Advertisement

Impact of Temporal Features of Cattle Exchanges on the Size and Speed of Epidemic Outbreaks

  • Aurore PayenEmail author
  • Lionel Tabourier
  • Matthieu Latapy
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10405)

Abstract

Databases recording cattle exchanges offer unique opportunities for a better understanding and fighting of disease spreading. Most studies model contacts with (sequences of) networks, but this approach neglects important dynamical features of exchanges, that are known to play a key role in spreading. We use here a fully dynamic modeling of contacts and empirically compare the spreading outbreaks obtained with it to the ones obtained with network approaches. We show that neglecting time information leads to significant over-estimates of actual sizes of spreading cascades, and that these sizes are much more heterogeneous than generally assumed. Our approach also makes it possible to study the speed of spreading, and we show that the observed speeds vary greatly, even for a same cascade size.

Notes

Acknowledgements

This work is funded in part by the European Commission H2020 FETPROACT 2016–2017 program under grant 732942 (ODYCCEUS), by the ANR (French National Agency of Research) under grants ANR-15-CE38-0001 (AlgoDiv) and ANR-13-CORD-0017-01 (CODDDE), by the French program “PIA - Usages, services et contenus innovants” under grant O18062-44430 (REQUEST), and by the Ile-de-France program FUI21 under grant 16010629 (iTRAC).

References

  1. 1.
    Bajardi, P., Barrat, A., Natale, F., Savini, L., Colizza, V.: Dynamical patterns of cattle trade movements. PLoS One 6(5), e19869 (2011)CrossRefGoogle Scholar
  2. 2.
    Bajardi, P., Barrat, A., Savini, L., Colizza, V.: Optimizing surveillance for livestock disease spreading through animal movements. J. R. Soc. Interface 9(76), 2814–2825 (2012)CrossRefGoogle Scholar
  3. 3.
    Belik, V., Fiebig, F., Lentz, H.H.K., Hövel, P.: Controlling contagious processes on temporal networks via adaptive rewiring (2015)Google Scholar
  4. 4.
    Broder, A., Kumar, R., Maghoul, F., Raghavan, P., Rajagopalan, S., Stata, R., Tomkins, A., Wiener, J.: Graph structure in the web. Comput. Netw. 33(1), 309–320 (2000)CrossRefGoogle Scholar
  5. 5.
    Büttner, K., Krieter, J., Traulsen, A., Traulsen, I.: Efficient interruption of infection chains by targeted removal of central holdings in an animal trade network. PLoS One 8(9), e74292 (2013)CrossRefGoogle Scholar
  6. 6.
    Büttner, K., Salau, J., Krieter, J.: Quality assessment of static aggregation compared to the temporal approach based on a pig trade network in Northern Germany. Prev. Vet. Med. 129, 1–8 (2016)CrossRefGoogle Scholar
  7. 7.
    Dube, C., Ribble, C., Kelton, D., Mcnab, B.: Comparing network analysis measures to determine potential epidemic size of highly contagious exotic diseases in fragmented monthly networks of dairy cattle movements in Ontario, Canada. Transboundary Emerg. Dis. 55, 382–392 (2008)CrossRefGoogle Scholar
  8. 8.
    Dutta, B.L., Ezanno, P., Vergu, E.: Characteristics of the spatio-temporal network of cattle movements in France over a 5-year period. Prev. Vet. Med. 117(1), 79–94 (2014)CrossRefGoogle Scholar
  9. 9.
    Holme, P., Liljeros, F.: Birth and death of links control disease spreading in empirical contact networks. Sci. Rep. 4, 4999 (2014)CrossRefGoogle Scholar
  10. 10.
    Holme, P., Saramäki, J.: Temporal networks. Phys. Rep. 519(3), 97–125 (2012)CrossRefGoogle Scholar
  11. 11.
    Lentz, H.H.K., Selhorst, T., Sokolov, I.M.: Unfolding accessibility provides a macroscopic approach to temporal networks. Phys. Rev. Lett. 110(11), 1–5 (2013)CrossRefGoogle Scholar
  12. 12.
    Natale, F., Giovannini, A., Savini, L., Palma, D., Possenti, L., Fiore, G., Calistri, P.: Network analysis of Italian cattle trade patterns and evaluation of risks for potential disease spread. Prev. Vet. Med. 92(4), 341–350 (2009)CrossRefGoogle Scholar
  13. 13.
    Newman, M.E.J.: Spread of epidemic disease on networks. Phys. Rev. E Stat. Nonlinear Soft Matter Phys. 66(1), 1–11 (2002)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Nöremark, M., Hakansson, N., Lewerin, S.S., Lindberg, A., Jonsson, A.: Network analysis of cattle and pig movements in Sweden: measures relevant for disease control and risk based surveillance. Prev. Vet. Med. 99(2–4), 78–90 (2011)CrossRefGoogle Scholar
  15. 15.
    Pastor-Satorras, R., Castellano, C., Van Mieghem, P., Vespignani, A.: Epidemic processes in complex networks. Rev. Mod. Phys. 87(3), 925–979 (2015)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Pellis, L., Ball, F., Bansal, S., Eames, K., House, T., Isham, V., Trapman, P.: Eight challenges for network epidemic models. Epidemics 10, 58–62 (2015)CrossRefGoogle Scholar
  17. 17.
    Rautureau, S., Dufour, B., Durand, B.: Vulnerability of animal trade networks to the spread of infectious diseases: a methodological approach applied to evaluation and emergency control strategies in cattle, France, 2005. Transbound. Emerg. Dis. 58(2), 110–120 (2011)CrossRefGoogle Scholar
  18. 18.
    Schärrer, S., Widgren, S., Schwermer, H., Lindberg, A., Vidondo, B., Zinsstag, J., Reist, M.: Evaluation of farm-level parameters derived from animal movements for use in risk-based surveillance programmes of cattle in switzerland. BMC Vet. Res. 11(1), 149 (2015)CrossRefGoogle Scholar
  19. 19.
    Vernon, M.C., Keeling, M.J.: Representing the UK’s cattle herd as static and dynamic networks. Proc. R. Soc. Biol. Sci. 276(1656), 469–476 (2009)CrossRefGoogle Scholar
  20. 20.
    Viard, J., Latapy, M.: Identifying roles in an ip network with temporal and structural density. In: 2014 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pp. 801–806. IEEE (2014)Google Scholar
  21. 21.
    Webb, C.R.: Investigating the potential spread of infectious diseases of sheep via agricultural shows in Great Britain. Epidemiol. Infect. 134(1), 31–40 (2006)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Aurore Payen
    • 1
    • 2
    Email author
  • Lionel Tabourier
    • 2
  • Matthieu Latapy
    • 2
  1. 1.AgroParisTechParisFrance
  2. 2.Sorbonne Universités, UPMC Univ Paris 06, CNRS, LIP6 UMR 7606ParisFrance

Personalised recommendations