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Maximizing the Likelihood of Detecting Outbreaks in Temporal Networks

  • Martin SterchiEmail author
  • Cristina Sarasua
  • Rolf Grütter
  • Abraham Bernstein
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 882)

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.

Keywords

Temporal networks Epidemic spreading Outbreak detection Source detection Submodular set functions Greedy optimization 

Notes

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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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Martin Sterchi
    • 1
    • 2
    • 3
    Email author
  • Cristina Sarasua
    • 1
  • Rolf Grütter
    • 2
  • Abraham Bernstein
    • 1
  1. 1.University of ZurichZurichSwitzerland
  2. 2.Swiss Federal Research Institute WSLBirmensdorfSwitzerland
  3. 3.University of Applied Sciences and Arts Northwestern Switzerland FHNWOltenSwitzerland

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