plot.atwins

Plot results of fitting a twins model using MCMC output. Plots similar
 to those in the Held et al. (2006) paper are generated.

hplot

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

plot.atwins function

<dl> <dt>x</dt>
<dd>An object of class <code>"atwins"</code> as returned by
 <code>algo.twins</code>.</dd> <dt>which</dt>
<dd>a vector containing the different plot types to show<dl>
 <dt>1</dt>
<dd>A plot of the observed time series Z is shown together
	with posterior means for the number of endemic cases (X) and number of
	epidemic cases (Y).</dd></dl></dd> <dt>2</dt>
<dd>This plot shows trace plots of the gamma parameters
	over all MCMC samples.</dd> <dt>3</dt>
<dd>This shows a trace plot of psi, which controls the
	overdispersion in the model.</dd> <dt>4</dt>
<dd>Autocorrelation functions for K and psi are shown in
	order to judge whether the MCMC sampler has converged.</dd> <dt>5</dt>
<dd>Shows a plot of the posterior mean of the seasonal
	model nu[t] together with 95% credibility intervals based on the
	quantiles of the posterior.</dd> <dt>6</dt>
<dd>Histograms illustrating the posterior density for K and
	psi. The first one corresponds to Fig. 4(f) in the paper.</dd> <dt>7</dt>
<dd>Histograms illustrating the predictive posterior
	density for the next observed number of cases Z[n+1]. Compare with Fig.5
	in the paper.</dd> 
</dl> <dt>ask</dt>
<dd>Boolean indicating whether to ask for a newline before
 showing the next plot (only if multiple are shown).</dd> <dt>...</dt>
<dd>Additional arguments for <code>stsplot_time</code>,
 used for plot type 1.</dd>

Arguments

Author

Plots for Fitted algo.twins Models — plot.atwins

<dl>

 <dt>x</dt>
<dd>An object of class <code>"atwins"</code> as returned by
 <code>algo.twins</code>.</dd>

 <dt>which</dt>
<dd>a vector containing the different plot types to show<dl>
 <dt>1</dt>
<dd>A plot of the observed time series Z is shown together
	with posterior means for the number of endemic cases (X) and number of
	epidemic cases (Y).</dd>

 <dt>2</dt>
<dd>This plot shows trace plots of the gamma parameters
	over all MCMC samples.</dd>

 <dt>3</dt>
<dd>This shows a trace plot of psi, which controls the
	overdispersion in the model.</dd>

 <dt>4</dt>
<dd>Autocorrelation functions for K and psi are shown in
	order to judge whether the MCMC sampler has converged.</dd>

 <dt>5</dt>
<dd>Shows a plot of the posterior mean of the seasonal
	model nu[t] together with 95% credibility intervals based on the
	quantiles of the posterior.</dd>

 <dt>6</dt>
<dd>Histograms illustrating the posterior density for K and
	psi. The first one corresponds to Fig. 4(f) in the paper.</dd>

 <dt>7</dt>
<dd>Histograms illustrating the predictive posterior
	density for the next observed number of cases Z[n+1]. Compare with Fig.5
	in the paper.</dd>

 
</dl></dd>

 <dt>ask</dt>
<dd>Boolean indicating whether to ask for a newline before
 showing the next plot (only if multiple are shown).</dd>

 <dt>...</dt>
<dd>Additional arguments for <code>stsplot_time</code>,
 used for plot type 1.</dd>

</dl>

Plots for Fitted <code>algo.twins</code> Models

plot.atwins: Plots for Fitted `algo.twins` Models

Description

Usage

Arguments

Author

Details

References

See Also