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An Autoregressive Disease Mapping Model for Spatio-Temporal Forecasting

Author

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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
    CIBER de Epidemiología y Salud Pública (CIBERESP), 28029 Madrid, Spain)

  • Miguel A. Martinez-Beneito

    (CIBER de Epidemiología y Salud Pública (CIBERESP), 28029 Madrid, Spain
    Departament d’Estadística i Investigació Operativa, Universitat de València, C/ Dr. Moliner, 50, Burjassot, 46100 Valencia, Spain)

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.

Suggested Citation

  • Francisca Corpas-Burgos & Miguel A. Martinez-Beneito, 2021. "An Autoregressive Disease Mapping Model for Spatio-Temporal Forecasting," Mathematics, MDPI, vol. 9(4), pages 1-17, February.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:4:p:384-:d:499484
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    References listed on IDEAS

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    4. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
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    1. Win Wah & Rob G. Stirling & Susannah Ahern & Arul Earnest, 2021. "Forecasting of Lung Cancer Incident Cases at the Small-Area Level in Victoria, Australia," IJERPH, MDPI, vol. 18(10), pages 1-13, May.

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