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Outbreak minimization v.s. influence maximization: an optimization framework

Authors
  • Cheng, Chun-Hung1
  • Kuo, Yong-Hong2
  • Zhou, Ziye3
  • 1 Logistics and Supply Chain MultiTech R&D Centre Limited, Unit 202, Level 2, Block B, Cyberport 4, 100 Cyberport Road, Hong Kong, Hong Kong, China , Hong Kong (China)
  • 2 the University of Hong Kong, Pokfulam Road, Hong Kong, China , Hong Kong (China)
  • 3 the Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China , Hong Kong (China)
Type
Published Article
Journal
BMC Medical Informatics and Decision Making
Publisher
Springer (Biomed Central Ltd.)
Publication Date
Oct 16, 2020
Volume
20
Issue
1
Identifiers
DOI: 10.1186/s12911-020-01281-0
Source
Springer Nature
Keywords
License
Green

Abstract

BackgroundAn effective approach to containing epidemic outbreaks (e.g., COVID-19) is targeted immunization, which involves identifying “super spreaders” who play a key role in spreading disease over human contact networks. The ultimate goal of targeted immunization and other disease control strategies is to minimize the impact of outbreaks. It shares similarity with the famous influence maximization problem studied in the field of social network analysis, whose objective is to identify a group of influential individuals to maximize the influence spread over social networks. This study aims to establish the equivalence of the two problems and develop an effective methodology for targeted immunization through the use of influence maximization.MethodsWe present a concise formulation of the targeted immunization problem and show its equivalence to the influence maximization problem under the framework of the Linear Threshold diffusion model. Thus the influence maximization problem, as well as the targeted immunization problem, can be solved by an optimization approach. A Benders’ decomposition algorithm is developed to solve the optimization problem for effective solutions.ResultsA comprehensive computational study is conducted to evaluate the performance and scalability of the optimization approach on real-world large-scale networks. Computational results show that our proposed approaches achieve more effective solutions compared to existing methods.ConclusionsWe show the equivalence of the outbreak minimization and influence maximization problems and present a concise formulation for the influence maximization problem under the Linear Threshold diffusion model. A tradeoff between computational effectiveness and computational efficiency is illustrated. Our results suggest that the capability of determining the optimal group of individuals for immunization is particularly crucial for the containment of infectious disease outbreaks within a small network. Finally, our proposed methodology not only determines the optimal solutions for target immunization, but can also aid policymakers in determining the right level of immunization coverage.

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