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Time Series Analysis of Hemorrhagic Fever with Renal Syndrome: A Case Study in Jiaonan County, China

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  • Shujuan Li
  • Wei Cao
  • Hongyan Ren
  • Liang Lu
  • Dafang Zhuang
  • Qiyong Liu

Abstract

Exact prediction of Hemorrhagic fever with renal syndrome (HFRS) epidemics must improve to establish effective preventive measures in China. A Seasonal Autoregressive Integrated Moving Average (SARIMA) model was applied to establish a highly predictive model of HFRS. Meteorological factors were considered external variables through a cross correlation analysis. Then, these factors were included in the SARIMA model to determine if they could improve the predictive ability of HFRS epidemics in the region. The optimal univariate SARIMA model was identified as (0,0,2)(1,1,1)12. The R2 of the prediction of HFRS cases from January 2014 to December 2014 was 0.857, and the Root mean square error (RMSE) was 2.708. However, the inclusion of meteorological variables as external regressors did not significantly improve the SARIMA model. This result is likely because seasonal variations in meteorological variables were included in the seasonal characteristics of the HFRS itself.

Suggested Citation

  • Shujuan Li & Wei Cao & Hongyan Ren & Liang Lu & Dafang Zhuang & Qiyong Liu, 2016. "Time Series Analysis of Hemorrhagic Fever with Renal Syndrome: A Case Study in Jiaonan County, China," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-10, October.
    • Handle: RePEc:plo:pone00:0163771
    DOI: 10.1371/journal.pone.0163771
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    References listed on IDEAS

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    1. Hualiang Lin & Liuqing Yang & Qiyong Liu & Tong Wang & Sarah Hossain & Suzanne Ho & Linwei Tian, 2012. "Time series analysis of Japanese encephalitis and weather in Linyi City, China," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 57(2), pages 289-296, April.
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