Abstract
Studying bacterial resistance has direct importance for the antimicrobial treatment of individual patients. In addition, surveillance data pooled from individual diagnostic reports help physicians to choose the most effective drug for empirical therapy. However, this is not the limit of what can be done with the resistance data. There is an increasing need to synthesize the available strands of data in order to construct mathematical models that can be used as tools to predict the likely outcomes of various antibiotic policy options.