Data & scripts_v2

ZIP folder containing all scripts, data and simulation results used in this study

Summary statistics for parameters estimated from the survey data.

<p>Summary statistics for parameters estimated from the survey data.</p

Impact of the number of introduction patches (<i>κ</i>) on the expected Fisher information for the sharka epidemic.

<p>For each <i>κ</i>, the estimation with the highest mean posterior log-likelihood was retained. For <i>κ</i><10 no introduction patch combination returned a finite posterior log-likelihood. The empirical approximation of the Fisher information was maximal at <i>κ</i> = 11.</p

Susceptible-Exposed-Hidden-Detected-Removed (<i>SEHDR</i>) model of an individual’s epidemiological status.

<p>At <i>T</i>₀, patch <i>i</i> is planted with infectious (<i>I</i>) or susceptible (<i>S</i>) individuals with probabilities <i>p</i>_<i>i</i> and 1-<i>p</i>_<i>i</i>, respectively. An individual passes between compartments at event times <i>T</i>_<i>E</i>, <i>T</i>_<i>H</i>, <i>T</i>_<i>D</i> and <i>T</i>_<i>R</i>. Apart from <i>T</i>₀, only the detection time <i>T</i>_<i>D</i> can be known (yellow); all other event times are censored (blue). Infectious individuals from both within and outside the patch contribute to the force of infection , which is the expected number of infectious events affecting an individual over time interval (<i>t</i>_<i>r</i>−1, <i>t</i>_<i>r</i>]. The probability that a given susceptible (<i>S</i>) individual becomes exposed (<i>E</i>) in this time interval is 1-exp(-), assuming independent infection events. A latent period of duration <i>T</i>_<i>H</i>-<i>T</i>_<i>E</i> follows, after which the individual becomes infectious (<i>H</i>). Infectious individuals are removed (<i>R</i>) only after detection (<i>D</i>) or when the entire patch is removed. For simplicity, the <i>i</i> and <i>t</i>_<i>r</i> subscripts are omitted in the figure.</p

Parameter sets for four estimation scenarios.

<p>Parameter sets for four estimation scenarios.</p

Video S5 from Using sensitivity analysis to identify key factors for the propagation of a plant epidemic

Simulations with fixed parameters, in the explicit landscap

Comparison of simulated and estimated dispersal kernels.

<p>From left to right: kernels with the minimum, lower quartile, median, upper quartile and maximum Kullback-Leibler (KL) distances (posterior mean), as estimated (red) under the most exhaustive scheme (Θ₄), based on simulated epidemics with short-, medium- and long-range kernels (from top to bottom; black). Kernels are represented by their marginal cumulative distribution function <i>F</i>^1<i>D</i> (with distance from the source represented on the log₁₀ scale). The mean KL distance is indicated for each estimation.</p

Estimated dispersal kernel for the sharka epidemic.

<p>The posterior marginal cumulative distribution function, <i>F</i>^1<i>D</i>, of the fitted dispersal kernel, obtained for <i>κ</i> = 11 (i.e. the number of introduction patches maximising the Fisher information). The plotted posterior distribution was obtained from 4000 MCMC samples. One line is plotted per sample.</p

Video S3 from Using sensitivity analysis to identify key factors for the propagation of a plant epidemic

Simulations with fixed parameters, in the explicit landscap

Supporting Information from Using sensitivity analysis to identify key factors for the propagation of a plant epidemic

PDF file containing supplemental figures and method