Using social network analysis to inform disease control interventions

- We analysed the network of livestock movements in a group of 112 corporate farms in New Zealand.
- The number and volume of movements were consistent over years while the identity of trading partners varied.
- The contact pattern was geographically clustered with only 3% of movement across the two main islands of New Zealand.
- Bridge-farms trading across islands as well as highly connected 'hubs' were efficient targets of movement control to prevent the spread of pathogens.

Contact patterns between individuals are an important determinant for the spread of infectious diseases in populations. Social network analysis (SNA) describes contact patterns and thus indicates how infectious pathogens may be transmitted. Here we explore network characteristics that may inform the development of disease control programes.This study applies SNA methods to describe a livestock movement network of 180 farms in New Zealand from 2006 to 2010. We found that the number of contacts was overall consistent from year to year, while the choice of trading partners tended to vary. This livestock movement network illustrated how a small number of farms central to the network could play a potentially dominant role for the spread of infection in this population. However, fragmentation of the network could easily be achieved by “removing” a small proportion of farms serving as bridges between otherwise isolated clusters, thus decreasing the probability of large epidemics.This is the first example of a comprehensive analysis of pastoral livestock movements in New Zealand. We conclude that, for our system, recording and exploiting livestock movements can contribute towards risk-based control strategies to prevent and monitor the introduction and the spread of infectious diseases in animal populations.