Abstract
Epidemic spreading occurs among animals, humans, or computers and causes substantial societal, personal, or economic losses if left undetected. Based on known temporal contact networks, we propose an outbreak detection method that identifies a small set of nodes such that the likelihood of detecting recent outbreaks is maximal. The two-step procedure involves (i) simulating spreading scenarios from all possible seed configurations and (ii) greedily selecting nodes for monitoring in order to maximize the detection likelihood. We find that the detection likelihood is a submodular set function for which it has been proven that greedy optimization attains at least 63% of the optimal (intractable) solution. The results show that the proposed method detects more outbreaks than benchmark methods suggested recently and is robust against badly chosen parameters. In addition, our method can be used for outbreak source detection. A limitation of this method is its heavy use of computational resources. However, for large graphs the method could be easily parallelized.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
We provide the dataset, the code, and additional results and figures under the following link: https://github.com/martinSter/Outbreak-Detection.
- 2.
The pig movement data contain private information and cannot be shared publicly. For research purposes, a data request can be sent to Identitas AG, Stauffacherstrasse 130A, 3014 Bern, Switzerland.
References
Aggarwal, C.C., Lin, S., Yu, P.S.: On influential node discovery in dynamic social networks. In: Proceedings of the 2012 SIAM International Conference on Data Mining, pp. 636–647 (2012)
Antulov-Fantulin, N., Lančić, A., Šmuc, T., Štefančić, H., Šikić, M.: Identification of patient zero in static and temporal networks: robustness and limitations. Phys. Rev. Lett. 114, 248701 (2015)
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)
Barrat, A., Barthlemy, M., Vespignani, A.: Dynamical Processes on Complex Networks, 1st edn. Cambridge University Press, New York (2008)
Budak, C., Agrawal, D., El Abbadi, A.: Limiting the spread of misinformation in social networks. In: Proceedings of the 20th International Conference on World Wide Web, pp. 665–674. ACM, New York (2011)
Christakis, N.A., Fowler, J.H.: Social network sensors for early detection of contagious outbreaks. PLoS ONE 5(9), 1–8 (2010)
Dubé, 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. Transbound. Emerg. Dis. 55(9–10), 382–392 (2008)
Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2003, pp. 137–146. ACM, New York (2003)
Krause, A., Golovin, D.: Submodular function maximization. In: Tractability: Practical Approaches to Hard Problems. Cambridge University Press (2014)
Leskovec, J., Krause, A., Guestrin, C., Faloutsos, C., VanBriesen, J., Glance, N.: Cost-effective outbreak detection in networks. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 420–429. ACM, New York (2007)
Nemhauser, G.L., Wolsey, L.A.: Best algorithms for approximating the maximum of a submodular set function. Math. Oper. Res. 3(3), 177–188 (1978)
Panagopoulos, G., Malliaros, F.D., Vazirgiannis, M.: DiffuGreedy: an influence maximization algorithm based on diffusion cascades. In: Aiello, L.M., Cherifi, C., Cherifi, H., Lambiotte, R., Lió, P., Rocha, L.M. (eds.) Complex Networks and Their Applications VII, pp. 392–404. Springer, Cham (2019)
Pastor-Satorras, R., Vespignani, A.: Epidemic spreading in scale-free networks. Phys. Rev. Lett. 86, 3200–3203 (2001)
Rocha, L.E.C., Liljeros, F., Holme, P.: Simulated epidemics in an empirical spatiotemporal network of 50,185 sexual contacts. PLoS Comput. Biol. 7(3), 1–9 (2011)
Schilling, R.L.: Measures, Integrals and Martingales. Cambridge University Press, Cambridge (2005)
Sterchi, M., Faverjon, C., Sarasua, C., Vargas, M.E., Berezowski, J., Bernstein, A., Grütter, R., Nathues, H.: The pig transport network in Switzerland: structure, patterns, and implications for the transmission of infectious diseases between animal holdings. PLoS ONE 14(5), 1–20 (2019)
Acknowledgement
This work was supported by the Swiss National Science Foundation (SNSF) NRP75, Project number \(407540\_167303\). M. Sterchi was partially supported by the Hasler foundation. We would like to thank Identitas AG for providing the pig movement data and Emily E. Raubach, Heiko Nathues, Beat Hulliger, and the anonymous reviewers for helpful comments.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Sterchi, M., Sarasua, C., Grütter, R., Bernstein, A. (2020). Maximizing the Likelihood of Detecting Outbreaks in Temporal Networks. In: Cherifi, H., Gaito, S., Mendes, J., Moro, E., Rocha, L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 882. Springer, Cham. https://doi.org/10.1007/978-3-030-36683-4_39
Download citation
DOI: https://doi.org/10.1007/978-3-030-36683-4_39
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-36682-7
Online ISBN: 978-3-030-36683-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)