MMRcoverageDE

Coverage levels at school entry for the first and second dose
 of the combined measles-mumps-rubella (MMR) vaccine in 2006, estimated 
 from children presenting vaccination documents at school entry examinations.

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

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

MMRcoverageDE function

A <code>data.frame</code> containing 19 rows and 5 columns with variables<dl>
 <dt>state</dt>
<dd>Names of states: the 16 federal states are
 followed by the total of Germany, as well as the total 
 of West and East Germany.</dd> <dt>nOfexaminedChildren</dt>
<dd>Number of children examined.</dd> <dt>withVaccDocument</dt>
<dd>Percentage of children who presented vaccination documents.</dd> <dt>MMR1</dt>
<dd>Percentage of children with vaccination documents, who
 received at least 1 dose of MMR vaccine.</dd> <dt>MMR2</dt>
<dd>Percentage of children with vaccination documents, who
 received at least 2 doses of MMR vaccine.</dd> 
</dl> 
 
Coverage levels were derived from vaccination documents presented
 at medical examinations, which are conducted by local health authorities
 at school entry each year. Records include information about the receipt 
 of 1st and 2nd doses of MMR, but no information about dates.
 Note that information from children who did not present a vaccination 
 document on the day of the medical examination, is not included in
 the estimated coverage.

Format

MMR coverage levels in the 16 states of Germany — MMRcoverageDE

A <code>data.frame</code> containing 19 rows and 5 columns with variables<dl>
 <dt>state</dt>
<dd>Names of states: the 16 federal states are
 followed by the total of Germany, as well as the total 
 of West and East Germany.</dd>

 <dt>nOfexaminedChildren</dt>
<dd>Number of children examined.</dd>

 <dt>withVaccDocument</dt>
<dd>Percentage of children who presented vaccination documents.</dd>

 <dt>MMR1</dt>
<dd>Percentage of children with vaccination documents, who
 received at least 1 dose of MMR vaccine.</dd>

 <dt>MMR2</dt>
<dd>Percentage of children with vaccination documents, who
 received at least 2 doses of MMR vaccine.</dd>

 
</dl> 
 
Coverage levels were derived from vaccination documents presented
 at medical examinations, which are conducted by local health authorities
 at school entry each year. Records include information about the receipt 
 of 1st and 2nd doses of MMR, but no information about dates.
 Note that information from children who did not present a vaccination 
 document on the day of the medical examination, is not included in
 the estimated coverage.

MMR coverage levels in the 16 states of Germany

MMRcoverageDE: MMR coverage levels in the 16 states of Germany

Description

Usage

Arguments

Format

References

See Also