PDF(Pubmed)
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).
CONCLUSIONS: 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.
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).
CONCLUSIONS: 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.
摘要：
背景：描述传染病在不同地区的传播动态对于有效的疾病监测至关重要。多变量时间序列（MTS）模型由于其在可解释性和预测性能方面的优势，已被广泛用于构建跨区域传染病传播网络。然而，恒定参数的假设经常忽略疾病传播率的动态变化，从而影响预警的准确性。本研究调查了时变MTS模型在多区域传染病监测中的适用性，并探索了模型选择的策略。
方法：本研究主要关注两个显著的时变MTS模型：时变参数-随机波动率-向量自回归(TVP-SV-VAR)模型和使用广义加性框架(tvvarGAM)的时变VAR模型，并旨在探索和验证其适用于传染病监测的条件。第一次,本研究提出了模型选择的时滞系数和空间稀疏性指标。这些指标量化了传染病数据的时间滞后和空间分布，分别。进行了从现实世界传染病监测中采用的模拟研究，以比较在时空变化和随机波动的各种情况下的模型性能。同时,我们说明了建模过程如何应用于四川省的流感样病例来帮助监测传染病，中国。
结果：当时空变化较小时（时间延迟系数：0.1-0.2，空间稀疏性：0.1-0.3），TVP-SV-VAR模型比tvvarGAM模型具有更小的拟合残差和参数估计的标准误差。相比之下,当时空变化增加时，tvvarGAM模型更可取（时间延迟系数：0.2-0.3，空间稀疏性：0.6-0.9）。
结论：本研究强调了在选择合适的传染病监测模型时考虑时空变化的重要性。通过将我们的新指标-时间延迟系数和空间稀疏性-纳入模型选择过程，这项研究可以提高传染病监测工作的准确性和有效性。这种方法不仅在本研究的背景下很有价值，而且对于在各种应用中改进时变MTS分析也具有更广泛的意义。
方法：本研究主要关注两个显著的时变MTS模型：时变参数-随机波动率-向量自回归(TVP-SV-VAR)模型和使用广义加性框架(tvvarGAM)的时变VAR模型，并旨在探索和验证其适用于传染病监测的条件。第一次,本研究提出了模型选择的时滞系数和空间稀疏性指标。这些指标量化了传染病数据的时间滞后和空间分布，分别。进行了从现实世界传染病监测中采用的模拟研究，以比较在时空变化和随机波动的各种情况下的模型性能。同时,我们说明了建模过程如何应用于四川省的流感样病例来帮助监测传染病，中国。
结果：当时空变化较小时（时间延迟系数：0.1-0.2，空间稀疏性：0.1-0.3），TVP-SV-VAR模型比tvvarGAM模型具有更小的拟合残差和参数估计的标准误差。相比之下,当时空变化增加时，tvvarGAM模型更可取（时间延迟系数：0.2-0.3，空间稀疏性：0.6-0.9）。
结论：本研究强调了在选择合适的传染病监测模型时考虑时空变化的重要性。通过将我们的新指标-时间延迟系数和空间稀疏性-纳入模型选择过程，这项研究可以提高传染病监测工作的准确性和有效性。这种方法不仅在本研究的背景下很有价值，而且对于在各种应用中改进时变MTS分析也具有更广泛的意义。
