PQStat

PQStat detects irregularly shaped spatial and space-time clusters of disease incidence using the CutL method to support epidemiological analyses and health-care planning.

Key Features:

• CutL Method Integration: Implements the CutL method to identify spatial clusters of arbitrary shape with disease incidence rates exceeding specified thresholds.
• Space-Time Cluster Detection: Extends the CutL method to space-time cluster detection, showing greater effectiveness than Kulldorff's scan statistic for irregularly shaped space-time clusters and comparable results for cylinder-shaped clusters.
• Threshold-Based Cluster Search: Searches for clusters where disease incidence rates surpass user-defined thresholds to allow targeted detection based on researcher-specified criteria.
• Simulation Analysis: Applied to complex data simulations, as demonstrated with Polish county health problem distributions from 2013 to 2017.

Scientific Applications:

• Epidemiological Surveillance: Identifies potential outbreaks and regions with higher-than-expected disease incidence for epidemiological investigation.
• Health-Care Planning and Targeted Interventions: Supports allocation and planning by locating spatial and space-time clusters that may require targeted public-health interventions.
• Spatio-Temporal Research: Enables analysis of disease distribution and spread across geographical areas and time periods, exemplified by analyses of Polish county data (2013–2017).

Methodology:

Implements the CutL method and its space-time extension to search for clusters where disease incidence rates exceed user-defined thresholds.