Vector Control Research Centre, Indira Nagar, Medical Complex, Pondicherry - 605 006, India.
Parasit Vectors. 2008 Feb 12;1(1):2. doi: 10.1186/1756-3305-1-2.
Mathematical models developed for describing the dynamics of transmission, infection, disease and control of lymphatic filariasis (LF) gained momentum following the 1997 World Health Assembly resolution and the launching of the Global Programme to Eliminate Lymphatic Filariasis (GPELF) in 2000. Model applications could provide valuable inputs for making decisions while implementing large scale programmes. However these models need to be evaluated at different epidemiological settings for optimization and fine-tuning with new knowledge and understanding on infection/disease dynamics.
EPIFIL and LYMFASIM are the two mathematical simulation models currently available for lymphatic filariasis transmission and control. Both models have been used for prediction and evaluation of control programmes under research settings. Their widespread application in evaluating large-scale elimination programmes warrants validation of assumptions governing the dynamics of infection and disease in different epidemiological settings. Furthermore, the predictive power of the models for decision support can be enhanced by generating knowledge on some important issues that pose challenges and incorporating such knowledge into the models. We highlight factors related to the efficacy of the drugs of choice, their mode of action, and the possibility that drug resistance may develop; the role of vector-parasite combinations; the magnitude of transmission thresholds; host-parasite interactions and their effects on the dynamics of infection and immunity; parasite biology, and progression to LF-associated disease.
The two mathematical models developed offer potential decision making tools for transmission and control of LF. In view of the goals of the GPELF, the predictive power of these models needs to be enhanced for their wide-spread application in large scale programmes. Assimilation and translation of new information into the models is a continuous process for which generation of new knowledge on a number of uncertainties is required. Particularly, a better understanding of the role of immune mechanisms in regulating infection and disease, the (direct or immune mediated) mode of action of current drugs, their effect on adult worms, their efficacy after repeated treatment, and the population genetics of drug resistance are important factors that could make the models more robust in their predictions of the impact of programmes to eliminate LF. However, if these models are to be user-friendly in the hands of programme managers (and not remain as research tools), it would be necessary to identify those factors which can be considered as the minimum necessary inputs/outputs in operational settings for easy evaluation and on-site decision making.
描述淋巴丝虫病（LF）传播、感染、疾病和控制动态的数学模型在 1997 年世界卫生大会决议和 2000 年启动全球消灭淋巴丝虫病规划（GPELF）之后得到了发展。模型应用可以为实施大规模规划提供有价值的决策依据。然而，这些模型需要在不同的流行病学环境中进行评估，以利用新的感染/疾病动态知识进行优化和微调。
EPIFIL 和 LYMFASIM 是目前用于淋巴丝虫病传播和控制的两种数学模拟模型。这两个模型都已用于研究环境下预测和评估控制规划。它们在评估大规模消除规划中的广泛应用需要验证不同流行病学环境下感染和疾病动态的假设。此外，通过生成关于一些构成挑战的重要问题的知识并将这些知识纳入模型中，可以提高模型对决策支持的预测能力。我们强调了与首选药物的疗效、作用模式以及药物耐药性可能发展的可能性、媒介-寄生虫组合的作用、传播阈值的大小、宿主-寄生虫相互作用及其对感染和免疫动态的影响、寄生虫生物学以及进展为与 LF 相关的疾病相关的因素。
开发的这两个数学模型为 LF 的传播和控制提供了潜在的决策工具。鉴于 GPELF 的目标，这些模型的预测能力需要得到增强，以便在大规模规划中广泛应用。将新信息吸收和转化到模型中是一个持续的过程，需要生成关于许多不确定性的新知识。特别是，更好地了解免疫机制在调节感染和疾病中的作用、当前药物的（直接或免疫介导）作用模式、它们对成虫的影响、重复治疗后的疗效以及药物耐药性的种群遗传学，是使模型在预测消除 LF 规划的影响方面更加稳健的重要因素。然而，如果这些模型要在规划管理人员手中变得易于使用（而不是作为研究工具），则有必要确定那些可以被视为在操作环境中进行简单评估和现场决策的最低必要投入/产出的因素。
