Spatial analysis of lumpy skin disease in Eurasia—Predicting areas at risk for further spread within the region

Alberto Allepuz, Jordi Casal, Daniel Beltrán-Alcrudo

Research output: Contribution to journalArticleResearch

5 Citations (Scopus)


© 2018 Blackwell Verlag GmbH Data from affected lumpy skin disease (LSD) locations between July 2012 and September 2018 in the Balkans, Caucasus, and Middle East were retrieved from FAO's Global Animal Disease Information System (EMPRES-i) from the European Commission's Animal Disease Notification System (ADNS) and completed with data from the official veterinary services of some countries. During this period, a total of 7,593 locations from 22 countries were affected. Within this period, over 46,000 cattle were clinically affected by LSD, 3,700 animals died and 17,500 were slaughtered due to culling policies to stop the spread of the disease. Most outbreaks occurred in 2016, between the months of May and November. The affected region was divided into a grid of 10 × 10 km cells and we fit a spatial regression model to analyse the association between the reported LSD outbreaks and climatic variables, land cover, and cattle density. The results showed big differences in the odds of being LSD positive due to the type of land cover: the odds of a cell being LSD positive was increased in areas mostly covered with croplands, grassland, or shrubland. The odds was also increased for higher cattle density, as well as areas with higher annual mean temperature and higher temperature diurnal range. The resulting model was utilized to predict the LSD risk in neighbouring unaffected areas in Europe, the Caucasus, and Central Asia, identifying several areas with high risk of spread. Results from this study provide useful information for the design of surveillance and awareness systems, and preventive measures, e.g., vaccination programmes.
Original languageEnglish
Pages (from-to)813-822
JournalTransboundary and Emerging Diseases
Publication statusPublished - 1 Mar 2019


  • lumpy skin disease
  • risk assessment
  • spatial analysis


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