This is our Friday rubric: every week a new Science Page from the Bob Morrison’s Swine Health Monitoring Project. The previous editions of the science page are available on our website.
This week, Dr. Dennis Makau from the VanderWaal lab is sharing a project on the importance of swine movement to identify farms with a high risk of disease outbreak.
- Animal movement is a key factor in the U.S. swine industry and is an important risk factor for disease transmission
- Animal movement data combined with social network analysis can inform risk-based surveillance and control
- Using production system movement data, it was possible to identify the time window of data needed to gauge connectivity and identify high-risk and high-spread farms
- Using previous data up to two years old is still better than choosing randomly implemented interventions to manage disease spread, especially in cases of outbreaks transmitted via animal movements
As a consequence of multi-site pig production practiced in North America, frequent and widespread animal movements create extensive networks of interaction between farms. Animal movements have been identified as one of the important risk factors for between-farm spread of diseases such as PRRSv. Social Network Analysis (SNA) has been used to understand disease transmission risks within these complex and dynamic production ecosystems and is particularly relevant for designing risk-based surveillance and control strategies targeting highly connected farms. SNA has also been used to identify high-risk disease spreaders and vulnerable farms in food animal production systems.
Inferences from SNA and the effectiveness of targeted strategies may be influenced by changes over time in the network structure. Since farm movements represent a temporally dynamic network, it is unclear how many months of data are required to gain a reliable picture of an individual farm’s connectivity pattern and the overall network structure. Understanding and characterizing the stability of a network structure is therefore critical for identifying effective intervention points for disease management in production systems. For example, if the network structure changes drastically through time, models or disease interventions using historical data will have little effectiveness in the current context.
In this study, we aimed to describe temporal stability and loyalty patterns of pig movement networks in the U.S. swine industry. The extent to which shipments between two specific farms are repeated (i.e., “loyalty” of farm contacts) can influence the rate at which the structure of a network changes over time, which may influence disease dynamics. A broader scale assessment is node-level stability, which measures a farm’s general pattern of connectedness in a network. Farm level connectedness may remain stable within a system network, even if the identity of its contacts changes. A farm’s connectedness in a network can be used to predict both its risk of being infected as well as its potential to be a super-spreader.
We analyzed a total of 282,807 animal movements among 2724 farms belonging to two production systems between 2014 and 2017. Loyalty trends were largely driven by contacts between sow farms and nurseries and between nurseries and finisher farms; mean loyalty (percent of contacts that were repeated at least once within a 52-week interval) of farm contacts was 51-60% for farm contacts involving wean pigs, and 12-22% for contacts involving feeder pigs. A cyclical pattern was observed for both weaned and feeder pig movements with episodes of increasing loyalty observed at intervals of 8 and 17-20 weeks, respectively. Network stability was achieved when six months of data were aggregated, and only small shifts in metrics were observed when adding more data.
The temporal stability observed in these networks suggests that identifying highly connected farms in retrospective network data (up to 24 months) is reliable for future planning, albeit with reduced effectiveness. The network stability that occurred at 6 months suggests that reliable identification of super-spreaders in a network can be inferred after 6 months of farm contacts, and appropriate interventions could be used to target these farms. However, more data from additional pig production systems would be necessary to establish if this time window is system specific or applies to other systems and thus can be used as a general rule.
We simulated the impact of targeted interventions and found that excluding 10-15% of highly connected farms was effective in breaking disease transmission and decreasing the level of connection within the system, which in turn would disrupt disease transmission via animal movements. In contrast to these findings, if farms were targeted for intervention randomly, more than 60% of the farms would have to be excluded to reach a similar level of impact. The temporal stability observed in these networks suggests that identifying highly connected farms in retrospective network data (up to 24 months) is reliable for future planning, albeit with reduced effectiveness when using older movement data.
Full paper can be found at: https://doi.org/10.1016/j.prevetmed.2021.105369