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The spatiotemporal distribution of lumpy skin disease virus

Gustavo Machado1, 2*, Fedor Korennoy3, Julio Alvarez4, Catalina Picasso Risso4, Andres M. Perez4 and Kimberly VanderWaal4
  • 1 North Carolina State University, United States
  • 2 Department of Population Health and Pathobiology, College of Veterinary Medicine, North Carolina State University, United States
  • 3 Federal Center for Animal Health (FGBI ARRIAH), Russia
  • 4 Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, United States

Background and specific objectives Lumpy Skin Disease (LSD) is a vector-borne disease of cattle caused by Capripoxvirus (Poxviridae family), referred to as LSD virus (LSDV). The morbidity rate varies between 10% and 20%, whereas the mortality rate does not usually exceed 5% (OIE, 2018). The main route of transmission often relates with mosquitoes and biting flies (Chihota et al., 2001; Tuppurainen et al., 2011; Tuppurainen & Oura, 2012). Therefore, LSDV transmission and spread linked to warm and humid weather conditions that are associated with high population densities of biting arthropods (Ochwo et al., 2018). Long-distance disease dispersal may also be facilitated by windborne carriage of vectors and their transportation via vehicles carrying hay and straw (Klausner et al., 2017). Even though the global distribution of LSDV has been restricted to a few regions, the identification of potential geographic distributions has not yet been determined, therefore is a need to calculate the likelihood of this virus reaching free areas. One way to approximate LSDV’s potential distribution is by correlating environmental abiotic conditions with disease occurrence location, via ecological niche models, alternatively Bayesian hierarchical statistical approaches allow the inclusion of spatially and temporally explicit features in regression models and capture how disease risk can be related to proximity with areas experiencing outbreaks (Lawson, 2018). In this study, we developed a novel and integrative approach, by combination of ecological niche modeling and fine spatiotemporally explicit Bayesian hierarchical model on LSDV outbreak occurrence data to estimate of the underlying LSDV risk. Methods We retrieved and curated LSDV outbreak data from Middle Eastern, Central Asian, and Eastern European countries in 2014-2016. Outbreaks were defined as the detection of one or more cases of LSDV per location. We used ecological niche modeling and Bayesian space-time model in combination to estimate the LSDV spatial risk. Results 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 modeling approaches. Areas of elevated LSDV risks were identified mainly in Russia, Turkey, Serbia, and Bulgaria (Fig. 1). Results suggest that, if current ecological and epidemiological conditions persist, further spread of LSDV in Eurasia may be expected (Fig. 2). Conclusions The ecological niche model developed in this study demonstrated the ability to estimate the past distribution of LSDV. Further studies to confirm the capacities of ecological niche model to forecast the potential distribution of LSDV are needed. Future models should also include future climate scenarios in order to identify potential shifts in the disease distribution especially in areas of transitional elevation, which may become suitable for the occurrence of potential vectors. The spatiotemporal patterns of LSDV occurrences modeled by the Bayesian hierarchical approach were heterogeneous, with temperature and precipitation increasing the relative risk and stronger winds reducing risk. With this study, we identified hotspot areas by using two modeling approaches, demonstrating how integrated approaches can better guide disease control and active surveillance efforts.

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Acknowledgements

This project was funded by the Department of Population Health and Pathobiology, College of Veterinary Medicine, North Carolina State University, provided startup funds for GM and JA. Additionally, the University of Minnesota Academic Health Center Grant-in-Aid program also provided funding the recipient of a Ramón y Cajal postdoctoral contract from the Spanish Ministry of Economy, Industry and Competitiveness (MINECO) (RYC-2016-20422).

References

Chihota, C.M., Rennie, L.F., Kitching, R.P., Mellor, P.S., 2001. Mechanical transmission of lumpy skin disease virus by Aedes aegypti (Diptera: Culicidae). Epidemiol. Infect. https://doi.org/10.1017/S0950268801005179 Klausner, Z., Fattal, E., Klement, E., 2017. Using Synoptic Systems’ Typical Wind Trajectories for the Analysis of Potential Atmospheric Long-Distance Dispersal of Lumpy Skin Disease Virus. Transbound. Emerg. Dis. https://doi.org/10.1111/tbed.12378 Lawson, A.B., 2018. Bayesian latent modeling of spatio-temporal variation in small-area health data. Wiley Interdiscip. Rev. Comput. Stat. https://doi.org/10.1002/wics.1441 Ochwo, S., VanderWaal, K., Munsey, A., Ndekezi, C., Mwebe, R., Okurut, A.R.A., Nantima, N., Mwiine, F.N. 2018. Spatial and temporal distribution of lumpy skin disease outbreaks in Uganda (2002-2016). BMC Vet. Res. https://doi.org/10.1186/s12917-018-1503-3 OIE. 2018a. Lumpy Skin Disease in: Technical disease cards. URL: http://www.oie.int/fileadmin/Home/eng/Animal_Health_in_the_World/docs/pdf/Disease_cards/LUMPY_SKIN_DISEASE_FINAL.pdf (accessed 17 December 2018) Tuppurainen, E.S.M., Stoltsz, W.H., Troskie, M., Wallace, D.B., Oura, C.A.L., Mellor, P.S., Coetzer, J.A.W., Venter, E.H., 2011. A Potential Role for Ixodid (Hard) Tick Vectors in the Transmission of Lumpy Skin Disease Virus in Cattle. Transbound. Emerg. Dis. https://doi.org/10.1111/j.1865-1682.2010.01184.x Tuppurainen, E.S.M., Oura, C.A.L., 2012. Review: Lumpy Skin Disease: An Emerging Threat to Europe, the Middle East and Asia. Transbound. Emerg. Dis. https://doi.org/10.1111/j.1865-1682.2011.01242.x

Keywords: Disease mapping, Bayesian hierarchical model estimation, Ecological niche modeling (ENM), spatial dynamics, Transboundary disease management

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: Spatial methods for environmental & exposure epidemiology and climate change

Citation: Machado G, Korennoy F, Alvarez J, Picasso Risso C, Perez AM and VanderWaal K (2019). The spatiotemporal distribution of lumpy skin disease virus. Front. Vet. Sci. Conference Abstract: GeoVet 2019. Novel spatio-temporal approaches in the era of Big Data. doi: 10.3389/conf.fvets.2019.05.00062

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Received: 01 Jun 2019; Published Online: 27 Sep 2019.

* Correspondence: Prof. Gustavo Machado, North Carolina State University, Raleigh, United States, gmachad@ncsu.edu