Refined stratified-worm-burden models that incorporate specific biological features of human and snail hosts provide better estimates of Schistosoma diagnosis, transmission, and control
Refined stratified-worm-burden models that incorporate specific biological features of human and snail hosts provide better estimates of Schistosoma diagnosis, transmission, and control
复制标题
DOI：
10.1186/s13071-016-1681-4
复制
发表时间：
2016-08-04
影响因子：
3.2
通讯作者：
Li, Emily
中科院分区：
文献类型：
--
作者：
Gurarie, David;King, Charles H.;Li, Emily
背景资料：血吸虫寄生虫维持一个复杂的传播过程，在一个最终的人类宿主，两个自由游泳的幼虫阶段和一个中间的蜗牛宿主之间循环。多种因素改变其传播和影响其控制，包括宿主种群和环境的异质性，人类蠕虫负担的聚集分布，以及寄生虫繁殖和宿主蜗牛生物学的特征。由于这些因素有助于增强局部传播，因此将其纳入血吸虫病控制计划结果的准确定量预测中非常重要。然而，它们的包含提出了许多数学和计算挑战。为了解决这些问题，我们最近开发了一个易于处理的分层蠕虫负担（SWB）模型，该模型介于简单的确定性平均蠕虫负担模型和计算密集型的自治代理模型之间。为了改进模型预测的准确性，我们通过纳入代表基本宿主生物学的因素来修改早期版本的SWB（寄生虫交配，聚集，密度依赖的繁殖力，和随机产卵释放）到人口结构的主机社区。我们还修改了传播模型的蜗牛成分，以反映人与蜗牛传播的饱和形式。新的模型使我们能够逼真地模拟过度分散的鸡蛋测试结果中观察到的个人水平的字段数据。我们进一步发展了一种贝叶斯型校正方法，该方法考虑了模型和数据的不确定性。肯尼亚沿海地区的埃及血吸虫感染。我们成功地推导出年龄特异性估计的蠕虫负担分布和蠕虫繁殖力和拥挤函数的儿童和成人。新的主观幸福感模型的估计值进行了比较，从旧的，简单的主观幸福感与一些显着的差异。我们验证了我们的新的SWB估计预测药物治疗为基础的控制结果为一个典型的Kenyan community.Conclusions：新版本的SWB模型提供了一个更好的工具来预测正在进行的血吸虫病控制计划的结果。它反映了寄生虫增加和延续传播的特征，同时它也很容易纳入诊断检测的差异和治疗覆盖率的人类亚群差异。一旦扩展到其他血吸虫物种和传播环境，它将提供一个有用和有效的工具，规划控制和消除战略。
Background: Schistosoma parasites sustain a complex transmission process that cycles between a definitive human host, two free-swimming larval stages, and an intermediate snail host. Multiple factors modify their transmission and affect their control, including heterogeneity in host populations and environment, the aggregated distribution of human worm burdens, and features of parasite reproduction and host snail biology. Because these factors serve to enhance local transmission, their inclusion is important in attempting accurate quantitative prediction of the outcomes of schistosomiasis control programs. However, their inclusion raises many mathematical and computational challenges. To address these, we have recently developed a tractable stratified worm burden (SWB) model that occupies an intermediate place between simpler deterministic mean worm burden models and the very computationally-intensive, autonomous agent models.Methods: To refine the accuracy of model predictions, we modified an earlier version of the SWB by incorporating factors representing essential in-host biology (parasite mating, aggregation, density-dependent fecundity, and random egg-release) into demographically structured host communities. We also revised the snail component of the transmission model to reflect a saturable form of human-to-snail transmission. The new model allowed us to realistically simulate overdispersed egg-test results observed in individual-level field data. We further developed a Bayesian-type calibration methodology that accounted for model and data uncertainties.Results: The new model methodology was applied to multi-year, individual-level field data on S. haematobium infections in coastal Kenya. We successfully derived age-specific estimates of worm burden distributions and worm fecundity and crowding functions for children and adults. Estimates from the new SWB model were compared with those from the older, simpler SWB with some substantial differences noted. We validated our new SWB estimates in prediction of drug treatment-based control outcomes for a typical Kenyan community.Conclusions: The new version of the SWB model provides a better tool to predict the outcomes of ongoing schistosomiasis control programs. It reflects parasite features that augment and perpetuate transmission, while it also readily incorporates differences in diagnostic testing and human sub-population differences in treatment coverage. Once extended to other Schistosoma species and transmission environments, it will provide a useful and efficient tool for planning control and elimination strategies.