summary.projections

This method summarises predicted epidemic trajectories contained in a
<code>projections</code> object by days, deriving the mean, standard deviation, and
user-specified quantiles for each day.

Provides functions and graphics for projecting daily incidence based on past incidence, and estimates of the serial interval and reproduction number. Projections are based on a branching process using a Poisson-distributed number of new cases per day, similar to the model used for estimating R in 'EpiEstim' or in 'earlyR', and described by Nouvellet et al. (2017) <doi:10.1016/j.epidem.2017.02.012>. The package provides the S3 class 'projections' which extends 'matrix', with accessors and additional helpers for handling, subsetting, merging, or adding these objects, as well as dedicated printing and plotting methods.

Thibaut Jombart

projections

Project Future Case Incidence

Pierre Nouvellet

Sangeeta Bhatia

Zhian N. Kamvar

Tim Taylor

Stephane Ghozzi

summary.projections function

<dl><dt>object</dt>
<dd>A <code>projections</code> object to summarise</dd>
<dt>quantiles</dt>
<dd>A <code>numeric</code> vector indicating which quantiles should be
computed; ignored if <code>FALSE</code> or of length 0</dd>
<dt>mean</dt>
<dd>a <code>logical</code> indicating of the mean should be computed</dd>
<dt>sd</dt>
<dd>a <code>logical</code> indicating of the standard deviation should be computed</dd>
<dt>min</dt>
<dd>a <code>logical</code> indicating of the minimum should be computed</dd>
<dt>max</dt>
<dd>a <code>logical</code> indicating of the maximum should be computed</dd>
<dt>...</dt>
<dd>only preesnt for compatibility with the generic
if (require(incidence)) 
i &lt;- incidence::incidence(as.Date('2020-01-23'))
si &lt;- c(0.2, 0.5, 0.2, 0.1)
R0 &lt;- 2p &lt;- project(x = i,
si = si,
R = R0,
n_sim = 2,
R_fix_within = TRUE,
n_days = 10,
model = "poisson"
)
summary(p)</dd></dl>

Arguments

Author

Summary for projections objects — summary.projections

<dl>

<dt>object</dt>
<dd>A <code>projections</code> object to summarise</dd>


<dt>quantiles</dt>
<dd>A <code>numeric</code> vector indicating which quantiles should be
computed; ignored if <code>FALSE</code> or of length 0</dd>


<dt>mean</dt>
<dd>a <code>logical</code> indicating of the mean should be computed</dd>


<dt>sd</dt>
<dd>a <code>logical</code> indicating of the standard deviation should be computed</dd>


<dt>min</dt>
<dd>a <code>logical</code> indicating of the minimum should be computed</dd>


<dt>max</dt>
<dd>a <code>logical</code> indicating of the maximum should be computed</dd>


<dt>...</dt>
<dd>only preesnt for compatibility with the generic
if (require(incidence)) 
i &lt;- incidence::incidence(as.Date('2020-01-23'))
si &lt;- c(0.2, 0.5, 0.2, 0.1)
R0 &lt;- 2p &lt;- project(x = i,
si = si,
R = R0,
n_sim = 2,
R_fix_within = TRUE,
n_days = 10,
model = "poisson"
)
summary(p)</dd>

</dl>

summary.projections: Summary for projections objects

Description

Usage

Arguments

Author