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globalchange > 过去全球变化的重建

DOI:	10.1371/journal.pone.0092945
论文题名:	Forecasting Influenza Epidemics from Multi-Stream Surveillance Data in a Subtropical City of China
作者:	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
刊名:	PLOS ONE
ISSN:	1932-6203
出版年:	2014
发表日期:	2014-3-27
卷:	9, 期:3
语种:	英语
英文关键词:	Influenza ; Infectious disease surveillance ; Forecasting ; Influenza viruses ; Subtropical regions ; Monte Carlo method ; China ; Seasons
英文摘要:	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.
URL:	http://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0092945&type=printable
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资源类型:	期刊论文
标识符:	http://119.78.100.158/handle/2HF3EXSE/20060
Appears in Collections:	过去全球变化的重建影响、适应和脆弱性科学计划与规划气候变化与战略全球变化的国际研究计划气候减缓与适应气候变化事实与影响

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journal.pone.0092945.PDF（2073KB）	期刊论文	作者接受稿	开放获取	CC BY-NC-SA	View Download

作者单位:

School of Public Health, The University of Hong Kong, Hong Kong Special Administrative Region, China;Shenzhen Center for Disease Control and Prevention, Shenzhen, China;Shenzhen Center for Disease Control and Prevention, Shenzhen, China;Shenzhen Center for Disease Control and Prevention, Shenzhen, China;School of Public Health, The University of Hong Kong, Hong Kong Special Administrative Region, China;School of Public Health, The University of Hong Kong, Hong Kong Special Administrative Region, China;Shenzhen Center for Disease Control and Prevention, Shenzhen, China;Shenzhen Center for Disease Control and Prevention, Shenzhen, China;Shenzhen Center for Disease Control and Prevention, Shenzhen, China;Shenzhen Center for Disease Control and Prevention, Shenzhen, China;Shenzhen Center for Disease Control and Prevention, Shenzhen, China;Shenzhen Center for Disease Control and Prevention, Shenzhen, China;School of Public Health, The University of Hong Kong, Hong Kong Special Administrative Region, China;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

Recommended Citation:

Pei-Hua Cao,Xin Wang,Shi-Song Fang,et al. Forecasting Influenza Epidemics from Multi-Stream Surveillance Data in a Subtropical City of China[J]. PLOS ONE,2014-01-01,9(3)

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