31: Modeling Count Time Series with Spatial Dependence: A COM-Poisson INGARCH Approach

Music City Center 

We introduce a novel time series model that integrates Integer-Valued Generalized Autoregressive Conditional Heteroskedasticity (INGARCH) dynamics with a COM-Poisson distribution, incorporating a spatial modeling term to account for spatial dependence. The COM-Poisson distribution allows for overdispersion and underdispersion in count data, making it more flexible for capturing real-world phenomena. The GARCH component models the time-varying conditional variance of the process, while the spatial term accounts for the influence of neighboring data points, enabling the model to address spatial correlations. This approach provides a comprehensive framework for analyzing time series count data with both heteroskedasticity and spatial dependence, which is particularly useful in fields such as epidemiology and infectious disease. The COM-Poisson INGARCH spatial model benefits public health and health policy researchers by allowing for more accurate predictions. This will assist public health officials and policymakers to make evidence-based decisions and improve public health outcomes. The model's performance is evaluated through simulation studies and applied to a real-world dataset.

COM-Poisson

Integer-valued GARCH models

Spatial Modeling

Time Series of Counts 

Section on Statistics in Epidemiology