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A Simulation-Based Study on the Comparison of Statistical and Time Series Forecasting Methods for Early Detection of Infectious Disease Outbreaks

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Listed:
  • Eunjoo Yang

    (Emergency Operations Center, Centers for Disease Control and Prevention, Cheongju 28644, Korea)

  • Hyun Woo Park

    (College of Electrical and Computer Engineering, Chungbuk National University, Cheongju 28644, Korea)

  • Yeon Hwa Choi

    (Emergency Operations Center, Centers for Disease Control and Prevention, Cheongju 28644, Korea)

  • Jusim Kim

    (Emergency Operations Center, Centers for Disease Control and Prevention, Cheongju 28644, Korea)

  • Lkhagvadorj Munkhdalai

    (College of Electrical and Computer Engineering, Chungbuk National University, Cheongju 28644, Korea)

  • Ibrahim Musa

    (College of Electrical and Computer Engineering, Chungbuk National University, Cheongju 28644, Korea)

  • Keun Ho Ryu

    (College of Electrical and Computer Engineering, Chungbuk National University, Cheongju 28644, Korea)

Abstract

Early detection of infectious disease outbreaks is one of the important and significant issues in syndromic surveillance systems. It helps to provide a rapid epidemiological response and reduce morbidity and mortality. In order to upgrade the current system at the Korea Centers for Disease Control and Prevention (KCDC), a comparative study of state-of-the-art techniques is required. We compared four different temporal outbreak detection algorithms: the CUmulative SUM (CUSUM), the Early Aberration Reporting System (EARS), the autoregressive integrated moving average (ARIMA), and the Holt-Winters algorithm. The comparison was performed based on not only 42 different time series generated taking into account trends, seasonality, and randomly occurring outbreaks, but also real-world daily and weekly data related to diarrhea infection. The algorithms were evaluated using different metrics. These were namely, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), F1 score, symmetric mean absolute percent error (sMAPE), root-mean-square error (RMSE), and mean absolute deviation (MAD). Although the comparison results showed better performance for the EARS C3 method with respect to the other algorithms, despite the characteristics of the underlying time series data, Holt–Winters showed better performance when the baseline frequency and the dispersion parameter values were both less than 1.5 and 2, respectively.

Suggested Citation

  • Eunjoo Yang & Hyun Woo Park & Yeon Hwa Choi & Jusim Kim & Lkhagvadorj Munkhdalai & Ibrahim Musa & Keun Ho Ryu, 2018. "A Simulation-Based Study on the Comparison of Statistical and Time Series Forecasting Methods for Early Detection of Infectious Disease Outbreaks," IJERPH, MDPI, vol. 15(5), pages 1-18, May.
    • Handle: RePEc:gam:jijerp:v:15:y:2018:i:5:p:966-:d:145887
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    References listed on IDEAS

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    1. Peter R. Winters, 1960. "Forecasting Sales by Exponentially Weighted Moving Averages," Management Science, INFORMS, vol. 6(3), pages 324-342, April.
    2. Holt, Charles C., 2004. "Author's retrospective on 'Forecasting seasonals and trends by exponentially weighted moving averages'," International Journal of Forecasting, Elsevier, vol. 20(1), pages 11-13.
    3. Mills,Terence C., 1991. "Time Series Techniques for Economists," Cambridge Books, Cambridge University Press, number 9780521405744, October.
    4. Holt, Charles C., 2004. "Forecasting seasonals and trends by exponentially weighted moving averages," International Journal of Forecasting, Elsevier, vol. 20(1), pages 5-10.
    5. Gabriel Bédubourg & Yann Le Strat, 2017. "Evaluation and comparison of statistical methods for early temporal detection of outbreaks: A simulation-based study," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-18, July.
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    2. Xueli Wang & Moqin Zhou & Jinzhu Jia & Zhi Geng & Gexin Xiao, 2018. "A Bayesian Approach to Real-Time Monitoring and Forecasting of Chinese Foodborne Diseases," IJERPH, MDPI, vol. 15(8), pages 1-13, August.

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