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Representing spatial dependence and spatial discontinuity in ecological epidemiology: a scale mixture approach

Peter Congdon

Published 2017 in Stochastic environmental research and risk assessment (Print)

ABSTRACT

Variation in disease risk underlying observed disease counts is increasingly a focus for Bayesian spatial modelling, including applications in spatial data mining. Bayesian analysis of spatial data, whether for disease or other types of event, often employs a conditionally autoregressive prior, which can express spatial dependence commonly present in underlying risks or rates. Such conditionally autoregressive priors typically assume a normal density and uniform local smoothing for underlying risks. However, normality assumptions may be affected or distorted by heteroscedasticity or spatial outliers. It is also desirable that spatial disease models represent variation that is not attributable to spatial dependence. A spatial prior representing spatial heteroscedasticity within a model accommodating both spatial and non-spatial variation is therefore proposed. Illustrative applications are to human TB incidence. A simulation example is based on mainland US states, while a real data application considers TB incidence in 326 English local authorities.

PUBLICATION RECORD

  • Publication year

    2017

  • Venue

    Stochastic environmental research and risk assessment (Print)

  • Publication date

    2017-02-01

  • Fields of study

    Medicine, Computer Science, Environmental Science

  • Identifiers
    DOI 10.1007/s00477-016-1292-9
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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