Giles John R, Peel Alison J, Wells Konstans, Plowright Raina K, McCallum Hamish, Restif Olivier
Department of Epidemiology Johns Hopkins University Bloomberg School of Public Health Baltimore MD USA.
Environmental Futures Research Institute Griffith University Brisbane Qld Australia.
Ecol Evol. 2021 Aug 27;11(18):12307-12321. doi: 10.1002/ece3.7830. eCollection 2021 Sep.
Outbreaks of infectious viruses resulting from spillover events from bats have brought much attention to bat-borne zoonoses, which has motivated increased ecological and epidemiological studies on bat populations. Field sampling methods often collect pooled samples of bat excreta from plastic sheets placed under-roosts. However, positive bias is introduced because multiple individuals may contribute to pooled samples, making studies of viral dynamics difficult. Here, we explore the general issue of bias in spatial sample pooling using Hendra virus in Australian bats as a case study. We assessed the accuracy of different under-roost sampling designs using generalized additive models and field data from individually captured bats and pooled urine samples. We then used theoretical simulation models of bat density and under-roost sampling to understand the mechanistic drivers of bias. The most commonly used sampling design estimated viral prevalence 3.2 times higher than individual-level data, with positive bias 5-7 times higher than other designs due to spatial autocorrelation among sampling sheets and clustering of bats in roosts. Simulation results indicate using a stratified random design to collect 30-40 pooled urine samples from 80 to 100 sheets, each with an area of 0.75-1 m, and would allow estimation of true prevalence with minimum sampling bias and false negatives. These results show that widely used under-roost sampling techniques are highly sensitive to viral presence, but lack specificity, providing limited information regarding viral dynamics. Improved estimation of true prevalence can be attained with minor changes to existing designs such as reducing sheet size, increasing sheet number, and spreading sheets out within the roost area. Our findings provide insight into how spatial sample pooling is vulnerable to bias for a wide range of systems in disease ecology, where optimal sampling design is influenced by pathogen prevalence, host population density, and patterns of aggregation.
蝙蝠溢出事件导致的传染性病毒爆发引起了人们对蝙蝠传播的人畜共患病的广泛关注，这推动了对蝙蝠种群的生态和流行病学研究的增加。现场采样方法通常从放置在栖息地下方的塑料布上收集蝙蝠排泄物的混合样本。然而，由于多个个体可能对混合样本有贡献，从而引入了正偏差，使得病毒动态研究变得困难。在这里，我们以澳大利亚蝙蝠中的亨德拉病毒为例，探讨空间样本混合中偏差的一般问题。我们使用广义相加模型以及来自单独捕获的蝙蝠和尿液混合样本的现场数据，评估了不同的栖息地下方采样设计的准确性。然后，我们使用蝙蝠密度和栖息地下方采样的理论模拟模型来了解偏差的机制驱动因素。最常用的采样设计估计的病毒流行率比个体水平数据高3.2倍，由于采样片之间的空间自相关以及蝙蝠在栖息地中的聚集，其正偏差比其他设计高5至7倍。模拟结果表明，采用分层随机设计，从面积为0.75至1平方米的80至100个采样片上收集30至40个尿液混合样本，将能够以最小的采样偏差和假阴性估计真实流行率。这些结果表明，广泛使用的栖息地下方采样技术对病毒的存在高度敏感，但缺乏特异性，提供的病毒动态信息有限。通过对现有设计进行微小改变，如减小采样片尺寸、增加采样片数量以及在栖息地区域内分散采样片，可以实现对真实流行率的更准确估计。我们的研究结果为了解空间样本混合在疾病生态学中的广泛系统中如何容易受到偏差影响提供了见解，其中最佳采样设计受病原体流行率、宿主种群密度和聚集模式的影响。
