Translating Big Data into Smart Data for Veterinary Epidemiology

VanderWaal, Kimberly; Morrison, Robert B.; Neuhauser, Claudia; Vilalta, Carles; Perez, Andres M.

FiGUre 1 | Characteristics of big data: volume, variety, velocity, and value. Arrows represent that data are progressively getting larger (more volume), more variable, and are accruing at faster rates than historically in the field of veterinary epidemiology. Italicized words are examples of types of data in veterinary epidemiology that meet some combination of volume, variety, and velocity. analytics to volume, variety, veracity, and velocity generates a fifth "V": the value of big data to create novel insights and inform decision-making. The analysis of big data, as applied to veterinary epidemiology, is not fundamentally novel compared to traditional or historical practices, but rather differs in complexity, scale, and scope. Veterinary epidemiological data that are or are becoming "big" include "-omics" data, geospatial data, publically available data repositories such as World Animal Health Information System 1 and EMPRES Global Animal Disease Information System (Empres-i 2 ), clinical data or digitized health records from both companion and food animals, data on animal movement from local to international scales, and production data from food animal industries (Figure 1 ) (14, 15, 19) . The analysis of such data can be used to understand health risks and minimize the impact of adverse animal health issues by, for example, increasing the effectiveness of control and surveillance by identifying high-risk populations through the analysis of spatial and animal movement data; combining disparate data or processes acting at multiple scales through epidemiological modeling approaches; and harnessing high velocity data to monitor animal health trends and for early detection of emerging health threats.