An Autoregressive Disease Mapping Model for Spatio-Temporal Forecasting

被引:4
|
作者
Corpas-Burgos, Francisca [1 ,2 ]
Martinez-Beneito, Miguel A. [2 ,3 ]
机构
[1] Fdn Fomento Invest Sanitaria & Biomed Comunitat V, Ave Cataluna 21, Valencia 46020, Spain
[2] CIBER Epidemiol & Salud Publ CIBERESP, Madrid 28029, Spain
[3] Univ Valencia, Dept Estadist & Invest Operat, C Dr Moliner 50, Valencia 46100, Spain
关键词
bayesian statistics; spatial statistics; spatio-temporal statistics; disease mapping; forecasting; mortality studies;
D O I
10.3390/math9040384
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
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.
引用
收藏
页码:1 / 17
页数:17
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