Research article Special Issues

Immunization strategies in directed networks

  • Received: 12 March 2020 Accepted: 20 May 2020 Published: 28 May 2020
  • Many complex systems can be modeled as directed networks, which can be regarded as a generalization of undirected networks. In this paper, epidemic dynamics and immunization strategies in directed networks are studied. First, a Susceptible-Infected-Susceptible (SIS) model on a directed network is established employing the mean-field method, and its dynamics and epidemic threshold of the network are studied. Then based on the continuous degree technique, namely, considering the degree of a node as a continuous variable, we propose a method to calculate the epidemic threshold of the immunized network. Besides, some immunization strategies, including optimal immunization, random immunization, combined targeted immunization, and combined acquaintance immunization, and three special networks are considered. Finally, through numerical analysis, all immunization strategies are simulated and compared on different types of networks. We find that the nodes with the largest product of in-degree and out-degree are the most worthy of being immunized.

    Citation: Junbo Jia, Wei Shi, Pan Yang, Xinchu Fu. Immunization strategies in directed networks[J]. Mathematical Biosciences and Engineering, 2020, 17(4): 3925-3952. doi: 10.3934/mbe.2020218

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  • Many complex systems can be modeled as directed networks, which can be regarded as a generalization of undirected networks. In this paper, epidemic dynamics and immunization strategies in directed networks are studied. First, a Susceptible-Infected-Susceptible (SIS) model on a directed network is established employing the mean-field method, and its dynamics and epidemic threshold of the network are studied. Then based on the continuous degree technique, namely, considering the degree of a node as a continuous variable, we propose a method to calculate the epidemic threshold of the immunized network. Besides, some immunization strategies, including optimal immunization, random immunization, combined targeted immunization, and combined acquaintance immunization, and three special networks are considered. Finally, through numerical analysis, all immunization strategies are simulated and compared on different types of networks. We find that the nodes with the largest product of in-degree and out-degree are the most worthy of being immunized.



    Dear Editorial Board Members,

    It is my pleasure to share with you the year-end report for AIMS Environmental Science. The journal continues to improve its quality as indicated by steady increases in the number of manuscripts received and the number of articles published over the past three years (Figure 1). We have received 69 submissions with 28 published online. The most downloaded and cited papers are listed in Tables 1 and 2. The top read article received more than 11390 downloads.

    Figure 1.  Manuscript statistics.
    Table 1.  The top 10 articles with most pdf download: (By December 31th 2019).
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    Feasibility study of a solar photovoltaic water pumping system for rural Ethiopia 2021
    Biophilic architecture: a review of the rationale and outcomes 2016
    Low temperature selective catalytic reduction of NOx with NH3 over Mn-based catalyst: A review 1834
    Remote sensing of agricultural drought monitoring: A state of art review 1808
    Challenges and opportunities in municipal solid waste management in Mozambique: a review in the light of nexus thinking 1643
    Nitrate pollution of groundwater by pit latrines in developing countries 1524
    Assessment of repeated harvests on mercury and arsenic phytoextraction in a multi-contaminated industrial soil 1506
    Urban agriculture in the transition to low carbon cities through urban greening 1463
    A state-and-transition simulation modeling approach for estimating the historical range of variability 1438

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    Table 2.  The top 10 articles with most cited: (By December 31th 2019).
    Title Number
    Biophilic architecture: a review of the rationale and outcomes 21
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    The mechanism of kaolin clay flocculation by a cation-independent bioflocculant produced by Chryseobacterium daeguense W6 12
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    Catalytic hydrothermal liquefaction (HTL) of biomass for bio-crude production using Ni/HZSM-5 catalysts 11
    Influence of everyday activities and presence of people in common indoor environments on exposure to airborne fungi 10

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    I would like to thank all the board members for serving on the Editorial Board and their dedication and contribution to the journal, especially to the editors for two special issues: Impacts of Microplastics in the Urban Environment Conference and Green built environment. The goal in 2020 is to solicit more manuscripts and increase paper citations. We will try our best to reduce the processing time and supply with a better experience for publication. To recognize the contribution of the Editorial Board members and authors during the years, we will continue to offer that (1) for authors invited, the article processing charge (APC) is automatically waived; (2) each editorial board member is entitled for some waivers. I am looking forward to continuing working with you to make the AIMS Environmental Science a sustainable and impactful journal. Please don’t hesitate to send me e-mails if you have new ideas and suggestions to help us to achieve this goal.

    Yifeng Wang, Ph.D.

    Editor in Chief, AIMS Environmental Science



    [1] M. Newman, Networks, Oxford University Press, 2018.
    [2] M. Faloutsos, P. Faloutsos, C. Faloutsos, On power-law relationships of the internet topology, Comput. Commun. Rev., 29 (1999), 251-262. doi: 10.1145/316194.316229
    [3] G. Kossinets, D. J. Watts, Empirical analysis of an evolving social network, Science, 311 (1999), 88-90.
    [4] D. J. Watts, S. H. Strogatz, Collective dynamics of small-world networks, Nature, 393 (1998), 440-442. doi: 10.1038/30918
    [5] J. Scott, Social network analysis, Sociology, 22 (1988), 109-127. doi: 10.1177/0038038588022001007
    [6] E. H. Davidson, D. H. Erwin, Gene regulatory networks and the evolution of animal body plans, Science, 311 (2006), 796-800. doi: 10.1126/science.1113832
    [7] P. Erdős, A. Rényi, On the evolution of random graphs, Publ. Math. Inst. Hung. Acad. Sci., 5 (1960), 17-60.
    [8] A. L. Barabási, R. Albert, Emergence of scaling in random networks, Science, 286 (1999), 509-512. doi: 10.1126/science.286.5439.509
    [9] M. E. J. Newman, M. Girvan, Finding and evaluating community structure in networks, Phys. Rev. E, 69 (2004), 026113. doi: 10.1103/PhysRevE.69.026113
    [10] A. Clauset, M. E. J. Newman, C. Moore, Finding community structure in very large networks, Phys. Rev. E, 70 (2004), 066111. doi: 10.1103/PhysRevE.70.066111
    [11] S. Fortunato, Community detection in graphs, Phys. Rep., 486 (2010), 75-174. doi: 10.1016/j.physrep.2009.11.002
    [12] H. Cherifi, G. Palla, B. K. Szymanski, X. Lu, On community structure in complex networks: Challenges and opportunities, Appl. Network Sci., 4 (2019), 1-35. doi: 10.1007/s41109-018-0108-x
    [13] A. Clauset, C. Moore, M. E. J. Newman, Hierarchical structure and the prediction of missing links in networks, Nature, 453 (2008), 98-101. doi: 10.1038/nature06830
    [14] R. Pastor-Satorras, A. Vespignani, Epidemic spreading in scale-free networks, Phys. Rev. Lett., 86 (2001), 3200. doi: 10.1103/PhysRevLett.86.3200
    [15] X. Fu, M. Small, G. Chen, Propagation Dynamics on Complex Networks: Models, Methods and Stability Analysis, John Wiley and Sons, 2013.
    [16] M. Jalili, M. Perc, Information cascades in complex networks, J. Complex Networks, 5 (2017), 665-693.
    [17] M. Nadini, K. Sun, E. Ubaldi, M. Starnini, A. Rizzo, N. Perra, Epidemic spreading in modular time-varying networks, Sci. Rep., 8 (2018), 1-11.
    [18] J. Jia, Z. Jin, X. Fu, Epidemic spread in directed interconnected networks, Commun. Nonlinear Sci. Numeri. Simul., 75 (2019), 1-13. doi: 10.1016/j.cnsns.2019.03.025
    [19] C. Pizzuti, A. Socievole, Epidemic spreading curing strategy over directed networks, in International Conference on Numerical Computations: Theory and Algorithms, Springer, Cham, (2020), 182-194.
    [20] L. Meyers, M. Newman, B. Pourbohloul, Predicting epidemics on directed contact networks, J. Theor. Biol., 240 (2006), 400-418. doi: 10.1016/j.jtbi.2005.10.004
    [21] X. Zhang, G. Sun, Y. Zhu, J. Ma, Z. Jin, Epidemic dynamics on semi-directed complex networks, Math. Biosci., 246 (2013), 242-251. doi: 10.1016/j.mbs.2013.10.001
    [22] M. Moslonka-Lefebvre, T. Harwood, M. J. Jeger, M. Pautasso, SIS along a continuum (SISc) epidemiological modelling and control of diseases on directed trade networks, Math. Biosci., 236 (2012), 44-52. doi: 10.1016/j.mbs.2012.01.004
    [23] D. H. Zanette, M. Kuperman, Effects of immunization in small-world epidemics, Phys. A: Stat. Mech. Appl., 309 (2002), 445-452. doi: 10.1016/S0378-4371(02)00618-0
    [24] R. Pastor-Satorras, A. Vespignani, Immunization of complex networks, Phys. Rev. E, 65 (2002), 036104. doi: 10.1103/PhysRevE.65.036104
    [25] N. Madar, T. Kalisky, R. Cohen, D. Ben-avraham, S. Havlin, Immunization and epidemic dynamics in complex networks, Eur. Phys. J. B, 38 (2004), 269-276. doi: 10.1140/epjb/e2004-00119-8
    [26] R. Cohen, S. Havlin, D. Ben-Avraham, Efficient immunization strategies for computer networks and populations, Phys. Rev. Lett., 91 (2003), 247901. doi: 10.1103/PhysRevLett.91.247901
    [27] D. Chakraborty, A. Singh, H. Cherifi, Immunization strategies based on the overlapping nodes in networks with community structure, in International Conference on Computational Social Networks, Springer, Cham, (2016), 62-73.
    [28] X. Fu, M. Small, D. M. Walker, H. Zhang, Epidemic dynamics on scale-free networks with piecewise linear infectivity and immunization, Phys. Rev. E, 77 (2008), 036113. doi: 10.1103/PhysRevE.77.036113
    [29] Y. Yang, A. McKhann, S. Chen, G. Harling, J. P. Onnela, Efficient vaccination strategies for epidemic control using network information, Epidemics, 27 (2019), 115-122. doi: 10.1016/j.epidem.2019.03.002
    [30] J. Jia, Z. Jin, L. Chang, X. Fu, Structural calculations and propagation modeling of growing networks based on continuous degree, Math. Biosci. Eng., 14 (2017), 1215-1232. doi: 10.3934/mbe.2017062
    [31] J. Joo, J. L. Lebowitz, Behavior of susceptible-infected-susceptible epidemics on heterogeneous networks with saturation, Phys. Rev. E, 69 (2004), 066105. doi: 10.1103/PhysRevE.69.066105
    [32] T. Zhou, J. Liu, W. Bai, G. Chen, B. Wang, Behaviors of susceptible-infected epidemics on scalefree networks with identical infectivity, Phys. Rev. E, 74 (2006), 056109. doi: 10.1103/PhysRevE.74.056109
    [33] C. Nowzari, V. M. Preciado, G. J. Pappas, Analysis and control of epidemics: A survey of spreading processes on complex networks, IEEE Control Syst. Mag., 36 (2016), 26-46.
    [34] R. Noldus, P. Van Mieghem, Assortativity in complex networks, J. Complex Networks, 3 (2015), 507-542.
    [35] N. Gupta, A. Singh, H. Cherifi, Community-based immunization strategies for epidemic control, in 2015 7th International Conference on Communication Systems and Networks (COMSNETS), IEEE, 2015.
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