Epidemiological research to investigate the spatial characteristics of poultry farms confirmed with avian influenza (AI) infection can help increase the efficacy of AI surveillance as well as AI control strategies. The spatial characteristics of poultry farms confirmed with AI infection can provide insights on effective AI-surveillance and AI-control strategies to policymakers by providing a visualization of the geographical pattern of AI distribution. The goal of the current study was to investigate the spatial characteristics of the risk of a farm being AI-positive by using data from routine AI-surveillance performed during the period 2014–2015. To achieve this goal, we applied a spatial model because it improves the estimation of the relative risk by taking into account spatial dependence between epidemiological units. The results revealed there was a lack of dependency between districts in the risk of a farm being AI-positive. The estimates for the spatial autocorrelation coefficient in the spatial model for chicken farms were 0.006 in 2014 (p = 0.9496) and -0.064 in 2015 (p = 0.6052) and for duck farms were -0.066 in 2014 (p = 0.4380) and 0.047 in 2015. Likewise, Moran’s I statistic estimates for chicken farms were 0.0243 in 2014 (p = 0.3183) and -0.0174 in 2015 (p = 0.5657) and for duck farms were -0.0342 in 2014 (p = 0.6678) and -0.0230 in 2015.

Abstract
INTRODUCTION
MATERIALS AND METHODS
    Data
    Spatial model
RESULTS
DISCUSSION
REFERENCES

• EuTteum Kim(College of Veterinary Medicine and Institute of Veterinary Science, Kangwon National University)
• Son-Il Pak(College of Veterinary Medicine and Institute of Veterinary Science, Kangwon National University) Corresponding Author.