Integrating independent spatio-temporal replications to assess population trends in disease spread

Infectious diseases spread at different rates across locations (e.g., states or cities). When we follow an outbreak, we observe a disease spread where infectious counts are collected at many locations throughout the epidemic. Current statistical methods available to model these outbreaks focus on a single monitored disease. We created a statistical method to collectively analyze replications of the same monitored disease.

The motivation for our method comes from a vision loss trial studying glaucoma progression. There are several locations within the eye where vision can be recorded. Individuals came in for multiple visits where vision loss was measured at these locations each time. The measurements collected from a single individual will be correlated (will behave similarly) which needs to be accounted for in the statistical method.

When vision loss is more severe, the variability of the measurements increases (i.e., the accuracy of the measurements decreases). We use the data collected from each individual to collectively analyze and make conclusions at the population level about glaucoma progression. These inferences will allow us to predict the disease progression within an individual accounting for the variability in the measurements. Vision loss change can be measured at each location in the eye for each person. These rates can help doctors understand what areas of the eye are affected more by the glaucoma progression.