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Updated: Aug 10, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Big problems in spatio-temporal disease mapping: Methods and software.

Erick Orozco-Acosta¹, Aritz Adin¹, María Dolores Ugarte¹

¹Department of Statistics, Computer Science and Mathematics, Public University of Navarre, Campus de Arrosadia, 31006 Pamplona, Spain; Institute for Advanced Materials and Mathematics (InaMat2), Public University of Navarre, Campus de Arrosadia, 31006 Pamplona, Spain.

Computer Methods and Programs in Biomedicine

|February 11, 2023

Summary

This summary is machine-generated.

This study introduces a scalable method for analyzing large, high-dimensional spatio-temporal areal data, improving relative risk estimation in fields like cancer epidemiology. The approach enables efficient Bayesian model fitting for complex datasets.

Keywords:

Cancer epidemiology Laplace approximations Massive data Non-stationary models Scalable modelling

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Area of Science:

Epidemiology
Statistical Modeling
Geospatial Analysis

Background:

Spatio-temporal models for areal data are vital in epidemiology.
Large datasets present computational challenges for traditional modeling approaches.
Accurate relative risk estimation is critical for public health interventions.

Purpose of the Study:

To propose a general procedure for analyzing high-dimensional spatio-temporal areal data.
To address computational limitations in fitting complex hierarchical models.
To enhance mortality/incidence relative risk estimation.

Main Methods:

Developed a pragmatic procedure for fitting hierarchical spatio-temporal models to large areal datasets.
Utilized integrated nested Laplace approximations over spatial partitions.
Employed parallel and distributed computing strategies to accelerate Bayesian model fitting.

Main Results:

Demonstrated superior performance of the proposed method over classical global models using simulated and real data.
Implemented the methodology in the open-source R package bigDM.
Included user-friendly vignettes to facilitate adoption by non-expert users.

Conclusions:

The proposed scalable methodology enables reliable risk estimates.
Facilitates the fitting of Bayesian hierarchical spatio-temporal models for high-dimensional data.
Offers a computationally efficient solution for complex epidemiological analyses.