Harnessing the Power of Open Data and Analytics for Characterizing the Role of Hydrological Processes in the Spread of a Vector-borne Livestock Disease in Multiple Watersheds across the Western US

Determining the connection between a vector-borne disease and the environment over which it spreads is complex due to the myriad of biotic and abiotic processes that are involved. Hydrological processes can play a major role in this spread when the vector has water-dependent lifecycle stages. This poster details an investigation into the spatiotemporal relationship between surface water and the spread of Vesicular Stomatitis Virus (VSV) in livestock on a landscape scale in multiple watersheds across the western US. The watersheds were chosen by which regions had relatively high VSV incidence in the past two outbreaks since 2004. The hydrological data used is a collection of stream gauge data for flow and temperature measurements, county-level GIS products for delineating streams and irrigation canals, and global remote sensing products describing the temporal variability of surface water - each of which are publicly available. These data sources vary in their temporal and spatial characteristics and are combined to build a more complete picture of the hydrological landscape in the watersheds of interest than if any source was used alone. This hydrological landscape is then coupled with VSV incidence records and genetic information to link the dynamics of disease spread to surface water in a connectivity analysis.

Although previous studies have investigated the relationship between VSV incidence and environmental variables, including hydrological data, this study focuses on the dynamics of the disease spread in VSV-prone landscapes with finer detail hydrological data. In order to achieve this goal, a variety of computational tools have been proven necessary, such as the R language for data wrangling and machine learning, Shiny for web-based interfaces for interactive visualization and sharing across a national trans-disciplinary team, Google Earth Engine for remote sensing data access and pre-processing, and parallel processing for executing the most computationally expensive analyses.