trajectory

Generic function to extract data from a simulated trajectory

Provides an efficient and very flexible framework to
conduct data-driven epidemiological modeling in realistic large
scale disease spread simulations. The framework integrates
infection dynamics in subpopulations as continuous-time Markov
chains using the Gillespie stochastic simulation algorithm and
incorporates available data such as births, deaths and movements
as scheduled events at predefined time-points. Using C code for
the numerical solvers and 'OpenMP' (if available) to divide work
over multiple processors ensures high performance when simulating
a sample outcome. One of our design goals was to make the package
extendable and enable usage of the numerical solvers from other R
extension packages in order to facilitate complex epidemiological
research. The package contains template models and can be extended
with user-defined models. For more details see the paper by
Widgren, Bauer, Eriksson and Engblom (2019)
<doi:10.18637/jss.v091.i12>. The package also provides
functionality to fit models to time series data using the
Approximate Bayesian Computation Sequential Monte Carlo
('ABC-SMC') algorithm of Toni and others (2009)
<doi:10.1098/rsif.2008.0172>.

Stefan Widgren

SimInf

A Framework for Data-Driven Stochastic Disease Spread
Simulations

Robin Eriksson

Stefan Engblom

Pavol Bauer

Thomas Rosendal

Ivana Rodriguez Ewerlöf

Attractive Chaos 

trajectory function

<dl><dt>model</dt>
<dd>the object to extract the trajectory from.</dd>
<dt>compartments</dt>
<dd>specify the names of the compartments to
extract data from. The compartments can be specified as a
character vector e.g. <code>compartments = c('S', 'I', 'R')</code>,
or as a formula e.g. <code>compartments = ~S+I+R</code> (see
&#8216;Examples&#8217;). Default (<code>compartments=NULL</code>) is to
extract the number of individuals in each compartment i.e. the
data from all discrete state compartments in the model. In
models that also have continuous state variables e.g. the
<code>SISe</code> model, they are also included.</dd>
<dt>index</dt>
<dd>indices specifying the subset of nodes to include
when extracting data. Default (<code>index = NULL</code>) is to
extract data from all nodes.</dd>
<dt>...</dt>
<dd>Additional arguments, see
<code>trajectory,SimInf_model-method</code></dd></dl>

Arguments

Generic function to extract data from a simulated trajectory — trajectory

<dl>

<dt>model</dt>
<dd>the object to extract the trajectory from.</dd>


<dt>compartments</dt>
<dd>specify the names of the compartments to
extract data from. The compartments can be specified as a
character vector e.g. <code>compartments = c('S', 'I', 'R')</code>,
or as a formula e.g. <code>compartments = ~S+I+R</code> (see
&#8216;Examples&#8217;). Default (<code>compartments=NULL</code>) is to
extract the number of individuals in each compartment i.e. the
data from all discrete state compartments in the model. In
models that also have continuous state variables e.g. the
<code>SISe</code> model, they are also included.</dd>


<dt>index</dt>
<dd>indices specifying the subset of nodes to include
when extracting data. Default (<code>index = NULL</code>) is to
extract data from all nodes.</dd>


<dt>...</dt>
<dd>Additional arguments, see
<code>trajectory,SimInf_model-method</code></dd>

</dl>

trajectory: Generic function to extract data from a simulated trajectory

Description

Usage

Arguments