Bayesian statistical methods are increasingly being used in the analysis of parasitological data. Here, the basis of differences between the Bayesian method and the classical or frequentist approach to statistical inference is explained. This is illustrated with practical implications of Bayesian analyses using prevalence estimation of strongyloidiasis and onchocerciasis as two relevant examples. The strongyloidiasis example addresses the problem of parasitological diagnosis in the absence of a gold standard, whereas the onchocerciasis case focuses on the identification of villages warranting priority mass ivermectin treatment. The advantages and challenges faced by users of the Bayesian approach are also discussed and the readers pointed to further directions for a more in-depth exploration of the issues raised. We advocate collaboration between parasitologists and Bayesian statisticians as a fruitful and rewarding venture for advancing applied research in parasite epidemiology and the control of parasitic infections.

中文翻译：

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贝叶斯统计为寄生虫学家。

贝叶斯统计方法越来越多地用于寄生虫学数据的分析。在此，说明了贝叶斯方法与经典或惯常方法进行统计推断的区别的基础。贝叶斯分析的实际意义说明了这一点，其中使用强病菌病和盘尾丝虫病的流行估计作为两个相关示例。圆线虫病的例子解决了在没有金标准的情况下进行寄生虫学诊断的问题，而盘尾丝虫病病例则侧重于确定需要优先使用伊维菌素大规模治疗的村庄。还讨论了贝叶斯方法用户所面临的优势和挑战，读者指出了对所提出问题进行更深入探索的进一步方向。