A Slaughterhouse-Linked Spatial Framework for Prioritizing Livestock Surveillance in Thailand

Area of Science:

• Veterinary epidemiology
• Spatial analysis
• Public health surveillance

Background:

• Slaughterhouses serve as crucial points for collecting livestock health data.
• Existing surveillance frameworks often lack integration of spatial distribution and population density for effective prioritization.
• Targeted surveillance is essential for efficient disease control in livestock populations.

Purpose of the Study:

• To develop and validate a spatial model for prioritizing livestock surveillance areas in Thailand.
• To integrate slaughterhouse distribution and livestock population density for enhanced surveillance planning.
• To identify high-priority districts for livestock disease monitoring.

Main Methods:

• A composite surveillance prioritization index was created by combining z-scores of slaughterhouse density and livestock population density at the district level.
• Kernel density estimation was used to map registered slaughterhouses, and census data mapped livestock populations.
• Spatial autocorrelation analysis (Global Moran's I, LISA) was performed to identify priority districts.

Main Results:

• The composite prioritization index varied across districts, with significant spatial autocorrelation (I = 0.642, p < 0.001).
• Local Indicators of Spatial Association (LISA) identified 120 High-High priority districts.
• Forty-two priority districts were selected, primarily located in the northeastern and west-central regions of Thailand.

Conclusions:

• The developed model provides a replicable and effective method for prioritizing livestock surveillance areas.
• Integrating slaughterhouse data with population density enhances the precision of surveillance efforts.
• This approach can be adapted for livestock-rearing regions globally that utilize slaughterhouses for surveillance.