Literature DB >> 21976638

Edge-based compartmental modelling for infectious disease spread.

Joel C Miller1, Anja C Slim, Erik M Volz.   

Abstract

The primary tool for predicting infectious disease spread and intervention effectiveness is the mass action susceptible-infected-recovered model of Kermack & McKendrick. Its usefulness derives largely from its conceptual and mathematical simplicity; however, it incorrectly assumes that all individuals have the same contact rate and partnerships are fleeting. In this study, we introduce edge-based compartmental modelling, a technique eliminating these assumptions. We derive simple ordinary differential equation models capturing social heterogeneity (heterogeneous contact rates) while explicitly considering the impact of partnership duration. We introduce a graphical interpretation allowing for easy derivation and communication of the model and focus on applying the technique under different assumptions about how contact rates are distributed and how long partnerships last.

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Year:  2011        PMID: 21976638      PMCID: PMC3306633          DOI: 10.1098/rsif.2011.0403

Source DB:  PubMed          Journal:  J R Soc Interface        ISSN: 1742-5662            Impact factor:   4.118


  35 in total

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Authors:  F Liljeros; C R Edling; L A Amaral; H E Stanley; Y Aberg
Journal:  Nature       Date:  2001-06-21       Impact factor: 49.962

2.  Modelling disease outbreaks in realistic urban social networks.

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Journal:  Nature       Date:  2004-05-13       Impact factor: 49.962

3.  A note on a paper by Erik Volz: SIR dynamics in random networks.

Authors:  Joel C Miller
Journal:  J Math Biol       Date:  2010-03-23       Impact factor: 2.259

4.  Network epidemic models with two levels of mixing.

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Journal:  Math Biosci       Date:  2008-01-11       Impact factor: 2.144

5.  Random graphs with clustering.

Authors:  M E J Newman
Journal:  Phys Rev Lett       Date:  2009-07-27       Impact factor: 9.161

6.  Percolation and epidemics in random clustered networks.

Authors:  Joel C Miller
Journal:  Phys Rev E Stat Nonlin Soft Matter Phys       Date:  2009-08-04

7.  Random graphs containing arbitrary distributions of subgraphs.

Authors:  Brian Karrer; M E J Newman
Journal:  Phys Rev E Stat Nonlin Soft Matter Phys       Date:  2010-12-30

8.  A high-resolution human contact network for infectious disease transmission.

Authors:  Marcel Salathé; Maria Kazandjieva; Jung Woo Lee; Philip Levis; Marcus W Feldman; James H Jones
Journal:  Proc Natl Acad Sci U S A       Date:  2010-12-13       Impact factor: 11.205

9.  Insights from unifying modern approximations to infections on networks.

Authors:  Thomas House; Matt J Keeling
Journal:  J R Soc Interface       Date:  2010-06-10       Impact factor: 4.118

10.  Model hierarchies in edge-based compartmental modeling for infectious disease spread.

Authors:  Joel C Miller; Erik M Volz
Journal:  J Math Biol       Date:  2012-08-22       Impact factor: 2.259

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  58 in total

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Authors:  Robert R Wilkinson; Frank G Ball; Kieran J Sharkey
Journal:  J Math Biol       Date:  2017-04-13       Impact factor: 2.259

2.  Population-level effects of suppressing fever.

Authors:  David J D Earn; Paul W Andrews; Benjamin M Bolker
Journal:  Proc Biol Sci       Date:  2014-01-22       Impact factor: 5.349

3.  A primer on the use of probability generating functions in infectious disease modeling.

Authors:  Joel C Miller
Journal:  Infect Dis Model       Date:  2018-09-25

4.  Dynamics of stochastic epidemics on heterogeneous networks.

Authors:  Matthew Graham; Thomas House
Journal:  J Math Biol       Date:  2013-04-30       Impact factor: 2.259

5.  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

6.  Pairwise and edge-based models of epidemic dynamics on correlated weighted networks.

Authors:  P Rattana; J C Miller; I Z Kiss
Journal:  Math Model Nat Phenom       Date:  2014-04-24       Impact factor: 4.157

7.  Heterogeneous network epidemics: real-time growth, variance and extinction of infection.

Authors:  Frank Ball; Thomas House
Journal:  J Math Biol       Date:  2017-01-17       Impact factor: 2.259

8.  Pairwise approximation for SIR-type network epidemics with non-Markovian recovery.

Authors:  G Röst; Z Vizi; I Z Kiss
Journal:  Proc Math Phys Eng Sci       Date:  2018-02-21       Impact factor: 2.704

9.  A note on the derivation of epidemic final sizes.

Authors:  Joel C Miller
Journal:  Bull Math Biol       Date:  2012-07-25       Impact factor: 1.758

10.  Exact Equations for SIR Epidemics on Tree Graphs.

Authors:  K J Sharkey; I Z Kiss; R R Wilkinson; P L Simon
Journal:  Bull Math Biol       Date:  2013-12-18       Impact factor: 1.758

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