Belsare Aniruddha, Gompper Matthew, Keller Barbara, Sumners Jason, Hansen Lonnie, Millspaugh Joshua
Boone & Crockett Quantitative Wildlife Center, Michigan State University, East Lansing, MI 48824, United States.
Department of Fish, Wildlife and Conservation Ecology, New Mexico State University, Las Cruces, NM 88003, United States.
MethodsX. 2020 Jun 11;7:100953. doi: 10.1016/j.mex.2020.100953. eCollection 2020.
Epidemiological surveillance for many important wildlife diseases relies on samples obtained from hunter-harvested animals. Statistical methods used to calculate sample size requirements assume that the target population is randomly sampled, and therefore the samples are representative of the population. But hunter-harvested samples may not be representative of the population due to disease distribution heterogeneities (e.g. spatial clustering of infected individuals), and harvest-related non-random processes like regulations, hunter selectivity, variable land access, and uneven hunter distribution. Consequently, sample sizes necessary for detection of disease are underestimated and disease detection probabilities are overestimated, resulting in erroneous inferences about disease presence and distribution. We have developed a modeling framework to support the design of efficient disease surveillance programs for wildlife populations. The constituent agent-based models can incorporate real-world heterogeneities associated with disease distribution, harvest, and harvest-based sampling, and can be used to determine population-specific sample sizes necessary for prompt detection of important wildlife diseases like chronic wasting disease and bovine tuberculosis. The modeling framework and its application has been described in detail by Belsare et al. [1]. Here we describe how model scenarios were developed and implemented, and how model outputs were analyzed. The main objectives of this methods paper are to provide users the opportunity to a) assess the reproducibility of the published model results, b) gain an in-depth understanding of model analysis, and c) facilitate adaptation of this modeling framework to other regions and other wildlife disease systems.•The two agent-based models, MOPOP and MOPOP, incorporate real-world heterogeneities underpinned by host characteristics, disease spread dynamics, and sampling biases in hunter-harvested deer.•The modeling framework facilitates iterative analysis of locally relevant disease surveillance scenarios, thereby facilitating sample size calculations for prompt and reliable detection of important wildlife diseases.•Insights gained from modeling studies can be used to inform the design of effective wildlife disease surveillance strategies.
许多重要野生动物疾病的流行病学监测依赖于从猎人捕获的动物身上获取的样本。用于计算样本量需求的统计方法假定目标种群是随机抽样的，因此样本能够代表总体。但由于疾病分布的异质性（例如受感染个体的空间聚集）以及与捕猎相关的非随机过程，如法规、猎人选择性、不同的土地获取情况和猎人分布不均等，猎人捕获的样本可能无法代表总体。因此，疾病检测所需的样本量被低估，疾病检测概率被高估，从而导致对疾病存在和分布的错误推断。我们开发了一个建模框架，以支持针对野生动物种群设计高效的疾病监测计划。基于主体的构成模型可以纳入与疾病分布、捕猎及基于捕猎的抽样相关的现实世界异质性，并可用于确定及时检测慢性消耗病和牛结核病等重要野生动物疾病所需的特定种群样本量。Belsare等人[1]已详细描述了该建模框架及其应用。在此，我们描述模型情景是如何开发和实施的，以及模型输出是如何分析的。本方法论文的主要目标是为用户提供机会，a）评估已发表模型结果的可重复性，b）深入了解模型分析，c）促进该建模框架适用于其他地区和其他野生动物疾病系统。
• 两个基于主体的模型，MOPOP和MOPOP，纳入了由宿主特征、疾病传播动态以及猎人捕获鹿时的抽样偏差所支撑的现实世界异质性。
• 该建模框架有助于对当地相关疾病监测情景进行迭代分析，从而便于计算样本量，以便及时、可靠地检测重要野生动物疾病。
• 从建模研究中获得的见解可用于为有效的野生动物疾病监测策略设计提供信息。
