Modelling the spread and mitigation of an emerging vector-borne pathogen: Citrus greening in the U.S.
Fig 3
Model selection and goodness-of-fit of the full HLB spread in the Lower Rio Grande Valley, Texas.
(A) Performance of the full model and four model variants in predicting the outcome of survey trials assessed by receiver operating characteristic (ROC) curves. The area under the curve (AUC) of the ROC curve measures the predictive capability of calibrated models when used as binary classifiers to separate positive from negative (1km x 1km) sites. Models were fitted to a randomly sampled portion of survey data up to August 2016, and tested using the remaining portion and data up to August 2017. (B) Performance of the full model in predicting the outcome of survey trials within and beyond the temporal scope of the Texas dataset used for parameter estimation in Table 1. The model was fitted to a randomly sampled portion (80%) of the training data (collected between December 2011 to August 2016) and tested using the remaining part (20%) of the training data and the testing data (collected between September 2016 and October 2018) for the ROC analysis. (C) Temporal progression of the prevalence of three infection categories (Exposed, Infectious, and Detected) for the full model compared with the surveillance data. Besides the medians of 1000 simulation realizations (solid lines), we also show 50%, 75%, and 95% credible intervals (shades of decreasing intensities). The vertical dotted line separates the training dataset (used for parameter estimation) from the test dataset. (D) Spatial autocorrelation scores using Moran’s I of the ‘detected’ categories of the model with the survey data at the end of August 2017: medians of 1000 simulation realizations (red line) with 50%, 75%, and 95% credible intervals (shades of decreasing intensities).