Mathematical Models as Tools for Evaluating the Effectiveness of Interventions: A Comment on Levin
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...
| Published in: | Clinical Infectious Diseases |
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| Main Author: | |
| Format: | Text |
| Language: | English |
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Oxford University Press
2001
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| Online Access: | http://cid.oxfordjournals.org/cgi/content/short/33/Supplement_3/S174 https://doi.org/10.1086/321845 |
| Summary: | 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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