Predicting of infected People with Corona virus (covid-19) by using Non-Parametric Quality Control Charts

Document Type : Research Paper

Authors

Department of statistics, college of administration and Economics, University of Baghdad, Iraq

Abstract

Quality control Charts were used to monitor the number of infections with the emerging corona virus (Covid-19) for the purpose of predicting the extent of the disease's control, knowing the extent of its spread, and determining the injuries if they were within or outside the limits of the control charts. The research aims to use each of the control chart of the (Kernel Principal Component Analysis Control Chart) and (K- Nearest Neighbor Control Chart). As (18) variables representing the governorates of Iraq were used, depending on the daily epidemiological position of the Public Health Department of the Iraqi Ministry of Health. To compare the performance of the charts, a measure of average length of run was adopted, as the results showed that the number of infection with the new Corona virus is out of control, and that the (KNN) chart had better performance in the short term with a relative equality in the performance of the two charts in the medium and long rang

Keywords

[1] M. Ahsan, M. Mashuri, H. Khusna and M. H. Lee, Multivariate Control Chart Based on Kernel PCA for Monitoring Mixed Variable and Attribute Quality Characteristics, Symmetry, 12 (11) (2020) 1838 .
[2] M. E. Camargo, A. I. d. S. Dullius, W. P. Filho, S. L. Russo, M. R. Cruz, A. Galelli, G. F. da Silva , Multivariate quality control basead on discriminant analysis-ajusted variables, Aust. J. Basic Appl. Sci., 6 (1) (2012) 207-212.
[3] N. Das Non-parametric Control Chart for Controlling Variability Based on Rank Test, Econ. Qual. Control, 23 (2) (2008) 227-242 .
[4] M. Fris´en, On multivariate control charts, Produ¸c˜ao, 21(2)(2011) 235-241.
[5] W. Gani, M. Limam, Performance Evaluation of One-Class Classification-based Control Charts through anIndustrial Application, Qual. Reliab. Eng., 29( 16) (2012) 841-854.
[6] W. Gani, M. Limam,A One-Class Classification-Based Control Chart Using the ??-Means Data Description Algorithm, J. Qual. Reliab. Eng., 2014 (2014) 1-9 .
[7] G. Han, K. M. B. Chong, A Study on the Median Run Length Performance of the Run Sum S Control Chart, Int. J. Mech. Eng. Rob. Res., 8 (6 ) (2019) 885-889.
[8] Q. P. He, J. Wang, Fault Detection Using the k-Nearest Neighbor Rule for Semiconductor Manufacturing Processes,IEEE Trans. Semicond. Manuf. , 20 (4 ) (2007) 345-354.
[9] A. GH. Jaber and F. H. Enad, The Using of Multivariate Parametric Hottling -T 2 and Non-Parametric Bootstrap Charts in Quality Control Using Simulation, Muthanna Journal of Administrative and Economic Sciences , 10 (3)(2020) 8-22.
[10] S. Kazemi and S. Niaki , Monitoring image-based processes using a PCA-based control chart and a classification technique, Decis. Sci. Lett., 10 (1)(2020) 39-52 .
[11] H. Kuswanto, M. Ahsan,Multivariate control chart based on PCA mix for variable and attribute quality characteristics, Prod. Manuf. Res., 6( 1 ) (2018) 364-384 .
[12] W. Li, C. Zhang, Nonparametric monitoring of multivariate data via KNN learning,Int. J. Prod. Res. ,2 (2020) 1-16 .
[13] M. Mashuri, H. Haryono, Tr(R2) control charts based on kernel density estimation for monitoring multivariate variability process, Cogent Eng., 6 (1) (2019) 1-37 .
[14] G. Verdier and A. Ferreira , Adaptive Mahalanobis Distance and k-Nearest Neighbor Rule for Fault Detection in Semiconductor Manufacturing Adaptive Mahalanobis Distance and k-Nearest Neighbor Rule for Fault Detection in Semiconductor Manufacturing , IEEE, 24( 1) (2009) 1-21 .
[15] T. Sukchotrat, S. B. Kim and F. Tsung, One-class classification-based control charts for multivariate process monitoring, IIE Trans. , 42 (2 ) (2010) 107- 120.
[16] https://www.mayoclinic.org/ar/diseases-conditions/coronavirus/symptoms-causes/syc-20479963.
Volume 13, Issue 1
March 2022
Pages 2115-2126
  • Receive Date: 02 May 2021
  • Revise Date: 20 October 2021
  • Accept Date: 30 October 2021