Abstract
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.
Highlights
Statistical techniques for disease mapping have received substantial attention during the last two decades [1,2,3]
As described in this paper, the autoregressive spatio-temporal model proposed by Martinez-Beneito et al (2008) [17] tends to estimate high values of the temporal correlation coefficient (ρ) for all the data sets studied in the Spatio-temporal Mortality Atlas of the Valencian Region (1987–2006)
We propose a modification of the spatio-temporal original AR model that includes a new random effect, which models the common spatial pattern for the whole period of study
Summary
Statistical techniques for disease mapping have received substantial attention during the last two decades [1,2,3]. Ocaña and Riola (2007) [5] showed that the aggregation of data over long time periods in disease mapping studies produces biased risk estimates, and it would be convenient to consider the temporal variation of risks in those models for correcting that bias For all these reasons, and for forecasting, spatio-temporal models should be borne in mind in very general contexts, mainly if the available period of study is long. We compare these proposals with two simpler spatio-temporal models proposed in the literature, which consider spatially varying quadratic and linear time trends [6,7], in order to check if less parameterized models could show greater ability for forecasting future out-of-sample risks. We give some conclusions (Section 6) about the results and models described in the previous sections
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
