Data from: Application of a Bayesian weighted surveillance approach for detecting chronic wasting disease in white-tailed deer

SUMMARY 1. Surveillance is critical for the early detection of emerging and re-emerging infectious diseases, and weighted surveillance uses heterogeneity in risk of infection to increase the sampling efficiency. 2. We apply a Bayesian approach to estimate weights for 16 surveillance classes of white-tailed deer in Wisconsin, USA, relative to hunter-harvested yearling males. We use these weights to conduct a surveillance program for detecting chronic wasting disease (CWD) in white-tailed deer at Shenandoah National Park (SHEN) in Virginia, USA. 3. Generally, for surveillance, risk of infection increased with age and was greater in males. Clinical suspect deer had the highest risk with weight estimates of 33.33 and 9.09, for community reported and hunter reported suspect deer, respectively, while fawns had the lowest risk with an estimated weight of 0.001. 4. We used surveillance weights for Wisconsin deer to determine sampling effort required to detect a CWD-positive case in SHEN if prevalence in yearling males ≥0.025. The sampling required to detect CWD was 37–91 adult deer, depending on the adult male:female ratio in the surveillance stream. We collected rectal biopsies from 49 and 21 adult female and male deer, respectively, and 10 additional samples from vehicle-killed deer. CWD was not detected and we concluded with 95% probability that prevalence in the reference population (yearling males) was between 0.0 to 3.6%. 5. Synthesis and applications. Our approach allows managers to estimate relative surveillance weights for different host classes and quantify limits of disease detection in real time when only a sample of animals from a population can be tested, resulting in considerable cost savings for agencies performing wildlife disease detection surveillance. Additionally, it provides a rigorous means of estimating prevalence limits when a disease/pathogen is not detected in a sample set, and is generalizable to other wildlife, domestic animal, and human disease systems which can be characterized by surveillance classes with heterogeneous probability of infection. This methodology is also extendable to other disciplines such as invasive species, environmental toxicology, and generally any ecological question seeking to efficiently use scarce financial and human resources to maximize the detection probability of a rare event.概述 1. 监测对于新发与再现传染病的早期发现至关重要，加权监测通过利用感染风险的异质性提升采样效率。 2. 我们采用贝叶斯方法，针对美国威斯康星州白尾鹿的16类监测群体，以猎获的周岁公鹿为参照估算其权重；并利用这些权重，在美国弗吉尼亚州仙纳度国家公园（Shenandoah National Park, SHEN）开展白尾鹿慢性消耗病（CWD）监测项目。 3. 总体而言，监测场景下的感染风险随年龄增长而升高，且雄性个体风险更高。临床疑似鹿只的感染风险最高，其中社区上报与猎人上报的疑似鹿只的权重估算值分别为33.33与9.09；而幼鹿风险最低，权重估算值仅为0.001。 4. 我们以威斯康星州白尾鹿的监测权重为基础，当周岁公鹿的患病率≥0.025时，测算出在仙纳度国家公园检出慢性消耗病阳性病例所需的采样工作量。根据监测群体中成年公母鹿的比例差异，所需采样的成年鹿数量为37~91头。我们分别采集了49头成年母鹿与21头成年公鹿的直肠活检样本，另从车辆致死鹿只中采集了10份样本。本次监测未检出慢性消耗病，据此我们以95%的置信度判定参照群体（周岁公鹿）的患病率介于0.0至3.6%之间。 5. 总结与应用：本方法可帮助管理者针对不同宿主群体估算相对监测权重，并在仅能对种群中部分个体开展检测的场景下，实时量化疾病检出的置信区间，为开展野生动物疾病监测的机构节省大量成本。此外，当样本集未检出某一疾病或病原体时，该方法可提供严谨的患病率上限估算方式，且可推广至其他以异质性感染风险监测群体为特征的野生动物、家畜及人类疾病系统。该方法还可拓展至入侵物种、环境毒理学等其他学科领域，以及所有旨在通过稀缺的财力与人力资源最大化稀有事件检出概率的生态学研究场景。