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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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A spatial scan statistic for group testing data.

Vincent Onyame1, Alexander C McLain1, Rahul Ghosal1

  • 1University of South Carolina, Department of Epidemiology and Biostatistics, Columbia, 29208, SC, USA.

Spatial and Spatio-Temporal Epidemiology
|June 12, 2026
PubMed
Summary
This summary is machine-generated.

Group testing efficiently surveils low-prevalence infections but struggles with spatial cluster detection. A new spatial scan statistic for group testing data successfully identified a Rickettsia infection cluster in South Carolina ticks.

Keywords:
Group testingLikelihood ratio testMonte carlo simulationSpatial scan statistic

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Spatial Analysis

Background:

  • Group testing combines samples to reduce costs for disease surveillance.
  • Traditional group testing methods face challenges in identifying spatial disease clusters.
  • Existing methods lack tailored approaches for variable pool sizes in group testing data.

Purpose of the Study:

  • To introduce a novel spatial scan statistic designed for group testing data with variable pool sizes.
  • To evaluate the performance of this new statistic in detecting spatial clusters.
  • To apply the method to real-world tick surveillance data for Rickettsia infections.

Main Methods:

  • Developed a spatial scan statistic using a likelihood ratio test.
  • Compared homogeneous and heterogeneous infection probability models.
  • Conducted simulation studies to assess power and Type I error rates.
  • Applied the statistic to pooled tick testing data from South Carolina.

Main Results:

  • The spatial scan statistic effectively detects spatial clusters in group testing data.
  • Geographically homogeneous pooling demonstrated improved statistical power compared to heterogeneous pooling.
  • A significant cluster of Rickettsia infection was identified in Amblyomma americanum ticks along South Carolina's southeast coast.

Conclusions:

  • The developed spatial scan statistic is a valuable tool for disease surveillance using group testing data.
  • Homogeneous pooling strategies enhance the detection of spatial disease clusters.
  • The method successfully identified a Rickettsia infection hotspot in ticks, aiding targeted surveillance efforts.