continue_abc

continue_abc,SimInf_abc-method

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 

continue_abc function

<dl><dt>object</dt>
<dd>The <code>SimInf_abc</code> object to continue from.</dd>
<dt>tolerance</dt>
<dd>A numeric matrix (number of summary statistics
\(\times\) number of generations) where each column contains
the tolerances for a generation and each row contains a
sequence of gradually decreasing tolerances. Can also be a
numeric vector if there is only one summary statistic. The
tolerance determines the number of generations of ABC-SMC to
run.</dd>
<dt>data</dt>
<dd>Optional data to be passed to the
<code>SimInf_abc@fn</code> function. Default is <code>NULL</code>.</dd>
<dt>verbose</dt>
<dd>prints diagnostic messages when <code>TRUE</code>. The
default is to retrieve the global option <code>verbose</code> and
use <code>FALSE</code> if it is not set.</dd>
<dt>post_gen</dt>
<dd>An optional function that, if non-NULL, is applied
after each completed generation. The function must accept one
argument of type <code>SimInf_abc</code> with the current state of
the fitting process. This function can be useful to, for
example, save and inspect intermediate results.</dd></dl>

Arguments

Run more generations of ABC SMC — continue_abc

<dl>

<dt>object</dt>
<dd>The <code>SimInf_abc</code> object to continue from.</dd>


<dt>tolerance</dt>
<dd>A numeric matrix (number of summary statistics
\(\times\) number of generations) where each column contains
the tolerances for a generation and each row contains a
sequence of gradually decreasing tolerances. Can also be a
numeric vector if there is only one summary statistic. The
tolerance determines the number of generations of ABC-SMC to
run.</dd>


<dt>data</dt>
<dd>Optional data to be passed to the
<code>SimInf_abc@fn</code> function. Default is <code>NULL</code>.</dd>


<dt>verbose</dt>
<dd>prints diagnostic messages when <code>TRUE</code>. The
default is to retrieve the global option <code>verbose</code> and
use <code>FALSE</code> if it is not set.</dd>


<dt>post_gen</dt>
<dd>An optional function that, if non-NULL, is applied
after each completed generation. The function must accept one
argument of type <code>SimInf_abc</code> with the current state of
the fitting process. This function can be useful to, for
example, save and inspect intermediate results.</dd>

</dl>

Run more generations of ABC SMC

continue_abc: Run more generations of ABC SMC

Description

Usage

Value

Arguments