Risk for African Swine Fever Introduction Into Kazakhstan

• Published in: Frontiers in veterinary science Vol. 8; p. 605910
• Main Authors: Schettino, Daniella N, Abdrakhmanov, Sarsenbay K, Beisembayev, Kanatzhan K, Korennoy, Fedor I, Sultanov, Akhmetzhan A, Mukhanbetkaliyev, Yersyn Y, Kadyrov, Ablaikhan S, Perez, Andres M
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 11.02.2021
• Subjects: African swine fever; conjoint analysis; epidemic; Kazakhstan; risk analysis; risk map; Veterinary Science; African swine fever; Kazakhstan; epidemic; risk map; conjoint analysis; risk analysis
• DOI: 10.3389/fvets.2021.605910
• ISSN: 2297-1769

African swine fever (ASF) is a disease of swine that is endemic to some African countries and that has rapidly spread since 2007 through many regions of Asia and Europe, becoming endemic in some areas of those continents. Since there is neither vaccine nor treatment for ASF, prevention is an important action to avoid the economic losses that this disease can impose on a country. Although the Republic of Kazakhstan has remained free from the disease, some of its neighbors have become ASF-infected, raising concerns about the potential introduction of the disease into the country. Here, we have identified clusters of districts in Kazakhstan at highest risk for ASF introduction. Questionnaires were administered, and districts were visited to collect and document, for the first time, at the district level, the distribution of swine operations and population in Kazakhstan. A snowball sampling approach was used to identify ASF experts worldwide, and a conjoint analysis model was used to elicit their opinion in relation to the extent at which relevant epidemiological factors influence the risk for ASF introduction into disease-free regions. The resulting model was validated using data from the Russian Federation and Mongolia. Finally, the validated model was used to rank and categorize Kazakhstani districts in terms of the risk for serving as the point of entry for ASF into the country, and clusters of districts at highest risk of introduction were identified using the normal model of the spatial scan statistic. Results here will help to allocate resources for surveillance and prevention activities aimed at early detecting a hypothetical ASF introduction into Kazakhstan, ultimately helping to protect the sanitary status of the country.