Data from: Imperfect pathogen detection from non-invasive skin swabs biases disease inference

1. Conservation managers rely on accurate estimates of disease parameters, such as pathogen prevalence and infection intensity, to assess disease status of a host population. However, these disease metrics may be biased if low-level infection intensities are missed by sampling methods or laboratory diagnostic tests. These false negatives underestimate pathogen prevalence and overestimate mean infection intensity of infected individuals.

2. Our objectives were two-fold. First, we quantified false negative error rates of Batrachochytrium dendrobatidis on non-invasive skin swabs collected from an amphibian community in El Copé, Panama. We swabbed amphibians twice in sequence, and we used a recently developed hierarchical Bayesian estimator to assess disease status of the population. Second, we developed a novel hierarchical Bayesian model to simultaneously account for imperfect pathogen detection from field sampling and laboratory diagnostic testing. We evaluated the performance of the model using simulations and varying sampling design to quantify the magnitude of bias in estimates of pathogen prevalence and infection intensity.

3. We show that Bd detection probability from skin swabs was related to host infection intensity, where Bd infections < 10 zoospores have < 95% probability of being detected. If imperfect Bd detection was not considered, then Bd prevalence was underestimated by as much as 16%. In the Bd-amphibian system, this indicates a need to correct for imperfect pathogen detection caused by skin swabs in persisting host communities with low-level infections. More generally, our results have implications for study designs in other disease systems, particularly those with similar objectives, biology, and sampling decisions.

4. Uncertainty in pathogen detection is an inherent property of most sampling protocols and diagnostic tests, where the magnitude of bias depends on the study system, type of infection, and false negative error rates. Given that it may be difficult to know this information in advance, we advocate that the most cautious approach is to assume all errors are possible and to accommodate them by adjusting sampling designs. The modeling framework presented here improves the accuracy in estimating pathogen prevalence and infection intensity.

1. 野生动物保护管理者需依托精准的疾病参数估计值——如病原体流行率（pathogen prevalence）与感染强度（infection intensity）——以评估宿主种群的疾病状况。然而，若采样方法或实验室诊断检测未能检出低水平感染，此类疾病指标可能产生偏差。这类假阴性结果会低估病原体流行率，同时高估受感染个体的平均感染强度。
2. 本研究的目标分为两部分：其一，针对从巴拿马埃尔科佩（El Copé）两栖动物群落采集的无创皮肤拭子样本，量化蛙壶菌（Batrachochytrium dendrobatidis，Bd）的假阴性误差率。我们对两栖动物先后开展两次拭样，并采用新近开发的分层贝叶斯估计器（hierarchical Bayesian estimator）评估种群疾病状况。其二，我们构建了一种全新的分层贝叶斯模型，可同时校正野外采样与实验室诊断检测中病原体检测不完全的问题。我们通过模拟实验与不同采样设计评估该模型的性能，以量化病原体流行率与感染强度估计值的偏差幅度。
3. 研究结果表明，皮肤拭子的Bd检测概率与宿主感染强度相关：当感染强度低于10个游动孢子（zoospores）时，检测概率不足95%。若未考虑Bd检测不完全的问题，Bd流行率的低估幅度可达16%。在Bd-两栖动物研究体系中，这提示对于存在低水平感染的存续宿主群落，需要校正皮肤拭子采样导致的病原体检测不完全偏差。从更广泛的视角来看，本研究结果可为其他疾病研究体系的实验设计提供参考，尤其是那些具有相似研究目标、生物学特征与采样策略的体系。
4. 病原体检测的不确定性是多数采样方案与诊断检测的固有属性，其偏差幅度取决于研究体系、感染类型与假阴性误差率。鉴于此类信息往往难以提前获取，我们建议最稳妥的策略是假设所有误差均可能发生，并通过优化采样设计加以校正。本文提出的建模框架可提升病原体流行率与感染强度的估计精度。