12: Bayesian Spatial Scan Statistics for Under-Reported Data

Music City Center 

The spatial scan statistic is a cornerstone for disease monitoring and outbreak detection. Common implementations typically assume that case counts are correctly observed. However, underreporting frequently occurs in real-world settings, leading to biased estimates and compromising the accuracy of disease surveillance efforts. This study proposes a novel Bayesian spatial scan statistic to address the challenges posed by under-reported case counts in outbreak detection. By accounting for misreporting, the proposed framework enhances the accuracy and robustness of disease cluster identification. Comparisons with existing methods and applications to COVID-19 data demonstrate its superior ability to provide reliable inferences despite reporting limitations.

Spatial Clustering

Underreporting

Bayesian Statistics

Spatial scan statistics

COVID-19 

Section on Statistics in Epidemiology