update.epidataCS

The <code><a href="/link/update?package=surveillance&version=1.23.1" data-mini-rdoc="surveillance::update">update</a></code> method for the <code>"epidataCS"</code> class
may be used to modify the hyperparameters $\epsilon$ (<code>eps.t</code>)
and $\delta$ (<code>eps.s</code>), the indicator matrix <code>qmatrix</code> determining
possible transmission between the event types, the numerical
accuracy <code>nCircle2Poly</code> of the polygonal approximation, and
the endemic covariates from <code>stgrid</code> (including the time intervals).
The update method will also update the auxiliary information contained
in an <code>"epidataCS"</code> object accordingly, e.g., the vector of potential
sources of each event, the influence regions, or the endemic covariates
copied from the new <code>stgrid</code>.

manip

methods

utilities

epidataCS_update

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

epidataCS_update function

<dl> <dt>object</dt>
<dd>an object of class <code>"epidataCS"</code>.</dd> <dt>eps.t</dt>
<dd>numeric vector of length 1 or corresponding to the number of events in
 <code>object$events</code>. The event data column <code>eps.t</code> specifies
 the maximum temporal influence radius (e.g., length of infectious
 period, time to culling, etc.) of the events.</dd> <dt>eps.s</dt>
<dd>numeric vector of length 1 or corresponding to the number of events in
 <code>object$events</code>. The event data column <code>eps.s</code> specifies
 the maximum spatial influence radius of the events.</dd> <dt>qmatrix</dt>
<dd>square indicator matrix (0/1 or TRUE/FALSE) for possible
 transmission between the event types.</dd> <dt>nCircle2Poly</dt>
<dd>accuracy (number of edges) of the polygonal approximation of a circle.</dd> <dt>stgrid</dt>
<dd>a new <code>data.frame</code> with endemic covariates, possibly
 transformed from or adding to the original <code>object$stgrid</code>.
 The grid must cover the same regions as the original, i.e.,
 <code>levels(object$stgrid$tile)</code> must remain identical.
 See <code>epidataCS</code> for a detailed description of the
 required format.</dd> <dt>...</dt>
<dd>unused (argument of the generic).</dd></dl>

Arguments

Author

The <code><a href='https://rdrr.io/r/stats/update.html'>update</a></code> method for the <code>"epidataCS"</code> class
may be used to modify the hyperparameters $\epsilon$ (<code>eps.t</code>)
and $\delta$ (<code>eps.s</code>), the indicator matrix <code>qmatrix</code> determining
possible transmission between the event types, the numerical
accuracy <code>nCircle2Poly</code> of the polygonal approximation, and
the endemic covariates from <code>stgrid</code> (including the time intervals).
The update method will also update the auxiliary information contained
in an <code>"epidataCS"</code> object accordingly, e.g., the vector of potential
sources of each event, the influence regions, or the endemic covariates
copied from the new <code>stgrid</code>.

Update method for "epidataCS" — epidataCS_update

<dl>

 <dt>object</dt>
<dd>an object of class <code>"epidataCS"</code>.</dd>

 <dt>eps.t</dt>
<dd>numeric vector of length 1 or corresponding to the number of events in
 <code>object$events</code>. The event data column <code>eps.t</code> specifies
 the maximum temporal influence radius (e.g., length of infectious
 period, time to culling, etc.) of the events.</dd>

 <dt>eps.s</dt>
<dd>numeric vector of length 1 or corresponding to the number of events in
 <code>object$events</code>. The event data column <code>eps.s</code> specifies
 the maximum spatial influence radius of the events.</dd>

 <dt>qmatrix</dt>
<dd>square indicator matrix (0/1 or TRUE/FALSE) for possible
 transmission between the event types.</dd>

 <dt>nCircle2Poly</dt>
<dd>accuracy (number of edges) of the polygonal approximation of a circle.</dd>

 <dt>stgrid</dt>
<dd>a new <code>data.frame</code> with endemic covariates, possibly
 transformed from or adding to the original <code>object$stgrid</code>.
 The grid must cover the same regions as the original, i.e.,
 <code>levels(object$stgrid$tile)</code> must remain identical.
 See <code>epidataCS</code> for a detailed description of the
 required format.</dd>

 <dt>...</dt>
<dd>unused (argument of the generic).</dd>

</dl>

Update method for <code>"epidataCS"</code>

epidataCS_update: Update method for `"epidataCS"`

Description

Usage

Value

Arguments

Author

See Also

Examples