A Deep Residual Network Integrating Spatial-temporal Properties to Predict Influenza Trends at an Intra-urban Scale
文献类型：会议论文
| 作者 | Guikai Xi; Ling Yin; Ye Li; Shujiang Mei |
| 出版日期 | 2018 |
| 会议日期 | 2018 |
| 会议地点 | Seattle |
| 英文摘要 | Infuenza is one of the most common causes of human illness and death; thus, accurate and timely predictions for infuenza trends are critical tasks for public health. Many studies have attempted to conduct infuenza prediction at or beyond the city scale; however, larger spatial scales are too coarse to help analyze infuenza epidemics or allow ofering precise interventions inside a city. Moreover, the existing prediction models often ignore the spatial correlations of infuenza activity between neighbouring regions although such correlations are potentially helpful in infuenza prediction. To address the above issues, this study proposes an infuenza prediction model based on a deep residual network that predicts infuenza trends by integrating the spatial-temporal properties of infuenza at an intra-urban scale. Using a real dataset of infuenza in Shenzhen City, China, we tested our prediction model on 10 districts within the city. Our results show that our proposed deep residual model outperforms four baseline models, including linear regression (LR),artifcial neural network (ANN), long short-term memory (LSTM) and spatiotemporal LSTM (ST-LSTM) models, thus demonstrating the efectiveness of the proposed prediction model. To our best knowledge, although deep-learning-based approaches have been shown to be useful in many felds in recent years, there has been no attempt to apply such approaches to infuenza prediction. Therefore, this study is an initial attempt to introduce a deep learning model into infuenza prediction. The proposed deep residual network is able to incorporate the spatial correlations of infuenza, and it has obvious potential for making infuenza predictions at fner spatial scales within a city, which can ofer critical support for preciser public health interventions. |
| 语种 | 英语 |
| URL标识 | 查看原文 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/14079]  |
| 专题 | 深圳先进技术研究院_数字所 |
| 推荐引用方式 GB/T 7714 | Guikai Xi,Ling Yin,Ye Li,et al. A Deep Residual Network Integrating Spatial-temporal Properties to Predict Influenza Trends at an Intra-urban Scale[C]. 见:. Seattle. 2018. |
入库方式： OAI收割
来源：深圳先进技术研究院
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