Cost-effective control of plant disease when epidemiological knowledge is incomplete: modelling Bahia bark scaling of citrus.
Cost-effective control of plant disease when epidemiological knowledge is incomplete: modelling Bahia bark scaling of citrus.
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DOI：
10.1371/journal.pcbi.1003753
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发表时间：
2014-08
影响因子：
4.3
通讯作者：
Gilligan CA
中科院分区：
文献类型：
--
作者：
Cunniffe NJ;Laranjeira FF;Neri FM;DeSimone RE;Gilligan CA
一个空间上明确的，随机模型开发的巴伊亚树皮缩放，在巴西东北部的柑橘生产的威胁，并用于评估流行病学原则的成本效益的疾病控制策略。该模型是通过马尔可夫链蒙特卡罗与数据增强来自以前报道的多年实验的疾病传播的快照拟合。拟合优度检验强烈支持该模型的拟合，即使该疾病的详细病因尚不清楚，也未明确纳入该模型。主要流行病学参数包括感染率、潜伏期和传播规模等都是根据传播数据估计的。这使我们能够扩大实验结果，以预测在典型大小的柑橘格罗夫中初始接种物水平对疾病进展的影响。两个文化的控制措施的效力进行了评估：改变寄主植物的间距，和roguing症状的树木。如果寄主之间的距离足够大，降低种植密度可以显著减缓疾病的传播。然而，低密度的格罗夫斯每公顷的植物较少。因此，生产性植物的最佳密度是在一个中间主机间距恢复。即使对有症状的植物的检测不完善，欺骗也可以导致非常有效的控制。然而，寻找疾病症状是有成本的。我们使用该模型来平衡成本的侦察对植物的数量损失的疾病，并显示如何确定一个流氓时间表，优化利润。我们确定的两个最优的最佳主机间距和最佳roguing时间表的权衡是适用于许多pathosystems。我们的工作演示了如何仔细参数化的数学模型可以用来找到这些最优。它还说明了数学模型如何在即使是最具挑战性的情况下使用，在这种情况下，基本的流行病学是不了解的。我们考虑如何数学模型可以用来通知植物病害的控制，即使病原体的身份和生物学没有很好地理解。这是经常发生的情况：当流行病的规模仍然很小，但那时人们可能知之甚少时，对新出现的流行病的控制最有可能产生重大影响。我们分析数据从一个实验区有关传播的巴伊亚树皮剥落的柑橘，在巴西东北部的经济上重要的疾病，通过拟合的数学模型，这也占了不确定性，疾病的传播。我们的模型捕捉了该疾病的流行病学特征，揭示了传播是局部的，疾病传播相对缓慢。我们使用该模型来调查基本的权衡相关的柑橘生产规模的文化病害控制。我们展示了如何定义最佳的种植密度，它平衡了疾病传播的速度，减少了种植植物的利润。我们还展示了如何寻找和去除植物病害的成本可以与减少疾病的平衡。我们的研究是第一次考虑如何使用参数化数学模型来设计植物病害的优化文化控制。
A spatially-explicit, stochastic model is developed for Bahia bark scaling, a threat to citrus production in north-eastern Brazil, and is used to assess epidemiological principles underlying the cost-effectiveness of disease control strategies. The model is fitted via Markov chain Monte Carlo with data augmentation to snapshots of disease spread derived from a previously-reported multi-year experiment. Goodness-of-fit tests strongly supported the fit of the model, even though the detailed etiology of the disease is unknown and was not explicitly included in the model. Key epidemiological parameters including the infection rate, incubation period and scale of dispersal are estimated from the spread data. This allows us to scale-up the experimental results to predict the effect of the level of initial inoculum on disease progression in a typically-sized citrus grove. The efficacies of two cultural control measures are assessed: altering the spacing of host plants, and roguing symptomatic trees. Reducing planting density can slow disease spread significantly if the distance between hosts is sufficiently large. However, low density groves have fewer plants per hectare. The optimum density of productive plants is therefore recovered at an intermediate host spacing. Roguing, even when detection of symptomatic plants is imperfect, can lead to very effective control. However, scouting for disease symptoms incurs a cost. We use the model to balance the cost of scouting against the number of plants lost to disease, and show how to determine a roguing schedule that optimises profit. The trade-offs underlying the two optima we identify—the optimal host spacing and the optimal roguing schedule—are applicable to many pathosystems. Our work demonstrates how a carefully parameterised mathematical model can be used to find these optima. It also illustrates how mathematical models can be used in even this most challenging of situations in which the underlying epidemiology is ill-understood. We consider how mathematical models can be used to inform the control of plant disease, even when the identity and biology of the pathogen are not well understood. This is often the case: control of emerging epidemics is most likely to have a significant effect when epidemics remain small, but little may then be known. We analyse data from an experimental plot concerning spread of Bahia bark scaling of citrus, an economically-important disease in north-eastern Brazil, by fitting a mathematical model, which also accounts for uncertainty, to disease spread. Our model captures the epidemiological features of the disease, revealing that transmission is localised and that disease spreads relatively slowly. We use the model to investigate fundamental trade-offs underlying cultural disease control at scales relevant to citrus production. We show how optimal planting densities can be defined, which balance slower spread of disease against the profit that would be lost by growing fewer plants. We also show how the cost of looking for and removing symptomatically diseased plants can be balanced against the reduced disease it leads to. Ours is the first study to consider how a parameterised mathematical model can be used to design optimised cultural controls of plant disease.
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影响因子：
2.7
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