Identifying equine premises at high risk of introduction of vector-borne diseases using geo-statistical and space-time analyses

Abstract

The identification of premises that may play an important role in the introduction or spread of animal diseases is fundamental to the development of risk-based surveillance and control programs. A combination of geo-statistical and cluster analysis methods was used to identify geographical areas and periods of time at highest risk for introduction of the African horse sickness virus (AHSV) into the Castile and Leon (CyL) region of Spain. Risk was estimated based on the predicted premises-specific abundance of Culicoides spp., a vector for AHSV, and on the frequency of equine introductions from outside regions. The largest abundance of Culicoides spp. was observed between May and September in the northern region of CyL. Six significant (P-value <0.01) space-time clusters of equine premises were found, at which presence of Culicoides spp. was predicted and live equidae were introduced from outside CyL. The clusters included 37 equine premises and took place between April and December. These results will contribute to updating plans for prevention of AHSV introduction and spread in Spain. The methodological approach developed here may be adapted and applied to design and establish risk-based surveillance and control programs for Spain and other European countries.