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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*
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:


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.

Copyright © Cambridge University Press 2020

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Cholewinska, P, Czyz, K, Nowakowski, P and Wyrostek, A (2019) The microbiome of the digestive system of ruminants – a review. Animal Health Research Reviews 20, 4253.Google Scholar
Deblais, L, Kathayat, D, Helmy, YA, Closs, G Jr and Rajashekara, G (2019) Translating ‘big data’: better understanding of host-pathogen interactions to control bacterial foodborne pathogens in poultry. Animal Health Research Reviews 20, 2141.Google Scholar
Nayeri, S, Sargolzaei, M and Tulpan, D (2019) A review of traditional and machine learning methods applied to animal breeding. Animal Health Research Reviews 20, 6681.CrossRefGoogle ScholarPubMed
Ouyang, ZB, Sargeant, J, Thomas, A, Wycherley, K, Ma, R, Esmaelbeigi, R, Versluis, A, Stacey, D, Stone, E, Poljak, Z and Bernardo, TM (2019) A scoping review of ‘big data’, ‘informatics’, and ‘bioinformatics’ in the animal health and veterinary medical literature. Animal Health Research Reviews 20, 320.CrossRefGoogle ScholarPubMed
Watson Self, SC, Liu, Y, Nordone, SK, Yabsley, MJ, Stockdale Walden, H, Lund, RB, Bowman, DD, Carpenter, C, McMahan, CS and Gettings, JR (2019) Canine vector-borne disease: mapping and the accuracy of forecasting using big data from the veterinary community. Animal Health Research Reviews 20, 8295.Google Scholar
Wisnieski, L, Norby, B, Pierce, SJ, Becker, T and Sordillo, LM (2019) Prospects for predictive modeling of transition cow diseases. Animal Health Research Reviews 20, 5465.CrossRefGoogle ScholarPubMed
Yousefi Naghani, S, Pljak, Z, Sharif, S and Dara, R (2019) A review of knowledge discovery process in control and mitigation of avian influenza. Animal Health Research Reviews 20, 96106.CrossRefGoogle ScholarPubMed