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Escherichia coli O157 infection on Scottish cattle farms: dynamics and control.

Zhang XS, Woolhouse ME - J R Soc Interface (2010)

Bottom Line: We first generate distributions of model parameter estimates using Markov chain Monte Carlo methods.Despite considerable uncertainty in parameter values, each set of parameter values within the 95th percentile range implies a fairly similar impact of interventions.Targeted interventions based on farm-level risk factors are more efficient than non-targeted interventions.

View Article: PubMed Central - PubMed

Affiliation: Centre for Infectious Diseases, University of Edinburgh, , Kings Buildings, West Mains Road, Edinburgh EH9 3JT, UK. xu-sheng.zhang@hpa.org.uk

ABSTRACT
In this study, we parametrize a stochastic individual-based model of the transmission dynamics of Escherichia coli O157 infection among Scottish cattle farms and use the model to predict the impacts of both targeted and non-targeted interventions. We first generate distributions of model parameter estimates using Markov chain Monte Carlo methods. Despite considerable uncertainty in parameter values, each set of parameter values within the 95th percentile range implies a fairly similar impact of interventions. Interventions that reduce the transmission coefficient and/or increase the recovery rate of infected farms (e.g. via vaccination and biosecurity) are much more effective in reducing the level of infection than reducing cattle movement rates, which improves effectiveness only when the overall control effort is small. Targeted interventions based on farm-level risk factors are more efficient than non-targeted interventions. Herd size is a major determinant of risk of infection, and our simulations confirmed that targeting interventions at farms with the largest herds is almost as effective as targeting based on overall risk. However, because of the striking characteristic that the infection force depends weakly on the number of infected farms, no interventions that are less than 100 per cent effective can eradicate E. coli O157 infection from Scottish cattle farms, implying that eliminating the disease is impractical.

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Related in: MedlinePlus

Univariate distributions of estimates of model parameters: (a) log β, (b) log γ, (c) a, and (d) log b. For better illustration, especially for small values, both γ and b are transformed as log γ and log b. The data are from four MCMC that started from different initial values of model parameters. After removing the first 5000 iterations for each chain, the iterations for four MCMC processes totalled 56 094.
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RSIF20100470F1: Univariate distributions of estimates of model parameters: (a) log β, (b) log γ, (c) a, and (d) log b. For better illustration, especially for small values, both γ and b are transformed as log γ and log b. The data are from four MCMC that started from different initial values of model parameters. After removing the first 5000 iterations for each chain, the iterations for four MCMC processes totalled 56 094.

Mentions: After removing the first 5000 iterations (assumed as the burn-in period), we obtained four convergent MCMCs of a total 56 094 iterations, which were characterized by the values of Gelman–Rubin R statistics [21]: 1.02, 1.03, 1.02, 1.00 for the four model parameters log β, γ, a, b and 1.02 for the natural log of the likelihood (l). The MCMC samplings of four univariate frequency distributions for model parameters are shown in figure 1. The distributions of both coefficient a and log β are very close to normal, while distributions of logarithm of both recovery rate γ and coefficient b can be approximated by gamma distributions with the shape parameter larger than unity. From the univariate distributions, we obtained the estimates of modal values and 95% percentiles, which are given in table 1.Table 1.


Escherichia coli O157 infection on Scottish cattle farms: dynamics and control.

Zhang XS, Woolhouse ME - J R Soc Interface (2010)

Univariate distributions of estimates of model parameters: (a) log β, (b) log γ, (c) a, and (d) log b. For better illustration, especially for small values, both γ and b are transformed as log γ and log b. The data are from four MCMC that started from different initial values of model parameters. After removing the first 5000 iterations for each chain, the iterations for four MCMC processes totalled 56 094.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3104328&req=5

RSIF20100470F1: Univariate distributions of estimates of model parameters: (a) log β, (b) log γ, (c) a, and (d) log b. For better illustration, especially for small values, both γ and b are transformed as log γ and log b. The data are from four MCMC that started from different initial values of model parameters. After removing the first 5000 iterations for each chain, the iterations for four MCMC processes totalled 56 094.
Mentions: After removing the first 5000 iterations (assumed as the burn-in period), we obtained four convergent MCMCs of a total 56 094 iterations, which were characterized by the values of Gelman–Rubin R statistics [21]: 1.02, 1.03, 1.02, 1.00 for the four model parameters log β, γ, a, b and 1.02 for the natural log of the likelihood (l). The MCMC samplings of four univariate frequency distributions for model parameters are shown in figure 1. The distributions of both coefficient a and log β are very close to normal, while distributions of logarithm of both recovery rate γ and coefficient b can be approximated by gamma distributions with the shape parameter larger than unity. From the univariate distributions, we obtained the estimates of modal values and 95% percentiles, which are given in table 1.Table 1.

Bottom Line: We first generate distributions of model parameter estimates using Markov chain Monte Carlo methods.Despite considerable uncertainty in parameter values, each set of parameter values within the 95th percentile range implies a fairly similar impact of interventions.Targeted interventions based on farm-level risk factors are more efficient than non-targeted interventions.

View Article: PubMed Central - PubMed

Affiliation: Centre for Infectious Diseases, University of Edinburgh, , Kings Buildings, West Mains Road, Edinburgh EH9 3JT, UK. xu-sheng.zhang@hpa.org.uk

ABSTRACT
In this study, we parametrize a stochastic individual-based model of the transmission dynamics of Escherichia coli O157 infection among Scottish cattle farms and use the model to predict the impacts of both targeted and non-targeted interventions. We first generate distributions of model parameter estimates using Markov chain Monte Carlo methods. Despite considerable uncertainty in parameter values, each set of parameter values within the 95th percentile range implies a fairly similar impact of interventions. Interventions that reduce the transmission coefficient and/or increase the recovery rate of infected farms (e.g. via vaccination and biosecurity) are much more effective in reducing the level of infection than reducing cattle movement rates, which improves effectiveness only when the overall control effort is small. Targeted interventions based on farm-level risk factors are more efficient than non-targeted interventions. Herd size is a major determinant of risk of infection, and our simulations confirmed that targeting interventions at farms with the largest herds is almost as effective as targeting based on overall risk. However, because of the striking characteristic that the infection force depends weakly on the number of infected farms, no interventions that are less than 100 per cent effective can eradicate E. coli O157 infection from Scottish cattle farms, implying that eliminating the disease is impractical.

Show MeSH
Related in: MedlinePlus