campyDE

Weekly number of reported campylobacteriosis cases in Germany,
 2002-2011, together with the corresponding absolute humidity (in g/m^3)
 that week. The absolute humidity was computed according to the
 procedure by Dengler (1997) using the means of representative weather
 station data from the German Climate service.

datasets

Statistical methods for the modeling and monitoring of time series
of counts, proportions and categorical data, as well as for the modeling
of continuous-time point processes of epidemic phenomena.
The monitoring methods focus on aberration detection in count data time
series from public health surveillance of communicable diseases, but
applications could just as well originate from environmetrics,
reliability engineering, econometrics, or social sciences. The package
implements many typical outbreak detection procedures such as the
(improved) Farrington algorithm, or the negative binomial GLR-CUSUM
method of Hoehle and Paul (2008) <doi:10.1016/j.csda.2008.02.015>.
A novel CUSUM approach combining logistic and multinomial logistic
modeling is also included. The package contains several real-world data
sets, the ability to simulate outbreak data, and to visualize the
results of the monitoring in a temporal, spatial or spatio-temporal
fashion. A recent overview of the available monitoring procedures is
given by Salmon et al. (2016) <doi:10.18637/jss.v070.i10>.
For the retrospective analysis of epidemic spread, the package provides
three endemic-epidemic modeling frameworks with tools for visualization,
likelihood inference, and simulation. hhh4() estimates models for
(multivariate) count time series following Paul and Held (2011)
<doi:10.1002/sim.4177> and Meyer and Held (2014) <doi:10.1214/14-AOAS743>.
twinSIR() models the susceptible-infectious-recovered (SIR) event
history of a fixed population, e.g, epidemics across farms or networks,
as a multivariate point process as proposed by Hoehle (2009)
<doi:10.1002/bimj.200900050>. twinstim() estimates self-exciting point
process models for a spatio-temporal point pattern of infective events,
e.g., time-stamped geo-referenced surveillance data, as proposed by
Meyer et al. (2012) <doi:10.1111/j.1541-0420.2011.01684.x>.
A recent overview of the implemented space-time modeling frameworks
for epidemic phenomena is given by Meyer et al. (2017)
<doi:10.18637/jss.v077.i11>.

Sebastian Meyer

surveillance

Temporal and Spatio-Temporal Modeling and Monitoring of Epidemic
Phenomena

Sophie Reichert

Andrea Riebler

Daniel Sabanes Bove

Stefan Steiner

Mikko Virtanen

Wei Wei

Valentin Wimmer

R Core Team 

Howard Burkom

Thais Correa

Mathias Hofmann

Christian Lang

Juliane Manitz

Dirk Schumacher

Leonhard Held

Michael Hoehle

Michaela Paul

Maelle Salmon

campyDE function

A <code>data.frame</code> containing the following columns<dl>
 <dt><code>date</code></dt>
<dd><code>Date</code> instance containing the Monday of the
 reporting week.</dd> <dt><code>case</code></dt>
<dd>Number of reported cases that week.</dd> <dt><code>state</code></dt>
<dd>Boolean indicating whether there is external knowledge
 about an outbreak that week</dd> <dt><code>hum</code></dt>
<dd>Mean absolute humidity (in g/m^3) of that week as
 measured by a single representative weather station.</dd> <dt><code>l1.hum</code>-<code>l5.hum</code></dt>
<dd>Lagged version (lagged by 1-5) of
 the <code>hum</code> covariate.</dd> <dt>newyears</dt>
<dd>Boolean indicating whether the reporting week
 corresponds to the first two weeks of the year (TRUE) or not
 (FALSE). Note: The first week of a year is here defined as the first
 reporting week, which has its corresponding Monday within new year.</dd> <dt>christmas</dt>
<dd>Boolean indicating whether the reporting week
 corresponds to the last two weeks of the year (TRUE) or not
 (FALSE). Note: This are the first two weeks before the
 <code>newyears</code> weeks.</dd>
 
 <dt>O104period</dt>
<dd>Boolean indicating whether the reporting week
 corresponds to the W21-W30 period of increased gastroenteritis
 awareness during the O104:H4 STEC outbreak.</dd> 
</dl>

Format

Campylobacteriosis and Absolute Humidity in Germany 2002-2011 — campyDE

A <code>data.frame</code> containing the following columns<dl>
 <dt><code>date</code></dt>
<dd><code>Date</code> instance containing the Monday of the
 reporting week.</dd>

 <dt><code>case</code></dt>
<dd>Number of reported cases that week.</dd>

 <dt><code>state</code></dt>
<dd>Boolean indicating whether there is external knowledge
 about an outbreak that week</dd>

 <dt><code>hum</code></dt>
<dd>Mean absolute humidity (in g/m^3) of that week as
 measured by a single representative weather station.</dd>

 <dt><code>l1.hum</code>-<code>l5.hum</code></dt>
<dd>Lagged version (lagged by 1-5) of
 the <code>hum</code> covariate.</dd>

 <dt>newyears</dt>
<dd>Boolean indicating whether the reporting week
 corresponds to the first two weeks of the year (TRUE) or not
 (FALSE). Note: The first week of a year is here defined as the first
 reporting week, which has its corresponding Monday within new year.</dd>

 <dt>christmas</dt>
<dd>Boolean indicating whether the reporting week
 corresponds to the last two weeks of the year (TRUE) or not
 (FALSE). Note: This are the first two weeks before the
 <code>newyears</code> weeks.</dd>
 
 <dt>O104period</dt>
<dd>Boolean indicating whether the reporting week
 corresponds to the W21-W30 period of increased gastroenteritis
 awareness during the O104:H4 STEC outbreak.</dd>

 
</dl>

Campylobacteriosis and Absolute Humidity in Germany 2002-2011

campyDE: Campylobacteriosis and Absolute Humidity in Germany 2002-2011

Description

Usage

Arguments

Format

References

Examples