Mathematical models as tools for evaluating the effectiveness of interventions: A comment on Levin

Possible interventions to minimize resistance rates are numerous and can in volve reduction and/or change in antimicrobial use, infection control, and vaccinations. As mathematical models are becoming more realistic they can b e useful to quantitatively evaluate the relative contribution of individual risk factors and for the planning of future intervention strategies. The f itness cost associated with resistance is an important parameter and small differences can have a profound effect on the results. The mathematical mod els presented for communities predicted that even with cessation of antibio tic 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 th e heterogeneity of open communities. Provision of susceptible strains from areas with low resistance rates to areas with high resistance rates can hav e a profound effect on the success of interventions to minimize resistance.