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Network analysis of pig movements in Argentina: identification of key farms in the spread of diseases and relationship with their biosecurity level

Laura V Alarcón1, 2*, Pablo A. CIPRIOTTI3, 4, Mariela Monterubbianessi5, Carlos Perfumo1, Enric Mateu2, 6 and Alberto Allepuz2, 6
  • 1 Facultad de Ciencias Veterinarias, Universidad Nacional de La Plata, Argentina
  • 2 Autonomous University of Barcelona, Spain
  • 3 Facultad de Agronomía, Universidad de Buenos Aires, Argentina
  • 4 National Council for Scientific and Technical Research (CONICET), Argentina
  • 5 National Service for Agrifood Health and Quality (SENASA), Argentina
  • 6 Center for Research in Animal Health, Spain

Animal movements are one of the major means for infectious disease transmission in livestock populations (Fèvre et al., 2006). Network analysis has been widely used in veterinary epidemiology to assess and describe the spread of infectious diseases based on the interactions among farms or individuals (Marquetoux et al., 2016). The purpose of our study was to characterize the network of pig movements in Argentina for a better understanding of potential disease spread together with the identification of super-spreader farms for targeted control and surveillance measures. The biosecurity level of those super-spreaders was evaluated in relation to their role in the transmission of diseases through animal movements. We adopted a spatially explicit approach where all pig commercial farms of Argentina were located by their geographic coordinates and movements data among them for the 2014-2017 period. Considering different types of risk for disease transmission such as animal category (breeders, weaners, etc.), origin and destination (Fèvre et al., 2006) and the pig production chain in Argentina, the following movement types were distinguished: animals of high genetic value sent to other farms, movements to or from markets, movements to finisher operations and animals sent to slaughterhouses. A network analysis was carried out considering the first three movement types, since the last one is expected to have little impact on disease spread. First, centrality and cohesion measures were calculated for each movement type and year. Next, to determine if the networks had a small-world (Newman, 2000), the average path length and the clustering coefficients were compared with the results of random Erdős–Rényi network simulations (Erdos, P. & A. Rényi, 1960), and scale free topology through the degree distributions were analyzed to determine if they fitted a power-law distribution following the guidelines proposed by Clauset et al. (2009). Furthermore, we used the degree and betweeness values to calculate the basic reproductive rate (R0) according to Woordhouse et al. (2005). To examine the role of each farm in the potential disease spread farms were removed one by one, starting by those with the highest degree and betweenness; the R0 was calculated for each reduced network and divided by the R0 of the full network according to Marquetoux et al., (2016). This allowed the identification of super-spreaders in those populations, that is, nodes accounting for most of the contacts and therefore having a major contribution on disease spread. Finally, we evaluated their external biosecurity scores according to Alarcón et al (2019). Table 1 shows the descriptive and cohesion measures at network level for the year 2017 (other years are shown in supplementary material S2). The number of nodes in the genetic network was higher than in the market and finisher networks, whereas density (the fraction of all possible edges realized in the network) was higher in the market and finisher networks. As shown by its diameter, the distance between the most separate farms/nodes in the genetic network is six hops, whereas in the market and finisher networks it is only two. The average path length from the different networks ranged from 1.00 to 1.75 and the clustering coefficient varied from 0 (market network) to 0.071 (genetic). Results for degree and betweenness (unweighted or weighted) at node level in the network for 2017 (Table 2) exhibited large variation between nodes and highly asymmetrical distributions for each measure. Supplementary material S4 shows these measures for the rest of the networks (2014-2016). For the genetic network, the clustering coefficient weighted by the number of moved animals was higher than for the Erdős–Rényi network (0.0071 in the original vs. percentile (2.5) = 0.001; percentile (97.5) = 0.005 in the random network) but lower for the finisher and market network. In contrast, when the comparison was made using the average path length values of the original networks, these were shorter than those 2.5 and 97.5 percentiles of the random network (1.76 vs. 7.86-8.25; 1.12 vs. 5.11-5.22 and 1.01 vs. 4.48-4.65 for the genetic, finisher and market networks, respectively). Evidenced that only the genetic network fulfilled the requirements of a small-world topology. In-degree and out-degree values at node level were fitted to a power-law distribution model and the tail of the observed distribution satisfied it (exponent alpha values: in-degree/out-degree: genetic network=4.17/1.81, finisher=2.29/2.43, market= 1.64/3.29. Xmin in-degree/out-degree: genetic network= 9/14, finisher= 31/9, market= 12/10). The goodness tests performed were unable to reject the null hypothesis (p-values=0.11-0.99) in all cases. Hence, the observed distributions evidenced scale free topology was plausible for all networks. Thus, disease could spread fast, preferably to highly connected nodes and with little chances of being contained. Throughout the study, 31 farms were identified as super-spreaders for all years, while other 55 were super-spreaders at least once, from an average of 1613 farms per year. Interestingly, removal of less than 5% of higher degree and betweenness farms resulted in a >90% reduction of R0 indicating that few farms have a key role in disease spread (Figure 1). When biosecurity scores of these farms were examined, it was evident that many were at risk of introducing and disseminating new pathogens across the whole of Argentina’s pig production network, since only three had a score higher than 0.5 (in the scale of 0 to 0.95) (Figure 2). Therefore, a targeted plan for motivating farmers, especially super-spreader owners, as regards biosecurity improvement should be a national priority. In addition, targeted surveillance for critical pathogens should focus on those farms. Risk analysis estimating the entry pathways of different pathogens to super-spreaders are also highly needed to reduce the risk of disease spread in the country. In summary, in this study we identified pig farms with a critical role in disease transmission in Argentina and we examined their biosecurity level. Our findings showed that supers-spreaders were also at risk of introducing diseases due to their limited biosecurity and are key targets for prevention and intervention actions.

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Acknowledgements

We would like to thank the Argentinean National Service for Health and AgriFood Quality (SENASA).

References

Alarcón L.V., Monterubbianesi M., Perelman S., Sanguinetti H. R., Perfumo C.J., Mateu E. &Allepuz A. (2019). Biosecurity Assessment of Argentinean Pig Farms. Prev. Vet. Med. (In press). Clauset, A., C.R. Shalizi, & M.E.J. Newman. (2009). Power-law distributions in empirical data. SIAM review 51, 661–703. DOI: 10.1137/070710111 Erdos, P. & A. Rényi. (1960). On the evolution of random graphs. Publ. Math. Inst.Hungar. Acad. Sci 5, 17–61. DOI: 10.1.1.153.5943 Fèvre, E.M., B.M.D.C. Bronsvoort, K.A. Hamilton & S. Cleaveland. (2006). Animal movements and the spread of infectious diseases. Trends Microbiol., 14, 125–131. DOI: 10.1016/j.tim.2006.01.004 Marquetoux, N., Stevenson M., Wilson, P., Ridler, A. & C. Heuer. (2016). Using social network analysis to inform disease control interventions. Prev. Vet. Med. 126, 94-104. doi: 10.1016/j.prevetmed.2016.01.022 Newman M. E. J. (2000). Models of the Small World. Journal of Statistical Physics, 101, 34. https://arxiv.org/abs/cond-mat/0001118v2 Woolhouse M.E.J., D. J. Shaw, L. Matthews, W.-C. Liu, D. J. Mellor & M. R. Thomas. (2005). Epidemiological implications of the contact network structure for cattle farms and the 20–80 rule. Biol. Lett. 1, 350–352. doi: 10.1098/rsbl.2005.0331

Keywords: Networdk analysis, Network topology analysis, biosecurity, Swine, Animal movement analysis, Transboundary diseases

Conference: GeoVet 2019. Novel spatio-temporal approaches in the era of Big Data, Davis, United States, 8 Oct - 10 Oct, 2019.

Presentation Type: Regular oral presentation

Topic: Spatial-explicit or spatio-temporal network analysis

Citation: Alarcón L, CIPRIOTTI PA, Monterubbianessi M, Perfumo C, Mateu E and Allepuz A (2019). Network analysis of pig movements in Argentina: identification of key farms in the spread of diseases and relationship with their biosecurity level. Front. Vet. Sci. Conference Abstract: GeoVet 2019. Novel spatio-temporal approaches in the era of Big Data. doi: 10.3389/conf.fvets.2019.05.00006

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Received: 19 Jun 2019; Published Online: 27 Sep 2019.

* Correspondence: Dr. Laura V Alarcón, Facultad de Ciencias Veterinarias, Universidad Nacional de La Plata, La Plata, Buenos Aires, Argentina, lalarcon@fcv.unlp.edu.ar