Computes the epidemiological growth rate, defined as the instantaneous
rate of change in log-incidence over time. Mathematically, it represents:
$$r_t = \log(I_t) - \log(I_{t-1}) = \log\left(\frac{I_t}{I_{t-1}}\right)$$
where \(I_t\) is incidence (see incidence()) at time \(t\).
Usage
growth_rate(fitted_model, ...)
# S3 method for class 'ps'
growth_rate(fitted_model, ...)
# S3 method for class 'rw'
growth_rate(fitted_model, ...)
# S3 method for class 'ps_single'
growth_rate(fitted_model, ...)
# S3 method for class 'rw_single'
growth_rate(fitted_model, ...)Value
named list of class EpiStrainDynamics.metric containing a dataframe
of the calculated metric outcome ($measure), the fit object ($fit), and the
constructed model object ($constructed_model). The measure data frame
contains the median of the epidemiological quantity (y), the 50% credible
interval of the quantity (lb_50 & ub_50), the 95% credible interval
(lb_95 & ub_95), the proportion greater than a defined threshold value
(prop), the pathogen name (pathogen), and the time label (time).
Details
Growth rate and \(R_t\) both describe transmission trends, but on
different scales: growth rate is a direct log-scale rate of change, while
Rt() additionally accounts for the generation interval to translate
that rate into an average number of secondary infections per case.
Growth rate is positive whenever \(R_t > 1\) and negative whenever
\(R_t < 1\), since both describe the same underlying growth or decline.
This metric quantifies the proportional change in disease incidence from one time period to the next on a logarithmic scale, where:
Positive values (\(r_t > 0\)) indicate exponential growth
Negative values (\(r_t < 0\)) indicate exponential decline
Values near zero (\(r_t \approx 0\)) indicate stable incidence
The magnitude indicates the rate of exponential change
For example:
A growth rate of 0.1 means incidence increased by approximately 10.5% (\(e^{0.1} - 1\))
A growth rate of -0.05 means incidence decreased by approximately 4.9%
A growth rate of 0 means no change in incidence
This metric function can be run directly on the fitted model output.
See also
Other metrics:
Rt(),
incidence(),
proportion()
Examples
if (FALSE) { # interactive()
mod <- construct_model(
pathogen_structure = single(
case_timeseries = sarscov2$cases,
time = sarscov2$date
),
method = random_walk()
)
fit <- fit_model(mod)
gr <- growth_rate(fit)
}