Infectious diseases are powerful ecological forces structuring ecosystems, causing devastating economic impacts and disrupting society. Successful prevention and control of pathogens requires knowledge of the current scope and severity of disease, as well as the ability to forecast future disease dynamics. Assessment of the current situation as well as prediction of the future conditions, rely on spatially referenced information regarding the presence or absence of a pathogen, and the prevalence of the pathogen in the population. In particular, knowledge about the location of the disease front is foundational for deploying disease countermeasures to prevent further disease spread and focusing control efforts to reduce disease intensity in affected areas. In this paper, we develop a model-based approach to designing sampling strategies for wildlife disease surveillance at the disease front. Specifically, we use a mechanistic spatio-temporal model based on an underlying partial differential equation to track the disease dynamics and predict the disease prevalence in the future. We also devise an optimal surveillance system design at the disease front that takes into account the practical constraints of sampling. We evaluate the effectiveness of our proposed design via a simulation study and demonstrate the application of the proposed approach by designing a surveillance strategy for the pathogen that causes white-nose syndrome.

传染病是构建生态系统的强大生态力量，造成毁灭性的经济影响并扰乱社会。成功预防和控制病原体需要了解当前疾病的范围和严重程度，以及预测未来疾病动态的能力。对当前情况的评估以及对未来状况的预测依赖于有关病原体是否存在以及病原体在人群中的流行程度的空间参考信息。特别是，了解疾病前线的位置是部署疾病对策以防止疾病进一步传播和集中控制工作以降低受影响地区疾病强度的基础。在本文中，我们开发了一种基于模型的方法来设计疾病前沿野生动物疾病监测的采样策略。具体来说，我们使用基于基础偏微分方程的机械时空模型来跟踪疾病动态并预测未来的疾病患病率。我们还在疾病前沿设计了一个最佳的监测系统设计，其中考虑了采样的实际限制。我们通过模拟研究评估了我们提出的设计的有效性，并通过设计引起白鼻综合症的病原体的监测策略来证明所提出的方法的应用。