Literature DB >> 12857951

Response of a deterministic epidemiological system to a stochastically varying environment.

J E Truscott1, C A Gilligan.   

Abstract

Fluctuations in the natural environment introduce variability into the biological systems that exist within them. In this paper, we develop a model for the influence of random fluctuations in the environment on a simple epidemiological system. The model describes the infection of a dynamic host population by an environmentally sensitive pathogen and is based on the infection of sugar beet plants by the endoparasitic slime-mold vector Polymyxa betae. The infection process is switched on only when the temperature is above a critical value. We discuss some of the problems inherent in modeling such a system and analyze the resulting model by using asymptotic techniques to generate closed-form solutions for the mean and variance of the net amount of new inoculum produced within a season. In this way, the variance of temperature profile can be linked with that of the inoculum produced in a season and hence the risk of disease. We also examine the connection between the model developed in this paper and discrete Markov-chain models for weather.

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Year:  2003        PMID: 12857951      PMCID: PMC166439          DOI: 10.1073/pnas.1436273100

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


  1 in total

1.  A Model for the Temporal Buildup of Polymyxa betae.

Authors:  C R Webb; C A Gilligan; M J Asher
Journal:  Phytopathology       Date:  1999-01       Impact factor: 4.025

  1 in total
  17 in total

1.  Prediction of invasion from the early stage of an epidemic.

Authors:  Francisco J Pérez-Reche; Franco M Neri; Sergei N Taraskin; Christopher A Gilligan
Journal:  J R Soc Interface       Date:  2012-04-18       Impact factor: 4.118

2.  Modelling predation as a capped rate stochastic process, with applications to fish recruitment.

Authors:  Alex James; Paul D Baxter; Jonathan W Pitchford
Journal:  J R Soc Interface       Date:  2005-12-22       Impact factor: 4.118

3.  Evolution of host resistance: looking for coevolutionary hotspots at small spatial scales.

Authors:  Anna-Liisa Laine
Journal:  Proc Biol Sci       Date:  2006-02-07       Impact factor: 5.349

4.  Stochastic environmental fluctuations drive epidemiology in experimental host-parasite metapopulations.

Authors:  Alison B Duncan; Andrew Gonzalez; Oliver Kaltz
Journal:  Proc Biol Sci       Date:  2013-08-21       Impact factor: 5.349

5.  Complexity and anisotropy in host morphology make populations less susceptible to epidemic outbreaks.

Authors:  Francisco J Pérez-Reche; Sergei N Taraskin; Luciano da F Costa; Franco M Neri; Christopher A Gilligan
Journal:  J R Soc Interface       Date:  2010-01-14       Impact factor: 4.118

6.  Invasion, persistence and control in epidemic models for plant pathogens: the effect of host demography.

Authors:  Nik J Cunniffe; Christopher A Gilligan
Journal:  J R Soc Interface       Date:  2009-07-22       Impact factor: 4.118

7.  Bayesian analysis of botanical epidemics using stochastic compartmental models.

Authors:  G J Gibson; A Kleczkowski; C A Gilligan
Journal:  Proc Natl Acad Sci U S A       Date:  2004-08-09       Impact factor: 11.205

Review 8.  Sustainable agriculture and plant diseases: an epidemiological perspective.

Authors:  Christopher A Gilligan
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2008-02-27       Impact factor: 6.237

9.  Impact of scale on the effectiveness of disease control strategies for epidemics with cryptic infection in a dynamical landscape: an example for a crop disease.

Authors:  Christopher A Gilligan; James E Truscott; Adrian J Stacey
Journal:  J R Soc Interface       Date:  2007-10-22       Impact factor: 4.118

10.  Optimising and communicating options for the control of invasive plant disease when there is epidemiological uncertainty.

Authors:  Nik J Cunniffe; Richard O J H Stutt; R Erik DeSimone; Tim R Gottwald; Christopher A Gilligan
Journal:  PLoS Comput Biol       Date:  2015-04-13       Impact factor: 4.779

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