Jornada Bibliography
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2019. Using machine learning to model complex landscapes: predicting the geographic range of Vesicular Stomatitis across the western United States. Ecological Society of America Abstracts.
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2019. Factors determining spread of Vesicular Stomatitis across heterogeneous landscapes: an application of landscape connectivity and AI in disease ecology. Ecological Society of America Abstracts.
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2018. Relationships among snow depth, snow water equivalent, streamflow and virus activity in two Colorado watersheds (2004 to 2016). Western Snow Conference. Vesicular stomatitis (VS) is the most common viral vesicular disease affecting livestock (horses, cattle, pigs) throughout the A
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2020. Big data-model integration and AI for vector-borne disease predictor. Ecosphere. 11(6):1-20.
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2018. Contributions of hydrology to Vesicular Stomatitis Virus emergence in the Western USA. Ecosystems. 22:416-433.
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2018. An integrated View of Complex Landscapes: A Big Data-Model Integration Approach to Transdisciplinary Science. Bioscience. 68:653-669.
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2020. Management strategies for reducing the risk of equines contracting Vesicular Stomatitis Virus (VSV) in the Western United States. Journal of Equine Veterinary Science.
