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Open Access Correspondence

Modelling the evolution of drug resistance in the presence of antiviral drugs

Jianhong Wu1*, Ping Yan2 and Chris Archibald2

Author Affiliations

1 Center for Disease Modeling, Department of Mathematics and Statistics, York University, 4700 Keele Street, Toronto, M3J 1P3, Canada

2 Surveillance and Risk Assessment Division, Centre for Infectious Diseases Prevention and Control, Public Health Agency of Canada, AL: 0602B – Tunney's Pasture, Room: 2351 – 100 Eglantine Driveway, Ottawa, K1A 0K9, Canada

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BMC Public Health 2007, 7:300  doi:10.1186/1471-2458-7-300

Published: 23 October 2007

Abstract

Background

The emergence of drug resistance in treated populations and the transmission of drug resistant strains to newly infected individuals are important public health concerns in the prevention and control of infectious diseases such as HIV and influenza. Mathematical modelling may help guide the design of treatment programs and also may help us better understand the potential benefits and limitations of prevention strategies.

Methods

To explore further the potential synergies between modelling of drug resistance in HIV and in pandemic influenza, the Public Health Agency of Canada and the Mathematics for Information Technology and Complex Systems brought together selected scientists and public health experts for a workshop in Ottawa in January 2007, to discuss the emergence and transmission of HIV antiviral drug resistance, to report on progress in the use of mathematical models to study the emergence and spread of drug resistant influenza viral strains, and to recommend future research priorities.

Results

General lectures and round-table discussions were organized around the issues on HIV drug resistance at the population level, HIV drug resistance in Western Canada, HIV drug resistance at the host level (with focus on optimal treatment strategies), and drug resistance for pandemic influenza planning.

Conclusion

Some of the issues related to drug resistance in HIV and pandemic influenza can possibly be addressed using existing mathematical models, with a special focus on linking the existing models to the data obtained through the Canadian HIV Strain and DR Surveillance Program. Preliminary statistical analysis of these data carried out at PHAC, together with the general model framework developed by Dr. Blower and her collaborators, should provide further insights into the mechanisms behind the observed trends and thus could help with the prediction and analysis of future trends in the aforementioned items. Remarkable similarity between dynamic, compartmental models for the evolution of wild and drug resistance strains of both HIV and pandemic influenza may provide sufficient common ground to create synergies between modellers working in these two areas. One of the key contributions of mathematical modeling to the control of infectious diseases is the quantification and design of optimal strategies, combining techniques of operations research with dynamic modeling would enhance the contribution of mathematical modeling to the prevention and control of infectious diseases.