research-article

A genetic algorithm for finding an optimal curing strategy for epidemic spreading in weighted networks

Authors:

Annalisa SocievoleAuthors Info & Claims

GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference

Pages 498 - 504

https://doi.org/10.1145/3205455.3205508

Published: 02 July 2018 Publication History

Abstract

Contact networks have been recognized to have a central role in the dynamic behavior of spreading processes. The availability of cost-optimal curing strategies, able to control the epidemic propagation, are of primary importance for the design of efficient treatments reducing the number of infected individuals and the extinction time of the infection. In this paper, we investigate the use of Genetic Algorithms for solving the problem of finding an optimal curing strategy in a network where a virus spreads following the Susceptible-Infected-Susceptible (SIS) epidemic model. Exploiting the N-Intertwined Mean-Field Approximation (NIMFA) of the SIS spreading process, we propose a constrained genetic algorithm which determines specific curing rates to each node composing the network, in order to minimize the total curing cost, while suppressing the epidemic. Experiments on both synthetic and real-world networks show that the approach finds solutions whose curing cost is lower than that obtained by a classical baseline method.

References

[1]

Réka Albert and Albert-László Barabási. 2002. Statistical mechanics of complex networks. Reviews of Modern Physics 74, 1 (2002), 47--97.

[2]

Christian Borgs, Jennifer Chayes, Ayalvadi Ganesh, and Amin Saberi. 2010. How to Distribute Antidote to Control Epidemics. Random Struct. Algorithms 37, 2 (Sept. 2010), 204--222.

Digital Library

[3]

Fernando Concatto, Wellington Zunino, Luigi A Giancoli, Rafael Santiago, and Luís C Lamb. 2017. Genetic algorithm for epidemic mitigation by removing relationships. In Proceedings of the Genetic and Evolutionary Computation Conference. ACM, 761--768.

Digital Library

[4]

Kalyanmoy Deb. 2000. An Efficient Constraint Handling Method for Genetic Algorithms. Computer Methods in Applied Mechanics and Engineering 186 (2000), 311--338.

[5]

Paul Erdős and Alfréd Rényi. 1960. On the evolution of random graphs. Publ. Math. Inst. Hung. Acad. Sci 5 (1960), 17--61.

[6]

Eric Gourdin, Jasmina Omic, and Piet Van Mieghem. 2011. Optimization of network protection against virus spread. In Proceedings of the 8th International Workshop on Design of Reliable Communication Networks (DRCN), 2011. 659--667.

[7]

Michael Grant, Stephen Boyd, and Yinyu Ye. 2008. CVX: Matlab software for disciplined convex programming. (2008).

[8]

Mayank Lahiri and Manuel Cebrian. 2010. The Genetic Algorithm as a General Diffusion Model for Social Networks. In AAAI.

Digital Library

[9]

Ana Lajmanovich and James A. Yorke. 1976. A deterministic model for gonorrhea in a nonhomogeneous population. Mathematical Biosciences 28, 3 (1976), 221 -- 236.

[10]

Jian-Qin Liao, Xiao-Bing Hu, Ming Wang, and Mark S Leeson. 2013. Epidemic modelling by ripple-spreading network and genetic algorithm. Mathematical Problems in Engineering 2013 (2013).

[11]

A.G. McKendrick. 1926. Applications of mathematics to medical problems. In Proceedings of Edin. Math Society, Vol. 14. 98--130.

[12]

M.E.J. Newman. 2002. Spread of epidemic disease on Networks. Physical Review E 66, 1 (2002), 016128.

[13]

Cameron Nowzari, Victor M. Preciado, and George J. Pappas. 2016. Analysis and Control of Epidemics: A Survey of Spreading Processes on Complex Networks. IEEE Control Systems 36, 1 (2016), 26--46.

[14]

Stefania Ottaviano, Francesco De Pellegrini, Stefano Bonaccorsi, and Piet Van Mieghem. 2017. Optimal curing policy for epidemic spreading over a community network with heterogeneous population. Information and Inference: A Journal of the IMA (2017).

[15]

KJ Parousis-Orthodoxou and DS Vlachos. 2014. Evolutionary Algorithm for Optimal Vaccination Scheme. In Journal of Physics: Conference Series, Vol. 490. IOP Publishing, 012027.

[16]

Romualdo Pastor-Satorras, Claudio Castellano, Piet Van Mieghem, and Alessandro Vespignani. 2015. Epidemic Processes in Complex Networks. Review of Modern Physics 87, 3 (2015), 925--979.

[17]

Romualdo Pastor-Satorras and Alessandro Vespignani. 2014. Epidemic Spreading in Scale-Free Networks. Physical Review Letters 86, 14 (2014), 99--108.

[18]

B. Aditya Prakash, Lada Adamic, Theodore Iwashyna, Hanghang Tong, and Christos Faloutsos. 2013. Fractional Immunization in Networks. In Proceedings of the SIAM Data Mining Conference. 659--667.

[19]

Victor M. Preciado, Michael Zargham, Chinwendu Enyioha, Ali Jadbabaie, and George J. Pappas. 2013. Optimal vaccine allocation to control epidemic outbreaks in arbitrary networks. In Proceedings of the 52nd IEEE Conference on Decision and Control, CDC 2013, December 10-13, 2013, Firenze, Italy. 7486--7491.

[20]

Victor M. Preciado, Michael Zargham, Chinwendu Enyioha, Ali Jadbabaie, and George J. Pappas. 2014. Optimal Resource Allocation for Network Protection Against Spreading Processes. IEEE Trans. Control of Network Systems 1, 1 (2014), 99--108.

[21]

Faryad Darabi Sahneh, Caterina Scoglio, and Piet Van Mieghem. 2013. Generalized Epidemic Mean-field Model for Spreading Processes over Multilayer Complex Networks. IEEE/ACM Trans. Netw. 21, 5 (Oct. 2013), 1609--1620.

Digital Library

[22]

R. H. Tütüncü, K. C. Toh, and M. J. Todd. 2003. Solving semidefinite-quadratic-linear programs using SDPT3. Mathematical Programming 95, 2 (01 Feb 2003), 189--217.

[23]

Piet Van Mieghem and Jasmina Omic. {n. d.}. In-Homogeneous Virus Spread in Networks. arxiv.1306.2588 ({n. d.}).

[24]

Piet Van Mieghem, Jasmina Omic, and Robert Kooij. 2009. Virus Spread in Networks. IEEE/ACM Trans. Netw. 17, 1 (Feb. 2009), 1--14.

Digital Library

[25]

Lieven Vandenberghe and Stephen Boyd. 1996. Semidefinite Programming. SIAM Rev. 38, 1 (March 1996), 49--95.

Digital Library

[26]

Yang Wang, Deepayan Chakrabarti, Chenxi Wang, and Christos Faloutsos. 2003. Epidemic Spreading in Real Networks: An Eigenvalue Viewpoint. In In Proc. International Symposium Rel. Distr. Syst. (SRDS). 25--34.

[27]

Duncan J Watts and Steven H Strogatz. 1998. Collective dynamics of 'small-world'networks. nature 393, 6684 (1998), 440.

[28]

Xiangping Zhai, Liang Zheng, Jianping Wang, and Chee Wei Tan. 2013. Optimization algorithms for epidemic evolution in broadcast networks. In Wireless Communications and Networking Conference (WCNC), 2013 IEEE. IEEE, 1540--1545.

Cited By

Feng YWang YWang BDing L(2024)Optimized Weights for Heterogeneous Epidemic Spreading Networks: A Constrained Cooperative Coevolution StrategyIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.332340011:3(3911-3919)Online publication date: Jun-2024
https://doi.org/10.1109/TCSS.2023.3323400
Pizzuti CSocievole A(2020)Constrained evolutionary algorithms for epidemic spreading curing policyApplied Soft Computing10.1016/j.asoc.2020.10617390(106173)Online publication date: May-2020
https://doi.org/10.1016/j.asoc.2020.106173
Pizzuti CSocievole A(2019)Epidemic Spreading Curing Strategy Over Directed NetworksNumerical Computations: Theory and Algorithms10.1007/978-3-030-40616-5_14(182-194)Online publication date: 15-Jun-2019
https://dl.acm.org/doi/10.1007/978-3-030-40616-5_14
Show More Cited By

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A genetic algorithm for finding an optimal curing strategy for epidemic spreading in weighted networks

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cover image ACM Conferences

GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference

July 2018

1578 pages

ISBN:9781450356183

DOI:10.1145/3205455

Editor:
Hernan Aguirre
Shinshu University
,
General Chair:
Keiki Takadama
The University of Electro-Communications

Copyright © 2018 ACM.

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Published: 02 July 2018

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GECCO '18

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GECCO '18: Genetic and Evolutionary Computation Conference

July 15 - 19, 2018

Kyoto, Japan

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Feng YWang YWang BDing L(2024)Optimized Weights for Heterogeneous Epidemic Spreading Networks: A Constrained Cooperative Coevolution StrategyIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.332340011:3(3911-3919)Online publication date: Jun-2024
https://doi.org/10.1109/TCSS.2023.3323400
Pizzuti CSocievole A(2020)Constrained evolutionary algorithms for epidemic spreading curing policyApplied Soft Computing10.1016/j.asoc.2020.10617390(106173)Online publication date: May-2020
https://doi.org/10.1016/j.asoc.2020.106173
Pizzuti CSocievole A(2019)Epidemic Spreading Curing Strategy Over Directed NetworksNumerical Computations: Theory and Algorithms10.1007/978-3-030-40616-5_14(182-194)Online publication date: 15-Jun-2019
https://dl.acm.org/doi/10.1007/978-3-030-40616-5_14
Pizzuti CSocievole A(2019)Optimal Curing Strategy Enhancement of Epidemic Processes with Self-adaptive SBX CrossoverArtificial Life and Evolutionary Computation10.1007/978-3-030-21733-4_12(151-162)Online publication date: 30-May-2019
https://doi.org/10.1007/978-3-030-21733-4_12

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