reconstruct_delay

Reconstruct a dist_spec from stored stan data and posterior

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

reconstruct_delay function

<dl><dt>object</dt>
<dd>A fitted model object containing fit and args</dd>
<dt>delay_name</dt>
<dd>The name of the delay (e.g., "generation_time")</dd></dl>

Arguments

Reconstruct a dist_spec from stored stan data and posterior — reconstruct_delay

<dl>

<dt>object</dt>
<dd>A fitted model object containing fit and args</dd>


<dt>delay_name</dt>
<dd>The name of the delay (e.g., "generation_time")</dd>

</dl>

Reconstruct a dist_spec from stored stan data and posterior

reconstruct_delay: Reconstruct a dist_spec from stored stan data and posterior

Description

Usage

Value

Arguments