Estimate growth rate from modelled incidence

This assumes a modelled incidence estimate that is log-normal. The exponential growth rate is the first derivative of the mu parameters of this log-normal. On the link scale these are normally distributed. This function assumes that the time series incidence estimates are uncorrelated to estimate the error in the growth rate, which is a conservative approach resulting in more uncertainty in growth rate than might be possible through other methods. This is all based on Savitsky-Golay filters applied to the normally distributed log-incidence estimates.

Usage

growth_rate_from_incidence(d = i_incidence_model, window = 7, deg = 2)

Arguments

d

a modelled incidence estimate - a dataframe with columns:

• time (ggoutbreak::time_period + group_unique) - A (usually complete) set of singular observations per unit time as a `time_period`

• incidence.fit (double) - an estimate of the incidence rate on a log scale

• incidence.se.fit (positive_double) - the standard error of the incidence rate estimate on a log scale

• incidence.0.025 (positive_double) - lower confidence limit of the incidence rate (true scale)

• incidence.0.5 (positive_double) - median estimate of the incidence rate (true scale)

• incidence.0.975 (positive_double) - upper confidence limit of the incidence rate (true scale)

Any grouping allowed.

window

the width of the Savitsky-Golay filter - must be odd

deg

the polynomial degree of the filter

Value

the timeseries with growth rate columns: A dataframe containing the following columns:

• time (ggoutbreak::time_period + group_unique) - A (usually complete) set of singular observations per unit time as a time_period

• growth.fit (double) - an estimate of the growth rate

• growth.se.fit (positive_double) - the standard error the growth rate

• growth.0.025 (double) - lower confidence limit of the growth rate

• growth.0.5 (double) - median estimate of the growth rate

• growth.0.975 (double) - upper confidence limit of the growth rate

Any grouping allowed.

Examples

data = test_poisson_growth_rate
tmp2 = data %>%
  poisson_glm_model(window=7,deg=1) %>%
  growth_rate_from_incidence(window = 13, deg=1)

if(interactive()) {
  plot_growth_rate(
      tmp2,
      date_labels="%b %y"
    )+
    sim_geom_function(data,colour="red")
}