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EWMA Control Chart Integrated with Time Series Models for COVID-19 Surveillance

Author

Listed:
  • Chen-Rui Hsu

    (Institute of Statistics, National Yang Ming Chiao Tung University, Hsinchu 300093, Taiwan)

  • Hsiuying Wang

    (Institute of Statistics, National Yang Ming Chiao Tung University, Hsinchu 300093, Taiwan)

Abstract

The global outbreak of coronavirus disease 2019 (COVID-19) has posed a severe threat to public health and caused widespread socioeconomic disruptions in the past several years. While the pandemic has subsided, it is essential to explore effective disease surveillance tools to aid in controlling future pandemics. Several studies have proposed methods to capture the epidemic trend and forecast new daily confirmed cases. In this study, we propose the use of exponentially weighted moving average (EWMA) control charts integrated with time series models to monitor the number of daily new confirmed cases of COVID-19. The conventional EWMA control chart directly monitors the number of daily new confirmed cases. The proposed methods, however, monitor the residuals of time series models fitted to these data. In this study, two time series models—the auto-regressive integrated moving average (ARIMA) model and the vector auto-regressive moving average (VARMA) model—are considered. The results are compared with those of the conventional EWMA control chart using three datasets from India, Malaysia, and Thailand. The findings demonstrate that the proposed method can detect disease outbreak signals earlier than conventional control charts.

Suggested Citation

  • Chen-Rui Hsu & Hsiuying Wang, 2024. "EWMA Control Chart Integrated with Time Series Models for COVID-19 Surveillance," Mathematics, MDPI, vol. 13(1), pages 1-15, December.
    • Handle: RePEc:gam:jmathe:v:13:y:2024:i:1:p:115-:d:1557145
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    References listed on IDEAS

    as
    1. Padhan, Rakesh & Prabheesh, K.P., 2021. "The economics of COVID-19 pandemic: A survey," Economic Analysis and Policy, Elsevier, vol. 70(C), pages 220-237.
    2. Wang, Hsiuying & Huwang, Longcheen & Yu, Jeng Hung, 2015. "Multivariate control charts based on the James–Stein estimator," European Journal of Operational Research, Elsevier, vol. 246(1), pages 119-127.
    3. Wang, Hsiuying, 2009. "Comparison of p control charts for low defective rate," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4210-4220, October.
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