Using Spatial Prediction Model to Analyze Driving Forces of the Beijing 2008 HFMD Epidemic
Identifieur interne : 002193 ( Main/Exploration ); précédent : 002192; suivant : 002194Using Spatial Prediction Model to Analyze Driving Forces of the Beijing 2008 HFMD Epidemic
Auteurs : Jiaojiao Wang [République populaire de Chine] ; Zhidong Cao [République populaire de Chine] ; Quanyi Wang [République populaire de Chine] ; Xiaoli Wang [République populaire de Chine] ; Hongbin Song [République populaire de Chine]Source :
- Lecture Notes in Computer Science [ 0302-9743 ]
Abstract
Abstract: Based on the spatial units of community, village and town in Beijing, the relationship betweent HFMD morbidity and the potential risk factors has been examined. According to the 6 selected risk factors (namely population density, disposable income of urban residents, the number of medical and health institutions, the number of hospital beds, average annual temperature and average annual relative humidity) significantly related to HFMD morbidity, the prediction performance of Classical Linear Regression Model(CLRM) and Spatial Lag Model(SLM) has been compared. The results showed that SLM achieved better effect and R square reached 0.82. It was showed that spatial effect played the crucial role in the HFMD morbidity prediction and its contribution attained 88%. However, CLRM showed low prediction accuracy and bias estimation. It was demonstrated that including spatial effect item into CLRM could greatly improve the performance of HFMD morbidity prediciton model.
Url:
DOI: 10.1007/978-3-642-22039-5_10
Affiliations:
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According to the 6 selected risk factors (namely population density, disposable income of urban residents, the number of medical and health institutions, the number of hospital beds, average annual temperature and average annual relative humidity) significantly related to HFMD morbidity, the prediction performance of Classical Linear Regression Model(CLRM) and Spatial Lag Model(SLM) has been compared. The results showed that SLM achieved better effect and R square reached 0.82. It was showed that spatial effect played the crucial role in the HFMD morbidity prediction and its contribution attained 88%. However, CLRM showed low prediction accuracy and bias estimation. It was demonstrated that including spatial effect item into CLRM could greatly improve the performance of HFMD morbidity prediciton model.</div></front></TEI><affiliations><list><country><li>République populaire de Chine</li></country><settlement><li>Pékin</li></settlement></list><tree><country name="République populaire de Chine"><noRegion><name sortKey="Wang, Jiaojiao" sort="Wang, Jiaojiao" uniqKey="Wang J" first="Jiaojiao" last="Wang">Jiaojiao Wang</name></noRegion><name sortKey="Cao, Zhidong" sort="Cao, Zhidong" uniqKey="Cao Z" first="Zhidong" last="Cao">Zhidong Cao</name><name sortKey="Song, Hongbin" sort="Song, Hongbin" uniqKey="Song H" first="Hongbin" last="Song">Hongbin Song</name><name sortKey="Wang, Quanyi" sort="Wang, Quanyi" uniqKey="Wang Q" first="Quanyi" last="Wang">Quanyi Wang</name><name sortKey="Wang, Xiaoli" sort="Wang, Xiaoli" uniqKey="Wang X" first="Xiaoli" last="Wang">Xiaoli Wang</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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