Dataset Information

Restif2006 - Whooping cough

ABSTRACT: Restif2006 - Whooping cough This model is described in the article: Integrating life history and cross-immunity into the evolutionary dynamics of pathogens. Restif O, Grenfell BT. Proc. Biol. Sci. 2006 Feb; 273(1585): 409-416 Abstract: Models for the diversity and evolution of pathogens have branched into two main directions: the adaptive dynamics of quantitative life-history traits (notably virulence) and the maintenance and invasion of multiple, antigenically diverse strains that interact with the host's immune memory. In a first attempt to reconcile these two approaches, we developed a simple modelling framework where two strains of pathogens, defined by a pair of life-history traits (infectious period and infectivity), interfere through a given level of cross-immunity. We used whooping cough as a potential example, but the framework proposed here could be applied to other acute infectious diseases. Specifically, we analysed the effects of these parameters on the invasion dynamics of one strain into a population, where the second strain is endemic. Whereas the deterministic version of the model converges towards stable coexistence of the two strains in most cases, stochastic simulations showed that transient epidemic dynamics can cause the extinction of either strain. Thus ecological dynamics, modulated by the immune parameters, eventually determine the adaptive value of different pathogen genotypes. We advocate an integrative view of pathogen dynamics at the crossroads of immunology, epidemiology and evolution, as a way towards efficient control of infectious diseases. This version of the model can be used for both the stochastic and the deterministic simulations described in the article. For deterministic interpretations with infinite population sizes, set the population size N = 1. The model reproduces the deterministic time courses. Stochastic interpretation with Copasi UI gave results similar to the article, but was not extensively tested. The initial conditions for competition simulations can be derived by equilibrating the system for one pathogen and then adding a starting concentration for the other. Originally created by libAntimony v1.3 (using libSBML 4.1.0-b1) This model is hosted on BioModels Database and identified by: BIOMD0000000249. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

DISEASE(S): Pertussis

SUBMITTER: Lukas Endler

PROVIDER: BIOMD0000000249 | BioModels | 2024-09-02

REPOSITORIES: BioModels

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	BIOMD0000000249?filename=BIOMD0000000249-biopax2.owl	Owl
	BIOMD0000000249?filename=BIOMD0000000249-biopax3.owl	Owl
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Publications

Integrating life history and cross-immunity into the evolutionary dynamics of pathogens.

Restif Olivier O Grenfell Bryan T BT

Proceedings. Biological sciences 20060201 1585

Models for the diversity and evolution of pathogens have branched into two main directions: the adaptive dynamics of quantitative life-history traits (notably virulence) and the maintenance and invasion of multiple, antigenically diverse strains that interact with the host's immune memory. In a first attempt to reconcile these two approaches, we developed a simple modelling framework where two strains of pathogens, defined by a pair of life-history traits (infectious period and infectivity), int ...[more]

PMID: 16615206

Publication: 1/3

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Project description:Restif2007 - Vaccination invasion This model is described in the article: Vaccination and the dynamics of immune evasion. Restif O, Grenfell BT. J R Soc Interface 2007 Feb; 4(12): 143-153 Abstract: Vaccines exert strong selective pressures on pathogens, favouring the spread of antigenic variants. We propose a simple mathematical model to investigate the dynamics of a novel pathogenic strain that emerges in a population where a previous strain is maintained at low endemic level by a vaccine. We compare three methods to assess the ability of the novel strain to invade and persist: algebraic rate of invasion; deterministic dynamics; and stochastic dynamics. These three techniques provide complementary predictions on the fate of the system. In particular, we emphasize the importance of stochastic simulations, which account for the possibility of extinctions of either strain. More specifically, our model suggests that the probability of persistence of an invasive strain (i) can be minimized for intermediate levels of vaccine cross-protection (i.e. immune protection against the novel strain) and (ii) is lower if cross-immunity acts through a reduced infectious period rather than through reduced susceptibility. This version of the model can be used for both the stochastic and the deterministic simulations described in the article. For deterministic interpretations with infinite population sizes, set the population size N = 1. The model does reproduces the deterministic time course. The initial values are set to the steady state values for a latent infection with strain 1 with an invading infection of strain 2 (I2=1e-06), 100 percent vaccination with a susceptibility reduction τ=0.7 at birth (p=1), and all other parameters as in figure 3 of the publication. To be compatible with older software tools, the english letter names instead of the greek symbols were used for parameter names: parameter symbol name transmission rate β beta recovery rate γ gamma birth/death rate μ mu rate of loss of natural immunity σ sigma rate of loss of vaccine immunity σ v sigmaV reduction of susceptibility by primary infection θ theta reduction of infection period by primary infection ν nu reduction of susceptibility by vaccination τ tau reduction of infection period by vaccination η eta Originally created by libAntimony v1.4 (using libSBML 3.4.1) This model is hosted on BioModels Database and identified by: BIOMD0000000294. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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