Détail du document
Identifiant
oai:pubmedcentral.nih.gov:6796...
Sujet
Research paperAuteur
Su, Kun Xu, Liang Li, Guanqiao Ruan, Xiaowen Li, Xian Deng, Pan Li, Xinmi Li, Qin Chen, Xianxian Xiong, Yu Lu, Shaofeng Qi, Li Shen, Chaobo Tang, Wenge Rong, Rong Hong, Boran Ning, Yi Long, Dongyan Xu, Jiaying Shi, Xuanling Yang, Zhihong Zhang, Qi Zhuang, Ziqi Zhang, Linqi Xiao, Jing Li, YafeiLangue
enEditeur
Elsevier
Catégorie
EBioMedicine
Année
2019
Date de référencement
16/01/2023
Mots clés
trends multi-source 2018 influenza data mape ili% activity chongqing seasonalRésumé
BACKGROUND: Early detection of influenza activity followed by timely response is a critical component of preparedness for seasonal influenza epidemic and influenza pandemic.
However, most relevant studies were conducted at the regional or national level with regular seasonal influenza trends.
There are few feasible strategies to forecast influenza activity at the local level with irregular trends.
METHODS: Multi-source electronic data, including historical percentage of influenza-like illness (ILI%), weather data, Baidu search index and Sina Weibo data of Chongqing, China, were collected and integrated into an innovative Self-adaptive AI Model (SAAIM), which was constructed by integrating Seasonal Autoregressive Integrated Moving Average model and XGBoost model using a self-adaptive weight adjustment mechanism.
SAAIM was applied to ILI% forecast in Chongqing from 2017 to 2018, of which the performance was compared with three previously available models on forecasting.
FINDINGS: ILI% showed an irregular seasonal trend from 2012 to 2018 in Chongqing.
Compared with three reference models, SAAIM achieved the best performance on forecasting ILI% of Chongqing with the mean absolute percentage error (MAPE) of 11·9%, 7·5%, and 11·9% during the periods of the year 2014–2016, 2017, and 2018 respectively.
Among the three categories of source data, historical influenza activity contributed the most to the forecast accuracy by decreasing the MAPE by 19·6%, 43·1%, and 11·1%, followed by weather information (MAPE reduced by 3·3%, 17·1%, and 2·2%), and Internet-related public sentiment data (MAPE reduced by 1·1%, 0·9%, and 1·3%).
INTERPRETATION: Accurate influenza forecast in areas with irregular seasonal influenza trends can be made by SAAIM with multi-source electronic data.
Su, Kun,Xu, Liang,Li, Guanqiao,Ruan, Xiaowen,Li, Xian,Deng, Pan,Li, Xinmi,Li, Qin,Chen, Xianxian,Xiong, Yu,Lu, Shaofeng,Qi, Li,Shen, Chaobo,Tang, Wenge,Rong, Rong,Hong, Boran,Ning, Yi,Long, Dongyan,Xu, Jiaying,Shi, Xuanling,Yang, Zhihong,Zhang, Qi,Zhuang, Ziqi,Zhang, Linqi,Xiao, Jing,Li, Yafei, 2019, Forecasting influenza activity using self-adaptive AI model and multi-source data in Chongqing, China, Elsevier
