Department of Infectious Disease Epidemiology, Faculty of Medicine, St. Mary's campus, Imperial College London, Norfolk Place, London W2 1PG, UK.
Adv Exp Med Biol. 2010;673:157-71. doi: 10.1007/978-1-4419-6064-1_11.
The planning and evaluation of parasitic control programmes are complicated by the many interacting population dynamic and programmatic factors that determine infection trends under different control options. A key need is quantification about the status of the parasite system state at any one given timepoint and the dynamic change brought upon that state as an intervention program proceeds. Here, we focus on the control and elimination of the vector-borne disease, lymphatic filariasis, to show how mathematical models of parasite transmission can provide a quantitative framework for aiding the design of parasite elimination and monitoring programs by their ability to support (1) conducting rational analysis and definition of endpoints for different programmatic aims or objectives, including transmission endpoints for disease elimination, (2) undertaking strategic analysis to aid the optimal design of intervention programs to meet set endpoints under different endemic settings and (3) providing support for performing informed evaluations of ongoing programs, including aiding the formation of timely adaptive management strategies to correct for any observed deficiencies in program effectiveness. The results also highlight how the use of a model-based framework will be critical to addressing the impacts of ecological complexities, heterogeneities and uncertainties on effective parasite management and thereby guiding the development of strategies to resolve and overcome such real-world complexities. In particular, we underscore how this approach can provide a link between ecological science and policy by revealing novel tools and measures to appraise and enhance the biological controllability or eradicability of parasitic diseases. We conclude by emphasizing an urgent need to develop and apply flexible adaptive management frameworks informed by mathematical models that are based on learning and reducing uncertainty using monitoring data, apply phased or sequential decision-making to address extant uncertainty and focus on developing ecologically resilient management strategies, in ongoing efforts to control or eliminate filariasis and other parasitic diseases in resource-poor communities.
寄生虫控制规划和评估的复杂性在于，许多相互作用的种群动态和计划因素决定了在不同控制选择下的感染趋势。一个关键需求是量化寄生虫系统在任何给定时间点的状态以及随着干预计划的进行对该状态带来的动态变化。在这里，我们专注于控制和消除媒介传播疾病——淋巴丝虫病，以展示寄生虫传播的数学模型如何通过以下能力为寄生虫消除和监测计划的设计提供定量框架：(1) 为不同计划目标或目的（包括消除疾病的传播终点）进行合理的分析和定义；(2) 进行战略分析，以帮助在不同流行环境下为实现设定的终点最优设计干预计划；(3) 为正在进行的项目进行知情评估提供支持，包括帮助制定及时的适应性管理策略，以纠正项目效果中任何观察到的缺陷。结果还强调了模型为基础的框架的使用对于解决生态复杂性、异质性和不确定性对有效寄生虫管理的影响并指导解决和克服这些现实世界复杂性的策略的发展至关重要。特别是，我们强调了这种方法如何通过揭示评估和增强寄生虫病的生物可控性或可消除性的新工具和措施，在生态科学和政策之间建立联系。我们最后强调迫切需要制定和应用基于数学模型的灵活适应性管理框架，这些框架基于使用监测数据进行学习和减少不确定性，应用阶段性或序贯决策来解决现有的不确定性，并专注于开发具有生态弹性的管理策略，以持续努力控制或消除资源匮乏社区中的丝虫病和其他寄生虫病。
