SimInf_pfilter-class

Class <code>"SimInf_pfilter"</code>

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 

SimInf_pfilter-class function

<dl>
<dt><code>model</code></dt>
<dd>A <code>SimInf_model</code> object with one filtered
trajectory attached.</dd></dl>
<dt><code>npart</code></dt>
<dd>An integer with the number of particles that was used
at each timestep.</dd>
<dt><code>loglik</code></dt>
<dd>The estimated log likelihood.</dd>
<dt><code>ess</code></dt>
<dd>A numeric vector with the effective sample size (ESS).
The effective sample size is computed as
$$\left(\sum_{i=1}^N\!(w_{t}^{i})^2\right)^{-1},$$ where $w_{t}^{i}$ is the normalized
weight of particle $i$ at time $t$.</dd>

Slots

Class "SimInf_pfilter" — SimInf_pfilter-class

<dl>
<dt><code>model</code></dt>
<dd>A <code>SimInf_model</code> object with one filtered
trajectory attached.</dd>


<dt><code>npart</code></dt>
<dd>An integer with the number of particles that was used
at each timestep.</dd>


<dt><code>loglik</code></dt>
<dd>The estimated log likelihood.</dd>


<dt><code>ess</code></dt>
<dd>A numeric vector with the effective sample size (ESS).
The effective sample size is computed as
$$\left(\sum_{i=1}^N\!(w_{t}^{i})^2\right)^{-1},$$ where $w_{t}^{i}$ is the normalized
weight of particle $i$ at time $t$.</dd>


</dl>

SimInf_pfilter-class: Class `"SimInf_pfilter"`

Description

Arguments

Slots