R/RcppExports.R

In landsepi: Landscape Epidemiology and Evolution

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#' @title Model for Landscape Epidemiology & Evolution
#' @name model_landsepi
#' @description Stochastic, spatially-explicit, demo-genetic model simulating the spread and evolution of a 
#' plant pathogen in a heterogeneous landscape.
#' 
#' @param time_param list of simulation parameters:\itemize{ 
#' \item Nyears = number cropping seasons, 
#' \item nTSpY = number of time-steps per cropping season.
#' }
#' @param area_vector a vector containing areas of polygons (i.e. fields), in surface units.
#' @param rotation_matrix a matrix 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. 
#' @param croptypes_cultivars_prop a matrix with three columns named 'croptypeID' for croptype index, 
#' 'cultivarID' for cultivar index and 'proportion' for the proportion of the cultivar within the croptype. 
#' @param dispersal list of dispersal parameters:\itemize{ 
#' \item disp_patho_clonal = vectorised dispersal matrix of the pathogen (clonal propagules), 
#' \item disp_patho_sex = vectorised dispersal matrix of the pathogen (sexual propagules), 
#' \item disp_host = vectorised dispersal matrix of the host.
#' }
#' @param inits list of initial conditions:\itemize{
#' \item pI0 = initial probability for the first host (whose index is 0) to be infectious (i.e. state I) at t=0.
#' }
#' @param seed seed (for random number generation).
#' @param cultivars_param list of parameters associated with each host genotype (i.e. cultivars) 
#' when cultivated in pure crops:\itemize{   
#' \item initial_density = vector of host densities (per surface unit) at the beginning of the cropping season,  
#' \item max_density = vector of maximum host densities (per surface unit) at the end of the cropping season, 
#' \item growth rate = vector of host growth rates, 
#' \item reproduction rate = vector of host reproduction rates, 
#' \item relative_yield_H = Yield of H individuals relative to H individuals (100%)
#' \item relative_yield_L = Yield of L individuals relative to H individuals
#' \item relative_yield_I = Yield of I individuals relative to H individuals
#' \item relative_yield_R = Yield of R individuals relative to H individuals
#' \item sigmoid_kappa_host = kappa parameter for the sigmoid invasion function (for host dispersal),
#' \item sigmoid_sigma_host = sigma parameter for the sigmoid invasion function (for host dispersal),
#' \item sigmoid_plateau_host = plateau parameter for the sigmoid invasion function (for host dispersal),
#' \item cultivars_genes_list = a list containing, for each host genotype, the indices of carried resistance genes,
#' } 
#' @param basic_patho_param list of i. pathogen aggressiveness parameters on a susceptible host 
#' for a pathogen genotype not adapted to resistance and ii. sexual reproduction parameters: \itemize{
#' \item infection_rate = maximal expected infection rate of a propagule on a healthy host, 
#' \item propagule_prod_rate = maximal expected reproduction_rate of an infectious host per timestep, 
#' \item latent_period_mean = minimal expected duration of the latent period, 
#' \item latent_period_var = variance of the latent period duration, 
#' \item infectious_period_mean = maximal expected duration of the infectious period, 
#' \item infectious_period_var = variance of the infectious period duration,
#' \item survival_prob = probability for a propagule to survive the off-season, 
#' \item repro_sex_prob = probability for an infectious host to reproduce via sex rather than via cloning, 
#' \item sigmoid_kappa = kappa parameter of the sigmoid contamination function, 
#' \item sigmoid_sigma = sigma parameter of the sigmoid contamination function, 
#' \item sigmoid_plateau = plateau parameter of the sigmoid contamination function,
#' \item sex_propagule_viability_limit = maximum number of cropping seasons up to which a sexual propagule is viable
#' \item sex_propagule_release_mean = average number of cropping seasons after which a sexual propagule is released.
#' \item clonal_propagule_gradual_release = whether or not clonal propagules surviving the bottleneck are gradually released along the following cropping season.
#' }
#' @param genes_param list of parameters associated with each resistance gene and with the evolution of 
#' each corresponding pathogenicity gene:\itemize{ 
#' \item target_trait = vector of aggressiveness components (IR, LAT, IP, or PR) targeted by resistance genes, 
#' \item efficiency = vector of resistance gene efficiencies (percentage of reduction of the targeted 
#' aggressiveness component: IR, 1/LAT, IP and PR), 
#' \item age_of_activ_mean = vector of expected delays to resistance activation (for APRs), 
#' \item age_of_activ_var = vector of variances of the delay to resistance activation (for APRs),  
#' \item mutation_prob = vector of mutation probabilities for pathogenicity genes (each of them corresponding to a resistance gene), 
#' \item Nlevels_aggressiveness = vector of 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), 
#' \item adaptation_cost = vector of adaptation penalties paid by pathogen genotypes fully adapted 
#' to the considered resistance genes on hosts that do not carry this gene, 
#' \item tradeoff_strength = vector of strengths of the trade-off relationships between the level of aggressiveness 
#' on hosts that do and do not carry the resistance genes.
#' }
#' @param treatment_param list of parameters related to pesticide treatments: \itemize{ 
#' \item treatment_degradation_rate = degradation rate (per time step) of chemical concentration,
#' \item treatment_efficiency = maximal efficiency of chemical treatments (i.e. fractional reduction 
#' of pathogen infection rate at the time of application),
#' \item treatment_timesteps = vector of time-steps corresponding to treatment application dates,
#' \item treatment_cultivars = vector of indices of the cultivars that receive treatments,
#' \item treatment_cost = cost of a single treatment application (monetary units/ha),
#' \item treatment_application_threshold = vector of thresholds (i.e. disease severity, one for each treated cultivar) above which the treatment is applied
#' }
#' 
#' @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.
#'  
#' @return A set of binary files is generated for every year of simulation and every compartment: 
#' \itemize{
#'  \item H: healthy hosts,
#'  \item Hjuv: juvenile healthy hosts (for host reproduction),
#'  \item L: latently infected hosts,
#'  \item I: infectious hosts,
#'  \item R: removed hosts,
#'  \item P: propagules.}
#' Each file indicates for every time-step the number of individuals in each field, and when 
#' appropriate for each host and pathogen genotypes). Additionally, a binary file called TFI is 
#' generated and gives the Treatment Frequency Indicator (expressed as the number of treatment applications 
#'  per polygon).
#' 
#' @examples
#' \dontrun{
#' #### Spatially-implicit simulation with 2 patches (S + R) during 3 years ####
#' 
#' ## Simulation parameters
#' time_param <- list(Nyears=3, nTSpY=120)
#' Npoly=2
#' Npatho=2
#' area <- c(100000, 100000)
#' cultivars <- as.list(rbind(loadCultivar(name="Susceptible", type="growingHost")
#' , loadCultivar(name="Resistant", type="growingHost")))
#' names(cultivars)[names(cultivars)=="cultivarName"] <- "name"
#' cultivars <- c(cultivars, list(sigmoid_kappa_host=0.002, sigmoid_sigma_host=1.001,
#'  sigmoid_plateau_host=1, cultivars_genes_list=list(numeric(0),0)))
#' rotation <- data.frame(year_1=c(0,1), year_2=c(0,1), year_3=c(0,1), year_4=c(0,1))
#' croptypes_cultivars_prop <- data.frame(croptypeID=c(0,1), cultivarID=c(0,1), proportion=c(1,1))
#' genes <- as.list(loadGene(name="MG", type="majorGene"))
#'     
#' ## run simulation
#' model_landsepi(seed=1,
#'                time_param = time_param,
#'                basic_patho_param = loadPathogen(disease = "rust"),
#'                inits = list(pI0=0.01), area_vector = area,
#'                dispersal = list(disp_patho_clonal=c(0.99,0.01,0.01,0.99),
#'                disp_patho_sex=c(1,0,0,1),
#'                disp_host=c(1,0,0,1)),
#'                rotation_matrix = as.matrix(rotation),
#'                croptypes_cultivars_prop = as.matrix(croptypes_cultivars_prop),
#'                cultivars_param = cultivars, genes_param = genes)  
#' 
#' ## Compute outputs
#' eco_param <- list(yield_perHa = cbind(H = as.numeric(cultivars$yield_H),
#'              L = as.numeric(cultivars$yield_L),
#'              I = as.numeric(cultivars$yield_I),
#'              R = as.numeric(cultivars$yield_R)),
#'              planting_cost_perHa = as.numeric(cultivars$planting_cost),
#'              market_value = as.numeric(cultivars$market_value))
#'             
#' evol_res <- evol_output(, time_param, Npoly, cultivars, genes)
#' epid_output(, time_param, Npatho, area, rotation
#' , croptypes_cultivars_prop, cultivars, eco_param)
#' 
#' 
#' 
#' 
#' #### 1-year simulation of a rust epidemic in pure susceptible crop in a single 1-km2 patch ####
#'## Simulation and pathogen parameters
#'time_param <- list(Nyears=1, nTSpY=120)
#'area <- c(1E6)
#'basic_patho_param = loadPathogen(disease = "rust")
#'## croptypes, cultivars and genes
#'rotation <- data.frame(year_1=c(0), year_2=c(0))
#'croptypes_cultivars_prop <- data.frame(croptypeID=c(0), cultivarID=c(0), proportion=c(1))
#'cultivars <- as.list(rbind(loadCultivar(name="Susceptible", type="growingHost")))
#'names(cultivars)[names(cultivars)=="cultivarName"] <- "name"
#'yield0 <- cultivars$yield_H + as.numeric(cultivars$yield_H==0)
#'cultivars <- c(cultivars, list(relative_yield_H = as.numeric(cultivars$yield_H / yield0)
#'     , relative_yield_L = as.numeric(cultivars$yield_L / yield0)
#'     , relative_yield_I = as.numeric(cultivars$yield_I / yield0)
#'     , relative_yield_R = as.numeric(cultivars$yield_R / yield0)
#'     , sigmoid_kappa_host=0.002, sigmoid_sigma_host=1.001, sigmoid_plateau_host=1
#'     , cultivars_genes_list=list(numeric(0))))
#'genes <-   list(geneName = character(0) , adaptation_cost = numeric(0)
#'     , mutation_prob = numeric(0)
#'     , efficiency = numeric(0) , tradeoff_strength = numeric(0)
#'     , Nlevels_aggressiveness = numeric(0)
#'     , age_of_activ_mean = numeric(0) , age_of_activ_var = numeric(0)
#'     , target_trait = character(0)
#'     , recombination_sd = numeric(0))
#'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))
#'
#'## run simulation
#'model_landsepi(seed=1, time_param = time_param
#'     , basic_patho_param = basic_patho_param
#'     , inits = list(pI0=5E-4), area_vector = area
#'     , dispersal = list(disp_patho_clonal=c(1), disp_patho_sex=c(1), disp_host=c(1))
#'     , rotation_matrix = as.matrix(rotation)
#'     , treatment_param = treatment
#'     , croptypes_cultivars_prop = as.matrix(croptypes_cultivars_prop)
#'     , cultivars_param = cultivars,  genes_param = genes)
#' }
#' @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. \emph{PLoS Computational Biology} 14(4):e1006067.
#' 
#' @export
model_landsepi <- function(time_param, area_vector, rotation_matrix, croptypes_cultivars_prop, dispersal, inits, seed, cultivars_param, basic_patho_param, genes_param, treatment_param) {
    invisible(.Call(`_landsepi_model_landsepi`, time_param, area_vector, rotation_matrix, croptypes_cultivars_prop, dispersal, inits, seed, cultivars_param, basic_patho_param, genes_param, treatment_param))
}