利用数学模型预测消除淋巴丝虫病规划影响的进展和挑战。

Wilma A Stolk, Sake J de Vlas, J Dik F Habbema
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引用次数: 37

摘要

淋巴丝虫病传播和控制的数学模拟模型是研究消除淋巴丝虫病前景的有用工具。目前正在使用两种模拟模型。第一个是EPIFIL,这是一个基于人群的确定性模型,模拟感染强度随时间的平均趋势。第二个是LYMFASIM,这是一个基于个体的随机模型,它模拟了模拟人群中每个个体感染和失去感染的情况,并考虑了个体特征。对于像印度本地治里(Pondicherry)这样由致倦库蚊传播班氏乌chereria bancrofti感染的地区,这些模型给出了将微丝虫病流行率降至0.5%以下所需的覆盖率和治疗轮数的类似预测。然而,由于使用了不同的消除指标(EPIFIL:最后一次治疗后微丝虫病患病率< 0.5%;在开始大规模治疗后的40年内,将微丝虫病患病率降至零。未来建模工作的两个主要挑战是:1)对其他地区的模型进行量化和验证,以调查与本地治里相比,其他媒介-寄生虫组合和流行水平的情况下的消除前景;2)应用这些模型来解决与正在进行的控制方案的监测和评价有关的一系列方案问题。模型的有用性可以通过几个扩展来增强;在模型中纳入不同的诊断测试和疾病的自然病史是特别相关的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Advances and challenges in predicting the impact of lymphatic filariasis elimination programmes by mathematical modelling.

Advances and challenges in predicting the impact of lymphatic filariasis elimination programmes by mathematical modelling.

Advances and challenges in predicting the impact of lymphatic filariasis elimination programmes by mathematical modelling.

Advances and challenges in predicting the impact of lymphatic filariasis elimination programmes by mathematical modelling.

Mathematical simulation models for transmission and control of lymphatic filariasis are useful tools for studying the prospects of lymphatic filariasis elimination. Two simulation models are currently being used. The first, EPIFIL, is a population-based, deterministic model that simulates average trends in infection intensity over time. The second, LYMFASIM, is an individual-based, stochastic model that simulates acquisition and loss of infection for each individual in the simulated population, taking account of individual characteristics. For settings like Pondicherry (India), where Wuchereria bancrofti infection is transmitted by Culex quinquefasciatus, the models give similar predictions of the coverage and number of treatment rounds required to bring microfilaraemia prevalence below a level of 0.5%. Nevertheless, published estimates of the duration of mass treatment required for elimination differed, due to the use of different indicators for elimination (EPIFIL: microfilaraemia prevalence < 0.5% after the last treatment; LYMFASIM: reduction of microfilaraemia prevalence to zero, within 40 years after the start of mass treatment). The two main challenges for future modelling work are: 1) quantification and validation of the models for other regions, for investigation of elimination prospects in situations with other vector-parasite combinations and endemicity levels than in Pondicherry; 2) application of the models to address a range of programmatic issues related to the monitoring and evaluation of ongoing control programmes. The models' usefulness could be enhanced by several extensions; inclusion of different diagnostic tests and natural history of disease in the models is of particular relevance.

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