Time evolution of epidemic disease on finite and infinite networks.

Mathematical models of infectious diseases, which are in principle analytically tractable, use two general approaches. The first approach, generally known as compartmental modeling, addresses the time evolution of disease propagation at the expense of simplifying the pattern of transmission. The second approach uses network theory to incorporate detailed information pertaining to the underlying contact structure among individuals while disregarding the progression of time during outbreaks. So far, the only alternative that enables the integration of both aspects of disease propagation simultaneously while preserving the variety of outcomes has been to abandon the analytical approach and rely on computer simulations. We offer an analytical framework, that incorporates both the complexity of contact network structure and the time progression of disease spread. Furthermore, we demonstrate that this framework is equally effective on finite- and "infinite"-size networks. This formalism can be equally applied to similar percolation phenomena on networks in other areas of science and technology.

[1]  Aravind Srinivasan,et al.  Modelling disease outbreaks in realistic urban social networks , 2004, Nature.

[2]  E. Volz SIR dynamics in random networks with heterogeneous connectivity , 2007, Journal of mathematical biology.

[3]  O. Diekmann Mathematical Epidemiology of Infectious Diseases , 1996 .

[4]  Alessandro Vespignani,et al.  Immunization of complex networks. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[5]  M. Newman,et al.  Random graphs with arbitrary degree distributions and their applications. , 2000, Physical review. E, Statistical, nonlinear, and soft matter physics.

[6]  Albert-László Barabási,et al.  Statistical mechanics of complex networks , 2001, ArXiv.

[7]  Lauren Ancel Meyers,et al.  SIR epidemics in dynamic contact networks , 2007, 0705.2105.

[8]  Albert-László Barabási,et al.  Evolution of Networks: From Biological Nets to the Internet and WWW , 2004 .

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

[10]  S. Redner,et al.  Introduction To Percolation Theory , 2018 .

[11]  A. Roddam Mathematical Epidemiology of Infectious Diseases: Model Building, Analysis and Interpretation O Diekmann and JAP Heesterbeek, 2000, Chichester: John Wiley pp. 303, £39.95. ISBN 0-471-49241-8 , 2001 .

[12]  M Marder Dynamics of epidemics on random networks. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[13]  M. Newman Spread of epidemic disease on networks. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

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

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

[16]  Alessandro Vespignani,et al.  Efficiency and reliability of epidemic data dissemination in complex networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[17]  R. Rosenfeld Nature , 2009, Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery.

[18]  Alessandro Vespignani,et al.  Evolution and structure of the Internet , 2004 .

[19]  Carson C. Chow,et al.  Small Worlds , 2000 .

[20]  V. Latora,et al.  Complex networks: Structure and dynamics , 2006 .

[21]  P. Kaye Infectious diseases of humans: Dynamics and control , 1993 .

[22]  Sergey N. Dorogovtsev,et al.  Evolution of Networks: From Biological Nets to the Internet and WWW (Physics) , 2003 .

[23]  K. Eames,et al.  Modelling disease spread through random and regular contacts in clustered populations. , 2008, Theoretical population biology.

[24]  M. Newman Properties of highly clustered networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[25]  Mark E. J. Newman,et al.  The Structure and Function of Complex Networks , 2003, SIAM Rev..

[26]  Herbert W. Hethcote,et al.  The Mathematics of Infectious Diseases , 2000, SIAM Rev..

[27]  Y. Moreno,et al.  Epidemic outbreaks in complex heterogeneous networks , 2001, cond-mat/0107267.

[28]  Marián Boguñá,et al.  Clustering in complex networks. I. General formalism. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[29]  David Bawden,et al.  Book Review: Evolution and Structure of the Internet: A Statistical Physics Approach. , 2006 .

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

[31]  F. Brauer,et al.  Mathematical Models in Population Biology and Epidemiology , 2001 .

[32]  D. Earn,et al.  A simple model for complex dynamical transitions in epidemics. , 2000, Science.

[33]  D. Cummings,et al.  Strategies for containing an emerging influenza pandemic in Southeast Asia , 2005, Nature.

[34]  C. Macken,et al.  Mitigation strategies for pandemic influenza in the United States. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[35]  Yamir Moreno,et al.  Dynamics of rumor spreading in complex networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[36]  Alessandro Vespignani,et al.  Epidemic dynamics and endemic states in complex networks. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.