Skip to contents

{landsepi} : Landscape Epidemiology and Evolution

Description

A stochastic, spatially-explicit, demo-genetic model simulating the spread and evolution of a plant pathogen in a heterogeneous landscape to assess resistance deployment strategies. It is based on a spatial geometry for describing the landscape and allocation of different cultivars, a dispersal kernel for the dissemination of the pathogen, and a SEIR (‘Susceptible-Exposed-Infectious-Removed’) structure with a discrete time step. It provides a useful tool to assess the performance of a wide range of deployment options with respect to their epidemiological, evolutionary and economic outcomes.

Authors and contributors

  • Loup Rimbaud. Author.
  • Marta Zaffaroni. Author.
  • Julien Papaix. Author.
  • Jean-François Rey. Author, maintainer.
  • Jean-Loup Gaussen. Contributor.
  • Manon Couty. Contributor.

Funding

This work benefited from :

  • ANR project “ArchiV” (2019–2023, grant n°ANR-18-CE32-0004-01),
  • AFB Ecophyto II-Leviers Territoriaux Project ”Médée” (2020–2023),
  • GRDC grant CSP00192 and the CSIRO/INRA linkage program,
  • ANR project ‘COMBINE’ (2022-2026, grant n°ANR-22-CE32-0004),
  • SPE project ‘DYNAMO’(2022-2024),
  • INRA Program ASC (2008-2014),

Citation

Rimbaud L, Zaffaroni M, Rey J, Papaïx J (2024). landsepi: Landscape Epidemiology and Evolution. R package version 1.4.0, https://cran.r-project.org/package=landsepi.

License

The package landsepi is under GNU GPL V2.
See LICENSE file.

Installation

Configuration and dependencies

Read file “DESCRIPTION” for details on configuration requirements (with linux and R).
The package for compiling needs R (version >= 4.2.0) as well as g++, gsl dev library and ffmpeg (for videos).

Under Linux OS:

sudo apt-get install g++ libgsl2 libgsl-dev ffmpeg

Under Windows use the zip.
> You can install Rtools and get g++, gsl and ffmpeg binaries under Windows (using pacman).

R packages dependencies :
* Rcpp * sp * Matrix (>= 1.3-0) * mvtnorm * fields * splancs * sf
* DBI * RSQLite * foreach * doParallel * deSolve

To install :

install.packages(c("Rcpp","sp","Matrix","mvtnorm","fields","splancs","sf","DBI","RSQLite","foreach","doParallel","deSolve"))

Install landsepi

To install the last version (from artifacts) :

Unzip the artifacts.zip archive to get the package.
And then run in a terminal (or using Rstudio interface)

# linux
R CMD INSTALL landsepi_*.*.*.tar.gz
# windows
R CMD INSTALL landsepi_*.*.*.zip

Development

Generate documentation before build and install

Open R :

library(Rcpp)
library(roxygen2)
Rcpp::compileAttributes(pkg=".")
roxygen2::roxygenize('.', roclets=c('rd', 'namespace'))

Build, check and install the package 

R CMD build . --resave-data
R CMD check --as-cran landsepi_*.*.*.tar.gz
R CMD INSTALL landsepi_*.*.*tar.gz

For debug mode :

R CMD INSTALL landsepi_*.*.*.tar.gz --with-keep.source --configure-args="--enable-debug=yes"

Package users: Documentation and demonstration

Take a look at the vignettes/

A complete description of the package and its functions is provided in the R documentation. Open R:

library(landsepi)
??landsepi   
## select <landsepi::landsepi-package> for a complete description of the package

## Also access the vignettes (tutorials) via:
browseVignettes("landsepi")

Run demonstrations (in 20-year simulations) for different deployment strategies:

demo_landsepi(strat = "MO") ## for a mosaic of cultivars
demo_landsepi(strat = "MI") ## for a mixture of cultivars
demo_landsepi(strat = "RO") ## for a rotation of cultivars
demo_landsepi(strat = "PY") ## for a pyramid of resistance genes

Shiny interface

You can try the online Landsepi R Shiny App
or run it into R. To run the shiny interface into R you will need the following packages:

Install packages : install.packages(c("shiny","DT", "shinyjs", "gridExtra", "png", "grid"
, "future", "promises", "tools", "shinyalert"))

The app can be launched with:

Get started

To get started see the first article

Future versions

Future versions of the package will include in particular:

  • Sets of pathogen parameters to simulate other pathosystems (e.g. cucumber mosaic virus [CMV] or potato virus Y [PVY] on pepper).
  • An updated version of the shiny app.
  • Pathogen adaptation to chemical treatments.