Fig 1.
Overview of the model for the Xylella fastidiosa outbreak among olive trees in southern Italy.
Boxes show the compartments of the epidemiological model (blue boxes), model for the regional disease surveillance and containment (bright orange), remote sensing of severe tree damage (dark orange) and summary statistics produced from the model for fitting to observed data with Approximate Bayesian Computation (ABC) (green). Influences of key parameters and variables are labelled (see main text and Table 1 for explanation).
Table 1.
Model parameters estimated with ABC, with details of their prior distributions (N = normal with sd being the standard deviation, U = uniform, DU = discrete uniform). The prior for β, TA and TD was a multivariate normal distribution with covariance (not given here) fitted to posterior estimates from [10].
Fig 2.
Maps showing the locations of Xylella fastidiosa inspections and laboratory-confirmed positive detections carried out in the modelled region and in each model year (May 1st to April 30th of the named years), which were used to determine the locations of inspections in the model simulations.
Each inspection covered a 100 x 100 m area, meaning up to four inspections per year in each model grid cell (200 x 200 m). The presumed approximate location of introduction is also shown. The base map is reproduced from the GADM Global administrative areas dataset, under Creative Commons Attribution-ShareAlike 2.0 (https://gadm.org/license.html).
Fig 3.
Accuracy of parameter estimation estimated with 10,000 leave-one-out cross validations (LOO CV) of the Approximate Bayesian Computation rejection sampling and acceptance threshold of 2x10-4.
Background shading shows the number of cross-validations. Where the highest densities of values align to the grey 1:1 lines, this indicates that parameter estimation is reliable. See Table 1 for full parameter explanations: β = transmission rate; TA = asymptomatic period (years); TD = desiccation period (years); bD = infectiveness of desiccated trees; mshort = short-range dispersal distance (km); mlong = long-distance dispersal distance (km); Y0 = Introduction year; v = visual inspection inefficiency; u = leaf sampling probability.
Fig 4.
Posterior estimates for the three identifiable parameters in the basic model shown as kernel density plots with vertical lines showing the medians (solid lines) and 95% credible intervals (dashed lines).
Grey background histograms show the uniform prior distributions.
Fig 5.
Average model Xylella fastidiosa spread, shown as the mean number of infected trees (IA + IS + ID) per grid cell at the end of each modelled year in single simulations with each of the 100 posterior parameter values.
Simulations started at the estimated introduction year for each posterior parameter, but only 2008/9 onwards is shown as there was little earlier spread. For scale, the grey background grid represents 20x20 km divisions. The base map is reproduced from the GADM Global administrative areas dataset, under Creative Commons Attribution-ShareAlike 2.0 (https://gadm.org/license.html).
Fig 6.
Modelled effect of containment measures on spread of Xylella fastidiosa in 100 simulations from the posterior model parameters.
Panels show the mean (a) trees infected and felled and (b) 99th percentile spread distance. Also shown is a “none” scenario in which no felling is applied after positive infection detections. Ribbons are bootstrapped 95% confidence intervals for the means.