Literature DB >> 26794321

Disease invasion risk in a growing population.

Sanling Yuan1, P van den Driessche2, Frederick H Willeboordse3, Zhisheng Shuai4, Junling Ma5.   

Abstract

The spread of an infectious disease may depend on the population size. For simplicity, classic epidemic models assume homogeneous mixing, usually standard incidence or mass action. For standard incidence, the contact rate between any pair of individuals is inversely proportional to the population size, and so the basic reproduction number (and thus the initial exponential growth rate of the disease) is independent of the population size. For mass action, this contact rate remains constant, predicting that the basic reproduction number increases linearly with the population size, meaning that disease invasion is easiest when the population is largest. In this paper, we show that neither of these may be true on a slowly evolving contact network: the basic reproduction number of a short epidemic can reach its maximum while the population is still growing. The basic reproduction number is proportional to the spectral radius of a contact matrix, which is shown numerically to be well approximated by the average excess degree of the contact network. We base our analysis on modeling the dynamics of the average excess degree of a random contact network with constant population input, proportional deaths, and preferential attachment for contacts brought in by incoming individuals (i.e., individuals with more contacts attract more incoming contacts). In addition, we show that our result also holds for uniform attachment of incoming contacts (i.e., every individual has the same chance of attracting incoming contacts), and much more general population dynamics. Our results show that a disease spreading in a growing population may evade control if disease control planning is based on the basic reproduction number at maximum population size.

Entities:  

Keywords:  Basic reproduction number; Dynamic contact network; Excess degree

Mesh:

Year:  2016        PMID: 26794321     DOI: 10.1007/s00285-015-0962-4

Source DB:  PubMed          Journal:  J Math Biol        ISSN: 0303-6812            Impact factor:   2.259


  9 in total

1.  Epidemic dynamics in finite size scale-free networks.

Authors:  Romualdo Pastor-Satorras; Alessandro Vespignani
Journal:  Phys Rev E Stat Nonlin Soft Matter Phys       Date:  2002-03-07

2.  Assortative mixing in networks.

Authors:  M E J Newman
Journal:  Phys Rev Lett       Date:  2002-10-28       Impact factor: 9.161

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.  Effective degree network disease models.

Authors:  Jennifer Lindquist; Junling Ma; P van den Driessche; Frederick H Willeboordse
Journal:  J Math Biol       Date:  2010-02-24       Impact factor: 2.259

5.  Epidemic models for complex networks with demographics.

Authors:  Zhen Jin; Guiquan Sun; Huaiping Zhu
Journal:  Math Biosci Eng       Date:  2014-12       Impact factor: 2.080

6.  A network with tunable clustering, degree correlation and degree distribution, and an epidemic thereon.

Authors:  Frank Ball; Tom Britton; David Sirl
Journal:  J Math Biol       Date:  2012-11-16       Impact factor: 2.259

7.  Spread of epidemic disease on networks.

Authors:  M E J Newman
Journal:  Phys Rev E Stat Nonlin Soft Matter Phys       Date:  2002-07-26

8.  Modeling control strategies of respiratory pathogens.

Authors:  Babak Pourbohloul; Lauren Ancel Meyers; Danuta M Skowronski; Mel Krajden; David M Patrick; Robert C Brunham
Journal:  Emerg Infect Dis       Date:  2005-08       Impact factor: 6.883

9.  SIR dynamics in random networks with heterogeneous connectivity.

Authors:  Erik Volz
Journal:  J Math Biol       Date:  2007-08-01       Impact factor: 2.259

  9 in total

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