Plots case counts with intervention status timelines

Source: R/intervention_epi.R

This function plots the daily case counts (epi-curve) for a given country or admin1 unit with as seven day rolling average. The status of select intervention information (border closures, household confinement, universal mask mandates, restaurant closures, primary school closures, and retail store closures) are also plotted. The admin1 plotting is only available for 11 countries.

intervention_epi(
  hit_data,
  country = NULL,
  admin1 = NULL,
  source = c("WHO", "ECDC"),
  case_threshold = 0,
  start_date = NULL,
  end_date = NULL,
  date_format = "%m/%d/%Y",
  include_title = TRUE
)

Arguments

hit_data

the full HIT-COVID database pulled from GitHub, pulled using hit_pull
country

country ISO 3166-1 alpha-3 code to plot (see geo_lookup for concordance of country codes to names)
admin1

first administrative unit GID codes to plot (see geo_lookup for concordance of admin 1 codes to names).
source

the source of the daily case count data, one of "ECDC" or "WHO" (default is "WHO").
case_threshold

threshold the total number of cases needs to exceed to start the plot (default is 0).
start_date

character string indicating the earliest date to plot (default is first day where the total number of cases is greater than the case_threshold).
end_date

character string indicating the latest date to plot (default is today)
date_format

character string indicating the format of the date inputs (default is "%m/%d/%Y").
include_title

logical indicating if the plot should have a title (default is TRUE)

Details

The HIT-COVID database must first be loaded with hit_pull and fed into this function. Only one country or admin1 unit can be plotted at a time and the resulting plot will include the case counts for this location with select intervention data. The user can specify the date range for the plot using start_date and end_date. Another way to restrict the plot is to change case_threshold. When the case_threshold is specified then the earliest date of the plot will be when the total number of cases in the country exceeded case_threshold. If both start_date and case_threshold are provided, start_date will be used as the beginning of the plot.

The source for the country level data is either the European Centre for Disease Control (ECDC) or the World Health Organization (WHO) with the WHO as the default. The source for the admin1 data varies depending on the package and can be seen in the reference section below. The admin1 case counts data is pulled using get_regional_data. As of now this is only available for 11 countries: Afghanistan, Belgium, Brazil, Canada, Columbia, Germany, India, Italy, Russia, UK, and USA. The admin1 units that are included in the HIT-COVID database can be found by using get_admin1. As of 11/30/2020 the admin1 data for India and Afghanistan does not work.

IMPORTANT note about intervention timelines printed under the epi curves. These final bars of each timeline represent the last logged status of an intervention. For some locations, the intervention data may not have been updated which means that older policies would appear to carry to the present when they are not still active. Care should be taken when interpretting these plots without knowledge of the completeness of the intervention data of the location of interest.

References

Sam Abbott, Katharine Sherratt, Jonnie Bevan, Hamish Gibbs, Joel Hellewell, James Munday, Paul Campbell and Sebastian Funk (2020). covidregionaldata: Subnational Data for the Covid-19 Outbreak. R package version 0.6.0. https://CRAN.R-project.org/package=covidregionaldata

ECDC national data: https://opendata.ecdc.europa.eu/covid19

WHO national data: https://covid19.who.int

Afghanistan admin1 data: https://data.humdata.org/dataset/afghanistan-covid-19-statistics-per-province

Belgium admin1 data: https://epistat.wiv-isp.be/covid/

Brazil admin1 data: https://github.com/wcota/covid19br

Canada admin1 data: https://health-infobase.canada.ca

Columbia admin1 data: https://github.com/danielcs88/colombia_covid-19

Germany admin1 data: https://npgeo-corona-npgeo-de.hub.arcgis.com/datasets/dd4580c810204019a7b8eb3e0b329dd6_0

India admin1 data: https://api.covid19india.org/csv/latest/state_wise_daily.csv

Italy admin1 data: https://raw.githubusercontent.com/pcm-dpc/COVID-19/master/dati-regioni/dpc-covid19-ita-regioni.csv

Russia admin1 data: https://raw.githubusercontent.com/grwlf/COVID-19_plus_Russia/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_RU.csv

UK admin1 data: https://coronavirus.data.gov.uk/details/cases

USA admin1 data: https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv

See also

hit_filter

Examples


# Puling the HIT-COVID database
hit_data <- hit_pull(add_first_case = FALSE)

# Plotting the case counts for India
intervention_epi(hit_data, country = "IND")
#> Registered S3 method overwritten by 'quantmod':
#>   method            from
#>   as.zoo.data.frame zoo 

# Starting when there were more than 100 cases
intervention_epi(hit_data, country = "IND", case_threshold = 100)

# Plotting the case counts for the New Zealand from February to May 
intervention_epi(hit_data, country = "NZL", start_date = "3/1/2020", end_date = "5/31/2020")

# Plotting the case counts for New Jersey, USA
intervention_epi(hit_data, admin1 = "USA.31_1")