bundeslanddata

Data: Bundeslaender (German Federal State Information)
Umlaute converted via 
<code>bd[] &lt;- lapply(bd, function(x) gsub("\xfc", "ue", x))</code>

datasets

Implements a discrete-event simulation model for a hospital resource planning problem.
The project is motivated by the challenges faced by health care institutions in the current COVID-19 pandemic.
It can be used by health departments to forecast demand for intensive care beds, ventilators, and staff resources.
Our modelling approach is inspired by "A novel modelling technique to predict resource requirements in critical care - a case study" (Lawton and McCooe 2019) and combines two powerful technologies:
(i) discrete event simulation using the 'simmer' package and
(ii) model-based optimization using 'SPOT'.
Ucar I, Smeets B, Azcorra A (2019) <doi:10.18637/jss.v090.i02>.
Bartz-Beielstein T, Lasarczyk C W G, Preuss M (2005) <doi:10.1109/CEC.2005.1554761>.
Lawton T, McCooe M (2019) <doi:10.7861/futurehosp.6-1-17>.

Thomas Bartz-Beielstein

babsim.hospital

Bartz & Bartz Simulation Hospital

Frederik Rehbach

Eva Bartz

Olaf Mersmann

Martin Zaefferer

bundeslanddata function

'data.frame':	obs. of 34 variables:<dl class="dl-horizontal">
 <dt>IdBundesland</dt><dd>int</dd>
 <dt>Bundesland</dt><dd>Factor w/ 16 levels</dd>
 <dt>Geschlecht</dt><dd>Factor w/ 2 levels</dd>
 <dt>Alter</dt><dd>int</dd>
 <dt>N</dt><dd>int population size</dd>
</dl>

Format

bundeslanddata data — bundeslanddata

'data.frame':	obs. of 34 variables:<dl class='dl-horizontal'>
 <dt>IdBundesland</dt><dd>int</dd>
 <dt>Bundesland</dt><dd>Factor w/ 16 levels</dd>
 <dt>Geschlecht</dt><dd>Factor w/ 2 levels</dd>
 <dt>Alter</dt><dd>int</dd>
 <dt>N</dt><dd>int population size</dd>
</dl>

bundeslanddata: bundeslanddata data

Description

Usage

Arguments

Format

Details