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Applications and Comparisons of Four Time Series Models in Epidemiological Surveillance Data

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  • Xingyu Zhang
  • Tao Zhang
  • Alistair A Young
  • Xiaosong Li

Abstract

Public health surveillance systems provide valuable data for reliable predication of future epidemic events. This paper describes a study that used nine types of infectious disease data collected through a national public health surveillance system in mainland China to evaluate and compare the performances of four time series methods, namely, two decomposition methods (regression and exponential smoothing), autoregressive integrated moving average (ARIMA) and support vector machine (SVM). The data obtained from 2005 to 2011 and in 2012 were used as modeling and forecasting samples, respectively. The performances were evaluated based on three metrics: mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE). The accuracy of the statistical models in forecasting future epidemic disease proved their effectiveness in epidemiological surveillance. Although the comparisons found that no single method is completely superior to the others, the present study indeed highlighted that the SVMs outperforms the ARIMA model and decomposition methods in most cases.

Suggested Citation

  • Xingyu Zhang & Tao Zhang & Alistair A Young & Xiaosong Li, 2014. "Applications and Comparisons of Four Time Series Models in Epidemiological Surveillance Data," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-16, February.
  • Handle: RePEc:plo:pone00:0088075
    DOI: 10.1371/journal.pone.0088075
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    1. Firmino, Paulo Renato Alves & de Sales, Jair Paulino & Gonçalves Júnior, Jucier & da Silva, Taciana Araújo, 2020. "A non-central beta model to forecast and evaluate pandemics time series," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    2. Hadi Bagheri & Leili Tapak & Manoochehr Karami & Zahra Hosseinkhani & Hamidreza Najari & Safdar Karimi & Zahra Cheraghi, 2020. "Forecasting the monthly incidence rate of brucellosis in west of Iran using time series and data mining from 2010 to 2019," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-18, May.
    3. Jerelyn Co & Jason Allan Tan & Ma. Regina Justina Estuar & Kennedy Espina, 2017. "Dengue Spread Modeling in the Absence of Sufficient Epidemiological Parameters: Comparison of SARIMA and SVM Time Series Models," Working papers Conference proceedings The Future of Ethics, Education and Research, October 16-17, 2017 22, Research Association for Interdisciplinary Studies.
    4. Sallahuddin Hassan & Zalila Othman, 2018. "Forecasting on the long-term sustainability of the employees provident fund in Malaysia via the Box-Jenkins’ ARIMA model," Business and Economic Horizons (BEH), Prague Development Center, vol. 14(1), pages 43-53, January.
    5. Sinan Keskin & Fatih Külahcı, 2023. "ARIMA model simulation for total electron content, earthquake and radon relationship identification," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 115(3), pages 1955-1976, February.
    6. Asmita Mahajan & Nonita Sharma & Silvia Aparicio-Obregon & Hashem Alyami & Abdullah Alharbi & Divya Anand & Manish Sharma & Nitin Goyal, 2022. "A Novel Stacking-Based Deterministic Ensemble Model for Infectious Disease Prediction," Mathematics, MDPI, vol. 10(10), pages 1-15, May.
    7. Gaetano Perone, 2020. "An ARIMA model to forecast the spread and the final size of COVID-2019 epidemic in Italy," Health, Econometrics and Data Group (HEDG) Working Papers 20/07, HEDG, c/o Department of Economics, University of York.
    8. Xingyu Zhang & Tao Zhang & Jiao Pei & Yuanyuan Liu & Xiaosong Li & Pau Medrano-Gracia, 2016. "Time Series Modelling of Syphilis Incidence in China from 2005 to 2012," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-18, February.
    9. Fanyu Meng & Wenwu Gong & Jun Liang & Xian Li & Yiping Zeng & Lili Yang, 2021. "Impact of different control policies for COVID-19 outbreak on the air transportation industry: A comparison between China, the U.S. and Singapore," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-19, March.
    10. Iman Khosravi & Yaser Jouybari-Moghaddam & Mohammad Reza Sarajian, 2017. "The comparison of NN, SVR, LSSVR and ANFIS at modeling meteorological and remotely sensed drought indices over the eastern district of Isfahan, Iran," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 87(3), pages 1507-1522, July.

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