antideriv_verhulst

Give the antiderivative of the logistic function from the Verhulst model.

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>.

Jean-Francois Rey

landsepi

Landscape Epidemiology and Evolution

Loup Rimbaud

Marta Zaffaroni

Jean-François Rey

Julien Papaix

Jean-Loup Gaussen

Manon Couty

antideriv_verhulst function

<dl><dt>x</dt>
<dd>timestep up to which antiderivative must be computed</dd>
<dt>initial_density</dt>
<dd>initial density</dd>
<dt>max_density</dt>
<dd>maximal density</dd>
<dt>growth_rate</dt>
<dd>growth rate</dd></dl>

Arguments

Antiderivative of the Verhulst logistic function — antideriv_verhulst

<dl>

<dt>x</dt>
<dd>timestep up to which antiderivative must be computed</dd>


<dt>initial_density</dt>
<dd>initial density</dd>


<dt>max_density</dt>
<dd>maximal density</dd>


<dt>growth_rate</dt>
<dd>growth rate</dd>

</dl>

antideriv_verhulst: Antiderivative of the Verhulst logistic function

Description

Usage

Value

Arguments

Details

Examples