Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/32603
Title: Forecasting influenza epidemics from multi-stream surveillance data in a subtropical city of China
Authors: Cao, PH
Wang, X
Fang, SS
Cheng, XW
Chan, KP
Wang, XL
Lu, X
Wu, CL
Tang, XJ
Zhang, RL
Ma, HW
Cheng, JQ
Wong, CM
Yang, L
Issue Date: 2014
Publisher: Public Library Science
Source: Plos one, 2014, v. 9, no. 3, e92945 How to cite?
Journal: PLoS ONE 
Abstract: 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.
URI: http://hdl.handle.net/10397/32603
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0092945
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