sample_nb

Sample from negative binomial model given a set of predictions

Nowcasting right-truncated epidemiological data is critical
for timely public health decision-making, as reporting delays can create
misleading impressions of declining trends in recent data. This package
provides nowcasting methods based on using empirical
delay distributions and uncertainty from past performance. It is also designed
to be used as a baseline method for developers of new nowcasting methods.
For more details on the performance of the method(s) in this package applied
to case studies of COVID-19 and norovirus, see our recent paper at
<https://wellcomeopenresearch.org/articles/10-614>. The package supports
standard data frame inputs with reference date, report date, and
count columns, as well as the direct use of reporting triangles, and is
compatible with 'epinowcast' objects. Alongside
an opinionated default workflow, it has a low-level pipe-friendly modular
interface, allowing context-specific workflows. It can accommodate a wide
spectrum of reporting schedules, including mixed patterns of reference and
reporting (daily-weekly, weekly-daily). It also supports sharing delay
distributions and uncertainty estimates between strata,
as well as custom uncertainty models and delay estimation methods.

Kaitlyn Johnson

baselinenowcast

Baseline Nowcasting for Right-Truncated Epidemiological Data

Emily Tyszka

Johannes Bracher

Sebastian Funk

Sam Abbott

Tim Taylor

sample_nb function

<dl><dt>pred</dt>
<dd>Vector of predictions.</dd>
<dt>uncertainty_params</dt>
<dd>Vector of uncertainty parameters.</dd></dl>

Arguments

Sample from negative binomial model given a set of predictions — sample_nb

<dl>

<dt>pred</dt>
<dd>Vector of predictions.</dd>


<dt>uncertainty_params</dt>
<dd>Vector of uncertainty parameters.</dd>

</dl>

sample_nb: Sample from negative binomial model given a set of predictions

Description

Usage

Value

Arguments

See Also

Examples