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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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A Bayesian modelling framework to quantify multiple sources of spatial variation for disease mapping.

Sophie A Lee1,2, Theodoros Economou3, Rachel Lowe1,2,4,5

  • 1Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK.

Journal of the Royal Society, Interface
|September 21, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces novel Bayesian models using penalized smoothing splines to capture complex spatial connectivity in infectious disease data. These flexible models accurately identify disease drivers and outperform existing frameworks.

Keywords:
hierarchical modellinginfectious disease dynamicsspatial analysisspatial connectivityspatial epidemiologyvector-borne disease

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Geographic Information Science

Background:

  • Spatial connectivity is crucial for infectious disease modeling.
  • Traditional Bayesian models require pre-defined spatial structures.
  • Existing methods may not capture complex or non-stationary spatial relationships.

Purpose of the Study:

  • To develop a flexible Bayesian hierarchical modeling approach for infectious disease data.
  • To incorporate spatial connectivity using penalized smoothing splines.
  • To identify and quantify multiple sources of spatial structure influencing disease spread.

Main Methods:

  • Applied penalized smoothing splines to geographic coordinates to create flexible 2D spatial surfaces.
  • Developed Bayesian hierarchical models incorporating these smooth surfaces as random effects.
  • Utilized Bayesian inference and simulation studies to analyze spatial structures and their contributions.

Main Results:

  • Penalized smoothing splines effectively model non-stationary spatial connectivity with minimal assumptions.
  • The approach accommodates various symmetric continuous connectivity measures, including human movement data.
  • Models demonstrated comparable or superior performance to existing frameworks in simulation studies.

Conclusions:

  • This novel method offers a flexible and powerful tool for modeling spatial infectious disease dynamics.
  • It allows for the investigation of multiple, potentially interacting, sources of spatial connectivity.
  • The approach facilitates hypothesis generation regarding disease drivers and spatial transmission patterns.