Leveraging spatiotemporal models and a transdisciplinary framework to anticipate the spread of Vesicular stomatitis (2019-2020)

Authors: Humphreys Jr, John; Peters, Debra; PELZEL-MCCLUSKEY, ANGELA - Animal And Plant Health Inspection Service (APHIS); Cohnstaedt, Lee; BURRUSS, N. DYLAN - New Mexico State University; Rodriguez, Luis

Submitted to: American Geophysical Union
Publication Type: Abstract Only
Publication Acceptance Date: 8/1/2020
Publication Date: 12/1/2020
Citation: Humphreys Jr, J.M., Peters, D.C., Pelzel-McCluskey, A.M., Cohnstaedt, L.W., Burruss, N., Rodriguez, L.L. 2020. Leveraging spatiotemporal models and a transdisciplinary framework to anticipate the weekly occurrence, intensity, and spread of vesicular stomatitis outbreaks in livestock 2019-2020. American Geophysical Union. Abstract.

Interpretive Summary:

Technical Abstract: Vesicular stomatitis (VS) is a vector-borne disease caused by an RNA virus that adversely affects livestock health and economically impacts both the agricultural industry and private livestock owners. Although several past studies have examined the environmental and climatic drivers of previous VS outbreaks, an early warning system is needed to anticipate VS outbreaks and case spatiotemporal distribution as events occur. Using VS outbreaks documented between 2004 and 2015, we present on a collaboratively developed integrated modeling framework to predict the weekly occurrence, intensity, and spread of VS disease in 2019 and 2020 as a demonstrative case study across the Western United States. Our integrative modeling approach incorporates machine learning techniques to identify environmental and climatic drivers of disease as well as Bayesian hierarchical modeling to account for biased sampling and spatiotemporal dynamics. Our VS early warning system provides a variety of outputs, including week-specific maps depicting predicted disease onset timing, environmental suitability, disease incidence, and relative risk based on livestock abundance and the suite of environmental and biotic drivers. Tools to help anticipate the times and locations most vulnerable to livestock disease are expected to benefit the agricultural industry, private livestock owners, and the government agencies responsible for disease surveillance and response. Our study offers important insights into vesicular stomatitis disease ecology and provides novel tools to help forecast livestock disease spatiotemporal dynamics at fine scales applicable to management.