×

Effects of heterogeneous self-protection awareness on resource-epidemic coevolution dynamics. (English) Zbl 1508.92244

Summary: Recent studies have demonstrated that the allocation of individual resources has a significant influence on the dynamics of epidemic spreading. In the real scenario, individuals have a different level of awareness for self-protection when facing the outbreak of an epidemic. To investigate the effects of the heterogeneous self-awareness distribution on the epidemic dynamics, we propose a resource-epidemic coevolution model in this paper. We first study the effects of the heterogeneous distributions of node degree and self-awareness on the epidemic dynamics on artificial networks. Through extensive simulations, we find that the heterogeneity of self-awareness distribution suppresses the outbreak of an epidemic, and the heterogeneity of degree distribution enhances the epidemic spreading. Next, we study how the correlation between node degree and self-awareness affects the epidemic dynamics. The results reveal that when the correlation is positive, the heterogeneity of self-awareness restrains the epidemic spreading. While, when there is a significant negative correlation, strong heterogeneous or strong homogeneous distribution of the self-awareness is not conducive for disease suppression. We find an optimal heterogeneity of self-awareness, at which the disease can be suppressed to the most extent. Further research shows that the epidemic threshold increases monotonously when the correlation changes from most negative to most positive, and a critical value of the correlation coefficient is found. When the coefficient is below the critical value, an optimal heterogeneity of self-awareness exists; otherwise, the epidemic threshold decreases monotonously with the decline of the self-awareness heterogeneity. At last, we verify the results on four typical real-world networks and find that the results on the real-world networks are consistent with those on the artificial network.

MSC:

92D30 Epidemiology
62P10 Applications of statistics to biology and medical sciences; meta analysis

Software:

plfit

References:

[1] Zingg, W.; Colombo, C.; Jucker, T.; Bossart, W.; Ruef, C., Impact of an outbreak of norovirus infection on hospital resources, Infection Control & Hospital Epidemiology, 26, 3, 263-267 (2005)
[2] Tsai, M.-T.; Ya-Ti, H., A resource-based perspective on retention strategies for nurse epidemiologists, J. Adv. Nurs., 61, 2, 188-200 (2008)
[3] Syed, A. M.; Hjarnoe, L.; Krumkamp, R.; Reintjes, R.; Aro, A. R., Developing policy options for sars and sars-like diseases-a delphi study, Glob Public Health, 5, 6, 663-675 (2010)
[4] Gostin, L. O.; Friedman, E. A., A retrospective and prospective analysis of the west african ebola virus disease epidemic: robust national health systems at the foundation and an empowered who at the apex, The Lancet, 385, 9980, 1902-1909 (2015)
[5] https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports
[6] WHO, Shortage of personal protective equipment endangering health workers worldwide, 2020b, https://www.who.int/news-room/detail/03-03-2020-shortage-of-personal-protective-equipment-endangering-health-workers-worldwide.
[7] Preciado, V. M.; Zargham, M.; Enyioha, C.; Jadbabaie, A.; Pappas, G., Optimal vaccine allocation to control epidemic outbreaks in arbitrary networks, 52nd IEEE Conference on Decision and Control, 7486-7491 (2013), IEEE
[8] Lokhov, A. Y.; Saad, D., Optimal deployment of resources for maximizing impact in spreading processes, Proceedings of the National Academy of Sciences, 114, 39, E8138-E8146 (2017)
[9] Li, S.; Zhao, D.; Wu, X.; Tian, Z.; Li, A.; Wang, Z., Functional immunization of networks based on message passing, Appl. Math. Comput., 366, 124728 (2020) · Zbl 1433.91115
[10] Zhang, H.; Yang, Z.; Pawelek, K. A.; Liu, S., Optimal control strategies for a two-group epidemic model with vaccination-resource constraints, Appl. Math. Comput., 371, 124956 (2020) · Zbl 1433.34061
[11] Zhang, H.; Wang, Z., Suppressing epidemic spreading by imitating hub nodes strategy, IEEE Transactions on Circuits and Systems II: Express Briefs, 1 (2019)
[12] Guo, Z.; Sun, G.; Wang, Z.; Jin, Z.; Li, L.; Li, C., Spatial dynamics of an epidemic model with nonlocal infection, Appl Math Comput, 377, 125158 (2020) · Zbl 1508.92256
[13] Watkins, N. J.; Nowzari, C.; Preciado, V. M.; Pappas, G. J., Optimal resource allocation for competitive spreading processes on bilayer networks, IEEE Trans. Control Network Syst., 5, 1, 298-307 (2016) · Zbl 1507.91162
[14] Nowzari, C.; Preciado, V. M.; Pappas, G. J., Optimal resource allocation for control of networked epidemic models, IEEE Trans. Control Network Syst., 4, 2, 159-169 (2015) · Zbl 1370.90138
[15] Chen, H.; Li, G.; Zhang, H.; Hou, Z., Optimal allocation of resources for suppressing epidemic spreading on networks, Physical Review E, 96, 1, 012321 (2017)
[16] Wu, Z.; Googan, J. M.M., Characteristics of and important lessons from the coronavirus disease 2019 (covid-19) outbreak in china: summary of a report of 72 314 cases from the chinese center for disease control and prevention, JAMA, 1, 711-911 (2020)
[17] Böttcher, L.; Woolley-Meza, O.; Araújo, N. A.; Herrmann, H. J.; Helbing, D., Disease-induced resource constraints can trigger explosive epidemics, Sci. Rep., 5, 16571 (2015)
[18] Chen, X.; Wang, R.; Tang, M.; Cai, S.; Stanley, H. E.; Braunstein, L. A., Suppressing epidemic spreading in multiplex networks with social-support, New J. Phys., 20, 1, 13007 (2018) · Zbl 1540.92183
[19] Chen, X.; Wang, W.; Cai, S.; Stanley, H. E.; Braunstein, L. A., Optimal resource diffusion for suppressing disease spreading in multiplex networks, J. Stat. Mech: Theory Exp., 2018, 5, 53501 (2018) · Zbl 1459.92119
[20] Zhan, X.; Liu, C.; Zhou, G.; Zhang, Z.; Sun, G.; Zhu, J. J.; Jin, Z., Coupling dynamics of epidemic spreading and information diffusion on complex networks, Appl. Math. Comput., 332, 437-448 (2018) · Zbl 1427.92097
[21] Kabir, K. A.; Kuga, K.; Tanimoto, J., Analysis of sir epidemic model with information spreading of awareness, Chaos, Solitons & Fractals, 119, 118-125 (2019) · Zbl 1448.92302
[22] Funk, S.; Gilad, E.; Jansen, V., Endemic disease, awareness, and local behavioural response, J. Theor. Biol., 264, 2, 501-509 (2010) · Zbl 1406.92567
[23] Wang, Z.; Andrews, M. A.; Wu, Z.-X.; Wang, L.; Bauch, C. T., Coupled disease-behavior dynamics on complex networks: a review, Phys Life Rev, 15, 1-29 (2015)
[24] Kabir, K. A.; Kuga, K.; Tanimoto, J., Effect of information spreading to suppress the disease contagion on the epidemic vaccination game, Chaos, Solitons & Fractals, 119, 180-187 (2019) · Zbl 1448.92272
[25] Kabir, K. A.; Kuga, K.; Tanimoto, J., The impact of information spreading on epidemic vaccination game dynamics in a heterogeneous complex network-a theoretical approach, Chaos, Solitons & Fractals, 132, 109548 (2020) · Zbl 1434.92033
[26] Granell, C.; Gómez, S.; Arenas, A., Dynamical interplay between awareness and epidemic spreading in multiplex networks, Phys. Rev. Lett., 111, 12, 128701 (2013)
[27] Funk, S.; Gilad, E.; Watkins, C.; Jansen, V. A., The spread of awareness and its impact on epidemic outbreaks, Proceedings of the National Academy of Sciences, 106, 16, 6872-6877 (2009) · Zbl 1203.91242
[28] Kabir, K. A.; Tanimoto, J., Analysis of epidemic outbreaks in two-layer networks with different structures for information spreading and disease diffusion, Commun. Nonlinear Sci. Numer. Simul., 72, 565-574 (2019) · Zbl 1464.92245
[29] Kabir, K. A.; Tanimotoc, J., Impact of awareness in metapopulation epidemic model to suppress the infected individuals for different graphs, The European Physical Journal B, 92, 9, 199 (2019) · Zbl 1516.92101
[30] Kan, J.; Zhang, H., Effects of awareness diffusion and self-initiated awareness behavior on epidemic spreading-an approach based on multiplex networks, Commun. Nonlinear Sci. Numer. Simul., 44, 193-203 (2017) · Zbl 1466.92189
[31] Pastor-Satorras, R.; Vespignani, A., Epidemic dynamics in finite size scale-free networks, Physical Review E, 65, 3, 035108 (2002)
[32] Barthélemy, M.; Barrat, A.; Pastor-Satorras, R.; Vespignani, A., Velocity and hierarchical spread of epidemic outbreaks in scale-free networks, Phys. Rev. Lett., 92, 17, 178701 (2004)
[33] Peng, R.; Zhao, X., A reaction-diffusion sis epidemic model in a time-periodic environment, Nonlinearity, 25, 5, 1451 (2012) · Zbl 1250.35172
[34] Kulik, J. A.; Mahler, H. I., Social support and recovery from surgery, Health Psychology, 8, 2, 221 (1989)
[35] Nausheen, B.; Gidron, Y.; Peveler, R.; Moss-Morris, R., Social support and cancer progression: a systematic review, J. Psychosom. Res., 67, 5, 403-415 (2009)
[36] Chen, X.; Cai, S.; Tang, M.; Wang, W.; Zhou, T.; Hui, P. M., Controlling epidemic outbreak based on local dynamic infectiousness on complex networks, Chaos: An Interdisciplinary Journal of Nonlinear Science, 28, 12, 123105 (2018) · Zbl 1404.92115
[37] Mackie, A. S.; Pilote, L.; Ionescu-Ittu, R.; Rahme, E.; Marelli, A. J., Health care resource utilization in adults with congenital heart disease, Am. J. Cardiol., 99, 6, 839-843 (2007)
[38] Sharkey, K. J., Deterministic epidemic models on contact networks: correlations and unbiological terms, Theor. Popul. Biol., 79, 4, 115-129 (2011) · Zbl 1403.92313
[39] Pastor-Satorras, R.; Castellano, C.; Mieghem, P. V.; Vespignani, A., Epidemic processes in complex networks, Rev. Mod Phys., 87, 3, 925 (2015)
[40] Funk, S.; Salathé, M.; Jansen, V. A., Modelling the influence of human behaviour on the spread of infectious diseases: a review, Journal of the Royal Society Interface, 7, 50, 1247-1256 (2010)
[41] Wang, W.; Tang, M.; Zhang, H.; Lai, Y., Dynamics of social contagions with memory of nonredundant information, Physical Review E, 92, 1, 012820 (2015)
[42] Wang, W.; Tang, M.; Stanley, H. E.; Braunstein, L. A., Unification of theoretical approaches for epidemic spreading on complex networks, Rep. Prog. Phys., 80, 3, 36603 (2017)
[43] Kuga, K.; Tanimoto, J., Impact of imperfect vaccination and defense against contagion on vaccination behavior in complex networks, J. Stat. Mech: Theory Exp., 2018, 11, 113402 (2018) · Zbl 1456.92082
[44] Schönfisch, B.; de Roos, A., Synchronous and asynchronous updating in cellular automata, BioSystems, 51, 3, 123-143 (1999)
[45] Fennell, P. G.; Melnik, S.; Gleeson, J. P., Limitations of discrete-time approaches to continuous-time contagion dynamics, Physical Review E, 94, 5, 052125 (2016)
[46] Cai, S.; Chen, X.; Ye, X.; Tang, M., Precisely identifying the epidemic thresholds in real networks via asynchronous updating, Appl. Math. Comput., 361, 377-388 (2019) · Zbl 1428.92103
[47] Molloy, M.; Reed, B., A critical point for random graphs with a given degree sequence, Random Structures & Algorithms, 6, 2-3, 161-180 (1995) · Zbl 0823.05050
[48] Catanzaro, M.; Boguñá, M.; Pastor-Satorras, R., Generation of uncorrelated random scale-free networks, Physical Review E, 71, 2, 027103 (2005)
[49] Boguná, M.; Pastor-Satorras, R.; Vespignani, A., Cut-offs and finite size effects in scale-free networks, The European Physical Journal B-Condensed Matter and Complex Systems, 38, 2, 205-209 (2004)
[50] Clauset, A.; Shalizi, C. R.; Newman, M. E., Power-law distributions in empirical data, SIAM Rev., 51, 4, 661-703 (2009) · Zbl 1176.62001
[51] Ferreira, S. C.; Castellano, C.; Pastor-Satorras, R., Epidemic thresholds of the susceptible-infected-susceptible model on networks: a comparison of numerical and theoretical results, Physical Review E, 86, 4, 41125 (2012)
[52] Pastor-Satorras, R.; Vespignani, A., Epidemic spreading in scale-free networks, Phys. Rev. Lett., 86, 14, 3200 (2001)
[53] Opsahl, T.; Agneessens, F.; Skvoretz, J., Node centrality in weighted networks: generalizing degree and shortest paths, Soc. Networks, 3, 32, 245-251 (2010)
[54] O.n. dataset, 2016, http://konect.uni-koblenz.de/networks/opsahl-openflights.
[55] Šubelj, L.; Bajec, M., Robust network community detection using balanced propagation, The European Physical Journal B, 81, 3, 353-362 (2011)
[56] Isella, L.; Stehlé, J.; Barrat, A.; Cattuto, C.; Pinton, J.-F.; den, W. V., Broeck, what’s in a crowd? analysis of face-to-face behavioral networks, J. Theor. Biol., 271, 1, 166-180 (2011) · Zbl 1405.92255
[57] I.n. dataset, 2016, http://konect.uni-koblenz.de/networks/sociopatterns-infectious/.
[58] Leskovec, J.; Mcauley, J. J., Learning to discover social circles in ego networks, Advances in Neural Information Processing Systems, 539-547 (2012)
[59] Arefin, M. R.; Masaki, T.; Kabir, K. A.; Tanimoto, J., Interplay between cost and effectiveness in influenza vaccine uptake: a vaccination game approach, Proceedings of the Royal Society A, 475, 2232, 20190608 (2019) · Zbl 1472.92196
[60] Kabir, K. A.; Jusup, M.; Tanimoto, J., Behavioral incentives in a vaccination-dilemma setting with optional treatment, Physical Review E, 100, 6, 062402 (2019)
[61] Alam, M.; Tanaka, M.; Tanimoto, J., A game theoretic approach to discuss the positive secondary effect of vaccination scheme in an infinite and well-mixed population, Chaos, Solitons & Fractals, 125, 201-213 (2019) · Zbl 1448.92268
[62] Tanimoto, J., Fundamentals of Evolutionary Game Theory and its Applications (2015), Springer · Zbl 1326.91001
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.