A spatial scan statistic for group testing data

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.