Mathematical Institute, University of Oxford, Oxford, United Kingdom.
Warwick Crop Centre, School of Life Sciences, University of Warwick, Coventry, United Kingdom.
PLoS Comput Biol. 2023 Feb 2;19(2):e1010884. doi: 10.1371/journal.pcbi.1010884. eCollection 2023 Feb.
Infectious diseases of plants present an ongoing and increasing threat to international biosecurity, with wide-ranging implications. An important challenge in plant disease management is achieving early detection of invading pathogens, which requires effective surveillance through the implementation of appropriate monitoring programmes. However, when monitoring relies on visual inspection as a means of detection, surveillance is often hindered by a long incubation period (delay from infection to symptom onset) during which plants may be infectious but not displaying visible symptoms. 'Sentinel' plants-alternative susceptible host species that display visible symptoms of infection more rapidly-could be introduced to at-risk populations and included in monitoring programmes to act as early warning beacons for infection. However, while sentinel hosts exhibit faster disease progression and so allow pathogens to be detected earlier, this often comes at a cost: faster disease progression typically promotes earlier onward transmission. Here, we construct a computational model of pathogen transmission to explore this trade-off and investigate how including sentinel plants in monitoring programmes could facilitate earlier detection of invasive plant pathogens. Using Xylella fastidiosa infection in Olea europaea (European olive) as a current high profile case study, for which Catharanthus roseus (Madagascan periwinkle) is a candidate sentinel host, we apply a Bayesian optimisation algorithm to determine the optimal number of sentinel hosts to introduce for a given sampling effort, as well as the optimal division of limited surveillance resources between crop and sentinel plants. Our results demonstrate that including sentinel plants in monitoring programmes can reduce the expected prevalence of infection upon outbreak detection substantially, increasing the feasibility of local outbreak containment.
植物传染病对国际生物安全构成持续且日益严重的威胁，具有广泛的影响。植物病害管理的一个重要挑战是实现入侵病原体的早期检测，这需要通过实施适当的监测计划来实现有效监测。然而，当监测依赖于视觉检查作为检测手段时，监测往往会受到较长的潜伏期（从感染到症状出现的延迟）的阻碍，在此期间，植物可能具有传染性但没有表现出可见的症状。“哨兵”植物——替代的易感宿主物种，它们更快地表现出感染的可见症状——可以引入到高危种群中，并纳入监测计划，作为感染的早期预警信号。然而，虽然哨兵宿主表现出更快的疾病进展，从而可以更早地检测到病原体，但这通常是有代价的：更快的疾病进展通常会促进更早的传播。在这里，我们构建了一个病原体传播的计算模型，以探讨这种权衡，并研究在监测计划中纳入哨兵植物如何有助于更早地发现入侵植物病原体。我们以 Xylella fastidiosa 感染 Olea europaea（欧洲橄榄）为例，作为当前备受关注的案例研究，Catharanthus roseus（马达加斯加长春花）是候选的哨兵宿主，我们应用贝叶斯优化算法来确定在给定采样工作量下引入哨兵宿主的最佳数量，以及在作物和哨兵植物之间分配有限监测资源的最佳方式。我们的结果表明，在监测计划中纳入哨兵植物可以大大降低爆发检测时感染的预期流行率，从而提高局部爆发控制的可行性。
