Using Quality Control Charts for Monitoring COVID-19 Daily Cases and Deaths in Türkiye

Yıl 2024, Cilt: 13 Sayı: 1, 134 - 152, 24.03.2024

Esra Polat

https://doi.org/10.17798/bitlisfen.1356364

Öz

The national partial curfews in Turkey, announced after normal time period in 2020’s summer, began on 18.11.2020. In this study, for the periods between 25.11.2020-31.05.2022, the daily new cases and for the periods between 27.03.2020-31.05.2022 deaths and fatality rates are monitored. In this research, Run charts, control charts of c and p are used for monitoring the COVID-19 process in Turkey. In the periods of national extended curfews (December 1, 2020–February 28, 2021), full lockdown (April 29–May 16, 2021) and gradual normalization (May 17– May 31, 2021) the number of daily cases and deaths show downward trend as it is expected. However, in the periods of local decision-making phase (March 1–29, 2021) and revised local decision-making phase (March 30–April 13, 2021) the number of new daily cases and deaths show upward trend. For the latest period between June 1, 2021-May 31, 2022, in winter season the number of deaths and daily cases increasing as it is expected then in spring season they decrease. The daily new cases and deaths due to COVID-19 monitored rapidly and effectively by control charts. It is obvious that some of the public did not internalize the negative impacts of the virus in 2020 and consequently disregarded health guidelines in 2021.

Anahtar Kelimeler

COVID-19, c-chart, p-chart, run charts, statistical process control

Etik Beyan

The study is complied with research and publication ethics

Kaynakça

• [1] M. T. Kartal, Ö. Depren, S. K. Depren, “The relationship between mobility and COVID-19 pandemic: Daily evidence from an emerging country by causality analysis”, Transportation Research Interdisciplinary Perspectives, vol. 10, pp. 1-10, 2021.
• [2] J. C. Benneyan, “Statistical Quality Control Methods in Infection Control and Hospital Epidemiology, Part II: Chart Use, Statistical Properties, and Research Issues”, Infection Control & Hospital Epidemiology, vol. 19, no. 4, pp. 265-283, 1998.
• [3] G. Suman and D. Prajapati, “Control Chart Applications in Healthcare: A Literature Review”, Int. J. Metrol. Qual. Eng., vol. 9, no. 5, pp. 1-21, 2018.
• [4] J. C. Benneyan, R. C. Lloyd, P. E. Plsek, “Statistical Process Control as a Tool for Research and Healthcare Improvement”, Qual Saf Health Care, vol. 12, pp. 458-464, 2003.
• [5] M. F. Mbaye, N. Sarr, B. Ngom, “Construction of Control Charts for Monitoring Various Parameters Related to the Management of the COVID-19 Pandemic”, Journal of Biosciences and Medicines, vol. 9, pp. 9-19, 2021.
• [6] M. Arafah, “Using the Laney p’ Control Chart for Monitoring COVID-19 Cases in Jordan”, Journal of Healthcare Engineering Vol. 2022, pp. 1-18, 2022.
• [7] B. J. Odunayo, E. Nnamdi, E. S. Oluwatosin, “Monitoring corona virus disease 2019 (COVID-19) pandemic outbreak in Africa”, MOJ App Bio Biomech., vol. 4, no. 3, pp. 67‒71, 2020.
• [8] B. J. Odunayo, E. S. Oluwatosin, E. Nnamdi, “On the Monitoring of Coronavirus (COVID-19) Disease in Africa”, The Parisian Academic Journal Applied and Natural Sciences, vol. 3, pp. 12-20, 2020.
• [9] H. M. Fawzy, A. Ghalib, “Predicting of infected People with Corona virus (covid-19) by using Non-Parametric Quality Control Charts”, Int. J. Nonlinear Anal. Appl., vol. 13, no. 1, pp. 2115–2126, 2022.
• [10] Y. Hidayat, D. S. Pangestu, T. Purwandari, “Covid-19 Time Series Data Quality Analysis (Case study at COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University)”, in Proceedings of the 2nd African International Conference on Industrial Engineering and Operations Management, Harare, Zimbabwe, December 7-10, 2020, pp. 1904-1910.
• [11] Y. Hidayat, F. Utama, T. Purwandari, “The Implementation of Tangent Control Chart for Identification of Countries that have Positive Covid-19 Cases Data Trend Similar to Indonesia”, in Proceedings of the 2nd African International Conference on Industrial Engineering and Operations Management, Harare, Zimbabwe, December 7-10, 2020, pp. 1917-1926.
• [12] N. H. Irhaif, Z. I. Kareem, A. D. A. Kareem, S. M. Al-joboori, “Using the moving average and exponentially weighted moving average with COVID 19”, Periodicals of Engineering and Natural Sciences Methodology/Method, vol. 9, no. 3, pp. 625-631, 2021.
• [13] Y. Mahmood, S. Ishtiaq, M. B. C. Khoo, S. Y. Teh, H. Khan, “Monitoring of three-phase variations in the mortality of COVID-19 pandemic using control charts: where does Pakistan stand?”, International Journal for Quality in Health Care, vol. 33, no. 2, pp. 1–8, 2021.
• [14] U. Mukhaiyar, K. N. Sari, Y. S. Afrianti, A. M. R. Saputra, “The P-chart Implementation in Evaluating the Number of New COVID-19 Cases”, in the 8th Symposium on Biomathematics (Symomath) 2021 AIP Conf. Proc. 2498, Published by AIP Publishing, 2021, pp. 020022-1–020022-10.
• [15] G. Parry, L. P. Provost, S. M. Provost, K. Little, R. J. Perla, “A hybrid Shewhart chart for visualizing and learning from epidemic data”, International Journal for Quality in Health Care, vol. 33, no. 4, pp. 1–10, 2021.
• [16] E. R. Rashed, M. E. Eissa, “Global assessment of morbidity and mortality pattern of CoVID-19: Descriptive statistics overview”, Iberoamerican Journal of Medicine, vol. 2, pp. 68-72, 2020.
• [17] B. P. Singh, J. V. Madhusudan, A. K. Tiwari, S. Singh, U. D. Das, “Evaluation of EWMA control charts for monitoring spread of transformed observations of COVID-19 in India”, Asian Journal of Research in Infectious Diseases., vol. 5, no. 2, pp. 25-36, 2020.
• [18] A. Yupaporn, S. Rapin, “EWMA control chart based on its first hitting time and coronavirus alert levels for monitoring symmetric COVID-19 cases”, Asian Pacific Journal of Tropical Medicine, vol. 14, no. 8, pp. 364-374, 2021.
• [19] M. Waqas, S. H. Xu, S. M. Anwar, Z. Rasheed, J. Shabbir, “The optimal control chart selection for monitoring COVID-19 phases: a case study of daily deaths in the USA”, Quality in Health Care, vol. 35, no. 3, pp.1-9, 2023.
• [20] M. N. İlhan, H. Tüzün, R. Kılıç, N. Yıldırım, “Nonpharmaceutical interventions in Turkey and worldwide during COVID-19 pandemic”, Turkish Journal of Medical Sciences, vol. 51, pp. 3207-3214, 2021.
• [21] J. Anhøj, A. V. Olesen, “Run Charts Revisited: A Simulation Study of Run Chart Rules for Detection of Non-Random Variation in Health Care Processes”, PLoS ONE, vol. 9, no. 11, e113825, pp. 1-13, 2014.
• [22] Run charts with R (Jacob Anhøj). [Online]. Available: https://cran.r-project.org/web/packages/qicharts/vignettes/runcharts.html [Accessed: 31 October, 2023].
• [23] J. Anhøj, “Diagnostic Value of Run Chart Analysis: Using Likelihood Ratios to Compare Run Chart Rules on Simulated Data Series”, PLoS ONE, vol. 10, no. 3, pp. 1-9, 2015.
• [24] S. W. Roberts. “Control Chart Tests Based on Geometric Moving Averages.” Technometrics, vol. 1, no. 3, pp. 239–250, 1959.
• [25] R. C. Lloyd, Quality Health Care: A Guide to Developing and Using Indicators, CHAPTER 9: Understanding Variation with Shewhart Charts, 2nd Edition, Jones & Bartlett Learning, 2019, pp. 211-258. [Online]. Available: https://www.cec.health.nsw.gov.au/__data/assets/pdf_file/0011/605657/Understanding-Variation-with-Shewart-Charts.pdf [Accessed: 2 December, 2023].
• [26] L P. Provost, S. Murray, The Health Care Data Guide: Learning from Data for Improvement. San Francisco: Jossey-Bass, 2011.
• [27] General Coronavirus Chart data from website of the Republic of Türkiye Ministry of Health. [Online]. Available: https://covid19.saglik.gov.tr/TR-66935/genel-koronavirus-tablosu.html [Accessed: 9 January, 2023].