Emerging infectious diseases (EIDs) of plants continue to devastate ecosystems and livelihoods worldwide. Effective management requires surveillance to detect epidemics at an early stage. However, despite the increasing use of risk-based surveillance programs in plant health, it remains unclear how best to target surveillance resources to achieve this. We combine a spatially explicit model of pathogen entry and spread with a statistical model of detection and use a stochastic optimisation routine to identify which arrangement of surveillance sites maximises the probability of detecting an invading epidemic. Our approach reveals that it is not always optimal to target the highest-risk sites and that the optimal strategy differs depending on not only patterns of pathogen entry and spread but also the choice of detection method. That is, we find that spatial correlation in risk can make it suboptimal to focus solely on the highest-risk sites, meaning that it is best to avoid ‘putting all your eggs in one basket’. However, this depends on an interplay with other factors, such as the sensitivity of available detection methods. Using the economically important arboreal disease huanglongbing (HLB), we demonstrate how our approach leads to a significant performance gain and cost saving in comparison with conventional methods to targeted surveillance.

植物的新发传染病（EID）继续破坏着全世界的生态系统和生计。有效的管理要求进行监视以及早发现流行病。但是，尽管在植物健康中越来越多地使用基于风险的监视程序，但仍不清楚如何最好地利用监视资源来实现这一目标。我们将病原体进入和传播的空间显式模型与检测的统计模型结合在一起，并使用随机优化例程来确定哪些监视站点安排可以最大程度地检测出入侵流行病的可能性。我们的方法表明，针对高风险部位并非总是最优的，而且最优策略不仅取决于病原体进入和传播的方式，还取决于检测方法的选择。那是，我们发现，风险中的空间相关性可能使仅将注意力集中在最高风险的站点上是次优的，这意味着最好避免“将所有鸡蛋放入一个篮子中”。但是，这取决于与其他因素的相互作用，例如可用检测方法的灵敏度。使用经济上重要的树栖疾病黄龙病（HLB），我们证明了与针对目标监视的常规方法相比，该方法如何显着提高性能并节省成本。