Strathprints logo
Strathprints Home | Open Access | Browse | Search | User area | Copyright | Help | Library Home | SUPrimo

When a predator avoids infected prey: a model-based theoretical study

Haque, M. and Greenhalgh, D. (2010) When a predator avoids infected prey: a model-based theoretical study. Mathematical Medicine and Biology, 27 (1). pp. 75-94.

Full text not available in this repository. (Request a copy from the Strathclyde author)


In this paper we study a predator-prey model with logistic growth in the prey population, where a disease spreads among the prey according to an susceptible-infected-susceptible (SIS) epidemic model. The predators do not consume infected prey. After a review of the literature we formulate the basic mathematical model. For simplicity, we work initially with a model involving the fractions of prey susceptible and infected and then translate the results back to the model with absolute numbers. Both local and global stability results are examined. For the model working with absolute numbers, we find six possible equilibria and three important threshold values determining the behaviour of the system. There is always a unique locally stable equilibrium. We make conjectures concerning the global behaviour of the system. Next the effect of predator removal on the ecoepidemiological system is examined. The penultimate section describes numerical simulations using realistic parameter values for a real-life situation. This is humans predating on fish (Atlantic cod) infected by bacterial fin rot. The simulations confirm our conjectures. A discussion concludes the paper.

Item type: Article
ID code: 28125
Keywords: ecoepidemiological model predator-prey system susceptible-infected-susceptible epidemic model disease equilibrium and stability analysis, Probabilities. Mathematical statistics, Neuroscience(all), Biochemistry, Genetics and Molecular Biology(all), Environmental Science(all), Modelling and Simulation, Applied Mathematics, Immunology and Microbiology(all), Pharmacology
Subjects: Science > Mathematics > Probabilities. Mathematical statistics
Department: Faculty of Science > Mathematics and Statistics
Depositing user: Mrs Carolynne Westwood
Date Deposited: 13 Oct 2010 15:18
Last modified: 10 Oct 2015 00:01
Related URLs:

Actions (login required)

View Item View Item