Optimal information dissemination strategy to promote preventivebehaviors in multilayer epidemic networks

  • Received: 01 August 2014 Accepted: 29 June 2018 Published: 01 February 2015
  • MSC : Primary: 58F15, 58F17; Secondary: 53C35.

  • Launching a prevention campaign to contain the spread of infection requires substantial financial investments; therefore, a trade-off exists between suppressing the epidemic and containing costs. Information exchange among individuals can occur as physical contacts (e.g., word of mouth, gatherings), which provide inherent possibilities of disease transmission, and non-physical contacts (e.g., email, social networks), through which information can be transmitted but the infection cannot be transmitted. Contact network (CN) incorporates physical contacts, and the information dissemination network (IDN) represents non-physical contacts, thereby generating a multilayer network structure. Inherent differences between these two layers cause alerting through CN to be more effective but more expensive than IDN. The constraint for an epidemic to die out derived from a nonlinear Perron-Frobenius problem that was transformed into a semi-definite matrix inequality and served as a constraint for a convex optimization problem. This method guarantees a dying-out epidemic by choosing the best nodes for adopting preventive behaviors with minimum monetary resources. Various numerical simulations with network models and a real-world social network validate our method.

    Citation: Heman Shakeri, Faryad Darabi Sahneh, Caterina Scoglio, Pietro Poggi-Corradini, Victor M. Preciado. Optimal information dissemination strategy to promote preventivebehaviors in multilayer epidemic networks[J]. Mathematical Biosciences and Engineering, 2015, 12(3): 609-623. doi: 10.3934/mbe.2015.12.609

    Related Papers:

  • Launching a prevention campaign to contain the spread of infection requires substantial financial investments; therefore, a trade-off exists between suppressing the epidemic and containing costs. Information exchange among individuals can occur as physical contacts (e.g., word of mouth, gatherings), which provide inherent possibilities of disease transmission, and non-physical contacts (e.g., email, social networks), through which information can be transmitted but the infection cannot be transmitted. Contact network (CN) incorporates physical contacts, and the information dissemination network (IDN) represents non-physical contacts, thereby generating a multilayer network structure. Inherent differences between these two layers cause alerting through CN to be more effective but more expensive than IDN. The constraint for an epidemic to die out derived from a nonlinear Perron-Frobenius problem that was transformed into a semi-definite matrix inequality and served as a constraint for a convex optimization problem. This method guarantees a dying-out epidemic by choosing the best nodes for adopting preventive behaviors with minimum monetary resources. Various numerical simulations with network models and a real-world social network validate our method.


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    [1] Cambridge University Press, 1994.
    [2] Science, 286 (1999), 509-512.
    [3] in Physics Reports, 544 (2014), 1-122.
    [4] Cambridge University Press, 2004.
    [5] ACM Trans. Inf. Syst. Secur., 10 (2008), 1-26.
    [6] Naval Research Logistics, 9 (1962), 181-186.
    [7] Sci. Rep., 2012.
    [8] in 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC), (2011), 3008-3013.
    [9] in 51st Annual Conference on Decision and Control (CDC), IEEE, (2012), 1657-1662.
    [10] Physical Review, 85 (2012), 066109.
    [11] Springer Graduate Texts in Mathematics (GTM), 2012.
    [12] Physical Review, 81 (2010), 036118.
    [13] in INFOCOM 2005. 24th Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings IEEE,2 (2005), 1455-1466.
    [14] Physical review letters, 111 (2013), 128701.
    [15] Available from: http://cvxr.com/cvx.
    [16] SIAM journal on matrix analysis and applications, 13 (1992), 1094-1122.
    [17] Cambridge University Press, 2012.
    [18] Science, 110 (2007), 245-259.
    [19] Society for Industrial and Applied Mathematics, 1994.
    [20] Oxford University Press, 2003.
    [21] in Global Conference on Signal and Information Processing (GlobalSIP), IEEE, (2013), 851-854.
    [22] Physical Review, 86 (2012), 026106.
    [23] PloS one, 8 (2013), e59028.
    [24] Nonlinear Dyn, 69 (2012), 1097-1104.
    [25] Journal of Theoretical Biology, 264 (2010), 95-103.
    [26] Computing, 93 (2011), 147-169.
    [27] IEEE/ACM Transactions on Networking, 17 (2009), 1-14.
    [28] Physical Review, 86 (2012), 036103.
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