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首页> 外文期刊>Parasitology >Bayesian geostatistical modelling for mapping schistosomiasis transmission.
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Bayesian geostatistical modelling for mapping schistosomiasis transmission.

机译：用于映射血吸虫病传播的贝叶斯地统计模型。

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摘要

Progress has been made in mapping and predicting the risk of schistosomiasis using Bayesian geostatistical inference. Applications primarily focused on risk profiling of prevalence rather than infection intensity, although the latter is particularly important for morbidity control. In this review, the underlying assumptions used in a study mapping Schistosoma mansoni infection intensity in East Africa are examined. We argue that the assumption of stationarity needs to be relaxed, and that the negative binomial assumption might result in misleading inference because of a high number of excess zeros (individuals without an infection). We developed a Bayesian geostatistical zero-inflated (ZI) regression model that assumes a non-stationary spatial process. Our model is validated with a high-quality georeferenced database from western Cote d'Ivoire, consisting of demographic, environmental, parasitological and socio-economic data. Nearly 40% of the 3818 participating schoolchildren were infected with S. mansoni, and the mean egg count among infected children was 162 eggs per gram of stool (EPG), ranging between 24 and 6768 EPG. Compared to a negative binomial and ZI Poisson and negative binomial models, the Bayesian non-stationary ZI negative binomial model showed a better fit to the data. We conclude that geostatistical ZI models produce more accurate maps of helminth infection intensity than the spatial negative binomial ones.
机译：利用贝叶斯地统计学推论在绘制和预测血吸虫病风险方面取得了进展。应用程序主要关注流行率的风险分析，而不是感染强度，尽管后者对于控制发病率特别重要。在这篇综述中，调查了映射东非曼氏血吸虫感染强度的研究中使用的基本假设。我们认为平稳性的假设需要放宽，负二项式假设可能会导致误导性推断，因为存在过多的零（个人没有感染）。我们开发了一个假设非平稳空间过程的贝叶斯地统计零膨胀（ZI）回归模型。我们的模型已通过科特迪瓦西部的高质量地理参考数据库进行了验证，该数据库包含人口，环境，寄生虫学和社会经济数据。 3818名参与的学童中有近40％感染了曼氏链球菌，感染儿童的平均卵数为每克粪便（EPG）162卵，EPG在24至6768之间。与负二项式和ZI泊松模型以及负二项式模型相比，贝叶斯非平稳ZI负二项式模型显示出更好的数据拟合性。我们得出的结论是，与空间负二项式方法相比，地统计ZI模型可生成更准确的蠕虫感染强度图。

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