Using a spatial filter and a geographic information system to improve rabies surveillance data

Area of Science:

• Veterinary Public Health
• Spatial Epidemiology
• Geographic Information Systems (GIS)

Background:

• Antirabies measures rely on accurate surveillance data.
• Kentucky faces a potential influx of raccoon rabies.
• Concerns exist regarding inconsistent rabies surveillance data quality across Kentucky counties.

Purpose of the Study:

• To present a geographic model for assessing rabies surveillance data quality.
• To identify counties with potentially inadequate animal submissions for rabies testing.
• To provide a foundational step for improving surveillance schemes.

Main Methods:

• Utilized a geographic information system (GIS) for spatial analysis.
• Developed a spatial filter model to compare central county submissions with surrounding areas.
• Incorporated county boundaries as the area of analysis.

Main Results:

• The model can identify counties with fewer animal submissions than their neighbors.
• This method aids in pinpointing surveillance "holes" or underreporting.
• The technique is adaptable to various geographic scales.

Conclusions:

• The GIS-based spatial filter model is a valuable tool for rabies surveillance.
• It offers a systematic approach to identifying areas needing enhanced rabies testing.
• Improving surveillance data quality is crucial for effective rabies prevention and control.