format_quantile_predictions

Helper function to format posterior samples into quantiles in the structure
expected by <code>scoringutils::as_forecast_quantile()</code>.

internal

Estimates the time-varying reproduction number, rate of
spread, and doubling time using a renewal equation approach combined
with Bayesian inference via Stan. Supports Gaussian process and
random walk priors for modelling changes in
transmission over time. Accounts for delays between infection and
observation (incubation period, reporting delays), right-truncation
in recent data, day-of-week effects, and observation overdispersion.
Can estimate relationships between primary and secondary outcomes
(e.g., cases to hospitalisations or deaths) and forecast both. Runs
across multiple regions in parallel. Based on
Abbott et al. (2020) <doi:10.12688/wellcomeopenres.16006.1> and
Gostic et al. (2020) <doi:10.1101/2020.06.18.20134858>.

Sebastian Funk

EpiNow2

Estimate and Forecast Real-Time Infection Dynamics

Sam Abbott

Joel Hellewell

Katharine Sherratt

Katelyn Gostic

Joe Hickson

Hamada S. Badr

Michael DeWitt

James M. Azam

Adrian Lison

Robin Thompson

Sophie Meakin

James Munday

Nikos Bosse

Paul Mee

Peter Ellis

Pietro Monticone

Lloyd Chapman

Andrew Johnson

Kaitlyn Johnson

Adam Howes

format_quantile_predictions function

<dl><dt>samples</dt>
<dd>Data.table with date and value columns</dd>
<dt>quantiles</dt>
<dd>Numeric vector of quantile levels</dd>
<dt>forecast_date</dt>
<dd>Date when the forecast was made</dd></dl>

Arguments

Format quantile predictions — format_quantile_predictions

<dl>

<dt>samples</dt>
<dd>Data.table with date and value columns</dd>


<dt>quantiles</dt>
<dd>Numeric vector of quantile levels</dd>


<dt>forecast_date</dt>
<dd>Date when the forecast was made</dd>

</dl>

Format quantile predictions

format_quantile_predictions: Format quantile predictions

Description

Usage

Value

Arguments