Event Abstract

Spatio-temporal analysis of the local African swine fever epidemics in the Russian Federation, 2007 - 2015

  • 1 Faculty of Geography, Lomonosov Moscow State University, Russia
  • 2 Federal Center for Animal Health (FGBI ARRIAH), Russia

Background and objectives African swine fever (ASF) is a contagious viral disease of domestic and wild pigs, which since its introduction in 2007 has been spreading in the territory of the Russian Federation (RF), affecting both pigs in backyard holdings and large commercial farms, and wild boars. The disease leads to nearly 100% mortality in infected animals and causes huge economic losses to the country’s pig industry (Klinovitskaja et al., 2016). In the course of epidemics, the disease demonstrates both local transmission, which happens within relatively limited area between neighboring farms, villages, etc., and far distant transmission that leads to infection of new regions (Oganesyan et al., 2013; Malogolovkin et al., 2015). Previous studies found 156 km to be an average distance of the ASF spread between farms (Korennoy et al., 2014) and 133 km to be a maximum distance of the disease cases’ clustering in wild boars (Iglesias et al., 2016). The objectives of the present study were: 1) to detect spatio-temporal clusters of the ASF outbreaks the RF, treated as local epidemics; 2) to analyze within-cluster dynamics of outbreaks emergence in domestic and wild pigs; and 3) to reveal associations between within-cluster disease spread patterns and some geographical factors, which supposedly facilitate the local transmission of the ASF virus. Methods A map of the ASF outbreaks in the RF 2007-2015 was created using the national ASF incidence records. The Kulldorff space-time scan statistics (Kulldorff et al., 2005) was employed as a cluster analysis method to detect local ASF epidemics in: 1) domestic pigs’ population (DP); 2) wild boars population (WB), and 3) cumulative population of both. The main quantitative parameters of clusters including the radius, duration and the observed number of outbreaks were obtained. The correlation between the number of outbreaks and geographical factors within each statistically significant cluster was estimated. Results A total of 17, 9 and 17 statistically significant ASF outbreaks’ clusters were detected in PD, WB and cumulative populations respectively. It was found, that all clusters in WB comprise at least one outbreak in DP within the same time period, while only 7 out of 17 clusters in DP comprise WB outbreaks. Majority of cumulative population’s clusters in southern regions of the RF starts from outbreaks in WB, while clusters in central and north regions mainly start from DP outbreaks. Strong positive correlation between the observed number of outbreaks in domestic pigs and the total motorway length, total pig population and the number of rural settlements within the clusters was detected (Pearson correlation coefficients r = 0.83, 0.80 and 0.89 respectively). The number of outbreaks in wild boars and the forest coverage within the clusters were also found to strongly correlate (r = 0.96). Conclusions Our results agree well with the findings of previous studies, which revealed associations between the local ASF spread patterns and density of domestic pigs, densities of major roads and some other human economic activity-related factors (Korennoy et al., 2014; Vergne et al., 2016). It can concluded that the wild boar population should not be seen as a primary source of the disease spread in the RF. The obtained results are expected to contribute into development of a geospatial model for assessment of risks of the ASF local spread in case of the virus introduction into new areas.


The reported study was funded by Russian Foundation for Basic Research (RFBR) according to the research project № 18-05-60037


1. Klinovitskaja, I.M., Gulenkin, V.M., Karaulov, A.K. Ocenka jekonomicheskih poter' pri likvidacii afrikanskoj chumy svinej v regionah RF v 2015 [Estimation of economic losses associated with the eradication of African swine fever in the regions of the RF] // BIO. 2016. Vol. 10 (193). P. 24 – 30. (in Russian) 2. Malogolovkin, A.S., Gogin, A.E., Kolbasov, D.V. Rossijskij scenarij afrikanskoj chumy svinej [Russian scenario of the African swine fever] // Farm Animals. 2015. Vol. 2. P. 56 – 63. 3. FAO. Afrikanskaja chuma svinej v Rossijskoj Federacii (2007 – 2012) [African swine fever in the Russian Federation (2007-2012)] // FAO Animal husbandry and animal health. 2014. Document №178. Rome. Available at: http://www.fao.org/3/a-i3748r.pdf (in Russian) 4. FAO. African swine fever in the Russian Federation: risk factors for Europe and beyond // 2013. Vol. 28. Available at: http://www.fao.org/docrep/018/aq240e/aq240e.pdf 5. Iglesias, I., Munoz, MJ., Montes, F., Perez, A., Gogin, A., Kolbasov, D., de la Torre, A. Reproductive ratio for the local spread of African swine fever in wild boars in the Russian Federation // Transboundary and Emerging Diseases. 2016. Vol. 63(6). P. e237 – e245. 6. Iglesias, I., Rodriguez, A., Feliziani., F., Rolesu, S., de la Torre, A. Spatio-temporal Analysis of African Swine Fever in Sardinia (2012 – 2014): Trends in Domestic Pigs and Wild Boar // Transboundary and Emerging Diseases. 2017. Vol. 64. P. 656 – 662. 7. Korennoy, F.I., Gulenkin, V.M., Malone, J.B., Mores, C.N., Dudnikov, S.A., Stevenson, M.A. Spatio-temporal modeling of the African swine fever epidemic in the Russian Federation, 2007 – 2012 // Spatial and Spatio-temporal Epidemiology. 2014. Vol. 11. P. 135 – 141 8. Kulldorff M, Heffernan R, Hartman J, Assuncao RM, Mostashari F. A space-time permutation scan statistic for the early detection of disease outbreaks // PLoS Medicine. 2005. Vol. 2. P. 216 - 224 9. Oganesyan, A.S., Petrova, O.N., Korennoy, F.I., Bardina, N.S., Gogin, A.E., Dudnikov, S.A. African swine fever in the Russian Federation: spatio-temporal analysis and epidemiological overview // Virus Research. 2013. Vol 173 (1). P. 204 – 211. 10. Vergne, T., Gogin, A., Pfeiffer, D. U. Statistical Exploration of Local Transmission Routes for African Swine Fever in Pigs in the Russian Federation, 2007–2014 // Transboundary and Emerging Diseases. 2017. Vol. 64 (2). P. 504 – 512. 11. Vergne, T., Korennoy, F., Combelles, L., Gogin, A., Pfeiffer, DU. Modelling African swine fever presence and reported abundance in the Russian Federation using national surveillance data from 2007 to 2014 // Spatial and Spatio-temporal Epidemiology. 2016. Vol. 19. P. 70 – 77

Keywords: African swine fever (ASF), Cluster analisys, Local epidemics, geospatial factors, Correlation analysis

Conference: GeoVet 2019. Novel spatio-temporal approaches in the era of Big Data, Davis, United States, 8 Oct - 10 Oct, 2019.

Presentation Type: Regular oral presentation

Topic: Special topic on African Swine Fever (ASF)

Citation: Malkhazova S, Korennoy F, Petrova O, Gulenkin V and Karaulov A (2019). Spatio-temporal analysis of the local African swine fever epidemics in the Russian Federation, 2007 - 2015. Front. Vet. Sci. Conference Abstract: GeoVet 2019. Novel spatio-temporal approaches in the era of Big Data. doi: 10.3389/conf.fvets.2019.05.00018

Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters.

The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated.

Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed.

For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions.

Received: 02 Jun 2019; Published Online: 27 Sep 2019.

* Correspondence: Mr. Fedor Korennoy, Federal Center for Animal Health (FGBI ARRIAH), Vladimir, Russia, fkorennoy@yandex.ru