Coupled effects of local movement and global interaction on contagion

Abstract By incorporating segregated spatial domain and individual-based linkage into the SIS (susceptible–infected–susceptible) model, we propose a generalized epidemic model which can change from the territorial epidemic model to the networked epidemic model. The role of the individual-based linkage between different spatial domains is investigated. As we adjust the timescale parameter τ from 0 to unity, which represents the degree of activation of the individual-based linkage, three regions are found. Within the region of 0 < τ < 0.02 , the epidemic is determined by local movement and is sensitive to the timescale τ . Within the region of 0.02 < τ < 0.5 , the epidemic is insensitive to the timescale τ . Within the region of 0.5 < τ < 1 , the outbreak of the epidemic is determined by the structure of the individual-based linkage. As we keep an eye on the first region, the role of activating the individual-based linkage in the present model is similar to the role of the shortcuts in the two-dimensional small world network. Only activating a small number of the individual-based linkage can prompt the outbreak of the epidemic globally. The role of narrowing segregated spatial domain and reducing mobility in epidemic control is checked. These two measures are found to be conducive to curbing the spread of infectious disease only when the global interaction is suppressed. A log–log relation between the change in the number of infected individuals and the timescale τ is found. By calculating the epidemic threshold and the mean first encounter time, we heuristically analyze the microscopic characteristics of the propagation of the epidemic in the present model.

[1]  N. Gupte,et al.  Heat flux distribution and rectification of complex networks , 2010 .

[2]  N. Johnson,et al.  Equivalent dynamical complexity in a many-body quantum and collective human system , 2010, 1011.6398.

[3]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[4]  Kazuyuki Aihara,et al.  Safety-Information-Driven Human Mobility Patterns with Metapopulation Epidemic Dynamics , 2012, Scientific Reports.

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

[6]  Tao Zhou,et al.  Diversity of individual mobility patterns and emergence of aggregated scaling laws , 2012, Scientific Reports.

[7]  V. Colizza,et al.  Metapopulation epidemic models with heterogeneous mixing and travel behaviour , 2014, Theoretical Biology and Medical Modelling.

[8]  V. M. Kenkre,et al.  Theory of home range estimation from displacement measurements of animal populations. , 2006, Journal of theoretical biology.

[9]  Bambi Hu,et al.  Epidemic spreading in community networks , 2005 .

[10]  Shlomo Havlin,et al.  Conditions for viral influence spreading through multiplex correlated social networks , 2014, 1404.3114.

[11]  Alessandro Vespignani,et al.  Epidemic modeling in metapopulation systems with heterogeneous coupling pattern: theory and simulations. , 2007, Journal of theoretical biology.

[12]  M. Newman,et al.  Scaling and percolation in the small-world network model. , 1999, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[13]  M. Serrano,et al.  Percolation and epidemic thresholds in clustered networks. , 2006, Physical review letters.

[14]  Reuven Cohen,et al.  Limited path percolation in complex networks. , 2007, Physical review letters.

[15]  V. M. Kenkre,et al.  Theory of the Transmission of Infection in the Spread of Epidemics: Interacting Random Walkers with and Without Confinement , 2014, Bulletin of mathematical biology.

[16]  George E. Tita,et al.  Human group formation in online guilds and offline gangs driven by a common team dynamic. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[17]  Xiaoming Xu,et al.  Percolation of a general network of networks. , 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.

[18]  Laxmidhar Behera,et al.  Optimal migration promotes the outbreak of cooperation in heterogeneous populations , 2012, ArXiv.

[19]  L. A. Braunstein,et al.  Epidemic Model with Isolation in Multilayer Networks , 2014, Scientific Reports.

[20]  Chuang Liu,et al.  Strong ties promote the epidemic prevalence in susceptible-infected-susceptible spreading dynamics , 2013, ArXiv.

[21]  Ingve Simonsen,et al.  Effects of City-Size Heterogeneity on Epidemic Spreading in a Metapopulation: A Reaction-Diffusion Approach , 2013 .

[22]  T. Hwa,et al.  Stripe formation in bacterial systems with density-suppressed motility. , 2012, Physical review letters.

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

[24]  Alessandro Vespignani,et al.  Invasion threshold in heterogeneous metapopulation networks. , 2007, Physical review letters.

[25]  Harry Eugene Stanley,et al.  Epidemics on Interconnected Networks , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[26]  M. Newman,et al.  Percolation and epidemics in a two-dimensional small world. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[27]  Zonghua Liu Effect of mobility in partially occupied complex networks. , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[28]  Zonghua Liu,et al.  Influence of dynamical condensation on epidemic spreading in scale-free networks. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[29]  Tian Qiu,et al.  Time scales of epidemic spread and risk perception on adaptive networks , 2010, 1011.1621.

[30]  Francesco De Pellegrini,et al.  Epidemic Outbreaks in Networks with Equitable or Almost-Equitable Partitions , 2014, SIAM J. Appl. Math..

[31]  T. Geisel,et al.  Natural human mobility patterns and spatial spread of infectious diseases , 2011, 1103.6224.

[32]  Frank Schweitzer,et al.  Coexistence of Social Norms Based on in- and Out-Group Interactions , 2007, Adv. Complex Syst..

[33]  Güler Ergün,et al.  Coupled Growing Networks , 2003, Adv. Complex Syst..

[34]  Pascal Crépey,et al.  Epidemic variability in complex networks. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[35]  Duanbing Chen,et al.  The small world yields the most effective information spreading , 2011, ArXiv.

[36]  Kerstin Sailer,et al.  Modeling workplace contact networks: The effects of organizational structure, architecture, and reporting errors on epidemic predictions , 2015, Network Science.

[37]  Matjaz Perc,et al.  Success-Driven Distribution of Public Goods Promotes Cooperation but Preserves Defection , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[38]  M. Newman,et al.  Epidemics and percolation in small-world networks. , 1999, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[39]  Attila Szolnoki,et al.  Correlation of positive and negative reciprocity fails to confer an evolutionary advantage: Phase transitions to elementary strategies , 2013, ArXiv.

[40]  H. Stanley,et al.  Robustness of a partially interdependent network formed of clustered networks. , 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.

[41]  Attila Szolnoki,et al.  Coevolutionary Games - A Mini Review , 2009, Biosyst..

[42]  Gerardo Chowell,et al.  The Western Africa Ebola Virus Disease Epidemic Exhibits Both Global Exponential and Local Polynomial Growth Rates , 2014, PLoS currents.

[43]  Pak Ming Hui,et al.  Separatrices between healthy and endemic states in an adaptive epidemic model , 2011 .

[44]  Tao Zhou,et al.  Epidemic Spreading in Weighted Networks: An Edge-Based Mean-Field Solution , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[45]  Jonathan R. Potts,et al.  Animal Interactions and the Emergence of Territoriality , 2011, PLoS Comput. Biol..

[46]  M. Kuperman,et al.  Small world effect in an epidemiological model. , 2000, Physical review letters.

[47]  Attila Szolnoki,et al.  Cyclic dominance in evolutionary games: a review , 2014, Journal of The Royal Society Interface.

[48]  S. Havlin,et al.  Scale-free networks are ultrasmall. , 2002, Physical review letters.

[49]  Romualdo Pastor-Satorras,et al.  Nature of the epidemic threshold for the susceptible-infected-susceptible dynamics in networks. , 2013, Physical review letters.

[50]  Hyunsuk Hong,et al.  Finite-size scaling of synchronized oscillation on complex networks. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[51]  Claudio J. Tessone,et al.  How can Social Herding Enhance Cooperation? , 2012, Adv. Complex Syst..

[52]  Ming Tang,et al.  An Efficient Immunization Strategy for Community Networks , 2013, PloS one.

[53]  Fabrice Rossi,et al.  A statistical network analysis of the HIV/AIDS epidemics in Cuba , 2014, Social Network Analysis and Mining.

[54]  Alan M. Frieze,et al.  Random graphs , 2006, SODA '06.

[55]  Harry Eugene Stanley,et al.  Catastrophic cascade of failures in interdependent networks , 2009, Nature.

[56]  P. V. Mieghem,et al.  Non-Markovian Infection Spread Dramatically Alters the Susceptible-Infected-Susceptible Epidemic Threshold in Networks , 2013 .

[57]  Esteban Moro,et al.  Impact of human activity patterns on the dynamics of information diffusion. , 2009, Physical review letters.

[58]  Steve Gregory,et al.  Efficient local behavioral change strategies to reduce the spread of epidemics in networks , 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.

[59]  H. Stanley,et al.  Effect of the interconnected network structure on the epidemic threshold. , 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.

[60]  Matjaz Perc,et al.  Collective behavior and evolutionary games - An introduction , 2013, 1306.2296.

[61]  Zonghua Liu,et al.  How community structure influences epidemic spread in social networks , 2008 .

[62]  Luca Giuggioli,et al.  Encounter times in overlapping domains: application to epidemic spread in a population of territorial animals. , 2012, Physical review letters.

[63]  EFFECTS OF AGING AND LINKS REMOVAL ON EPIDEMIC DYNAMICS IN SCALE-FREE NETWORKS , 2004, cond-mat/0402063.

[64]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[65]  Marián Boguñá,et al.  Epidemic spreading on interconnected networks , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[66]  H. Stanley,et al.  Networks formed from interdependent networks , 2011, Nature Physics.

[67]  Ming Tang,et al.  Epidemic spreading by objective traveling , 2009 .

[68]  Riccardo Zecchina,et al.  Bayesian inference of epidemics on networks via Belief Propagation , 2013, Physical review letters.

[69]  Attila Szolnoki,et al.  Self-organization towards optimally interdependent networks by means of coevolution , 2014, ArXiv.

[70]  Yoshiharu Maeno,et al.  Detecting a trend change in cross-border epidemic transmission , 2013, 1307.5300.

[71]  V. Colizza,et al.  Human mobility and time spent at destination: impact on spatial epidemic spreading. , 2013, Journal of theoretical biology.

[72]  Attila Szolnoki,et al.  Evolutionary dynamics of group interactions on structured populations: a review , 2013, Journal of The Royal Society Interface.