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Optimising and Communicating Options for the Control of Invasive Plant Disease When There Is Epidemiological Uncertainty

Fig 2

Optimal control when there is uncertainty.

(a) Epidemic impact κE (total number of hosts lost to disease or control) as a function of the cull radius, L. The optimum value of L depends on the percentile of the distribution of κE that is being optimised (e.g. the optimum L would be 159m if the objective were to minimise median κE, whereas it would be 194m if minimising κE on the 95th percentile). The shape of the distribution of κE varies with L (insets A to F; distributions renormalized to the same height by scaling all distributions relative to the largest value in each). (b) Risk of failure. Given a notion of “acceptable risk” (i.e. a value of Ω, the threshold κE as a percentage of the total population), the probability of failing to achieve κE < Ω is shown. Dotted line marks radii with < 10% risk of failure for Ω = 20% (range 122m < L < 329m). (c) Effect of the initial level of infection, E0, on the response of median κE to L (dots show minimum median κE for each E0). (d) and (e) Effect of the scale of dispersal (α) and rate of infection (β) on the optimal L (shown in d) and median κE at optimal L to optimise median κE (shown in e). The white dots on (d) and (e) indicate the default values of α and β; the white squares show the values of α and β fitted by Cook et al. [24] (and used in the studies of Parnell et al. [11,12]) cf. S3 Text.

Fig 2