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Contribution Details

Type Journal Article
Scope Discipline-based scholarship
Title Outbreak detection for temporal contact data
Organization Unit
  • Martin Sterchi
  • Cristina Sarasua
  • Rolf Grütter
  • Abraham Bernstein
Item Subtype Original Work
Refereed Yes
Status Published in final form
  • English
Journal Title Applied Network Science
Publisher SpringerOpen
Geographical Reach international
ISSN 2364-8228
Volume 6
Number 1
Page Range 17
Date 2021
Abstract Text Epidemic spreading is a widely studied process due to its importance and possibly grave consequences for society. While the classical context of epidemic spreading refers to pathogens transmitted among humans or animals, it is straightforward to apply similar ideas to the spread of information (e.g., a rumor) or the spread of computer viruses. This paper addresses the question of how to optimally select nodes for monitoring in a network of timestamped contact events between individuals. We consider three optimization objectives: the detection likelihood, the time until detection, and the population that is affected by an outbreak. The optimization approach we use is based on a simple greedy approach and has been proposed in a seminal paper focusing on information spreading and water contamination. We extend this work to the setting of disease spreading and present its application with two example networks: a timestamped network of sexual contacts and a network of animal transports between farms. We apply the optimization procedure to a large set of outbreak scenarios that we generate with a susceptible-infectious-recovered model. We find that simple heuristic methods that select nodes with high degree or many contacts compare well in terms of outbreak detection performance with the (greedily) optimal set of nodes. Furthermore, we observe that nodes optimized on past periods may not be optimal for outbreak detection in future periods. However, seasonal effects may help in determining which past period generalizes well to some future period. Finally, we demonstrate that the detection performance depends on the simulation settings. In general, if we force the simulator to generate larger outbreaks, the detection performance will improve, as larger outbreaks tend to occur in the more connected part of the network where the top monitoring nodes are typically located. A natural progression of this work is to analyze how a representative set of outbreak scenarios can be generated, possibly taking into account more realistic propagation models.
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Digital Object Identifier 10.1007/s41109-021-00360-z
Other Identification Number merlin-id:20797
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Funders The work in this paper was supported by the Swiss National Science Foundation (SNSF) NRP75, Project Number 407540_167303. In addition, MS was supported by the Hasler foundation (Grant No. 18050).