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

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 se...

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Bibliographic Details
Published in:PloS one 2014-03, Vol.9 (3), p.e92945-e92945
Main Authors: Cao, Pei-Hua, Wang, Xin, Fang, Shi-Song, Cheng, Xiao-Wen, Chan, King-Pan, Wang, Xi-Ling, Lu, Xing, Wu, Chun-Li, Tang, Xiu-Juan, Zhang, Ren-Li, Ma, Han-Wu, Cheng, Jin-Quan, Wong, Chit-Ming, Yang, Lin
Format: Article
Language:English
Subjects:
Analysis
Automation
Biology and Life Sciences
China - epidemiology
Cities
Consultation
Data transmission
Disease control
Disease prevention
Epidemics
Forecasting
Forecasts and trends
Geography
Health aspects
Humans
Influenza
Influenza, Human - epidemiology
Laboratories
Mathematical models
Medicine and Health Sciences
Models, Theoretical
Morbidity
Mortality
Population Surveillance - methods
Prognosis
Public health
Reproducibility of Results
Research and Analysis Methods
Seasonal variations
Sensitivity analysis
Streams
Surveillance
Viruses
Wavelet analysis
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Summary: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. 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. Both influenza virology data and ILI consultation rates in Shenzhen demonstrated a significant annual seasonal cycle (p
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0092945