Mathematical models as tools for evaluating the effectiveness of interventions: a comment on Levin.
To access full text version of this article. Please click on the hyperlink "View/Open" at the bottom of this page Possible interventions to minimize resistance rates are numerous and can involve reduction and/or change in antimicrobial use, infection control, and vaccinations. As mathemati...
| Published in: | Clinical Infectious Diseases |
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| Format: | Article in Journal/Newspaper |
| Language: | English |
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The University of Chicago Press
2008
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| Online Access: | http://hdl.handle.net/2336/33573 https://doi.org/10.1086/321845 |
| Summary: | To access full text version of this article. Please click on the hyperlink "View/Open" at the bottom of this page Possible interventions to minimize resistance rates are numerous and can involve reduction and/or change in antimicrobial use, infection control, and vaccinations. As mathematical models are becoming more realistic they can be useful to quantitatively evaluate the relative contribution of individual risk factors and for the planning of future intervention strategies. The fitness cost associated with resistance is an important parameter and small differences can have a profound effect on the results. The mathematical models presented for communities predicted that even with cessation of antibiotic use, the decline in resistance frequency would be slow. This contrasts with successful interventions in Finland and Iceland. Future models have to include important variables such as herd immunity and take into account the heterogeneity of open communities. Provision of susceptible strains from areas with low resistance rates to areas with high resistance rates can have a profound effect on the success of interventions to minimize resistance. |
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