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‘Big Data’ in animal health research – opportunities and challenges

Published online by Cambridge University Press:  20 July 2020

Janet I. MacInnes Open the ORCID record for Janet I. MacInnes [Opens in a new window]
Janet I. MacInnes*
Affiliation:
Department of Pathobiology, Ontario Veterinary College, University of Guelph, Guelph, Canada
*
Author for correspondence: Janet I. MacInnes, Department of Pathobiology, Ontario Veterinary College, University of Guelph, Guelph, Canada. E-mail: [email protected]
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Abstract

Automated systems for high-input data collection and data storage have led to exponential growth in the availability of information. Such datasets and the tools applied to them have been referred to as ‘big data’. Starting with a systematic review of the terms ‘informatics, bioinformatics and big data’ in animal health this special issue of AHRR illustrates some big-data applications with papers on how the use of various omics methods may be used to facilitate the development of improved diagnostics, therapeutics, and vaccines for foodborne pathogens in poultry and on how a better understanding of rumen microbiota could lead to improved feed absorption while minimizing methane production. Other papers in this issue cover the use of big data modeling in dairy cattle for more effective disease interventions and machine learning tools for livestock breeding. The final two reviews describe the use of big data in better vector-borne pathogen forecasts with canine seroprevalence maps and modeling approaches to understand the transmission of avian influenza virus. Although a lot of technical and ethical issues remain with the use of big data, these reviews illustrate the tremendous potential that big-data systems have to revolutionize animal health research.

Keywords

Animal healthbig datamachine learningmodelingprediction
Type
Opinion
Information
Animal Health Research Reviews , Volume 21 , Issue 1 , June 2020 , pp. 1 - 2
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Copyright
Copyright © Cambridge University Press 2020

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References

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