Ducheyne Els, Miranda Chueca Miguel A, Lucientes J, Calvete C, Estrada R, Boender Gert-Jan, Goossens Els, De Clercq Eva M, Hendrickx Guy
Geospat Health. 2013 Nov;8(1):241-54. doi: 10.4081/gh.2013.70.
In this paper we present a novel methodology applied in Spain to model spatial abundance patterns of potential vectors of disease at a medium spatial resolution of 5 x 5 km using a countrywide database with abundance data for five Culicoides species, random regression Forest modelling and a spatial dataset of ground measured and remotely sensed eco-climatic and environmental predictor variables. First the probability of occurrence was computed. In a second step a direct regression between the probability of occurrence and trap abundance was established to verify the linearity of the relationship. Finally the probability of occurrence was used in combination with the set of predictor variables to model abundance. In each case the variable importance of the predictors was used to biologically interpret results and to compare both model outputs, and model performance was assessed using four different accuracy measures. Results are shown for C. imicola, C. newsteadii, C. pulicaris group, C. punctatus and C. obsoletus group. In each case the probability of occurrence is a good predictor of abundance at the used spatial resolution of 5 x 5 km. In addition, the C. imicola and C. obsoletus group are highly driven by summer rainfall. The spatial pattern is inverse between the two species, indicating that the lower and upper thresholds are different. C. pulicaris group is mainly driven by temperature. The patterns for C. newsteadii and C. punctatus are less clear. It is concluded that the proposed methodology can be used as an input to transmission-infection-recovery (TIR) models and R0 models. The methodology will become available to the general public as part of the VECMAP™ software.
在本文中，我们介绍了一种在西班牙应用的新方法，该方法使用一个包含5种库蠓物种丰度数据的全国性数据库、随机回归森林建模以及一组地面测量和遥感生态气候与环境预测变量的空间数据集，在5×5千米的中等空间分辨率下对疾病潜在媒介的空间丰度模式进行建模。首先计算出现概率。第二步，建立出现概率与诱捕丰度之间的直接回归，以验证两者关系的线性。最后，将出现概率与预测变量集结合起来对丰度进行建模。在每种情况下，利用预测变量的重要性对结果进行生物学解释并比较两个模型的输出，同时使用四种不同的准确性度量来评估模型性能。展示了伊氏库蠓、纽氏库蠓、普氏库蠓组、斑点库蠓和废弃库蠓组的结果。在每种情况下，出现概率都是在5×5千米的所用空间分辨率下丰度的良好预测指标。此外，伊氏库蠓和废弃库蠓组受夏季降雨的影响很大。这两个物种的空间模式相反，表明下限和上限不同。普氏库蠓组主要受温度驱动。纽氏库蠓和斑点库蠓的模式不太清晰。得出的结论是，所提出的方法可作为传播-感染-恢复（TIR）模型和R0模型的输入。该方法将作为VECMAP™软件的一部分向公众提供。
