These are the datasets required to reproduce the figures in Mastin et al. "Optimising risk-based surveillance for early detection of invasive plant pathogens". dpcData This dataset presents the disease progression curves under the two forms of pathogen entry, over 1,000 model realisations. Figures: Figure 1E, Figure 1F entryType: Distribution of primary incursions (variable (i.e. Travel Census or flat). runNum: number of the simulation realisation time: time since initial pathogen entry propInfCells: proportion of cells infected propInfHost: proportion of hosts infected dpcSummary This dataset presents the simplified disease progression curves shown in Figure 1. Note that these are intended for visualisation only and do not represent any individual epidemic trajectories. time: time since initial pathogen entry. Figures: Figure 1E, Figure 1F percentile: percentile in question propInfHostVar: xth percentile of proportion of hosts infected at the given timepoint under the variable entry model propInfHostFlat: xth percentile of proportion of hosts infected at the given timepoint under the flat entry model spatialData_baselines This dataset presents the study landscape, the mean end prevalence under the two forms of pathogen entry (over 1,000 model realisations), and the locations of selected sites using a variety of selection strategies. Figures: Figure 2A, Figure 2C, Figure 2D, Figure 3A, Figure 3B, Figure 3C hostID: numeric ID of host site if in study landscape easting: UTM zone 17N easting coordinate of cell centroid northing: UTM zone 17N northing coordinate of cell centroid ratePrimInf: relative rate of primary incursions meanRiskVar: mean end prevalence under variable entry distribution (i.e. Travel Census model). This is the baseline scenario. meanRiskFlat: mean end prevalence under flat entry distribution. optSelSite: indicator variable for whether site was selected under the optimised strategy shown in Figure 2A. optClusterID: cluster ID for optSelSite sites. These are in numeric groups, but the numbers do not indicate anything. rankedSelSite: indicator variable for whether site was selected under the risk ranked strategy shown in Figure 2B. rankedClusterID: cluster ID for rankedSelSite sites. These are in numeric groups, but the numbers do not indicate anything. stochasticSelSite: indicator variable for whether site was selected under the stochastic risk strategy shown in Figure 2C. stochasticClusterID: cluster ID for stochasticSelSite sites. These are in numeric groups, but the numbers do not indicate anything. optSelLowSe: indicator variable for whether site was selected under the optimised strategy with a low diagnostic sensitivity, as shown in Figure 3A. optLowSeClusterID: cluster ID for optSelLowSe sites. These are in numeric groups, but the numbers do not indicate anything. optSelModSe: indicator variable for whether site was selected under the optimised strategy with a moderate diagnostic sensitivity, as shown in Figure 3B. optModSeClusterID: cluster ID for optSelModSe sites. These are in numeric groups, but the numbers do not indicate anything. optSelHighSe: indicator variable for whether site was selected under the optimised strategy with a high diagnostic sensitivity, as shown in Figure 3C. optHighSeClusterID: cluster ID for optSelHighSe sites. These are in numeric groups, but the numbers do not indicate anything. ofProgressionExample This dataset presents an example of the progression of the objective function over 15,0000 iterations of an optimisation routine. Figures: Figure 2B of: Objective function (i.e. the detection probability) iteration: Iteration number of the simulated annealing algorithm loessLine: Smoothed progression of the OF as shown in Figure 2B optimisationOutputs_testSens This dataset presents the detection probability and number of clusters under a range of different site selection strategies and different diagnostic sensitivities. Figures: Figure 4A, Figure 4B, Figure 4C, Figure 4D, Supplementary Figure 5A, Supplementary Figure 5B selType: Selection strategy (optimised (opt) / product of citrus density and relative rate of primary incursions (entry_spread) / citrus density (citrus) / relatve rate of primary incursions (entry) / random (rand)) sensitivity: Diagnostic sensitivity. selRun: Run of site selection strategy. detProb: Probability of detection. clustNum: Number of clusters. optimisationOutputs_numSites This dataset presents the detection probability under a range of different site selection strategies and different numbers of selected sites. Figures: Figure 5A, Supplementary Figure 6 selType: Selection strategy (optimised (opt) / product of citrus density and relative rate of primary incursions (entry_spread) / citrus density (citrus) / relatve rate of primary incursions (entry) / random (rand)) numSites: Number of selected sites. selRun: Run of site selection strategy. detProb: Probability of detection. costEstimates Figures: Figure 5B This dataset presents the number of sites required to achieve different mean detection probabilities, and the associated total annual cost of surveillance. selType: Selection strategy (optimised (opt) / product of citrus density and relative rate of primary incursions (entry_spread) / citrus density (citrus) / relatve rate of primary incursions (entry) / random (rand)) detProbEst: Estimated mean detection probability numSitesReq: Estimated number of sites needing to be visited to achieve detProbEst. annualCost: Estimated annual cost of surveillance under when sampling numSitesReq per year. modelMisspecification Figures: Figure 6A, Figure 6B This dataset presents the detection probability for the baseline model under a range of different site selection strategies based upon different assumptions regarding primary incursion rate and distribution. selType: Selection strategy (optimised (opt) / product of citrus density and relative rate of primary incursions (entry_spread) / citrus density (citrus) / relatve rate of primary incursions (entry) / random (rand)) entryTypeAssumed: Assumed distribution of primary incursions (variable (i.e. Travel Census or flat). primInfRate: Assumed rate of primary incursions. selRun: Run of site selection strategy. detProb: Probability of detection. spatialData_primaryInf This dataset presents the study landscape, the mean end prevalence under the three rates and two forms of pathogen entry (over 1,000 model realisations), and the locations of selected sites using a variety of selection strategies. Figures: Supplementary Figure 1A, Supplementary Figure 1B, Supplementary Figure 1C, Supplementary Figure 1D, Supplementary Figure 1E, Supplementary Figure 1F, Supplementary Figure 2A, Supplementary Figure 2B, Supplementary Figure 2C, Supplementary Figure 2D, Supplementary Figure 2E, Supplementary Figure 2F easting: UTM zone 17N easting coordinate of cell centroid northing: UTM zone 17N northing coordinate of cell centroid meanEndPrevVar005: mean end prevalence under variable entry distribution (i.e. Travel Census model) with 0.05 entries per year meanEndPrevVar050: mean end prevalence under variable entry distribution (i.e. Travel Census model) with 0.50 entries per year meanEndPrevVar500: mean end prevalence under variable entry distribution (i.e. Travel Census model) with 5.00 entries per year optSelVar005: indicator variable for whether site was selected under variable entry with 0.05 entries per year optVar005ClusterID: cluster ID for optSelVar005 sites. These are in numeric groups, but the numbers do not indicate anything. optSelVar050: indicator variable for whether site was selected under variable entry with 0.50 entries per year optVar050ClusterID: cluster ID for optSelVar050 sites. These are in numeric groups, but the numbers do not indicate anything. optSelVar500: indicator variable for whether site was selected under variable entry with 5.00 entries per year optVar500ClusterID: cluster ID for optSelVar500 sites. These are in numeric groups, but the numbers do not indicate anything. meanEndPrevFlat005: mean end prevalence under flat entry distribution with 0.05 entries per year meanEndPrevFlat050: mean end prevalence under flat entry distribution with 0.50 entries per year meanEndPrevFlat500: mean end prevalence under flat entry distribution with 5.00 entries per year optSelFlat005: indicator variable for whether site was selected under flat entry with 0.05 entries per year optFlat005ClusterID: cluster ID for optSelFlat005 sites. These are in numeric groups, but the numbers do not indicate anything. optSelFlat050: indicator variable for whether site was selected under flat entry with 0.50 entries per year optFlat050ClusterID: cluster ID for optSelFlat050 sites. These are in numeric groups, but the numbers do not indicate anything. optSelFlat500: indicator variable for whether site was selected under flat entry with 5.00 entries per year optFlat500ClusterID: cluster ID for optSelFlat500 sites. These are in numeric groups, but the numbers do not indicate anything. optimisationOutputs_primaryInf This dataset presents the detection probability and number of clusters under a range of different site selection strategies and different rates and distributions of primary incursions. Figures: Supplementary Figure 3A, Supplementary Figure 3B, Supplementary Figure 3C, Supplementary Figure 3D selType: Selection strategy (optimised (opt) / product of citrus density and relative rate of primary incursions (entry_spread) / citrus density (citrus) / relatve rate of primary incursions (entry) / random (rand)) entryType: Distribution of primary incursions (variable (i.e. Travel Census or flat). primInfRate: Assumed rate of primary incursions. selRun: Run of site selection strategy. detProb: Probability of detection. clustNum: Number of clusters. samplingInterval This dataset presents the mean detection probability under a range of different site selection strategies and different sampling intervals. Figures: Supplementary Figure 4 selType: Selection strategy (optimised (opt) / product of citrus density and relative rate of primary incursions (entry_spread) / citrus density (citrus) / relatve rate of primary incursions (entry) / random (rand)) delta: Numeric sampling interval in years. deltaString: Sampling interval. detProb: Probability of detection. ofPlots This dataset presents example of the progression of the objective function under a range of different parameters. Figures: Supplementary Figure 7 iteration: Iteration number of the simulated annealing algorithm initTemp: estimate of the initial temperature coolRate: estimate of the cooling rate detProb: Detection probability (i.e the objective function)