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Using geospatial methods to measure the risk of environmental persistence of avian influenza virus in South Carolina

Andrew Fox1*, Mary Jane McCool-Eye2, Chloe Stenkamp-Strahm3, John Humphreys4, Anglela James5, David South6 and Sheryl Magzamen6
  • 1 Animal and Plant Health Inspection Service (USDA), United States
  • 2 Center for Epidemiology and Animal Health, Animal and Plant Health Inspection Service (USDA), United States
  • 3 College of Veterinary Medicine and Biomedical Sciences, Colorado State University, United States
  • 4 Department of Fisheries and Wildlife, College of Agriculture & Natural Resources, Michigan State University, United States
  • 5 Center for Epidemiology and Animal Health, Animal and Plant Health Inspection Service (USDA), United States
  • 6 Department of Environmental and Radiological Health Sciences, College of Veterinary Medicine and Biomedical Sciences, Colorado State University, United States

Avian influenza (AIV) is a highly contagious virus that can infect both wild birds and domestic poultry. Introductions of AIV into domestic poultry can be initiated by exposures to infectious wild birds or poultry, fomites, or to virus surviving in the environment. This study aimed to define areas within the state of South Carolina at heightened risk for environmental persistence of AIV using geospatial methods. Environmental factors known to influence AIV survival were identified through an evaluation of the published literature, and corresponding data were located and downloaded from publicly available sources. Data layers were resampled to a 1km resolution for four seasons (breeding, fall migration, winter, and spring migration) and weighted based on their influence on virus survivability. Maps showing the risk of AIV persistence in the environment were created from these layers using ESRI’s ArcGIS Predictive Analysis Add-in Tool. This risk was defined using five categories following the World Organization for Animal Health Risk Assessment Guidelines. Less than 1% of the 1km grid cells in South Carolina showed a high risk of AIV persistence in the four seasons assessed. A greater number of grid cells showed either moderate (1 - 2%) or low risk (17 – 19%) of AIV environmental persistence, with an increase in risk during winter and spring migration. AIV persistence risk was then aggregated to a county level and commercial poultry operation data were overlaid with the AIV risk maps. Approximately 2% (breeding season) - 17% (winter season) of counties with high or very high environmental risk also had medium to very high numbers of commercial poultry operations. Validation demonstrated a statistically significant association between modeled AIV persistence risk and the spatial location of wild bird species among which AIV is known to circulate. Results can be used to improve surveillance activities and to inform biosecurity practices and emergency preparedness efforts within South Carolina. Methods developed here have been applied to other states and may be used to develop national models.

References

CIESIN, Center for International Earth Science Information Network, Columbia University. 2016. Gridded Population of the World, Version 4 (GPWv4): Administrative Unit Center Points with Population Estimates. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://dx.doi.org/10.7927/H4F47M2C. Diggle, P., Menezes, R., Su, T. 2010. Geostatistical inference under preferential sampling. Journal of the Royal Statistical Society: Series C (Applied Statistics), 59(2):191-232. Freeman, E. A., Moisen, G., 2008. PresenceAbsence: An r package for presence absence analysis. Journal of Statistical Software 23 (11), 1-31. Hijmans, R. J., Cameron, S., Parra, J., Jones, P., Jarvis, A., 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25, 1965-1978. URL http://www.worldclim.org/. Humphreys, J. M., Elsner, J. B., Jagger, T. H., Pau, S., 2017. A Bayesian geostatistical approach to modeling global distributions of Lygodium microphyllum under projected climate warming. Ecological Modelling 363, 192-206. Lindgren, F., Rue, H., 2015. Bayesian Spatial Modelling with R-INLA. Journal of Statistical Software 63 (19), 1-25. Lindgren, F., Rue, H., Lindstrom, J., 2011. An explicit link between Gaussian fields and Gaussian Markov random field: The stochastic partial differential equations approach. Journal of the Royal Statistical Society. Series B: Statistical Methodology 73, 423-498. R Core Team, 2017. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/. Renner, I. W., Elith, J., Baddeley, A., Fithian, W., Hastie, T., Phillips, S. J., Popovic, G., Warton, D. I., 2015. Point process models for presence-only analysis. Methods in Ecology and Evolution 6 (4), 366-379. Rue, H., Martino, S., Chopin, N., 2009. Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. Journal of the Royal Statistical Society. Series B: Statistical Methodology 71 (2), 319-392. Title, P. O. and Bemmels, J. B. 2018). ENVIREM: an expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling. Ecography, 41: 291-307. Wood C, Sullivan B, Iliff M, Fink D, Kelling S. 2011. eBird: engaging birders in science and conservation. PLOS Biology, 9(12):e1001220.

Keywords: Avian influenza (AI), Waterfowl abundance, geospatial, Risk mapping, modeling

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: Spatio-temporal surveillance and modeling approaches

Citation: Fox A, McCool-Eye M, Stenkamp-Strahm C, Humphreys J, James A, South D and Magzamen S (2019). Using geospatial methods to measure the risk of environmental persistence of avian influenza virus in South Carolina. Front. Vet. Sci. Conference Abstract: GeoVet 2019. Novel spatio-temporal approaches in the era of Big Data. doi: 10.3389/conf.fvets.2019.05.00020

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Received: 30 May 2019; Published Online: 27 Sep 2019.

* Correspondence: Mx. Andrew Fox, Animal and Plant Health Inspection Service (USDA), Riverdale Park, United States, andrew.m.fox@aphis.usda.gov