准确计算微小隐孢子虫的难度使得建议或 合理执行针对这种病原体的监管指南。最近的调查显示 计算微小隐孢子虫和微小隐孢子虫置信区间的统计方法 处理过程中的浓度和清除量。这些方法解释了各种原因 分析误差（代表性抽样、随机分析误差和非恒定分析误差） 回收率）与微小隐球菌浓度和计数有关。采用泊松分布 用于模拟真实样本数（代表性抽样），二项分布用于建模 卵囊的恢复分数（随机分析误差）和β分布来描述 非恒定分析回收率。这一基本统计框架用于:证明分析回收率曲线和 以浓度为基础的监管限值对原料和原料中微小隐球菌浓度的可行性 净化水；并提出采样目标，以提高病原体的可靠性 浓度和去除数据。 包括7个参考文献、表格、图表。

The difficulty in accurately enumerating C. parvum has made it impractical to suggest or reasonably enforce regulatory guidelines for this pathogen. Recent investigations have presented statistical approaches for the calculation of confidence intervals for both C. parvum concentrations and removals by treatment processes. These approaches account for the various analytical errors (representative sampling, random analytical error, and non-constant analytical recovery) associated with C. parvum concentration and enumeration. A Poisson distribution was utilized to model true sample counts (representative sampling), a binomial distribution to model the recovered fraction of oocysts (random analytical error), and a Beta distribution to describe non-constant analytical recovery. This fundamental statistical framework was utilized to: demonstrate the relationship between analytical recovery profiles and the feasibility of concentration-based regulatory limits for C. parvum concentrations in raw and treated waters; and, propose sampling objectives to increase the reliability of pathogen concentration and removal data. Includes 7 references, tables, figures.