default_fill_missing_obs

internal

Estimates the time-varying reproduction number,
rate of spread, and doubling time using a range of open-source tools
(Abbott et al. (2020) <doi:10.12688/wellcomeopenres.16006.1>), and
current best practices (Gostic et al. (2020)
<doi:10.1101/2020.06.18.20134858>). It aims to help users avoid some
of the limitations of naive implementations in a framework that is
informed by community feedback and is actively supported.

Sebastian Funk

EpiNow2

Estimate Real-Time Case Counts and Time-Varying Epidemiological
Parameters

Sam Abbott

Joel Hellewell

Katharine Sherratt

Katelyn Gostic

Joe Hickson

Hamada S. Badr

Michael DeWitt

James M. Azam

Robin Thompson

Sophie Meakin

James Munday

Nikos Bosse

Paul Mee

Peter Ellis

Pietro Monticone

Lloyd Chapman

Andrew Johnson

Kaitlyn Johnson

EpiForecasts 

default_fill_missing_obs function

<dl><dt>data</dt>
<dd>A <code>&lt;data.frame&gt;</code> of disease reports (confirm) by date (date).
<code>confirm</code> must be numeric and <code>date</code> must be in date format. Optionally
this can also have a logical <code>accumulate</code> column which indicates whether
data should be added to the next data point. This is useful when modelling
e.g. weekly incidence data. See also the <code>fill_missing()</code> function which
helps add the <code>accumulate</code> column with the desired properties when dealing
with non-daily data. If any accumulation is done this happens after
truncation as specified by the <code>truncation</code> argument.</dd>
<dt>obs</dt>
<dd>A list of options as generated by <code>obs_opts()</code> defining the
observation model. Defaults to <code>obs_opts()</code>.</dd>
<dt>obs_column</dt>
<dd>Character (default: "confirm"). If given, only the column
specified here will be used for checking missingness. This is useful if
using a data set that has multiple columns of hwich one of them
corresponds to observations that are to be processed here.</dd></dl>

Arguments

Temporary function to support the transition to full support of missing
data. — default_fill_missing_obs

<dl>

<dt>data</dt>
<dd>A <code>&lt;data.frame&gt;</code> of disease reports (confirm) by date (date).
<code>confirm</code> must be numeric and <code>date</code> must be in date format. Optionally
this can also have a logical <code>accumulate</code> column which indicates whether
data should be added to the next data point. This is useful when modelling
e.g. weekly incidence data. See also the <code>fill_missing()</code> function which
helps add the <code>accumulate</code> column with the desired properties when dealing
with non-daily data. If any accumulation is done this happens after
truncation as specified by the <code>truncation</code> argument.</dd>


<dt>obs</dt>
<dd>A list of options as generated by <code>obs_opts()</code> defining the
observation model. Defaults to <code>obs_opts()</code>.</dd>


<dt>obs_column</dt>
<dd>Character (default: "confirm"). If given, only the column
specified here will be used for checking missingness. This is useful if
using a data set that has multiple columns of hwich one of them
corresponds to observations that are to be processed here.</dd>

</dl>

Temporary function to support the transition to full support of missing
data.

default_fill_missing_obs: Temporary function to support the transition to full support of missing data.

Description

Usage

Value

Arguments