Efficiency proportion estimations for Thai people infected coronavirus 2019 (COVID-19) in Thailand by Jackknifing method and bootstrapping method

Thammarat Panityakul, Ronnason Chinram, Wandee Wanishsakpong

Abstract


This research aims to estimate the proportion of Thai people infected with COVID-19 and to compare the efficiency of estimates between the Jackknifing method and the Bootstrapping method. Data sample sizes of 100, 200, 400, 800 and 1,609 were obtained from the Department of Disease Control in March 2020, then repeatedly resampled by each method for 1,000 times. The proportion estimated of Thai people infected with COVID-19 by the Jackknifing method was 91.05034% with the width of the confidence interval at 0.06219% percent and the coefficient of variation of 0.0195%. The estimate of the proportion infected using the Bootstrapping method was 91.04842% with the width of the confidence interval 2.86047% and the coefficient of variation at 0.78527%. It was found that the estimations of proportion by the Jackknifing Method had a lower coefficient of variation and a smaller width of the confidence interval than did the Bootstrapping Method and increasing sample sizes led to a decrease in the coefficient of variation and the width of the confidence interval. The results found that the estimate of proportion infected was more efficient with the Jackknifing Method than with the Bootstrapping Method.

Full Text: PDF

Published: 2021-08-02

How to Cite this Article:

Thammarat Panityakul, Ronnason Chinram, Wandee Wanishsakpong, Efficiency proportion estimations for Thai people infected coronavirus 2019 (COVID-19) in Thailand by Jackknifing method and bootstrapping method, Commun. Math. Biol. Neurosci., 2021 (2021), Article ID 65

Copyright © 2021 Thammarat Panityakul, Ronnason Chinram, Wandee Wanishsakpong. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Commun. Math. Biol. Neurosci.

ISSN 2052-2541

Editorial Office: office@scik.org

 

Copyright ©2021 CMBN