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.
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- 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.
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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.
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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
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