Suppressing epidemic spreading in multiplex networks with social-support

Although suppressing the spread of a disease is usually achieved by investing in public resources, in the real world only a small percentage of the population have access to government assistance when there is an outbreak, and most must rely on resources from family or friends. We study the dynamics of disease spreading in social-contact multiplex networks when the recovery of infected nodes depends on resources from healthy neighbors in the social layer. We investigate how degree heterogeneity affects the spreading dynamics. Using theoretical analysis and simulations we find that degree heterogeneity promotes disease spreading. The phase transition of the infected density is hybrid and increases smoothly from zero to a finite small value at the first invasion threshold and then suddenly jumps at the second invasion threshold. We also find a hysteresis loop in the transition of the infected density. We further investigate how an overlap in the edges between two layers affects the spreading dynamics. We find that when the amount of overlap is smaller than a critical value the phase transition is hybrid and there is a hysteresis loop, otherwise the phase transition is continuous and the hysteresis loop vanishes. In addition, the edge overlap allows an epidemic outbreak when the transmission rate is below the first invasion threshold, but suppresses any explosive transition when the transmission rate is above the first invasion threshold.

[1]  Sergio Gómez,et al.  On the dynamical interplay between awareness and epidemic spreading in multiplex networks , 2013, Physical review letters.

[2]  Ginestra Bianconi,et al.  Percolation in multiplex networks with overlap. , 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.

[3]  M. Newman,et al.  Network theory and SARS: predicting outbreak diversity , 2004, Journal of Theoretical Biology.

[4]  Margaret L Brandeau,et al.  Dynamic resource allocation for epidemic control in multiple populations. , 2002, IMA journal of mathematics applied in medicine and biology.

[5]  Elchanan Mossel,et al.  Spectral redemption in clustering sparse networks , 2013, Proceedings of the National Academy of Sciences.

[6]  J. Hammersley,et al.  Monte Carlo Methods , 1965 .

[7]  Matjaz Perc,et al.  The Matthew effect in empirical data , 2014, Journal of The Royal Society Interface.

[8]  C. Yang,et al.  Crossover phenomena of percolation transition in evolution networks with hybrid attachment , 2016, Chaos.

[9]  Krishna P. Gummadi,et al.  On the evolution of user interaction in Facebook , 2009, WOSN '09.

[10]  Mason A. Porter,et al.  Author Correction: The physics of spreading processes in multilayer networks , 2016, 1604.02021.

[11]  O. Sporns,et al.  The economy of brain network organization , 2012, Nature Reviews Neuroscience.

[12]  A A Stinnett,et al.  Mathematical programming for the efficient allocation of health care resources. , 1996, Journal of health economics.

[13]  J. A. Almendral,et al.  Explosive transitions in complex networks’ structure and dynamics: Percolation and synchronization , 2016, 1610.01361.

[14]  Cristopher Moore,et al.  A message-passing approach for recurrent-state epidemic models on networks , 2015, Physical review. E, Statistical, nonlinear, and soft matter physics.

[15]  M. Drummond,et al.  Health Care Technology: Effectiveness, Efficiency and Public Policy@@@Methods for the Economic Evaluation of Health Care Programmes , 1988 .

[16]  Brian Karrer,et al.  Message passing approach for general epidemic models. , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[17]  Ming Tang,et al.  Recovery rate affects the effective epidemic threshold with synchronous updating , 2016, Chaos.

[18]  Romualdo Pastor-Satorras,et al.  Epidemic thresholds of the Susceptible-Infected-Susceptible model on networks: A comparison of numerical and theoretical results , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[19]  J. Borge-Holthoefer,et al.  Discrete-time Markov chain approach to contact-based disease spreading in complex networks , 2009, 0907.1313.

[20]  Raissa M. D'Souza,et al.  Anomalous critical and supercritical phenomena in explosive percolation , 2015, Nature Physics.

[21]  D Helbing,et al.  Connectivity disruption sparks explosive epidemic spreading. , 2016, Physical review. E.

[22]  R. Pastor-Satorras,et al.  Generation of uncorrelated random scale-free networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[23]  N A M Araújo,et al.  Explosive Percolation Via Control of the Largest Cluster , 2010, Physical review letters.

[24]  H. J. Herrmann,et al.  Disease-induced resource constraints can trigger explosive epidemics , 2014, Scientific Reports.

[25]  L. D. Valdez,et al.  Failure-recovery model with competition between failures in complex networks: a dynamical approach , 2016, 1606.03494.

[26]  Alessandro Vespignani,et al.  Epidemic spreading in scale-free networks. , 2000, Physical review letters.

[27]  S. Syme,et al.  Social support and health. , 1986 .

[28]  Conrado J. Pérez Vicente,et al.  Diffusion dynamics on multiplex networks , 2012, Physical review letters.

[29]  J. Sachs,et al.  The economic burden of malaria. , 2001, The American journal of tropical medicine and hygiene.

[30]  Harold W. Gutch,et al.  Continuous Percolation with Discontinuities , 2012 .

[31]  T. Seeman.  Social ties and health: the benefits of social integration. , 1996, Annals of epidemiology.

[32]  M. Minkler,et al.  Social support and health. , 1982, Patient education newsletter.

[33]  R. Guimerà,et al.  The worldwide air transportation network: Anomalous centrality, community structure, and cities' global roles , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[34]  B. Squires.  Methods for the Economic Evaluation of Health Care Programmes. , 1987 .

[35]  M. Brandeau,et al.  Resource allocation for control of infectious diseases in multiple independent populations: beyond cost-effectiveness analysis. , 2003, Journal of health economics.

[36]  M. Peiris,et al.  Clinical features and rapid viral diagnosis of human disease associated with avian influenza A H5N1 virus , 1998, The Lancet.

[37]  Alessandro Vespignani,et al.  Cut-offs and finite size effects in scale-free networks , 2003, cond-mat/0311650.

[38]  W. Team.  Ebola Virus Disease in West Africa — The First 9 Months of the Epidemic and Forward Projections , 2014 .

[39]  Wei Wang,et al.  Unification of theoretical approaches for epidemic spreading on complex networks , 2016, Reports on progress in physics. Physical Society.

[40]  Marc Timme,et al.  Self-organized adaptation of a simple neural circuit enables complex robot behaviour , 2010, ArXiv.

[41]  Lada A. Adamic,et al.  Power-Law Distribution of the World Wide Web , 2000, Science.

[42]  Yi Guan,et al.  Fatal outcome of human influenza A (H5N1) is associated with high viral load and hypercytokinemia , 2006, Nature Medicine.

[43]  Piet Van Mieghem,et al.  Epidemic processes in complex networks , 2014, ArXiv.

[44]  J. Kirigia,et al.  Economic burden of cholera in the WHO African region , 2009 .

[45]  Cristopher Moore,et al.  A message-passing approach for threshold models of behavior in networks , 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.