STANOVA: a smoothed-ANOVA-based model for spatio-temporal disease mapping

Torres-Avilés, Francisco; Martínez-Beneito, Miguel A.

doi:10.1007/s00477-014-0888-1

Cited by 5 publications

(4 citation statements)

References 22 publications

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“…Considering different

and

parameters for each element in the basis of functions allows reproducing different spatial strengths and variabilities, respectively, for any moment of the study period. Therefore, this reproduces a non-separable spatio-temporal correlation structure ( Torres-Avilés and Martinez-Beneito, 2015 ). These assumptions seem reasonable when the pandemic could show different spatial features at their different phases or waves.…”

Section: Methodsmentioning

confidence: 59%

Spatio-temporal small area surveillance of the COVID-19 pandemic

2022

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“…Considering different

and

Section: Methodsmentioning

confidence: 59%

Spatio-temporal small area surveillance of the COVID-19 pandemic

2022

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“…Thus SANOVA (Zhang et al, 2009; Marí Dell’Olmo et al, 2014) is a method for multivariate modelling which allows to structure in some specific ways the covariance structure between geographical patterns. This particular feature of SANOVA makes it also suitable for modelling complex dependence relationships like in multidimensional settings, even for structured factors (Torres-Avilés and Martinez-Beneito, 2015). Nevertheless, SANOVA has several drawbacks in comparison to multidimensional modelling.…”

Section: Discussionmentioning

confidence: 99%

Towards a Multidimensional Approach to Bayesian Disease Mapping

Martínez-Beneito¹,

Botella-Rocamora²,

Banerjee³

2017

Bayesian Anal.

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Multivariate disease mapping enriches traditional disease mapping studies by analysing several diseases jointly. This yields improved estimates of the geographical distribution of risk from the diseases by enabling borrowing of information across diseases. Beyond multivariate smoothing for several diseases, several other variables, such as sex, age group, race, time period, and so on, could also be jointly considered to derive multivariate estimates. The resulting multivariate structures should induce an appropriate covariance model for the data. In this paper, we introduce a formal framework for the analysis of multivariate data arising from the combination of more than two variables (geographical units and at least two more variables), what we have called Multidimensional Disease Mapping. We develop a theoretical framework containing both separable and non-separable dependence structures and illustrate its performance on the study of real mortality data in Comunitat Valenciana (Spain).

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“…Many proposals can be found in the Bayesian literature for the spatio-temporal modeling of risks. These proposals have followed very different approaches, ranging from spatially dependent temporal parametric models [6][7][8] to more flexible and parameterized spatio-temporal spline models [9][10][11][12][13]. However, the ability of disease mapping models to predict future mortality or disease incidence has been hardly explored [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

An Autoregressive Disease Mapping Model for Spatio-Temporal Forecasting

Corpas-Burgos

Martínez-Beneito

2021

Mathematics

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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.

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STANOVA: a smoothed-ANOVA-based model for spatio-temporal disease mapping

Cited by 5 publications

References 22 publications

Spatio-temporal small area surveillance of the COVID-19 pandemic

Spatio-temporal small area surveillance of the COVID-19 pandemic

Towards a Multidimensional Approach to Bayesian Disease Mapping

An Autoregressive Disease Mapping Model for Spatio-Temporal Forecasting

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