Spatio-temporal modeling of the African swine fever epidemic in the Russian Federation, 2007-2012

• Published in: Spatial and spatio-temporal epidemiology Vol. 11; pp. 135 - 141
• Main Authors: Korennoy, F I, Gulenkin, V M, Malone, J B, Mores, C N, Dudnikov, S A, Stevenson, M A
• Format: Journal Article
• Language: English
• Published: Netherlands 01.10.2014
• Subjects: African Swine Fever - epidemiology; Animals; Epidemics - statistics & numerical data; Epidemics - veterinary; Humans; Risk Factors; Russia - epidemiology; Socioeconomic Factors; Spatio-Temporal Analysis; Swine; Russia; Maxent; Risk mapping; African swine fever; Spatio-temporal modeling
• ISSN: 1877-5845; 1877-5853
• DOI: 10.1016/j.sste.2014.04.002

In 2007 African swine fever (ASF) entered Georgia and in the same year the disease entered the Russian Federation. From 2007 to 2012 ASF spread throughout the southern region of the Russian Federation. At the same time several cases of ASF were detected in the central and northern regions of the Russian Federation, forming a northern cluster of outbreaks in 2011. This northern cluster is of concern because of its proximity to mainland Europe. The aim of this study was to use details of recorded ASF outbreaks and human and swine population details to estimate the spatial distribution of ASF risk in the southern region of the European part of the Russian Federation. Our model of ASF risk was comprised of two components. The first was an estimate of ASF suitability scores calculated using maximum entropy methods. The second was an estimate of ASF risk as a function of Euclidean distance from index cases. An exponential distribution fitted to a frequency histogram of the Euclidean distance between consecutive ASF cases had a mean value of 156 km, a distance greater than the surveillance zone radius of 100-150 km stated in the ASF control regulations for the Russian Federation. We show that the spatial and temporal risk of ASF expansion is related to the suitability of the area of potential expansion, which is in turn a function of socio-economic and geographic variables. We propose that the methodology presented in this paper provides a useful tool to optimize surveillance for ASF in affected areas.