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Regularized spatial and spatio-temporal cluster detection.

Maria E Kamenetsky1, Junho Lee2, Jun Zhu3

  • 1Department of Population Health Sciences, University of Wisconsin-Madison, Madison, WI 53726, USA.

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|June 12, 2022
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Summary
This summary is machine-generated.

This study introduces a new high-dimensional data analysis method for detecting spatial and spatio-temporal clusters. The approach uses Poisson regression with a Lasso penalty for efficient and accurate cluster identification in public health applications.

Keywords:
LassoPoisson regressionQuasi-likelihoodSpatial cluster detectionSpatial scan statisticSpatio-temporal cluster detection

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Spatial Analysis

Background:

  • Spatial and spatio-temporal cluster detection are crucial for public health surveillance.
  • Traditional methods often employ space and time scan statistics for cluster identification.

Purpose of the Study:

  • To develop a novel, computationally efficient method for high-dimensional spatial and spatio-temporal cluster detection.
  • To apply a regression framework with Lasso penalty for improved cluster analysis.

Main Methods:

  • Recasting cluster detection within a high-dimensional data analytical framework using Poisson or quasi-Poisson regression.
  • Implementing a fast, computationally efficient method with a sparse matrix representation for potential cluster effects.
  • Utilizing (quasi-)information criteria for selecting the number of clusters and tuning parameters.

Main Results:

  • The proposed method demonstrates effective performance in identifying spatial and spatio-temporal clusters.
  • Simulation studies evaluated the method's false positive detection rate and statistical power.
  • The approach was successfully applied to real-world breast cancer incidence data from Japan.

Conclusions:

  • The novel regression-based approach offers an efficient and accurate alternative for spatial and spatio-temporal cluster detection.
  • This method has significant implications for public health surveillance and epidemiological research.
  • The study highlights the utility of high-dimensional data analysis techniques in identifying disease clusters.