A novel method to select time-varying multivariate time series models for the surveillance of infectious diseases
This study emphasized the importance of considering spatio-temporal variations when selecting appropriate models for infectious disease surveillance. By incorporating our novel indicators-the time delay coefficient and spatial sparsity-into the model selection process, the study could enhance the accuracy and effectiveness of infectious disease monitoring efforts. This approach was not only valuable in the context of this study, but also has broader implications for improving time-varying MTS analyses in various applications.
Authors
- Chen Yang (ORCID: https://orcid.org/0000-0001-9996-7534)
- Yue Ma (ORCID: https://orcid.org/0000-0002-1980-7520)
- Miaoshuang Chen
- Yang Weng (ORCID: https://orcid.org/0000-0003-0209-5595)
- Tao Zhang (ORCID: https://orcid.org/0000-0001-7279-8929)
- Huimin Wang (ORCID: https://orcid.org/0009-0006-3932-1604)
- Wenhui Zhu (ORCID: https://orcid.org/0009-0000-5207-6283)
- Jie Yu (ORCID: https://orcid.org/0000-0003-3806-2015)
- Xinyue Han (ORCID: https://orcid.org/0000-0002-8082-0863)
- Changhong Yang (ORCID: https://orcid.org/0000-0003-1743-879X)
- Fei Yin (ORCID: https://orcid.org/0000-0003-3486-4926)
- Qiao Deng (ORCID: https://orcid.org/0000-0001-7335-7199)
Institutions
- Sichuan University (CN)
- West China Hospital of Sichuan University (CN)
- Sichuan Center for Disease Control and Prevention (CN)
Publication Details
- Journal
- BMC Infectious Diseases
- Published
- 2024-08-15
- DOI
- https://doi.org/10.1186/s12879-024-09718-x
- Citations
- 1
- Primary Topic
- Data-Driven Disease Surveillance
- Type
- article
- Field-Weighted Citation Impact
- 0.39
Funders
- National Natural Science Foundation of China
- Sichuan Province Science and Technology Support Program