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Spatio-temporal disease risk estimation using clustering-based adjacency modelling.

Xueqing Yin1, Gary Napier1, Craig Anderson1

  • 1School of Mathematics and Statistics, 3526University of Glasgow, UK.

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Summary
This summary is machine-generated.

This study introduces a new two-stage Bayesian method to detect disease risk clusters and discontinuities in spatial data. The approach improves disease mapping by identifying localized high or low-risk areas over time.

Keywords:
Clusteringconditional autoregressive modelsneighbourhood matrixspatio-temporal modelling

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Geographic Information Systems (GIS)

Background:

  • Conditional autoregressive models commonly analyze spatial autocorrelation in disease counts using neighbourhood matrices.
  • Standard models mask disease risk discontinuities, hindering the detection of localized risk clusters.

Purpose of the Study:

  • To propose a novel two-stage modelling approach to identify and account for clusters and discontinuities in disease risk.
  • To allow for static or dynamically evolving clusters/discontinuities over time.

Main Methods:

  • A two-stage Bayesian spatio-temporal disease mapping model is developed.
  • Stage one generates candidate neighbourhood matrices representing potential cluster structures.
  • Stage two estimates the optimal neighbourhood structure as a parameter within the model.

Main Results:

  • Simulations demonstrate the effectiveness of the proposed methodology.
  • The method was applied to respiratory disease risk in Greater Glasgow, Scotland (2011-2017).

Conclusions:

  • The novel methodology successfully identifies and models disease risk clusters and discontinuities.
  • This approach enhances spatio-temporal disease mapping by revealing localized risk patterns.