Robust parameter estimation techniques for stochastic within-host macroparasite models
Robust parameter estimation techniques for stochastic within-host macroparasite models
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DOI：
10.1016/s0022-5193(03)00266-2
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发表时间：
2003-12-21
影响因子：
2
通讯作者：
Ferguson, NM
中科院分区：
文献类型：
--
作者：
Riley, S;Donnelly, CA;Ferguson, NM
我们提出了一个随机模型的宿主内的人口动态的淋巴丝虫病，并使用模拟拟合优度（GOF）的方法来估计免疫参数和它们的置信区间从实验数据。各种确定性矩封闭近似的随机系统进行了探讨，并与模拟结果进行了比较。对于最大GOF参数估计值，没有一种封闭方法准确地再现了随机模型的行为。然而，随机模型的直接分析表明，在数据中观察到的高水平的变化可以复制，而不需要参数在主机之间变化。这表明，所观察到的寄生虫负荷的聚集可能是由针对寄生虫幼虫的有效免疫应答的发展中的随机变化动态产生的。(C)2003爱思唯尔有限公司。保留所有权利。
We present a stochastic model of the within-host population dynamics of lymphatic filariasis, and use a simulated goodness-of-fit (GOF) method to estimate immunological parameters and their confidence intervals from experimental data. A variety of deterministic moment closure approximations to the stochastic system are explored and compared with simulation results. For the maximum GOF parameter estimates, none of the methods of closure accurately reproduce the behaviour of the stochastic model. However, direct analysis of the stochastic model demonstrates that the high levels of variation observed in the data can be reproduced without requiring parameters to vary between hosts. This indicates that the observed aggregation of parasite load may be dynamically generated by random variation in the development of an effective immune response against parasite larvae. (C) 2003 Elsevier Ltd. All rights reserved.