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A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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Modeling disease incidence data with spatial and spatio temporal dirichlet process mixtures.

Athanasios Kottas1, Jason A Duan, Alan E Gelfand

  • 1Department of Applied Mathematics and Statistics, 1156 High Street, University of California, Santa Cruz, CA 95064, USA. thanos@ams.ucsc.edu

Biometrical Journal. Biometrische Zeitschrift
|October 11, 2007
PubMed
Summary

This study introduces advanced Bayesian nonparametric spatial models for analyzing disease incidence data. The novel approach enhances understanding of geographic disease patterns and their temporal dynamics.

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Spatial Statistics

Background:

  • Disease incidence and mortality data are often collected as regional rates or counts over time.
  • Analyzing complex spatial patterns in disease data requires sophisticated statistical methods.

Purpose of the Study:

  • To propose and evaluate Bayesian nonparametric spatial modeling approaches for disease incidence data.
  • To develop a flexible hierarchical model incorporating spatial random effects using Dirichlet process priors.
  • To extend the model for spatio-temporal analysis of disease patterns.

Main Methods:

  • Hierarchical modeling with spatial random effects specified by a Dirichlet process prior.
  • Utilizing a log-Gaussian process for a latent incidence rate surface, followed by block averaging.
  • Implementing posterior inference via Gibbs sampling.
  • Introducing a dynamic formulation for spatio-temporal extensions.

Main Results:

  • The proposed Bayesian nonparametric approach provides a robust framework for spatial and spatio-temporal disease data analysis.
  • The model effectively captures latent incidence rate surfaces and regional variations.
  • Demonstrated utility with simulated data and a real-world lung cancer incidence dataset from Ohio.

Conclusions:

  • Bayesian nonparametric spatial modeling offers a powerful tool for analyzing regional disease incidence data.
  • The developed methodology is suitable for both static spatial and dynamic spatio-temporal analyses.
  • The approach provides valuable insights into disease distribution and trends over time and space.