Forecasting influenza epidemics from multi-stream surveillance data in a subtropical city of China

Pei Hua Cao, Xin Wang, Shi Song Fang, Xiao Wen Cheng, King Pan Chan, Xi Ling Wang, Xing Lu, Chun Li Wu, Xiu Juan Tang, Ren Li Zhang, Han Wu Ma, Jin Quan Cheng, Chit Ming Wong, Lin Yang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

10 Citations (Scopus)


Background: Influenza has been associated with heavy burden of mortality and morbidity in subtropical regions. However, timely forecast of influenza epidemic in these regions has been hindered by unclear seasonality of influenza viruses. In this study, we developed a forecasting model by integrating multiple sentinel surveillance data to predict influenza epidemics in a subtropical city Shenzhen, China. Methods: Dynamic linear models with the predictors of single or multiple surveillance data for influenza-like illness (ILI) were adopted to forecast influenza epidemics from 2006 to 2012 in Shenzhen. Temporal coherence of these surveillance data with laboratory-confirmed influenza cases was evaluated by wavelet analysis and only the coherent data streams were entered into the model. Timeliness, sensitivity and specificity of these models were also evaluated to compare their performance. Results: Both influenza virology data and ILI consultation rates in Shenzhen demonstrated a significant annual seasonal cycle (p<0.05) during the entire study period, with occasional deviations observed in some data streams. The forecasting models that combined multi-stream ILI surveillance data generally outperformed the models with single-stream ILI data, by providing more timely, sensitive and specific alerts. Conclusions: Forecasting models that combine multiple sentinel surveillance data can be considered to generate timely alerts for influenza epidemics in subtropical regions like Shenzhen.
Original languageEnglish
Article numbere92945
JournalPLoS ONE
Issue number3
Publication statusPublished - 27 Mar 2014

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)

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