Occurrence of PRRS lineages is not equal in different years, systems or production types
Occurrence of specific PRRS lineages is associated with movement of animals
Continuous surveillance for PRRS occurrence is important in understanding its determinants and might be able to provide insights that can
help on its prevention
By utilizing a dataset of 1901 PRRS sequences provided by the Morrison Swine Health Monitoring Project (MSHMP) participants over 3 recent years, the spatiotemporal patterns in the occurrence of different lineages of PRRSV was described and the extent to which the network of pig movement between farms determines the occurrence of PRRS from similar lineages was investigated.
PRRS lineages occurred at different frequencies across geographically overlapping production systems. Preliminary analysis showed that the relative frequency in which specific lineages occur increase while others are decrease over time. The rate at which these changes occur appears to be system-specific. Some lineages were also more common in farms of specific production types (i.e. sow farm or nurseries). As expected, farms that were connected via pig movements were more likely to share the same lineages than expected by chance across all years.
These findings suggest that system-specific characteristics partially drive PRRS occurrence over time and across farms of different production types. Our results also
indicate that animal movement between farms is a driver of PRRS occurrence, strengthening this hypothesis of viral transmission.
Additional research is needed to quantify risks and develop mitigation measures related to animal movement.
Big data can be defined as the daunting accumulation of abundant and diverse information. While recording data is the first step to measure progress or quickly identify an issue, the large amount of information collected can make it difficult to analyze.
At the University of Minnesota, one of the main projects using big data is the Morrison’s Swine Health Monitoring Program. This ongoing project collects veterinary reports and diagnostic results from numerous swine producers on a daily basis. The compiled information is then analyzed, interpreted and reported back as smart data to the participants every week. Smart data is commonly defined as a piece of information useful enough to make educated decisions.
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.