Author: VanderWaal, Kimberly; Morrison, Robert B.; Neuhauser, Claudia; Vilalta, Carles; Perez, Andres M.
Title: Translating Big Data into Smart Data for Veterinary Epidemiology Document date: 2017_7_17
ID: 03tx0rni_3
Snippet: Risk of infection is rarely homogenous in a population, and the ability to identify heterogeneities in risk allows for targeted surveillance and control measures. Risk-based strategies are typically more cost-effective than non-targeted strategies, both in terms of early detection and rapid control of a disease (10, (20) (21) (22) . Because movement of animals between locations is a key risk factor for many infectious diseases, many countries now.....
Document: Risk of infection is rarely homogenous in a population, and the ability to identify heterogeneities in risk allows for targeted surveillance and control measures. Risk-based strategies are typically more cost-effective than non-targeted strategies, both in terms of early detection and rapid control of a disease (10, (20) (21) (22) . Because movement of animals between locations is a key risk factor for many infectious diseases, many countries now implement mandatory animal traceability programs (23) (24) (25) (26) . For example, national or multinational programs, such as the European Union's Trade Control and Expert System and the United Kingdom's Cattle Tracing System, track shipments of production animals across space and time, generating a rich source of information for rapid response to health threats (27) (28) (29) . In the absence of national regulatory frameworks, large production companies often keep records on the movement of animals between company farms (30) . Movement data from a single swine production company in the US contained information on the origin and destination of 9.1 million pigs annually, totaling ~25,000 per day. Such databases can be used to construct contact networks that represent potential transmission pathways in a population, and social network analysis can be used to quantify the connectivity of each node within the network and to assess the population's vulnerability to infectious disease epidemics (26, (31) (32) (33) . Identifying premises that likely play critical roles in disease spread, such as highly connected farms or farms lying on bottlenecks within the network, can inform control measures that are more effective at limiting disease spread than non-targeted approaches (10, 21, 25) . Given the high velocity nature of animal movement data, it is relatively easy to envision how risk estimates could be updated in near real-time, provided that data are efficiently captured in the field, analyzed, and reported to decision-makers.
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