Skip to main content

Modeling spatial and temporal variability by Bayesian multilevel model


The early detection of outbreaks of diseases is one of the most challenging objectives of epidemiological surveillance systems. In order to achieve this goal, the primary foundation is using those big surveillance data for understanding and controlling the spatiotemporal variability of disease through populations. Typically, public health’s surveillance system would generate data with the big data characteristics of high volume, velocity, and variety. One common question of big data analysis is most of the data have the multilevel or hierarchy structure, in other word the big data are non-independent. Traditional multilevel or hierarchical model can only deal with 2 or 3 hierarchical data structure, which bound health big data further research for modeling, forecast and early-warning in the public health surveillance, in particular involving complex spatial and temporal variability of Infectious Diseases in the reality. 


The purpose of this article was to quantitative analyses the spatial variability and temporal variability of influenza like illness (ILI) by a three-level Poisson model, which means to explain the spatial and temporal level effects by introducing the random effects. 

Submitted by Magou on