The Potential for Big Data in Animal Disease Surveillance in Ireland

Animal Health Surveillance is the systematic collection, collation, analysis interpretation, and dissemination of animal health and welfare data from defined populations. This process is essentially about gathering intelligence to detect either novel animal health-related events or increases in animal health-related events as early as possible to better inform risk management at all levels within the industry (1). The incursion of an exotic disease, such as Foot and Mouth disease, is probably the most significant event from a national animal health perspective. The early detection of a newly emerging disease or the re-emergence of a disease which was previously eradicated from the state before it becomes widespread in the population is another important objective of a well functioning surveillance system. From a trade perspective, it is hugely important to demonstrate freedom from specific diseases, and this requires objective evidence upon which to base any declarations of freedom. While the presence of endemic diseases will be well established, there is a specific need to monitor the occurrence of endemic disease to identify any spikes in their occurrence and to take appropriate risk mitigation actions. Antimicrobial resistance (AMR) is emerging as one of the most significant public health concerns of the twenty-first century. The use of antimicrobials in livestock has come under increased scrutiny as a result. The monitoring of the use of the levels of antimicrobials in livestock and the emergence of resistance among animal pathogens are two important surveillance priorities in the years ahead. Animal health surveillance is a part of animal disease risk management in that the information garnered should lead to better informed risk mitigation strategies. These risk mitigation strategies must deal with risks that are well established as well as risks which are not yet fully understood, and therein lies the challenge.