty of Science, Helwan, Egypt.
⁴ Pan African Institute of Basic Science, Technology and Innovation, Nairobi, Kenya.

Abstract

The objective of this study is to construct a new distribution known as the weighted Burr-Hatke distribution (WBHD). The PDF and CDF of the WBHD are derived in a closed form. Moments, incomplete moments, and the quantile function of the proposed distribution are derived mathematically. Eleven estimate techniques for estimating the distribution parameters are discussed, and numerical simulations are utilised to evaluate the various approaches using partial and overall rankings. According to the findings of this study, it is recommended that the maximum product of spacing (MPSE) estimator of the WBHD is the best estimator according to overall rank table. The actuarial measurements were derived to the suggested distribution. By contrasting the WBHD with other competitive distributions using two different actual data sets collected from the COVID-19 mortality rates, we show the importance and flexibility of the WBHD.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

COVID-19* / epidemiology
Humans
Models, Statistical
Saudi Arabia / epidemiology

Grants and funding

This project was supported by the Deanship of Scientific Research at Prince Sattam Bin Abdulaziz University under the research project(PSAU-2022/02/20114).