runSimul.RdRuns 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.
runSimul(
params,
graphic = TRUE,
writeTXT = TRUE,
videoMP4 = FALSE,
keepRawResults = FALSE
)a LandsepiParams Object containing all simulation parameters. Must be initialised
with createSimulParams and updated using set*() methods
(see vignettes for details).
a logical indicating if graphics must be generated (TRUE, default) or not (FALSE).
a logical indicating if outputs must be written in text files (TRUE, default) or not (FALSE).
a logical indicating if a video must be generated (TRUE) or not (FALSE, default). Works only if graphic=TRUE and audpc_rel is computed.
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.
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 polygon, 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).
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 or polygon), 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.
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.