Plot an incidence or proportion vs. growth phase diagram

Plot an incidence or proportion vs. growth phase diagram

Usage

plot_growth_phase(
  modelled = i_timestamped,
  timepoints = NULL,
  duration = max(dplyr::count(modelled)$n),
  interval = 7,
  mapping = if (interfacer::is_col_present(modelled, class)) ggplot2::aes(colour = class)
    else ggplot2::aes(),
  cis = TRUE,
  ...
)

Arguments

modelled

Either:

A dataframe containing the following columns:

• time (as.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 (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)

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

• growth.se.fit (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

No mandatory groupings.

No default value.

OR:

A dataframe containing the following columns:

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

• proportion.fit (double) - an estimate of the proportion on a logit scale

• proportion.se.fit (double) - the standard error of proportion estimate on a logit scale

• proportion.0.025 (proportion) - lower confidence limit of proportion (true scale)

• proportion.0.5 (proportion) - median estimate of proportion (true scale)

• proportion.0.975 (proportion) - upper confidence limit of proportion (true scale)

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

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

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

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

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

No mandatory groupings.

No default value.

timepoints

timepoints (as Date or time_period vector) of dates to plot phase diagrams. If multiple this will result in a sequence of plots as facets. If NULL (the default) it will be the last time point in the series

duration

the length of the growth rate phase trail

interval

the length of time between markers on the phase plot

mapping

a ggplot2::aes() mapping

cis

should the phases be marked with confidence intervals?

...

Arguments passed on to geom_events

events

Significant events or time spans

A dataframe containing the following columns:

• label (character) - the event label

• start (date) - the start date, or the date of the event

• end (date) - the end date or NA if a single event

No mandatory groupings.

A default value is defined.

Value

a ggplot timeseries

Examples

# example code

tmp = growthrates::england_covid %>%
  time_aggregate(count=sum(count))

tmp_pop = growthrates::england_demographics %>%
  dplyr::ungroup() %>%
  dplyr::summarise(population = sum(population))

# If the incidence is normalised by population
tmp2 = tmp %>%
  poisson_locfit_model() %>%
  normalise_incidence(tmp_pop)

timepoints = as.Date(c("Lockdown 1" = "2020-03-30", "Lockdown 2" = "2020-12-31"))

plot_growth_phase(tmp2, timepoints, duration=108)