runSimul: Run a simulation
In landsepi: Landscape Epidemiology and Evolution

runSimul

R Documentation

Run a simulation

Description

Runs a simulation with landsepi, a 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

runSimul(
  params,
  graphic = TRUE,
  writeTXT = TRUE,
  videoMP4 = FALSE,
  keepRawResults = FALSE
)

Arguments

`params`	a LandsepiParams Object containing all simulation parameters. Must be initialised with `createSimulParams` and updated using `set*()` methods (see vignettes for details).
`graphic`	a logical indicating if graphics must be generated (TRUE, default) or not (FALSE).
`writeTXT`	a logical indicating if outputs must be written in text files (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 audpc_rel is computed.
`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, assumptions and outputs, and our vignettes for tutorials (browseVignettes("landsepi")). The function runs the model simulation using a LandsepiParams object. 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.

Value

A list containing all required 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: 
### Here is an example of simulation of a mosaic of three cultivars (S + R1 + R2). See our 
## tutorials for more examples.
## Initialisation
simul_params <- createSimulParams(outputDir = getwd())
## Seed & Time parameters
simul_params <- setSeed(simul_params, seed = 1)
simul_params <- setTime(simul_params, Nyears = 10, nTSpY = 120)
## Pathogen & inoculum parameters
simul_params <- setPathogen(simul_params, loadPathogen("rust"))
simul_params <- setInoculum(simul_params, 5e-4)
## Landscape & dispersal
simul_params <- setLandscape(simul_params, loadLandscape(1))
simul_params <- setDispersalPathogen(simul_params, loadDispersalPathogen[[1]])
## Genes
gene1 <- loadGene(name = "MG 1", type = "majorGene")
gene2 <- loadGene(name = "MG 2", type = "majorGene")
genes <- data.frame(rbind(gene1, gene2), stringsAsFactors = FALSE)
simul_params <- setGenes(simul_params, genes)
## Cultivars
cultivar1 <- loadCultivar(name = "Susceptible", type = "growingHost")
cultivar2 <- loadCultivar(name = "Resistant1", type = "growingHost")
cultivar3 <- loadCultivar(name = "Resistant2", type = "growingHost")
cultivars <- data.frame(rbind(cultivar1, cultivar2, cultivar3), stringsAsFactors = FALSE)
simul_params <- setCultivars(simul_params, cultivars)
## Allocate genes to cultivars
simul_params <- allocateCultivarGenes(simul_params, "Resistant1", c("MG 1"))
simul_params <- allocateCultivarGenes(simul_params, "Resistant2", c("MG 2"))
## Allocate cultivars to croptypes
croptypes <- loadCroptypes(simul_params, names = c("Susceptible crop"
, "Resistant crop 1"
, "Resistant crop 2"))
croptypes <- allocateCroptypeCultivars(croptypes, "Susceptible crop", "Susceptible")
croptypes <- allocateCroptypeCultivars(croptypes, "Resistant crop 1", "Resistant1")
croptypes <- allocateCroptypeCultivars(croptypes, "Resistant crop 2", "Resistant2")
simul_params <- setCroptypes(simul_params, croptypes)
## Allocate croptypes to landscape        
rotation_sequence <- croptypes$croptypeID ## No rotation -> 1 rotation_sequence element
rotation_period <- 0 ## same croptypes every years
prop <- c(1 / 3, 1 / 3, 1 / 3) ## croptypes proportions
aggreg <- 10 ## aggregated landscape
simul_params <- allocateLandscapeCroptypes(simul_params, rotation_period = rotation_period,
rotation_sequence = rotation_sequence,
rotation_realloc = FALSE, prop = prop, aggreg = aggreg)
## list of outputs to be generated
simul_params <- setOutputs(simul_params, loadOutputs())
## Check simulation parameters
checkSimulParams(simul_params)
## Save deployment strategy into GPKG file
simul_params <- saveDeploymentStrategy(simul_params)
## Run simulation
runSimul(simul_params)

### Simulation of rust epidemics in a 1-km^2 patch cultivated with a susceptible wheat cultivar
seed=10
Nyears=5
disease="rust"
hostType="growingHost"
simul_params <- createSimulParams(outputDir = getwd())

## Seed and time parameters
simul_params <- setSeed(simul_params, seed)
simul_params <- setTime(simul_params, Nyears, nTSpY=120)

simul_params <- setPathogen(simul_params, loadPathogen(disease))
myLand <- Polygons(list(Polygon(matrix(c(0,0,1,1,0,1,1,0)*1000, nrow=4))), "ID1")
myLand <- SpatialPolygons(list(myLand))
simul_params <- setLandscape(simul_params, myLand)

## Simulation, pathogen, landscape and dispersal parameters
simul_params <- setInoculum(simul_params, 5e-4)
simul_params <- setDispersalPathogen(simul_params, c(1))

## Cultivars
simul_params <- setCultivars(simul_params, loadCultivar(name = "Susceptible", type = hostType))

## Croptypes
croptype <- data.frame(croptypeID = 0, croptypeName = c("Fully susceptible crop"), Susceptible = 1)
simul_params <- setCroptypes(simul_params, croptype)
simul_params <- allocateLandscapeCroptypes(simul_params,
rotation_period = 0, rotation_sequence = list(c(0)),
rotation_realloc = FALSE, prop = 1, aggreg = 1)

## list of outputs to be generated
outputlist <- loadOutputs(epid_outputs = "all", evol_outputs = "")
simul_params <- setOutputs(simul_params, outputlist)

## Check, save and run simulation
checkSimulParams(simul_params)
runSimul(simul_params, graphic = TRUE)

## End(Not run)

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