Literature DB >> 19391800

Time evolution of epidemic disease on finite and infinite networks.

Pierre-André Noël1, Bahman Davoudi, Robert C Brunham, Louis J Dubé, Babak Pourbohloul.   

Abstract

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.

Entities:  

Year:  2009        PMID: 19391800     DOI: 10.1103/PhysRevE.79.026101

Source DB:  PubMed          Journal:  Phys Rev E Stat Nonlin Soft Matter Phys        ISSN: 1539-3755


  12 in total

1.  Edge-based compartmental modelling for infectious disease spread.

Authors:  Joel C Miller; Anja C Slim; Erik M Volz
Journal:  J R Soc Interface       Date:  2011-10-05       Impact factor: 4.118

2.  Epidemic spread in networks: Existing methods and current challenges.

Authors:  Joel C Miller; Istvan Z Kiss
Journal:  Math Model Nat Phenom       Date:  2014-01       Impact factor: 4.157

Review 3.  Coevolution spreading in complex networks.

Authors:  Wei Wang; Quan-Hui Liu; Junhao Liang; Yanqing Hu; Tao Zhou
Journal:  Phys Rep       Date:  2019-07-29       Impact factor: 25.600

4.  Spread of infectious disease through clustered populations.

Authors:  Joel C Miller
Journal:  J R Soc Interface       Date:  2009-03-04       Impact factor: 4.118

5.  Link removal for the control of stochastically evolving epidemics over networks: a comparison of approaches.

Authors:  Eva A Enns; Margaret L Brandeau
Journal:  J Theor Biol       Date:  2015-02-16       Impact factor: 2.405

6.  Untangling the Interplay between Epidemic Spread and Transmission Network Dynamics.

Authors:  Christel Kamp
Journal:  PLoS Comput Biol       Date:  2010-11-18       Impact factor: 4.475

7.  Early real-time estimation of the basic reproduction number of emerging or reemerging infectious diseases in a community with heterogeneous contact pattern: Using data from Hong Kong 2009 H1N1 Pandemic Influenza as an illustrative example.

Authors:  Kin On Kwok; Bahman Davoudi; Steven Riley; Babak Pourbohloul
Journal:  PLoS One       Date:  2015-09-15       Impact factor: 3.240

8.  Epidemic progression on networks based on disease generation time.

Authors:  Bahman Davoudi; Flavia Moser; Fred Brauer; Babak Pourbohloul
Journal:  J Biol Dyn       Date:  2013       Impact factor: 2.179

9.  Complex social contagion makes networks more vulnerable to disease outbreaks.

Authors:  Ellsworth Campbell; Marcel Salathé
Journal:  Sci Rep       Date:  2013       Impact factor: 4.379

10.  Inferring population-level contact heterogeneity from common epidemic data.

Authors:  J Conrad Stack; Shweta Bansal; V S Anil Kumar; Bryan Grenfell
Journal:  J R Soc Interface       Date:  2012-11-08       Impact factor: 4.118

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