Wu Jianhong, Yan Ping, Archibald Chris
Center for Disease Modeling, Department of Mathematics and Statistics, York University, 4700 Keele Street, Toronto, M3J 1P3, Canada.
BMC Public Health. 2007 Oct 23;7:300. doi: 10.1186/1471-2458-7-300.
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.
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.
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.
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.
在接受治疗的人群中出现耐药性以及耐药菌株传播给新感染个体，是预防和控制诸如艾滋病毒和流感等传染病过程中重要的公共卫生问题。数学建模有助于指导治疗方案的设计，还能帮助我们更好地理解预防策略的潜在益处和局限性。
为了进一步探索艾滋病毒耐药性建模与大流行性流感耐药性建模之间的潜在协同作用，加拿大公共卫生局以及信息技术与复杂系统数学部于2007年1月在渥太华召集了选定的科学家和公共卫生专家参加研讨会，讨论艾滋病毒抗病毒药物耐药性的出现和传播，汇报使用数学模型研究耐药性流感病毒株的出现和传播方面的进展，并推荐未来的研究重点。
围绕人群层面的艾滋病毒耐药性问题、加拿大西部的艾滋病毒耐药性、宿主层面的艾滋病毒耐药性（重点是最佳治疗策略）以及大流行性流感规划的耐药性等问题组织了主题演讲和圆桌讨论。
艾滋病毒和大流行性流感耐药性的一些相关问题可能可以通过现有的数学模型来解决，特别要关注将现有模型与通过加拿大艾滋病毒毒株和耐药性监测项目获得的数据相联系。加拿大公共卫生局对这些数据进行的初步统计分析，连同布洛尔博士及其合作者开发的通用模型框架，应能进一步深入了解观察到的趋势背后的机制，从而有助于预测和分析上述项目未来的趋势。艾滋病毒和大流行性流感野生株及耐药株进化的动态、分区模型之间的显著相似性，可能为这两个领域的建模者之间创造协同作用提供足够的共同基础。数学建模对传染病控制的关键贡献之一是最佳策略的量化和设计，将运筹学技术与动态建模相结合将增强数学建模对传染病预防和控制的贡献。
