Document details for 'Stochastic effects in a model of nematode infection in ruminants'

Authors Marion, G., Renshaw, E. and Gibson, G.J.
Publication details IMA Journal of Mathematics Applied in Medicine and Biology 15, 97-116.
Keywords ecology stochastic modelling
Abstract We illustrate the importance of stochastic effects in population models of biological systems and demonstrate a number of analytic and simulation-based approaches that can usefully be applied to such models. In so doing, we compare the stochastic approach to the more usual deterministic one. The model studied represents the gastro-intestinal infection of ruminants by nematodes when the hosts maintain a fixed density. The incorporation of a feed-back mechanism, which accounts for the immune response of the infected animals, results in a highly non-linear model; similar forms of non-linearity are a feature of many plausible models in population biology. In the absence of an analytic solution to the full stochastic model we explore a number of approximations and compare them to simulations of the full stochastic process. We explore three modes of behaviour of the system. In the endemic regime the stochastic system fluctuates widely around the non-zero fixed points of the deterministic model. In the managed regime, where the system is subject to external periodic perturbation, stochastic effects are negligible. Finally, we find that in a regime in which the deterministic model predicts the long-term persistence of oscillations the stochastic model shows that extinction can occur. Of the approximation procedures we consider, the Normal approximation to the full stochastic process is the most generally applicable, and also the most accurate in the light of simulation results. Local linearization provides reasonably accurate prediction of the variance-covariance structure, and a transfer function approach allows calculation of the time-lagged auto- and cross-correlations in the endemic regime. Linearization of the stochastic updates themselves results in poor prediction of the population variances.
Last updated 2003-05-12
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