Multiple Source Spatial Cluster Detection Through Multi-criteria Analysis

Authors

  • Luiz H. Duczmal Universidade Federal de Minas Gerais, Belo Horizonte, MG, BRAZIL
  • Alexandre C. L. Almeida Universidade Federal de S?嬣o Jo?嬣o del-Rei, Ouro Branco, MG, BRAZIL
  • Fabio R. da Silva Universidade Federal de Minas Gerais, Belo Horizonte, MG, BRAZIL
  • Martin Kulldorff Harvard Medical School, Boston, MA

DOI:

https://doi.org/10.5210/ojphi.v5i1.4420

Abstract

Multiple data sources are essential to provide more reliable information regarding the emergence of potential health threats, compared to single source methods. However, only ad hoc procedures have been devised to address the problem of locating, among the many potential solutions, which is the most likely cluster, and determining its significance. We incorporate information from multiple data streams of disease surveillance to achieve more coherent spatial cluster detection by using statistical tools from multi-criteria analysis. Our approach defines in an optimal way, how spatial disease clusters found by the spatial scan statistic can be interpreted in terms of their significance.

Author Biography

Luiz H. Duczmal, Universidade Federal de Minas Gerais, Belo Horizonte, MG, BRAZIL

Luiz Duczmal is Associate Professor in the Statistics Department at Universidade Federal de Minas Gerais, Brazil. His main research interests are spatial statistics, spatial cluster detection, optimization and operational research.

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Published

2013-03-23

How to Cite

Duczmal, L. H., Almeida, A. C. L., da Silva, F. R., & Kulldorff, M. (2013). Multiple Source Spatial Cluster Detection Through Multi-criteria Analysis. Online Journal of Public Health Informatics, 5(1). https://doi.org/10.5210/ojphi.v5i1.4420

Issue

Section

Oral Presentations: Cluster Detection