Translating Big Data into Smart Data for Veterinary Epidemiology

Abstract

The increasing availability and complexity of data has led to new opportunities and challenges in veterinary epidemiology around how to translate abundant, diverse, and rapidly growing “big” data into meaningful insights for animal health. Big data analytics are used to understand health risks and minimize the impact of adverse animal health issues through identifying high-risk populations, combining data or processes acting at multiple scales through epidemiological modeling approaches, and harnessing high velocity data to monitor animal health trends and detect emerging health threats. The advent of big data requires the incorporation of new skills into veterinary epidemiology training, including, for example, machine learning and coding, to prepare a new generation of scientists and practitioners to engage with big data. Establishing pipelines to analyze big data in near real-time is the next step for progressing from simply having “big data” to create “smart data,” with the objective of improving understanding of health risks, effectiveness of management and policy decisions, and ultimately preventing or at least minimizing the impact of adverse animal health issues.

Global trends in infectious diseases of swine

“…This effect could also be interpreted in terms of biosecurity, where countries may be more motivated to conduct research on swine pathogens as part of efforts to quantify the risk of disease introduction. New collaborations and data sharing models that are international in scope, span geographical regions, and break historical or language barriers will generate novel linkages and allow the field to take greater advantage of animal health data that is becoming bigger, richer, and more complex (20).…”

The Politics of Digital Agricultural Technologies: A Preliminary Review

“…For example, sound analysis is being used to identify respiratory disease in pigs in Europe (Ferrari et al ) and detect stress in laying hens in South Korea (Lee et al ), and biosensors are being used for early detection of respiratory disease pigs in the UK (Cowton et al ) and in calves in Japan (Nogami et al ). Big data is also being used at the level of veterinary epidemiology, identifying high risk populations so that surveillance and monitoring can be targeted efficiently (Van der Waal et al ). Many countries now have mandatory animal traceability programmes to track farm animal movements e.g.…”

Automated prediction of mastitis infection patterns in dairy herds using machine learning

“…ML has been used within the field of cattle medicine, for example in attempting to predict fertility outcomes 23 , high somatic cell counts 24 , and the onset of calving 25 . With the advent of increased "big data" within farm animal medicine, the potential to translate this into "smart data" is increasing 26 ; making full use of data already being collected. Machine learning has been applied to epidemiological classification problems within cattle medicine, such as the prediction of bovine viral diarrhoea virus exposure at herd level 27 , and the distribution of exposure of herds to liver fluke 28 , and has recently been applied in the investigation of mastitis pathogen (Streptococcus uberis) transmission patterns in cattle 29 as well as in the diagnosis of both subclinical 30,31 and clinical 32 mastitis at an individual animal level.…”