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

## Fig 4

(a) Epidemic time (τ_{E}); i.e. the time for the epidemic to be controlled, as a function of the cull radius *L*. The black dots on the x-axis mark *L* = 159m, the cull radius at which the median epidemic impact (κ_{E}) is minimised, and *L* = 63m, the radius at which the median epidemic time (τ_{E}) is maximised. (b) Normalised epidemic cost () for a number of values of the weighting factor, η. and are the epidemic impact and epidemic time normalised according to their maximum values over all cull radii considered, whereas η controls the relative weighting given to potential global impacts of the epidemic outside the area of immediate interest. The inset shows the value of *L* at which the minimum Ψ_{E} is obtained as a function of η. As global impacts are increasingly weighted (*η* → 1), the optimum cull radius increases, despite the increased number of local removals that would then result. (c) Area under the disease progress curve (*A*_{E}) as a function of cull radius, *L*. The inset shows the logarithm of *A*_{E} as *L* is changed. (d) The probability of escape, *p*_{E}, as function of the cull radius, *L*, for different values of the connectivity parameter λ. (e) Normalised epidemic cost () in which the probability of escape rather than the time until eradication is included, for a number of values of the weighting factor, δ, and for fixed connectivity parameter λ = 10^{-5} d^{-1}. The inset shows the value of *L* at which the optimum ζ_{E} is obtained for 0 ≤ δ ≤ 1. (f) Robustness to the value of λ. The inset to panel (e) is repeated for a number of values of λ; as potential global impacts are increasingly weighted (*δ* → 1), the optimal cull radius again becomes larger, for each value of λ we consider.