rt_empirical

Compute empirical \(R_t\), via Cori et al. (2013) method.

A suite of methods to fit and predict case count data using
a compartmental SIRS (Susceptible – Infectious – Recovered – Susceptible)
model, based on an assumed specification of the effective reproduction
number. The significance of this approach is that it relates epidemic
progression to the average number of contacts of infected individuals,
which decays as a function of the total susceptible fraction remaining
in the population. The main functions are pred.curve(), which computes
the epidemic curve for a set of parameters, and estimate.mle(), which
finds the best fitting curve to observed data. The easiest way to pass
arguments to the functions is via a config file, which contains input
settings required for prediction, and the package offers two methods,
navigate_to_config() which points the user to the configuration file,
and re_predict() for starting the fit-predict process.
Razvan G. Romanescu et al. (2023) <doi:10.1016/j.epidem.2023.100708>.

Razvan Romanescu

REffectivePred

Pandemic Prediction Model in an SIRS Framework

Songdi Hu

Md Ashiqul Haque

Olivier Tremblay-Savard

rt_empirical function

<dl><dt>cases</dt>
<dd>Vector of (confirmed) cases.</dd>
<dt>window_size</dt>
<dd>The maximum value for the serial interval.</dd>
<dt>serial_mean</dt>
<dd>Mean of the serial interval on the log scale.</dd>
<dt>serial_var</dt>
<dd>Variance of the serial interval on the log scale.</dd></dl>

Arguments

Empirical estimate of \(R_t\) — rt_empirical

<dl>

<dt>cases</dt>
<dd>Vector of (confirmed) cases.</dd>


<dt>window_size</dt>
<dd>The maximum value for the serial interval.</dd>


<dt>serial_mean</dt>
<dd>Mean of the serial interval on the log scale.</dd>


<dt>serial_var</dt>
<dd>Variance of the serial interval on the log scale.</dd>

</dl>

rt_empirical: Empirical estimate of \(R_t\)

Description

Usage

Value

Arguments