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

Svetlana Malkhazova1, Fedor Korennoy2*, Olga Petrova2, Vladimir Gulenkin2 and Anton Karaulov2
  • 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.

Acknowledgements

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

References

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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

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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