Communicable Diseases Surveillance Centre, Abton House, Wedal Road, Cardiff CF4 3QX, Wales, UK
Keywords: resistance , modelling , mathematical modelling
The ratchet paper1 has provoked an encouraging amount of correspondence.24 Mathematical modelling is a powerful tool in comparison of the ramifications of our assumptions and hypotheses against real data. However, as the correspondence indicates, firm detailed data on the behaviour of resistance clinically, in individual patients, hospitals and the general population are scarce. We need to collect (and find mechanisms to share) such data much more assiduously and systematically.5 This is not a plea for huge databases that will remain unused. Rather it is a path to completion of the scientific cyclehypothesis, modelling, comparison with reality and revision of assumptionsthat will achieve the deep understanding required to stem the rise in resistance. We have a pressing clinical problem of worldwide significance that interests the modellers. They need hypotheses with clear overt assumptions to model and data for comparisonsgive them some clues!
Transparency declarations
The author has no affiliations with the pharmaceutical industry or related commercial concerns.
References
1.
Magee JT. The resistance ratchet: theoretical implications of cyclic selection pressure. J Antimicrob Chemother 2005; 56: 42730.
2.
Huovinen P. Mathematical modeltell us the future! J Antimicrob Chemother 2005; 56: 2578.
3.
Magee JT. Resistance ratchet effect: author's response. J Antimicrob Chemother 2005; 56: 431.
4. Tam VH, Nikolaou M. Mathematical modelling of resistance emergence. J Antimicrob Chemother 2005; 56: 983.
5.
Magee JT, Heginbothom ML, Mason BW. Finding a strategy: the case for co-operative research on resistance epidemiology. J Antimicrob Chemother 2005; 55: 62833.
|