Pei-Hua Cao: School of Public Health, The University of Hong Kong, Hong Kong Special Administrative Region, China.
Xin Wang: Shenzhen Center for Disease Control and Prevention, Shenzhen, China.
Shi-Song Fang: Shenzhen Center for Disease Control and Prevention, Shenzhen, China.
Xiao-Wen Cheng: Shenzhen Center for Disease Control and Prevention, Shenzhen, China.
King-Pan Chan: School of Public Health, The University of Hong Kong, Hong Kong Special Administrative Region, China.
Xi-Ling Wang: School of Public Health, The University of Hong Kong, Hong Kong Special Administrative Region, China.
Xing Lu: Shenzhen Center for Disease Control and Prevention, Shenzhen, China.
Chun-Li Wu: Shenzhen Center for Disease Control and Prevention, Shenzhen, China.
Xiu-Juan Tang: Shenzhen Center for Disease Control and Prevention, Shenzhen, China.
Ren-Li Zhang: Shenzhen Center for Disease Control and Prevention, Shenzhen, China.
Han-Wu Ma: Shenzhen Center for Disease Control and Prevention, Shenzhen, China.
Jin-Quan Cheng: Shenzhen Center for Disease Control and Prevention, Shenzhen, China.
Chit-Ming Wong: School of Public Health, The University of Hong Kong, Hong Kong Special Administrative Region, China.
Lin Yang: School of Public Health, The University of Hong Kong, Hong Kong Special Administrative Region, China; School of Nursing, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China.
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.
References
J Infect Dis. 2013 Jan 1;207(1):106-14
[PMID: 22930808]
