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...
| Main Authors: | , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2014-01-01
|
| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC3968046?pdf=render |
| _version_ | 1818338220014829568 |
|---|---|
| author | 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 |
| author_facet | 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 |
| author_sort | Pei-Hua Cao |
| collection | DOAJ |
| description | 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<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.Forecasting models that combine multiple sentinel surveillance data can be considered to generate timely alerts for influenza epidemics in subtropical regions like Shenzhen. |
| first_indexed | 2024-12-13T15:07:38Z |
| format | Article |
| id | doaj.art-10b05ec8651b40a5af8b5663aba86465 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-13T15:07:38Z |
| publishDate | 2014-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-10b05ec8651b40a5af8b5663aba864652022-12-21T23:40:57ZengPublic Library of Science (PLoS)PLoS ONE1932-62032014-01-0193e9294510.1371/journal.pone.0092945Forecasting influenza epidemics from multi-stream surveillance data in a subtropical city of China.Pei-Hua CaoXin WangShi-Song FangXiao-Wen ChengKing-Pan ChanXi-Ling WangXing LuChun-Li WuXiu-Juan TangRen-Li ZhangHan-Wu MaJin-Quan ChengChit-Ming WongLin YangInfluenza 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<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.Forecasting models that combine multiple sentinel surveillance data can be considered to generate timely alerts for influenza epidemics in subtropical regions like Shenzhen.http://europepmc.org/articles/PMC3968046?pdf=render |
| spellingShingle | 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 Forecasting influenza epidemics from multi-stream surveillance data in a subtropical city of China. PLoS ONE |
| title | Forecasting influenza epidemics from multi-stream surveillance data in a subtropical city of China. |
| title_full | Forecasting influenza epidemics from multi-stream surveillance data in a subtropical city of China. |
| title_fullStr | Forecasting influenza epidemics from multi-stream surveillance data in a subtropical city of China. |
| title_full_unstemmed | Forecasting influenza epidemics from multi-stream surveillance data in a subtropical city of China. |
| title_short | Forecasting influenza epidemics from multi-stream surveillance data in a subtropical city of China. |
| title_sort | forecasting influenza epidemics from multi stream surveillance data in a subtropical city of china |
| url | http://europepmc.org/articles/PMC3968046?pdf=render |
| work_keys_str_mv | AT peihuacao forecastinginfluenzaepidemicsfrommultistreamsurveillancedatainasubtropicalcityofchina AT xinwang forecastinginfluenzaepidemicsfrommultistreamsurveillancedatainasubtropicalcityofchina AT shisongfang forecastinginfluenzaepidemicsfrommultistreamsurveillancedatainasubtropicalcityofchina AT xiaowencheng forecastinginfluenzaepidemicsfrommultistreamsurveillancedatainasubtropicalcityofchina AT kingpanchan forecastinginfluenzaepidemicsfrommultistreamsurveillancedatainasubtropicalcityofchina AT xilingwang forecastinginfluenzaepidemicsfrommultistreamsurveillancedatainasubtropicalcityofchina AT xinglu forecastinginfluenzaepidemicsfrommultistreamsurveillancedatainasubtropicalcityofchina AT chunliwu forecastinginfluenzaepidemicsfrommultistreamsurveillancedatainasubtropicalcityofchina AT xiujuantang forecastinginfluenzaepidemicsfrommultistreamsurveillancedatainasubtropicalcityofchina AT renlizhang forecastinginfluenzaepidemicsfrommultistreamsurveillancedatainasubtropicalcityofchina AT hanwuma forecastinginfluenzaepidemicsfrommultistreamsurveillancedatainasubtropicalcityofchina AT jinquancheng forecastinginfluenzaepidemicsfrommultistreamsurveillancedatainasubtropicalcityofchina AT chitmingwong forecastinginfluenzaepidemicsfrommultistreamsurveillancedatainasubtropicalcityofchina AT linyang forecastinginfluenzaepidemicsfrommultistreamsurveillancedatainasubtropicalcityofchina |