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Surveillance for Outbreak Detection in Livestock-Trade Networks

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Abstract

We analyze an empirical, temporal network of livestock trade and present numerical results of epidemiological dynamics. The considered network is the backbone of the pig trade in Germany, which forms a major route of disease spreading between agricultural premises. The network is comprised of farms that are connected by a link, if animals are traded between them. We propose a concept for epidemic surveillance, which is generally performed on a subset of the system due to limited resources. The goal is to identify agricultural holdings that are more likely to be infected during the early phase of an epidemic outbreak. These farms, which we call sentinels, are excellent candidates to monitor the whole network. To identify potential sentinel nodes, we determine most probable transmission routes by calculating functional clusters. These clusters are formed by nodes that – chosen as seed for an outbreak – have similar invasion paths. We find that it is indeed possible to group the German pig-trade network in such clusters. Furthermore, we select sentinels by choosing nodes out of every cluster. We argue that any epidemic outbreak can be reliably detected at an early stage by monitoring a small number of those sentinels. Considering a susceptible-infected-recovered model, we show that an outbreak can be detected with only 18 sentinels out of almost 100,000 farms with a probability of 65% in approximately 13 days after first infection. This finding can be further improved by including nodes with the largest in-component (highest vulnerability), which increases the detection probability to 86% within 8 days after first occurrence of the disease.

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Notes

  1. 1.

    Agrarpolitischer Bericht der Bundesregierung (2015). Bundesministerium für Ernährung und Landwirtschaft (BMEL), available as http://www.bmel.de/SharedDocs/Downloads/Broschueren/Agrarbericht2015.html

  2. 2.

    Bayerisches Staatsministerium für Ernährung, Landwirtschaft und Forsten (StMELF). Herkunftssicherungs- und Informationssystem für Tiere, available from: www.hi-tier.de

References

  1. Keeling, M.J., Rohani, P.: Modeling Infectious Diseases in Humans and Animals. Princeton University Press, Princeton (2008)

    MATH  Google Scholar 

  2. Funk, S., Gilad, E., Watkins, C., Jansen, V.A.: Proc. Natl. Acad. Sci. 106, 6872 (2009)

    CrossRef  Google Scholar 

  3. Anderson, R.H., May, R.M.: Infectious Diseases of Humans: Dynamics and Control. Oxford University Press, Oxford/New York (1992)

    Google Scholar 

  4. Murray, J.D.: Mathematical Biology: I. An Introduction Interdisciplinary Applied Mathematics. Springer, New York (2002)

    MATH  Google Scholar 

  5. Diekmann, O., Heesterbeek, H., Britton, T.: Mathematical Tools for Understanding Infectious Disease Dynamics. Princeton University Press, Princeton (2013)

    MATH  Google Scholar 

  6. Fritzemeier, J., Teuffert, J., Greiser-Wilke, I., Staubach, C., Schlüter, H., Moennig, V.: Vet. Microbiol. 77, 29 (2000)

    CrossRef  Google Scholar 

  7. Hethcote, H.W.: SIAM Rev. 42, 599 (2000)

    MathSciNet  CrossRef  Google Scholar 

  8. Bajardi, P., Barrat, A., Savini, L., Colizza, V.: J. Roy. Soc. Interface. 9, 2814 (2012)

    CrossRef  Google Scholar 

  9. Koher, A., Lentz, H.H.K., Hövel, P., Sokolov, I.: PLoS One. 11, e0151209 (2016)

    CrossRef  Google Scholar 

  10. Newman, M.E.J.: Phys. Rev. E. 66, 016128 (2002)

    MathSciNet  CrossRef  Google Scholar 

  11. Konschake, M., Lentz, H.H.K., Conraths, F., Hövel, P., Selhorst, T.: PLoS One. 8, e55223 (2013)

    CrossRef  Google Scholar 

  12. Vernon, M.C., Keeling, M.J.: Proc. R. Soc. Lond. B. Biol. Sci. 276, 469 (2009)

    CrossRef  Google Scholar 

  13. Holme, P., Saramäki, J.: Phys. Rep. 519, 97 (2012)

    CrossRef  Google Scholar 

  14. Casteigts, A., Flocchini, P., Quattrociocchi, W., Santoro, N.: Int. J. Parallel Emergent Distrib. Syst. 27, 387 (2012)

    CrossRef  Google Scholar 

  15. Holme, P.: EPJ B. 88, 1 (2015)

    Google Scholar 

  16. Bajardi, P., Barrat, A., Natale, F., Savini, L., Colizza, V.: PLoS One. 6, e19869 (2011)

    CrossRef  Google Scholar 

  17. Rocha, L.E., Liljeros, F., Holme, P.: PLoS Comput. Biol. 7, e1001109 (2011)

    CrossRef  Google Scholar 

  18. Valdano, E., Ferreri, L., Poletto, C., Colizza, V.: Phys. Rev. X. 5, 021005 (2015)

    Google Scholar 

  19. Lentz, H.H.K., Koher, A., Hövel, P., Gethmann, J., Sauter-Louis, C., Selhorst, T., Conraths, F.: PLoS One. 11, e0155196 (2016)

    CrossRef  Google Scholar 

  20. Wu, H., Cheng, J., Huang, S., Ke, Y., Lu, Y., Xu, Y.: Proc. VLDB Endowment. 7, 721 (2014)

    CrossRef  Google Scholar 

  21. Newman, M.E.J.: Networks: An Introduction. Oxford University Press, Inc., New York (2010)

    CrossRef  MATH  Google Scholar 

  22. Barabasi, A.L.: Network Science. Cambridge University Press, Cambridge (2016)

    MATH  Google Scholar 

  23. Lü, L., Chen, D., Ren, X.-L., Zhang, Q.-M., Zhang, Y.-C., Zhou, T.: Phys. Rep. 650, 1 (2016)

    MathSciNet  CrossRef  Google Scholar 

  24. Dorogovtsev, S.N., Mendes, J.F.F., Samukhin, A.N.: Phys. Rev. E. 64, 025101 (2001)

    CrossRef  Google Scholar 

  25. Pastor-Satorras, R., Vespignani, A.: Phys. Rev. Lett. 86, 3200 (2001)

    CrossRef  Google Scholar 

  26. Morone, F., Makse, H.A.: Nature. 524, 65 (2015)

    CrossRef  Google Scholar 

  27. Brockmann, D., Helbing, D.: Science. 342, 1337–1342 (2013)

    CrossRef  Google Scholar 

  28. Iannelli, F., Koher, A., Brockmann, D., Hövel, P., Sokolov, I.M.: Phys. Rev. E. 95, 012313 (2017)

    Google Scholar 

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Acknowledgements

This work was supported by Deutscher Akademischer Austauschdienst (DAAD) within the PPP-PROCOPE scheme. FS, AK, and PH acknowledge funding by Deutsche Forschungs- gemeinschaft in the framework of Collaborative Research Center 910. The work is partially funded by the EC-ANIHWA Contract No. ANR-13-ANWA-0007-03 (LIVEepi) to VC.

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Correspondence to Frederik Schirdewahn .

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Appendix

Appendix

Fig. 10.15
figure 15

Entropy H(t 0 , t) of the eight largest clusters not mentioned in the main text (for cluster 4 see Fig. 10.14) over time (red dots), minimum entropy (blue empty dots), and their difference (yellow bars)

Fig. 10.16
figure 16

Entropy H(t 0 , t) of the clusters 9–18 except for cluster 15, which is shown in Fig. 10.14, over time (red dots), minimum entropy (blue empty dots), and their difference (yellow bars)

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Schirdewahn, F., Colizza, V., Lentz, H.H.K., Koher, A., Belik, V., Hövel, P. (2017). Surveillance for Outbreak Detection in Livestock-Trade Networks. In: Masuda, N., Holme, P. (eds) Temporal Network Epidemiology. Theoretical Biology. Springer, Singapore. https://doi.org/10.1007/978-981-10-5287-3_10

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