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

Fig 4

Local vs. global control.

(a) Epidemic time (τE); 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 (κE) is minimised, and L = 63m, the radius at which the median epidemic time (τE) 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 ΨE 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 (AE) as a function of cull radius, L. The inset shows the logarithm of AE as L is changed. (d) The probability of escape, pE, 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 ζE 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.

Fig 4

doi: https://doi.org/10.1371/journal.pcbi.1004211.g004