Picture of flying drone

Award-winning sensor signal processing research at Strathclyde...

The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of Open Access research papers by University of Strathclyde researchers, including by Strathclyde researchers involved in award-winning research into technology for detecting drones. - but also other internationally significant research from within the Department of Electronic & Electrical Engineering.

Strathprints also exposes world leading research from the Faculties of Science, Engineering, Humanities & Social Sciences, and from the Strathclyde Business School.

Discover more...

The probability of extinction in a bovine respiratory syncytial virus epidemic model

Griffiths, Martin and Greenhalgh, David (2011) The probability of extinction in a bovine respiratory syncytial virus epidemic model. Mathematical Biosciences, 231 (2). pp. 144-158.

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

Abstract

Backward bifurcation is a relatively recent yet well-studied phenomenon associated with deterministic epidemic models. It allows for the presence of multiple subcritical endemic equilibria, and is generally found only in models possessing a reasonable degree of complexity. One particular aspect of backward bifurcation that appears to have been virtually overlooked in the literature is the potential influence its presence might have on the behaviour of any analogous stochastic model. Indeed, the primary aim of this paper is to investigate this possibility. Our approach is to compare the theoretical probabilities of extinction, calculated via a particular stochastic formulation of a deterministic model exhibiting backward bifurcation, with those obtained from a series of stochastic simulations. We have found some interesting links in the behaviour between the deterministic and stochastic models, and are able to offer plausible explanations for our observations.