File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1016/j.fmre.2021.07.007
- Scopus: eid_2-s2.0-85119596043
- WOS: WOS:000855406200001
Supplementary
- Citations:
- Appears in Collections:
Article: Adaptively temporal graph convolution model for epidemic prediction of multiple age groups
Title | Adaptively temporal graph convolution model for epidemic prediction of multiple age groups |
---|---|
Authors | |
Keywords | Graph convolution model Infectious disease prediction Multiple age group Multivariate time series Public health |
Issue Date | 2022 |
Citation | Fundamental Research, 2022, v. 2, n. 2, p. 311-320 How to Cite? |
Abstract | Introduction: Multivariate time series prediction of infectious diseases is significant to public health, and the deep learning method has attracted increasing attention in this research field. Material and methods: An adaptively temporal graph convolution (ATGCN) model, which learns the contact patterns of multiple age groups in a graph-based approach, was proposed for COVID- 19 and influenza prediction. We compared ATGCN with autoregressive models, deep sequence learning models, and experience- based ATGCN models in short-term and long-term prediction tasks. Results: Results showed that the ATGCN model performed better than the autoregressive models and the deep sequence learning models on two datasets in both short-term (12.5% and 10% improvements on RMSE) and long-term (12.4% and 5% improvements on RMSE) prediction tasks. And the RMSE of ATGCN predictions fluctuated least in different age groups of COVID- 19 (0.029 ± 0.003) and influenza (0.059±0.008). Compared with the Ones-ATGCN model or the Pre-ATGCN model, the ATGCN model was more robust in performance, with RMSE of 0.0293 and 0.06 on two datasets when horizon is one. Discussion: Our research indicates a broad application prospect of deep learning in the field of infectious disease prediction. Transmission characteristics and domain knowledge of infectious diseases should be further applied to the design of deep learning models and feature selection. Conclusion: The ATGCN model addressed the multivariate time series forecasting in a graph-based deep learning approach and achieved robust prediction on the confirmed cases of multiple age groups, indicating its great potentials for exploring the implicit interactions of multivariate variables. |
Persistent Identifier | http://hdl.handle.net/10722/330742 |
ISI Accession Number ID |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Wang, Yuejiao | - |
dc.contributor.author | Zeng, Dajun Daniel | - |
dc.contributor.author | Zhang, Qingpeng | - |
dc.contributor.author | Zhao, Pengfei | - |
dc.contributor.author | Wang, Xiaoli | - |
dc.contributor.author | Wang, Quanyi | - |
dc.contributor.author | Luo, Yin | - |
dc.contributor.author | Cao, Zhidong | - |
dc.date.accessioned | 2023-09-05T12:13:47Z | - |
dc.date.available | 2023-09-05T12:13:47Z | - |
dc.date.issued | 2022 | - |
dc.identifier.citation | Fundamental Research, 2022, v. 2, n. 2, p. 311-320 | - |
dc.identifier.uri | http://hdl.handle.net/10722/330742 | - |
dc.description.abstract | Introduction: Multivariate time series prediction of infectious diseases is significant to public health, and the deep learning method has attracted increasing attention in this research field. Material and methods: An adaptively temporal graph convolution (ATGCN) model, which learns the contact patterns of multiple age groups in a graph-based approach, was proposed for COVID- 19 and influenza prediction. We compared ATGCN with autoregressive models, deep sequence learning models, and experience- based ATGCN models in short-term and long-term prediction tasks. Results: Results showed that the ATGCN model performed better than the autoregressive models and the deep sequence learning models on two datasets in both short-term (12.5% and 10% improvements on RMSE) and long-term (12.4% and 5% improvements on RMSE) prediction tasks. And the RMSE of ATGCN predictions fluctuated least in different age groups of COVID- 19 (0.029 ± 0.003) and influenza (0.059±0.008). Compared with the Ones-ATGCN model or the Pre-ATGCN model, the ATGCN model was more robust in performance, with RMSE of 0.0293 and 0.06 on two datasets when horizon is one. Discussion: Our research indicates a broad application prospect of deep learning in the field of infectious disease prediction. Transmission characteristics and domain knowledge of infectious diseases should be further applied to the design of deep learning models and feature selection. Conclusion: The ATGCN model addressed the multivariate time series forecasting in a graph-based deep learning approach and achieved robust prediction on the confirmed cases of multiple age groups, indicating its great potentials for exploring the implicit interactions of multivariate variables. | - |
dc.language | eng | - |
dc.relation.ispartof | Fundamental Research | - |
dc.subject | Graph convolution model | - |
dc.subject | Infectious disease prediction | - |
dc.subject | Multiple age group | - |
dc.subject | Multivariate time series | - |
dc.subject | Public health | - |
dc.title | Adaptively temporal graph convolution model for epidemic prediction of multiple age groups | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1016/j.fmre.2021.07.007 | - |
dc.identifier.scopus | eid_2-s2.0-85119596043 | - |
dc.identifier.volume | 2 | - |
dc.identifier.issue | 2 | - |
dc.identifier.spage | 311 | - |
dc.identifier.epage | 320 | - |
dc.identifier.eissn | 2667-3258 | - |
dc.identifier.isi | WOS:000855406200001 | - |