Bibliographic Details
| Title: |
A novel method to select time-varying multivariate time series models for the surveillance of infectious diseases |
| Authors: |
Jie Yu, Huimin Wang, Miaoshuang Chen, Xinyue Han, Qiao Deng, Chen Yang, Wenhui Zhu, Yue Ma, Fei Yin, Yang Weng, Changhong Yang, Tao Zhang |
| Source: |
BMC Infectious Diseases, Vol 24, Iss 1, Pp 1-16 (2024) |
| Publisher Information: |
BMC, 2024. |
| Publication Year: |
2024 |
| Collection: |
LCC:Infectious and parasitic diseases |
| Subject Terms: |
Infectious disease, Surveillance and early warning, Spatio-temporal pattern, Multivariate time series, Time-varying parameter, Infectious and parasitic diseases, RC109-216 |
| More Details: |
Abstract Background Describing the transmission dynamics of infectious diseases across different regions is crucial for effective disease surveillance. The multivariate time series (MTS) model has been widely adopted for constructing cross-regional infectious disease transmission networks due to its strengths in interpretability and predictive performance. Nevertheless, the assumption of constant parameters frequently disregards the dynamic shifts in disease transmission rates, thereby compromising the accuracy of early warnings. This study investigated the applicability of time-varying MTS models in multi-regional infectious disease monitoring and explored strategies for model selection. Methods This study focused on two prominent time-varying MTS models: the time-varying parameter-stochastic volatility-vector autoregression (TVP-SV-VAR) model and the time-varying VAR model using the generalized additive framework (tvvarGAM), and intended to explore and verify their applicable conditions for the surveillance of infectious diseases. For the first time, this study proposed the time delay coefficient and spatial sparsity indicators for model selection. These indicators quantify the temporal lags and spatial distribution of infectious disease data, respectively. Simulation study adopted from real-world infectious disease surveillance was carried out to compare model performances under various scenarios of spatio-temporal variation as well as random volatility. Meanwhile, we illustrated how the modelling process could help the surveillance of infectious diseases with an application to the influenza-like case in Sichuan Province, China. Results When the spatio-temporal variation was small (time delay coefficient: 0.1–0.2, spatial sparsity:0.1–0.3), the TVP-SV-VAR model was superior with smaller fitting residuals and standard errors of parameter estimation than those of the tvvarGAM model. In contrast, the tvvarGAM model was preferable when the spatio-temporal variation increased (time delay coefficient: 0.2–0.3, spatial sparsity: 0.6–0.9). Conclusion 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. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
1471-2334 |
| Relation: |
https://doaj.org/toc/1471-2334 |
| DOI: |
10.1186/s12879-024-09718-x |
| Access URL: |
https://doaj.org/article/cffd13fea3864a8e865225bc1811ea12 |
| Accession Number: |
edsdoj.ffd13fea3864a8e865225bc1811ea12 |
| Database: |
Directory of Open Access Journals |
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