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ARS Home » Plains Area » Fort Collins, Colorado » Center for Agricultural Resources Research » Rangeland Resources & Systems Research » Research » Publications at this Location » Publication #352800

Title: Developing a predictive model for vesicular stomatitis: a USDA-ARS grand challenge

item Peters, Debra
item BURRUSS, DYLAN - New Mexico State University
item Rodriguez, Luis
item McVey, David
item Elias, Emile
item PELZEL-MCCLUSKEY, ANGELA - Animal And Plant Health Inspection Service (APHIS)
item Derner, Justin
item Pauszek, Steven
item SAVOY, HEATHER - New Mexico State University
item Peck, Dannele

Submitted to: International Society for Veterinary Epidemiology and Economics
Publication Type: Proceedings
Publication Acceptance Date: 7/16/2018
Publication Date: N/A
Citation: N/A

Interpretive Summary:

Technical Abstract: Objective: The USDA Agricultural Research Service (ARS) Grand Challenge calls for collaboration across projects, locations, and scientific disciplines to address the nation’s agricultural research needs. One specific need is to combat agricultural pathogens, such as vesicular stomatitis virus (VSV), which damages livestock health, depletes veterinary resources, and disrupts trade. A trans-disciplinary team of researchers—from veterinary science, entomology, epidemiology, microbial genomics, hydrology, ecology, economics, and rangeland livestock management— has formed to elucidate virus-vector-host interactions and ecological factors associated with VSV outbreaks in the western U.S. Methods: The VSV Grand Challenge team is using big data, spatio-temporal modeling, and machine-learning to test hypotheses about phylogeographic relationships, landscape-level characteristics, and premises-level conditions associated with VSV occurrence among cattle and horses. A “data-cube” containing 1550 VSV occurrences and 300 environmental variables have been constructed on a daily, seasonal, or annual time-step over a 1 million km2 area of the western U.S. The data-cube informs vector distribution and regression models of VSV occurrence. Results: The month of VS onset was estimated based on latitude, elevation, and precipitation. Relationships between VS occurrence and environmental conditions differ for incursion years versus expansion years. During incursion years, rainfall, vegetation, surface runoff, streamflow, and nighttime temperatures are significant predictors (R2=0.23). All first-incidents of VS occurred following peak annual streamflow, with 89% occurring after streams returned to baseflow conditions. During expansion years, rainfall, vegetation, soil moisture, and daytime temperatures are significant (R2=0.73). Conclusions: These results ultimately inform the team’s on-going development of a VSV early warning system, while providing management-relevant insights for land managers, livestock owners and veterinarians. Two years into a five-year project, practical recommendations have also emerged for building an effective and productive trans-disciplinary Grand Challenge team.