Department of Statistics, College of Liberal Arts and Sciences, Iowa State University, Ames, IA, United States.
Department of Veterinary Diagnostic and Production Animal Medicine, College of Veterinary Medicine, Iowa State University, Ames, IA, United States.
Prev Vet Med. 2021 Feb;187:105233. doi: 10.1016/j.prevetmed.2020.105233. Epub 2020 Dec 10.
In this study, five spatially balanced sampling methods, i.e., generalized random-tessellation stratified (GRTS), local pivotal method (LPM), spatially correlated Poisson sampling (SCPS), local cube method (LCUBE), and balanced acceptance sampling (BAS) were compared to simple random sampling (SRS) based on a livestock disease transmission model on a hypothetical region (195 km × 300 km) populated with 6000 farms in terms of the probability of detection by sample size. Given a fixed sample size, four of the five spatially balanced sampling methods provided better performance than SRS, i.e., higher probabilities of detecting at least one infected farms over a range of regional prevalence evaluated (1%-5%). That is, for any given probability of detection, spatially balanced methods required testing fewer farms than SRS. In an era of pandemics, active regional surveillance for early detection of emerging pathogens becomes urgent, yet shrinking budgets impose intractable constraints. The better performance and higher efficiency of spatially balanced sampling methods suggests a potential improvement in regional livestock disease surveillances and a partial solution to the challenge of affordable surveillance.
在这项研究中，我们比较了五种空间平衡抽样方法（广义随机网格分层抽样（GRTS）、局部枢轴方法（LPM）、空间相关泊松抽样（SCPS）、局部立方方法（LCUBE）和平衡接受抽样（BAS））与简单随机抽样（SRS），依据是在一个假设的区域（195 公里×300 公里）上的牲畜疾病传播模型，该区域居住着 6000 个农场，以样本量为基础的检测概率。在固定样本量的情况下，五种空间平衡抽样方法中的四种方法的性能优于 SRS，即在评估的区域流行率范围内（1%-5%），检测到至少一个感染农场的概率更高。也就是说，对于任何给定的检测概率，空间平衡方法所需的检测农场数量都少于 SRS。在大流行时代，主动进行区域监测以早期发现新出现的病原体变得紧迫，但不断缩减的预算带来了难以克服的限制。空间平衡抽样方法的更好性能和更高效率表明，区域牲畜疾病监测可能会得到改善，这也是解决负担得起的监测这一挑战的部分方法。
