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Using an Adjusted Serfling Regression Model to Improve the Early Warning at the Arrival of Peak Timing of Influenza in Beijing

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  • Xiaoli Wang
  • Shuangsheng Wu
  • C Raina MacIntyre
  • Hongbin Zhang
  • Weixian Shi
  • Xiaomin Peng
  • Wei Duan
  • Peng Yang
  • Yi Zhang
  • Quanyi Wang

Abstract

Serfling-type periodic regression models have been widely used to identify and analyse epidemic of influenza. In these approaches, the baseline is traditionally determined using cleaned historical non-epidemic data. However, we found that the previous exclusion of epidemic seasons was empirical, since year-year variations in the seasonal pattern of activity had been ignored. Therefore, excluding fixed ‘epidemic’ months did not seem reasonable. We made some adjustments in the rule of epidemic-period removal to avoid potentially subjective definition of the start and end of epidemic periods. We fitted the baseline iteratively. Firstly, we established a Serfling regression model based on the actual observations without any removals. After that, instead of manually excluding a predefined ‘epidemic’ period (the traditional method), we excluded observations which exceeded a calculated boundary. We then established Serfling regression once more using the cleaned data and excluded observations which exceeded a calculated boundary. We repeated this process until the R2 value stopped to increase. In addition, the definitions of the onset of influenza epidemic were heterogeneous, which might make it impossible to accurately evaluate the performance of alternative approaches. We then used this modified model to detect the peak timing of influenza instead of the onset of epidemic and compared this model with traditional Serfling models using observed weekly case counts of influenza-like illness (ILIs), in terms of sensitivity, specificity and lead time. A better performance was observed. In summary, we provide an adjusted Serfling model which may have improved performance over traditional models in early warning at arrival of peak timing of influenza.

Suggested Citation

  • Xiaoli Wang & Shuangsheng Wu & C Raina MacIntyre & Hongbin Zhang & Weixian Shi & Xiaomin Peng & Wei Duan & Peng Yang & Yi Zhang & Quanyi Wang, 2015. "Using an Adjusted Serfling Regression Model to Improve the Early Warning at the Arrival of Peak Timing of Influenza in Beijing," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-14, March.
  • Handle: RePEc:plo:pone00:0119923
    DOI: 10.1371/journal.pone.0119923
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    References listed on IDEAS

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    Cited by:

    1. Yuan Jiang & Ye-qing Tong & Bin Fang & Wen-kang Zhang & Xue-jie Yu, 2022. "Applying the Moving Epidemic Method to Establish the Influenza Epidemic Thresholds and Intensity Levels for Age-Specific Groups in Hubei Province, China," IJERPH, MDPI, vol. 19(3), pages 1-16, February.
    2. Xunjie Cheng & Tao Chen & Yang Yang & Jing Yang & Dayan Wang & Guoqing Hu & Yuelong Shu, 2018. "Using an innovative method to develop the threshold of seasonal influenza epidemic in China," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-13, August.

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