husO104Hosp

Data contain the date of hospitalization for 630 hemolytic-uremic
 syndrome (HUS) cases during the large STEC outbreak in Germany,
 2011. Note: Only HUS cases which ultimately had a hospitalization
 date available/reported are included in the data set. The total number
 of HUS cases during the outbreak was 855 -- see Höhle and
 an der Heiden (2014) as well as Frank et al. (2011) for details. 
For each HUS case the attribute <code>dHosp</code> contains the date of
 hospitalization and the attribute <code>dReport</code> contains the date of
 first arrival of this hospitalization date at the Robert Koch
 Institute (RKI). As described in Höhle and an der Heiden
 (2014) the mechanisms of the delay were complicated and should be
 interpreted with care. For example, the case report could have arrived
 earlier, but without information about the hospitalization date.
The resulting reporting triangle corresponds to Fig. 1 of the Web
 appendix of Höhle and an der Heiden (2014). This means
 that the reports which arrived with a delay longer than 15 days are
 set to have have arrived after 15 days. Altogether, this gives small
 discrepancies when compared with the results of the paper. However, as
 mentioned in the paper, longer delays were not very relevant for the
 nowcasting.

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

husO104Hosp function

Format

Hospitalization date for HUS cases of the STEC outbreak in Germany, 2011 — husO104Hosp

Hospitalization date for HUS cases of the STEC outbreak in Germany, 2011

husO104Hosp: Hospitalization date for HUS cases of the STEC outbreak in Germany, 2011

Description

Usage

Arguments

Format

References