Monitoring Serially Correlated Data by Two CUSUM Charts (Case Study: Numbers of Patients with Covid-19) | ||
| Journal of Quality Engineering and Production Optimization | ||
| مقاله 6، دوره 8، شماره 2، اسفند 2023، صفحه 141-150 اصل مقاله (1.32 M) | ||
| نوع مقاله: 18th IIIE conference selected papers | ||
| شناسه دیجیتال (DOI): 10.22070/jqepo.2023.16031.1232 | ||
| نویسندگان | ||
| Ahmad Hakimi؛ Hiva Farughi* ؛ Jamal Arkat | ||
| Department of Industrial Engineering, University of Kurdistan | ||
| چکیده | ||
| Statistical process control charts are utilized in many industries, including manufacturing, environmental monitoring and improvement, disease surveillance, and others. The use of statistical process control charts is common for independent process observations at different times. However, in the case of sequential data, correlation between the data is typically present. Therefore, the creation of control charts specifically for monitoring serially correlated data is essential. The Covid-19 epidemic is a severe global issue, with evidence indicating that infected individuals can transmit the virus to others, whose symptoms may appear several days later. This study aims to monitor the condition of Covid-19 patients over a specific period time using serial data. Two new CUSUM charts are used to track the number of Covid-19 patients in Iran, Japan, and Italy, with separate results presented and explained for each country. Additionally, a sensitivity analysis is conducted on key factors, yielding similar results, and the two control charts are compared. | ||
| کلیدواژهها | ||
| Covid-19؛ CUSUM chart؛ Data correlation؛ Patients number؛ Process monitoring | ||
| مراجع | ||
|
Alshraideh, H., and Khatatbeh, E. (2014). A Gaussian process control chart for monitoring autocorrelated process data. Journal of Quality Technology, 46(4), 317-322.
Angulo, F. J., Finelli, L., & Swerdlow, D. L. (2021). Estimation of US SARS-CoV-2 infections, symptomatic infections, hospitalizations, and deaths using seroprevalence surveys. JAMA network open, 4(1), 25-33.
Apley D.W., and Tsung, F. (2002). The autoregressive T 2 chart for monitoring univariate autocorrelated processes, Journal of Quality Technology, 34(1), 80-96.
Apley, D.W. and Lee, H.C., (2008). Robustness comparison of exponentially weighted moving-average charts on autocorrelated data and on residuals. Journal of Quality technology, 40(4), 428-447.
Aykroyd, R. G., Leiva, V., & Ruggeri, F. (2019). Recent developments of control charts, identification of big data sources and future trends of current research. Technological Forecasting and Social Change, 144, 221-232.
Ayyoubzadeh, S. M., Zahedi, H., Ahmadi, M., & Kalhori, S. R. N. (2020). Predicting COVID-19 incidence through analysis of google trends data in Iran: data mining and deep learning pilot study. JMIR public health and surveillance, 6(2), 50-56.
Capizzi, G., and Masarotto, G. (2008). Practical design of generalized likelihood ratio control charts for autocorrelated data. Technometrics, 50(3), 357-370.
Daneshmandi, A. A., Noorossana, R., & Farahbakhsh, K. (2020). Developing statistical process control to monitor the values education process. Journal of Quality Engineering and Production Optimization, 5(1), 33-54.
Derakhshani, R., Esmaeeli, H., & Amiri, A. (2021). Monitoring binary response profiles in multistage processes. Journal of Quality Engineering and Production Optimization, 6(2), 97-114.
Harris, T.J., and Ross, W.H. (1991). Statistical process control procedures for correlated observations, The canadian journal of chemical engineering, 69(1), 48-57.
Johnson, R.A., and Bagshaw, M. (1974). The effect of serial correlation on the performance of CUSUM tests, Technometrics, 16(1), 103-112.
Kim, S.H., Alexopoulos, C., Tsui, K.L., and Wilson, J.R. (2007). A distribution-free tabular CUSUM chart for autocorrelated data, IIE Transactions, 39(3), 317-330.
Lee, H. C., & Apley, D. W. (2011). Improved design of robust exponentially weighted moving average control charts for autocorrelated processes. Quality and Reliability Engineering International, 27(3), 337-352.
Li, J., Jeske, D. R., Zhou, Y., & Zhang, X. (2019). A wavelet‐based nonparametric CUSUM control chart for autocorrelated processes with applications to network surveillance. Quality and Reliability Engineering International, 35(2), 644-658.
Li, J., & Qiu, P. (2016). Nonparametric dynamic screening system for monitoring correlated longitudinal data. IIE Transactions, 48(8), 772-786.
Li, W., & Qiu, P. (2020). A general charting scheme for monitoring serially correlated data with short-memory dependence and nonparametric distributions. IISE Transactions, 52(1), 61-74.
Loredo, E., Jearkpaporn, D., and Borror, C. (2002). Model-based control chart for autoregressive and correlated data, Quality and reliability engineering international, 18(6), 489-496.
Middeldorp, S., Coppens, M., van Haaps, T. F., Foppen, M., Vlaar, A. P., Müller, M. C., ... & van Es, N. (2020). Incidence of venous thromboembolism in hospitalized patients with COVID‐19. Journal of Thrombosis and Haemostasis, 18(8), 1995-2002.
Montgomery, D.C. (2019), Introduction to statistical quality control. John wiley & sons.
Montgomery, D.C., and Mastrangelo, C.M. (1991). Some statistical process control methods for autocorrelated data. Journal of quality technology, 23(3), 179-193.
Qiu, P., Li, W., & Li, J. (2020). A new process control chart for monitoring short-range serially correlated data. Technometrics, 62(1), 71-83.
Qiu, P., & Xie, X. (2022). Transparent sequential learning for statistical process control of serially correlated data. Technometrics, 64(4), 487-501
Runger, G.C., (2002). Assignable causes and autocorrelation: control charts for observations or residuals?. Journal of Quality Technology, 34(2), 165-170.
Xue, L., & Qiu, P. (2021). A nonparametric CUSUM chart for monitoring multivariate serially correlated processes. Journal of Quality Technology, 53(4), 396-409.
Yuan, M., Boston-Fisher, N., Luo, Y., Verma, A., & Buckeridge, D. L. (2019). A systematic review of aberration detection algorithms used in public health surveillance. Journal of Biomedical Informatics, 94, 103-116.
Zhou, Q., & Qiu, P. (2022). Phase I monitoring of serially correlated nonparametric profiles by mixed‐effects modeling. Quality and Reliability Engineering International, 38(1), 134-152. | ||
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