Function reference • growthrates

Growth rate models

doubling_time(): Doubling time from growth rate

multinomial_nnet_model(): Multinomial time-series model.

normalise_incidence(): Calculate a normalised incidence rate per capita

normalise_incidence.incidence(): Calculate a normalised incidence rate per capita

normalise_incidence.proportion(): Calculate a normalised incidence rate per capita

normalise_proportion(): Calculate a normalised risk ration from proportions

poisson_glm_model(): Poisson time-series model.

poisson_locfit_model(): Poisson time-series model.

proportion_glm_model(): Binomial time-series model.

proportion_locfit_model(): A binomial proportion estimate and associated exponential growth rate

rt_epiestim(): EpiEstim reproduction number

rt_from_growth_rate(): Wallinga-Lipsitch reproduction number

rt_from_incidence(): Reproduction number from modelled incidence

Visualisations

geom_events(): Add time series event markers to a timeseries plot.

plot_growth_phase(): Plot an incidence or proportion vs. growth phase diagram

plot_growth_rate(): Growth rate timeseries diagram

plot_incidence(): Plot an incidence timeseries

plot_multinomial(): Plot a multinomial proportions mode

plot_proportion(): Plot a proportions timeseries

plot_rt(): Reproduction number timeseries diagram

Time series functions

as.Date(<time_period>) as.POSIXct(<time_period>): Convert time period to dates

as.time_period() c(<time_period>) `[`(<time_period>) `[<-`(<time_period>) `[[`(<time_period>) `[[<-`(<time_period>) is.time_period() print(<time_period>): Convert to a time period class

cut_date(): Places a set of dates within a regular time series

date_seq(<Date>): Expand a date vector to the full range of possible dates

date_seq(): Create the full sequence of values in a vector

date_seq(<time_period>): Expand a time_period vector to the full range of possible times

date_to_time(): Convert a set of dates to numeric timepoints

fdmy(): Format date as dmy

is.Date(): Check whether vector is a date

labels(<time_period>): Label a time period

max_date(): The maximum of a set of dates

min_date(): The minimum of a set of dates

time_aggregate(): Aggregate time series data preserving the time series

time_summarise(): Summarise data from a line list to a time-series of counts.

time_to_date(): Convert a set of timepoints to dates

Data Documentation

covid_infectivity_profile: The covid_infectivity_profile dataframe structure specification

england_consensus_growth_rate: The SPI-M-O England consensus growth rate

england_consensus_rt: The SPI-M-O England consensus reproduction number

england_covid: Daily COVID-19 case counts by age group in England

england_covid_pcr_positivity: England COVID-19 PCR test positivity

england_demographics: England demographics

england_events: Key dated in the COVID-19 response in England

england_variants: Counts of COVID-19 variants

germany_covid: Weekly COVID-19 case counts by age group in Germany

germany_demographics: Germany demographics

Others

breaks_log1p(): A scales breaks generator for log1p scales

date_seq(<numeric>): Create the full sequence of values in a vector

england_covid_proportion: England COVID by age group for ascertainment

england_nhs_app: NHS COVID-19 app data

england_ons_infection_survey: The england_ons_infection_survey dataset

logit_trans(): logit scale

reband_discrete(): Reband any discrete distribution

scale_y_log1p(): A log1p y scale

scale_y_logit(): A logit y scale

wallinga_lipsitch(): Calculate the reproduction number from a growth rate estimate and an infectivity profile