An Autoregressive Disease Mapping Model for Spatio-Temporal Forecasting

One of the more evident uses of spatio-temporal disease mapping is forecasting the spatial distribution of diseases for the next few years following the end of the period of study. Spatio-temporal models rely on very different modeling tools (polynomial fit, splines, time series, etc.), which could show very different forecasting properties. In this paper, we introduce an enhancement of a previous autoregressive spatio-temporal model with particularly interesting forecasting properties, given its reliance on time series modeling. We include a common spatial component in that model and show how that component improves the previous model in several ways, its predictive capabilities being one of them. In this paper, we introduce and explore the theoretical properties of this model and compare them with those of the original autoregressive model. Moreover, we illustrate the benefits of this new model with the aid of a comprehensive study on 46 different mortality data sets in the Valencian Region (Spain) where the benefits of the new proposed model become evident.

Automated summary

The use of spatio-temporal disease mapping for forecasting spatial distribution of diseases relies on various modeling tools and methods. This study introduces an enhanced autoregressive spatio-temporal model with a common spatial component, showing improved predictive capabilities compared to the original model, as illustrated through a comprehensive study on mortality data sets in the Valencian Region of Spain.