summary,SimInf_pmcmc-method

Detailed summary of a <code>SimInf_pmcmc</code> object

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> or the Particle Markov Chain Monte
Carlo ('PMCMC') algorithm of 'Andrieu' and others (2010)
<doi:10.1111/j.1467-9868.2009.00736.x>.

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 

summary,SimInf_pmcmc-method function

<dl><dt>object</dt>
<dd>The <code>SimInf_pmcmc</code> object</dd>
<dt>...</dt>
<dd>Not used.</dd></dl>

Arguments

Detailed summary of a SimInf_pmcmc object — summary,SimInf_pmcmc-method

<dl>

<dt>object</dt>
<dd>The <code>SimInf_pmcmc</code> object</dd>


<dt>...</dt>
<dd>Not used.</dd>

</dl>

summary,SimInf_pmcmc-method: Detailed summary of a `SimInf_pmcmc` object

Description

Usage

Value

Arguments