An Autoregressive Disease Mapping Model for Spatio-Temporal Forecasting

Francisca Corpas-Burgos and Miguel A. Martinez-Beneito
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Francisca Corpas-Burgos: Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana (FISABIO), Av. Cataluña, 21, 46020 Valencia, Spain
Miguel A. Martinez-Beneito: CIBER de Epidemiología y Salud Pública (CIBERESP), 28029 Madrid, Spain

Mathematics, 2021, vol. 9, issue 4, 1-17

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.

Keywords: bayesian statistics; spatial statistics; spatio-temporal statistics; disease mapping; forecasting; mortality studies (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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