A spatio-temporal stochastic model to assess resistance deployment strategies
against plant pathogens. The model is based on stochastic geometry for describing the
landscape and the resistant hosts, a dispersal kernel for the dissemination of the
pathogen, and a SEIR (Susceptible-Exposed-Infectious-Removed) architecture to simulate
plant response to disease.
Loup Rimbaud, Julien Papaïx, Jean-François Rey, Luke G Barrett,
Peter H Thrall (2018) <doi:10.1371/journal.pcbi.1006067>.

c(person("Loup", "Rimbaud", role = "aut", email = "loup.rimbaud@inra.fr"),
person("Julien", "Papaix", role = "aut", email = "julien.papaix@inra.fr"),
person("Jean-Francois", "Rey", role = "cre", email = "jean-francois.rey@inra.fr"))

c(person("Loup", "Rimbaud", role = "aut", email = "loup.rimbaud@inrae.fr"),
person("Julien", "Papaix", role = "aut", email = "julien.papaix@inrae.fr"),
person("Jean-Francois", "Rey", role = "cre", email = "jean-francois.rey@inrae.fr"),
person("Jean-Loup", "Gaussen", role = "ctb", email = "jean-loup-thomas.gaussen@inrae.fr"))
Loup Rimbaud [aut],
Julien Papaix [aut],
Jean-Francois Rey [cre],
Jean-Loup Gaussen [ctb]
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.
Loup Rimbaud, Julien Papaïx, Jean-François Rey, Luke G Barrett,
Peter H Thrall (2018) <doi:10.1371/journal.pcbi.1006067>.
Rcpp (>= 0.9.0), stats (>= 3.0.2), Matrix, mvtnorm, fields,
splancs, sf, DBI, RSQLite, foreach, parallel, doParallel
'Math-Functions.R' 'RcppExports.R' 'graphics.R' 'AgriLand.R'
'Class-LandsepiParams.R' 'GPKGTools.R' 'tools.R'
'Methods-LandsepiParams.R' 'catch-routine-registration.R'
'demo_landsepi.R' 'landsepi.R' 'output.R' 'runShiny.R'
'simul_landsepi.R'
testthat, shiny, shinyjs, DT, knitr, rmarkdown