Accounting for spatial correlation in the scan statistic

TL;DR

This paper investigates how spatial correlation and overdispersion affect the accuracy of the spatial scan statistic and proposes a modified method to reduce false positives in hotspot detection.

Contribution

The paper introduces a modified spatial scan statistic that accounts for spatial correlation and overdispersion, improving false positive control in disease hotspot detection.

Findings

Ignoring spatial correlation increases false positives.

The modified statistic reduces false alarms in simulations.

Application to real data demonstrates practical relevance.

Abstract

The spatial scan statistic is widely used in epidemiology and medical studies as a tool to identify hotspots of diseases. The classical spatial scan statistic assumes the number of disease cases in different locations have independent Poisson distributions, while in practice the data may exhibit overdispersion and spatial correlation. In this work, we examine the behavior of the spatial scan statistic when overdispersion and spatial correlation are present, and propose a modified spatial scan statistic to account for that. Some theoretical results are provided to demonstrate that ignoring the overdispersion and spatial correlation leads to an increased rate of false positives, which is verified through a simulation study. Simulation studies also show that our modified procedure can substantially reduce the rate of false alarms. Two data examples involving brain cancer cases in New Mexico and chickenpox incidence data in France are used to illustrate the practical relevance of the modified procedure.