Approximate cross-validatory predictive checks in disease mapping models

2003 ◽  
Vol 22 (10) ◽  
pp. 1649-1660 ◽  
Author(s):  
E. C. Marshall ◽  
D. J. Spiegelhalter
Keyword(s):  
Disease Mapping ◽  
Mapping Models

scholarly journals Imputation of confidential data sets with spatial locations using disease mapping models

2014 ◽  
Vol 33 (11) ◽  
pp. 1928-1945 ◽  
Author(s):  
Thais Paiva ◽  
Avishek Chakraborty ◽  
Jerry Reiter ◽  
Alan Gelfand
Keyword(s):  
Disease Mapping ◽  
Data Sets ◽  
Confidential Data ◽  
Spatial Locations ◽  
Mapping Models

Effects of Residual Smoothing on the Posterior of the Fixed Effects in Disease-Mapping Models

Biometrics ◽  
2006 ◽  
Vol 62 (4) ◽  
pp. 1197-1206 ◽  
Author(s):  
Brian J. Reich ◽  
James S. Hodges ◽  
Vesna Zadnik
Keyword(s):  
Fixed Effects ◽  
Disease Mapping ◽  
Mapping Models ◽  
Residual Smoothing

On fitting spatio-temporal disease mapping models using approximate Bayesian inference

2014 ◽  
Vol 23 (6) ◽  
pp. 507-530 ◽  
Author(s):  
María Dolores Ugarte ◽  
Aritz Adin ◽  
Tomas Goicoa ◽  
Ana Fernandez Militino
Keyword(s):  
Bayesian Inference ◽  
Disease Mapping ◽  
Spatio Temporal ◽  
Approximate Bayesian ◽  
Mapping Models ◽  
Approximate Bayesian Inference

scholarly journals Disease mapping models for data with weak spatial dependence or spatial discontinuities

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Helena Baptista ◽  
Peter Congdon ◽  
Jorge M. Mendes ◽  
Ana M. Rodrigues ◽  
Helena Canhão ◽  
...  
Keyword(s):  
Spatial Epidemiology ◽  
Disease Mapping ◽  
Gaussian Random Field ◽  
Spatial Smoothing ◽  
Public Health Perspective ◽  
The Public ◽  
Spatially Adaptive ◽  
Traditional Approaches ◽  
Bayesian Disease Mapping ◽  
Mapping Models

AbstractRecent advances in the spatial epidemiology literature have extended traditional approaches by including determinant disease factors that allow for non-local smoothing and/or non-spatial smoothing. In this article, two of those approaches are compared and are further extended to areas of high interest from the public health perspective. These are a conditionally specified Gaussian random field model, using a similarity-based non-spatial weight matrix to facilitate non-spatial smoothing in Bayesian disease mapping; and a spatially adaptive conditional autoregressive prior model. The methods are specially design to handle cases when there is no evidence of positive spatial correlation or the appropriate mix between local and global smoothing is not constant across the region being study. Both approaches proposed in this article are producing results consistent with the published knowledge, and are increasing the accuracy to clearly determine areas of high- or low-risk.

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