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Non-stationary Bayesian spatial model for disease mapping based on sub-regions.

Esmail Abdul-Fattah1, Elias Krainski1, Janet Van Niekerk1

  • 1Statistics Program, CEMSE Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.

Statistical Methods in Medical Research
|April 10, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a flexible non-stationary spatial model to improve disease mapping by capturing complex spatial patterns. The new Bayesian model enhances interpretability and is demonstrated using dengue risk in Brazil.

Keywords:
Integrated Nested Laplace ApproximationsNon-stationarybesag modeldisease mappingpenalizing complexity priorspatial model

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Area of Science:

  • Spatial statistics
  • Bayesian modeling
  • Disease mapping

Background:

  • The Besag model is a standard Bayesian spatial model for disease mapping.
  • Existing models often assume spatial stationarity, which may not hold in complex geographical areas.
  • There is a need for models that can capture varying spatial dependence structures.

Purpose of the Study:

  • To extend the Besag model to a non-stationary spatial model for irregular lattice data.
  • To improve the capture of complex spatial dependence patterns and enhance model interpretability.
  • To develop a flexible modeling framework for non-stationary effects in spatial data.

Main Methods:

  • Developed a non-stationary Bayesian spatial model using multiple precision parameters.
  • Introduced a joint penalized complexity prior for local precision parameters to prevent overfitting.
  • Derived methods for estimating and interpreting non-stationary spatial effects.
  • Created an R package (fbesag) for practical application.

Main Results:

  • The proposed non-stationary model effectively captures complex spatial dependence.
  • Modeling dengue risk in Brazil revealed limitations of the stationary assumption and estimated interesting risk profiles.
  • The model was also applied to investigate the spatial stationarity of different causes of death in Brazil.

Conclusions:

  • The developed non-stationary spatial model offers improved flexibility and interpretability for disease mapping.
  • Accounting for spatial non-stationarity is crucial for accurate risk estimation in complex geographical settings.
  • The methodology provides a foundation for modeling non-stationary effects in other domains, including time series analysis.