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Transbound Emerg Dis. 2019 Sep;66(5):2045-2057. doi: 10.1111/tbed.13253. Epub 2019 Jun 17.

Mapping changes in the spatiotemporal distribution of lumpy skin disease virus.

Author information

1
Department of Population Health and Pathobiology, College of Veterinary Medicine, North Carolina State University, Raleigh, North Carolina.
2
Federal Center for Animal Health (FGBI ARRIAH), Vladimir, Russia.
3
VISAVET Health Surveillance Center, Universidad Complutense, Madrid, Spain.
4
Departamento de Sanidad Animal, Facultad de Veterinaria, Universidad Complutense, Madrid, Spain.
5
Department of Veterinary Population Medicine, University of Minnesota, St. Paul, Minnesota.

Abstract

Lumpy skin disease virus (LSDV) is an infectious disease of cattle transmitted by arthropod vectors which results in substantial economic losses due to impact on production efficiency and profitability, and represents an emerging threat to international trade of livestock products and live animals. Since 2015, the disease has spread into the Northern Hemisphere including Azerbaijan, Kazakhstan, the Russian Federation and the Balkans. The rapid expansion of LSDV in those regions represented the emergence of the virus in more temperate regions than those in which LSDV traditionally occurred. The goal of this study was to assess the risk for further LSDV spread through the (a) analysis of environmental factors conducive for LSDV, and (b) estimate of the underlying LSDV risk, using a combination of ecological niche modelling and fine spatiotemporally explicit Bayesian hierarchical model on LSDV outbreak occurrence data. We used ecological niche modelling to estimate the potential distribution of LSDV outbreaks for 2014-2016. That analysis resulted in a spatial representation of environmental limits where, if introduced, LSDV is expected to efficiently spread. The Bayesian space-time model incorporated both environmental factors and the changing spatiotemporal distribution of the disease to capture the dynamics of disease spread and predict areas in which there is an increased risk for LSDV occurrence. Variables related to the average temperature, precipitation, wind speed, as well as land cover and host densities were important drivers explaining the observed distribution of LSDV in both modelling approaches. Areas of elevated LSDV risks were identified mainly in Russia, Turkey, Serbia and Bulgaria. The results suggest that, if current ecological and epidemiological conditions persist, further spread of LSDV in Eurasia may be expected. The results presented here advance our understanding of the ecological requirements of LSDV in temperate regions and may help in the design and implementation of prevention and surveillance strategies in the region.

KEYWORDS:

Bayesian hierarchical model; disease mapping; ecological niche modelling; spatial dynamics; transboundary disease

PMID:
31127984
DOI:
10.1111/tbed.13253

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