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An Autoregressive Disease Mapping Model for Spatio-Temporal Forecasting

Authors :
Francisca Corpas-Burgos
Miguel A. Martinez-Beneito
Source :
Mathematics, Volume 9, Issue 4, Mathematics, Vol 9, Iss 384, p 384 (2021)
Publication Year :
2021
Publisher :
Multidisciplinary Digital Publishing Institute, 2021.

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.

Details

Language :
English
ISSN :
22277390
Database :
OpenAIRE
Journal :
Mathematics
Accession number :
edsair.doi.dedup.....53dbdb063f6b380e0d5a194be733f754
Full Text :
https://doi.org/10.3390/math9040384