Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/32603
DC Field | Value | Language |
---|---|---|
dc.contributor | School of Nursing | - |
dc.creator | Cao, PH | - |
dc.creator | Wang, X | - |
dc.creator | Fang, SS | - |
dc.creator | Cheng, XW | - |
dc.creator | Chan, KP | - |
dc.creator | Wang, XL | - |
dc.creator | Lu, X | - |
dc.creator | Wu, CL | - |
dc.creator | Tang, XJ | - |
dc.creator | Zhang, RL | - |
dc.creator | Ma, HW | - |
dc.creator | Cheng, JQ | - |
dc.creator | Wong, CM | - |
dc.creator | Yang, L | - |
dc.date.accessioned | 2015-06-23T09:16:36Z | - |
dc.date.available | 2015-06-23T09:16:36Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/32603 | - |
dc.language.iso | en | en_US |
dc.publisher | Public Library of Science | en_US |
dc.rights | © 2014 Cao et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. | en_US |
dc.rights | The following publication: Cao P-H, Wang X, Fang S-S, Cheng X-W, Chan K-P, Wang X-L, et al. (2014) Forecasting Influenza Epidemics from Multi-Stream Surveillance Data in a Subtropical City of China. PLoS ONE 9(3): e92945 is available at https://doi.org/10.1371/journal.pone.0092945 | en_US |
dc.title | Forecasting influenza epidemics from multi-stream surveillance data in a subtropical city of China | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 9 | en_US |
dc.identifier.issue | 3 | en_US |
dc.identifier.doi | 10.1371/journal.pone.0092945 | en_US |
dcterms.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. | - |
dcterms.abstract | 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. | - |
dcterms.abstract | 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. | - |
dcterms.abstract | Conclusions: Forecasting models that combine multiple sentinel surveillance data can be considered to generate timely alerts for influenza epidemics in subtropical regions like Shenzhen. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | PLoS one, 2014, v. 9, no. 3, e92945 | - |
dcterms.isPartOf | PLoS one | - |
dcterms.issued | 2014 | - |
dc.identifier.isi | WOS:000333677500059 | - |
dc.identifier.scopus | 2-s2.0-84899796391 | - |
dc.identifier.pmid | 24676091 | - |
dc.identifier.eissn | 1932-6203 | en_US |
dc.identifier.rosgroupid | r68221 | - |
dc.description.ros | 2013-2014 > Academic research: refereed > Publication in refereed journal | en_US |
dc.description.validate | 201810_a bcma | en_US |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_IR/PIRA | en_US |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Cao_Forecasting_influenza_epidemics.PDF | 2.07 MB | Adobe PDF | View/Open |
Page views
125
Last Week
1
1
Last month
Citations as of Apr 21, 2024
Downloads
101
Citations as of Apr 21, 2024
SCOPUSTM
Citations
11
Last Week
0
0
Last month
0
0
Citations as of Apr 26, 2024
WEB OF SCIENCETM
Citations
11
Last Week
0
0
Last month
0
0
Citations as of Apr 25, 2024
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.