simul_landsepi: Simulation with input parameters as data.frames.
In landsepi: Landscape Epidemiology and Evolution

simul_landsepi

R Documentation

Simulation with input parameters as data.frames.

Description

Stochastic, spatially-explicit, demo-genetic model simulating the spread and evolution of a pathogen in a heterogeneous landscape and generating a wide range of epidemiological, evolutionary and economic outputs.

Usage

simul_landsepi(
  seed = 12345,
  time_param = list(Nyears = 5, nTSpY = 120),
  croptype_names = c("Susceptible crop"),
  croptypes_cultivars_prop = data.frame(croptypeID = 0, cultivarID = 0, proportion = 1),
  cultivars = data.frame(cultivarName = "Susceptible", initial_density = 0.1, max_density
    = 2, growth_rate = 0.1, reproduction_rate = 0, yield_H = 2.5, yield_L = 0, yield_I =
    0, yield_R = 0, planting_cost = 225, market_value = 200),
  cultivars_genes_list = list(numeric(0)),
  genes = data.frame(geneName = character(0), mutation_prob = numeric(0), efficiency =
    numeric(0), tradeoff_strength = numeric(0), Nlevels_aggressiveness = numeric(0),
    adaptation_cost = numeric(0), age_of_activ_mean = numeric(0), age_of_activ_var =
    numeric(0), target_trait = character(0), recombination_sd = numeric(0)),
  landscape = NULL,
  area = 1e+06,
  rotation = data.frame(year_1 = c(0), year_2 = c(0), year_3 = c(0), year_4 = c(0),
    year_5 = c(0), year_6 = c(0)),
  basic_patho_param = list(name = "rust", survival_prob = 1e-04, repro_sex_prob = 0,
    infection_rate = 0.4, propagule_prod_rate = 3.125, latent_period_mean = 10,
    latent_period_var = 9, infectious_period_mean = 24, infectious_period_var = 105,
    sigmoid_kappa = 5.333, sigmoid_sigma = 3, sigmoid_plateau = 1,
    sex_propagule_viability_limit = 1, sex_propagule_release_mean = 1,
    clonal_propagule_gradual_release = 0),
  repro_sex_prob = rep(0, time_param$nTSpY + 1),
  disp_patho_clonal = c(1),
  disp_patho_sex = c(1),
  disp_host = c(1),
  treatment = list(treatment_degradation_rate = 0.1, treatment_efficiency = 0,
    treatment_timesteps = logical(0), treatment_cultivars = logical(0), treatment_cost =
    0, treatment_application_threshold = logical(0)),
  pI0 = 5e-04,
  epid_outputs = "all",
  evol_outputs = "all",
  thres_breakdown = 50000,
  audpc100S = 0.76,
  writeTXT = TRUE,
  graphic = TRUE,
  videoMP4 = FALSE,
  keepRawResults = FALSE
)

Arguments

`seed`	an integer used as seed value (for random number generator).
`time_param`	a list of simulation parameters: Nyears = number cropping seasons, nTSpY = number of time-steps per cropping season.
`croptype_names`	a vector of croptypes names.
`croptypes_cultivars_prop`	a dataframe with three columns named 'croptypeID' for croptype index, 'cultivarID' for cultivar index and 'proportion' for the proportion of the cultivar within the croptype.
`cultivars`	a dataframe of parameters associated with each host genotype (i.e. cultivars) when cultivated in pure crops. Columns of the dataframe are: cultivarName: cultivar names, initial_density: host densities (per square meter) at the beginning of the cropping season as if cultivated in pure crop, max_density: maximum host densities (per square meter) at the end of the cropping season as if cultivated in pure crop, growth_rate: host growth rates, reproduction rate: host reproduction rates, yield_H: theoretical yield (in weight or volume units / ha / cropping season) associated with hosts in sanitary status H as if cultivated in pure crop, yield_L: theoretical yield (in weight or volume units / ha / cropping season) associated with hosts in sanitary status L as if cultivated in pure crop, yield_I: theoretical yield (in weight or volume units / ha / cropping season) associated with hosts in sanitary status I as if cultivated in pure crop, yield_R: theoretical yield (in weight or volume units / ha / cropping season) associated with hosts in sanitary status R as if cultivated in pure crop, planting_cost = planting costs (in monetary units / ha / cropping season) as if cultivated in pure crop, market_value = market values of the production (in monetary units / weight or volume unit).
`cultivars_genes_list`	a list containing, for each host genotype, the indices of carried resistance genes.
`genes`	a data.frame of parameters associated with each resistance gene and with the evolution of each corresponding pathogenicity gene. Columns of the dataframe are: geneName: names of resistance genes, target_trait: aggressiveness components (IR, LAT, IP, or PR) targeted by resistance genes, efficiency: resistance gene efficiencies (percentage of reduction of targeted aggressiveness components: IR, 1/LAT, IP and PR), age_of_activ_mean: expected delays to resistance activation (for APRs), age_of_activ_var: variances of the delay to resistance activation (for APRs), mutation_prob: mutation probabilities for pathogenicity genes (each of them corresponding to a resistance gene), Nlevels_aggressiveness: number of adaptation levels related to each resistance gene (i.e. 1 + number of required mutations for a pathogenicity gene to fully adapt to the corresponding resistance gene), adaptation_cost: fitness penalties paid by pathogen genotypes fully adapted to the considered resistance genes on host that do not carry the resistance genes, tradeoff_strength: strengths of the trade-off relationships between the level of aggressiveness on hosts that do and do not carry the resistance genes. recombination_sd: standard deviation of the normal distribution used for recombination of quantitative traits during sexual reproduction (infinitesimal model)
`landscape`	a sp object containing the landscape (required only if videoMP4=TRUE).
`area`	a vector containing polygon areas (must be in square meters).
`rotation`	a dataframe containing for each field (rows) and year (columns, named "year_1", "year_2", etc.), the index of the cultivated croptype. Importantly, the matrix must contain 1 more column than the real number of simulated years.
`basic_patho_param`	a list of i. pathogen aggressiveness parameters on a susceptible host for a pathogen genotype not adapted to resistance and ii. sexual reproduction parameters: infection_rate = maximal expected infection rate of a propagule on a healthy host, propagule_prod_rate = maximal expected effective propagule production rate of an infectious host per time step, latent_period_mean = minimal expected duration of the latent period, latent_period_var = variance of the latent period duration, infectious_period_mean = maximal expected duration of the infectious period, infectious_period_var = variance of the infectious period duration, survival_prob = probability for a propagule to survive the off-season, repro_sex_prob = probability for an infectious host to reproduce via sex rather than via cloning, sigmoid_kappa = kappa parameter of the sigmoid contamination function, sigmoid_sigma = sigma parameter of the sigmoid contamination function, sigmoid_plateau = plateau parameter of the sigmoid contamination function, sex_propagule_viability_limit = maximum number of cropping seasons up to which a sexual propagule is viable sex_propagule_release_mean = average number of seasons after which a sexual propagule is released, clonal_propagule_gradual_release = Whether or not clonal propagules surviving the bottleneck are gradually released along the following cropping season.
`repro_sex_prob`	a vector of size `time_param$nSTpY+1` giving the probability for an infectious host to reproduce via sex rather than via cloning for every time step,
`disp_patho_clonal`	a vectorized matrix giving the probability of pathogen dispersal from any field of the landscape to any other field.
`disp_patho_sex`	a vectorized matrix giving the probability of pathogen dispersal for sexual propagules from any field of the landscape to any other field.
`disp_host`	a vectorized matrix giving the probability of host dispersal from any field of the landscape to any other field
`treatment`	list of parameters related to pesticide treatments: treatment_degradation_rate = degradation rate (per time step) of chemical concentration, treatment_efficiency = maximal efficiency of chemical treatments (i.e. fractional reduction of pathogen infection rate at the time of application), treatment_timesteps = vector of time-steps corresponding to treatment application dates, treatment_cultivars = vector of indices of the cultivars that receive treatments, treatment_cost = cost of a single treatment application (monetary units/ha) treatment_application_threshold = vector of thresholds (i.e. disease severity, one for each treated cultivar) above which the treatment is applied in a polygon
`pI0`	initial probability for the first host (whose index is 0) to be infectious (i.e. state I) at the beginning of the simulation. Must be between 0 and 1.
`epid_outputs`	a character string (or a vector of character strings if several outputs are to be computed) specifying the type of epidemiological and economic outputs to generate (see details): "audpc" : Area Under Disease Progress Curve (average number of diseased host individuals per time step and square meter) "audpc_rel" : Relative Area Under Disease Progress Curve (average proportion of diseased host individuals relative to the total number of existing hosts) "gla" : Green Leaf Area (average number of healthy host individuals per time step and square meter) "gla_rel" : Relative Green Leaf Area (average proportion of healthy host individuals relative to the total number of existing hosts) "eco_yield" : total crop yield (in weight or volume units per ha) "eco_cost" : operational crop costs (in monetary units per ha) "eco_product" : total crop products (in monetary units per ha) "eco_margin" : Margin (products - operational costs, in monetary units per ha) "contrib": contribution of pathogen genotypes to LIR dynamics "HLIR_dynamics", "H_dynamics", "L_dynamics", "IR_dynamics", "HLI_dynamics", etc.: Epidemic dynamics related to the specified sanitary status (H, L, I or R and all their combinations). Graphics only, works only if graphic=TRUE. "all" : compute all these outputs (default) "" : none of these outputs will be generated.
`evol_outputs`	a character string (or a vector of character strings if several outputs are to be computed) specifying the type of evolutionary outputs to generate : "evol_patho": Dynamics of pathogen genotype frequencies "evol_aggr": Evolution of pathogen aggressiveness "durability": Durability of resistance genes "all": compute all these outputs (default) "": none of these outputs will be generated.
`thres_breakdown`	an integer (or vector of integers) giving the threshold (i.e. number of infections) above which a pathogen genotype is unlikely to go extinct, used to characterise the time to invasion of resistant hosts (several values are computed if several thresholds are given in a vector).
`audpc100S`	the audpc in a fully susceptible landscape (used as reference value for graphics).
`writeTXT`	a logical indicating if outputs must be written in text files (TRUE, default) or not (FALSE).
`graphic`	a logical indicating if graphics must be generated (TRUE, default) or not (FALSE).
`videoMP4`	a logical indicating if a video must be generated (TRUE) or not (FALSE, default). Works only if graphic=TRUE and epid_outputs="audpc_rel" (or epid_outputs="all").
`keepRawResults`	a logical indicating if binary files must be kept after the end of the simulation (default=FALSE). Careful, many files may be generated if keepRawResults=TRUE.

Details

See ?landsepi for details on the model and assumptions. Briefly, the model is stochastic, spatially explicit (the basic spatial unit is an individual field), based on a SEIR (‘susceptible-exposed-infectious-removed’, renamed HLIR for 'healthy-latent-infectious-removed' to avoid confusions with 'susceptible host') structure with a discrete time step. It simulates the spread and evolution (via mutation, recombination through sexual reproduction, selection and drift) of a pathogen in a heterogeneous cropping landscape, across cropping seasons split by host harvests which impose potential bottlenecks to the pathogen. A wide array of resistance deployment strategies (possibly including chemical treatments) can be simulated and evaluated using several possible outputs to assess the epidemiological, evolutionary and economic performance of deployment strategies (See ?epid_output and ?evol_output for details).

Value

A list containing all outputs that have been required via "epid_outputs" and "evol_outputs". A set of text files, graphics and a video showing epidemic dynamics can be generated. If keepRawResults=TRUE, a set of binary files is generated for every year of simulation and every compartment:

H: healthy hosts,
Hjuv: juvenile healthy hosts (for host reproduction),
L: latently infected hosts,
I: infectious hosts,
R: removed hosts,
P: propagules.

Each file indicates for every time-step the number of individuals in each field, and when appropriate for each host and pathogen genotype. Additionally, a binary file called TFI is generated and gives the Treatment Frequency Indicator (expressed as the number of treatment applications per polygon).

References

Rimbaud L., Papaïx J., Rey J.-F., Barrett L. G. and Thrall P. H. (2018). Assessing the durability andefficiency of landscape-based strategies to deploy plant resistance to pathogens. PLoS Computational Biology 14(4):e1006067.

Examples

## Not run: 
#### Spatially-implicit simulation with a single 1-km^2 patch 100% cultivated 
# with a susceptible cultivar

simul_landsepi()

#### Spatially-implicit simulation with 2 patches (S + R) during 3 years ####

## Simulation parameters
time_param <- list(Nyears = 3, nTSpY = 120)
area <- c(100000, 100000)
rotation <- data.frame(year_1 = c(0, 1), year_2 = c(0, 1), year_3 = c(0, 1), year_4 = c(0, 1))
croptype_names <- c("Susceptible crop", "Resistant crop")
croptypes_cultivars_prop <- data.frame(
  croptypeID = c(0, 1),
  cultivarID = c(0, 1),
  proportion = c(1, 1)
)
cultivars <- rbind(
  loadCultivar(name = "Susceptible", type = "growingHost"),
  loadCultivar(name = "Resistant", type = "growingHost")
)
genes <- loadGene(name = "MG", type = "majorGene")
cultivars_genes_list <- list(numeric(0), 0)

## Run simulation
simul_landsepi(
  seed = 12345, time_param, croptype_names, croptypes_cultivars_prop, cultivars,
  cultivars_genes_list, genes, landscape = NULL, area, rotation,
  basic_patho_param = loadPathogen(disease = "rust"),
  repro_sex_prob = rep(0, time_param$nTSpY+1),
  disp_patho_clonal = c(0.99, 0.01, 0.01, 0.99),
  disp_patho_sex = c(0.99, 0.01, 0.01, 0.99),
  disp_host = c(1, 0, 0, 1),
  pI0 = 5e-4
)


#### Spatially-explicit simulation with built-in landscape during 10 years ####
# Generate a mosaic of four croptypes in balanced proportions
# and medium level of spatial aggregation

## Simulation and Landscape parameters
Nyears <- 10
landscape <- loadLandscape(1)
Npoly <- length(landscape)
library(sf)
area <- st_area(st_as_sf(landscape))
rotation <- AgriLand(landscape, Nyears,
  rotation_period = 1, rotation_realloc = FALSE,
  rotation_sequence = c(0, 1, 2, 3),
  prop = rep(1 / 4, 4), aggreg = 0.5, graphic = TRUE, outputDir = getwd()
)
rotation <- data.frame(rotation)[, 1:(Nyears + 1)]
croptype_names <- c("Susceptible crop"
, "Resistant crop 1"
, "Resistant crop 2"
, "Resistant crop 3")
croptypes_cultivars_prop <- data.frame(croptypeID = c(0, 1, 2, 3), cultivarID = c(0, 1, 2, 3),
                                       proportion = c(1, 1, 1, 1))
cultivars <- data.frame(rbind(
  loadCultivar(name = "Susceptible", type = "growingHost"),
  loadCultivar(name = "Resistant1", type = "growingHost"),
  loadCultivar(name = "Resistant2", type = "growingHost"),
  loadCultivar(name = "Resistant3", type = "growingHost")
), stringsAsFactors = FALSE)
genes <- data.frame(rbind(
  loadGene(name = "MG 1", type = "majorGene"),
  loadGene(name = "MG 2", type = "majorGene"),
  loadGene(name = "MG 3", type = "majorGene")
), stringsAsFactors = FALSE)
cultivars_genes_list <- list(numeric(0), 0, 1, 2)

## Run simulation
simul_landsepi(
  seed = 12345, time_param = list(Nyears = Nyears, nTSpY = 120),
  croptype_names, croptypes_cultivars_prop, cultivars,
  cultivars_genes_list, genes, landscape, area, rotation,
  basic_patho_param = loadPathogen(disease = "rust"),
  repro_sex_prob = rep(0, 120+1),
  disp_patho_clonal = loadDispersalPathogen(1)[[1]],
  disp_patho_sex = as.numeric(diag(Npoly)),
  disp_host = as.numeric(diag(Npoly)),
  pI0 = 5e-4
)

## End(Not run)

landsepi documentation built on July 26, 2023, 5:36 p.m.