posterior_to_normal

Helper function to create a Normal distribution from a row of posterior
summary statistics, with consistent rounding.

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

posterior_to_normal function

<dl><dt>posterior</dt>
<dd>Data frame with <code>mean</code> and <code>sd</code> columns from Stan output</dd>
<dt>idx</dt>
<dd>Integer index into the posterior data frame</dd></dl>

Arguments

Create a Normal distribution from posterior samples — posterior_to_normal

<dl>

<dt>posterior</dt>
<dd>Data frame with <code>mean</code> and <code>sd</code> columns from Stan output</dd>


<dt>idx</dt>
<dd>Integer index into the posterior data frame</dd>

</dl>

Create a Normal distribution from posterior samples

posterior_to_normal: Create a Normal distribution from posterior samples

Description

Usage

Value

Arguments