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Fast Bayesian Classification for Disease Mapping and the Detection of Disease Clusters

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Quantitative Methods in Environmental and Climate Research

Abstract

We propose a framework fast method for detecting clusters of disease based on generalized spatial scan statistics set in the context of Bayesian Hierarchical Models. The approach models spatio-temporal clusters of disease as dummy variables as part of a Generalized Linear Mixed Model.

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Author information

Authors and Affiliations

  1. Department of Mathematics, School of Industrial Engineering, Universidad de Castilla-La Mancha, Albacete, Spain

    V. Gómez-Rubio

  2. College of Public Health and Human Sciences, Oregon State University, Corvallis, OR, USA

    John Molitor

  3. Faculty of Health and Medicine, Lancaster University, Lancaster, UK

    Paula Moraga

Authors
  1. V. Gómez-Rubio
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  2. John Molitor
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  3. Paula Moraga
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Corresponding author

Correspondence to V. Gómez-Rubio .

Editor information

Editors and Affiliations

  1. Department of Management, Economics and Quantitative Methods, University of Bergamo, Bergamo, Italy

    Michela Cameletti

  2. Department of Management, Information and Production Engineering, University of Bergamo, Dalmine, Bergamo, Italy

    Francesco Finazzi

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Gómez-Rubio, V., Molitor, J., Moraga, P. (2018). Fast Bayesian Classification for Disease Mapping and the Detection of Disease Clusters. In: Cameletti, M., Finazzi, F. (eds) Quantitative Methods in Environmental and Climate Research. Springer, Cham. https://doi.org/10.1007/978-3-030-01584-8_1

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