For documentation and a more detailed tutorial see: https://hopkinsidd.github.io/hitRcovid/
The Health Intervention Tracking for COVID-19 (HIT-COVID) project tracks the implementation and relaxation of public health and social measures (PHSMs) taken by governments to slow transmission of SARS-COV-2 globally. Hundreds of volunteer data contributors were trained, provided with standardized field definitions and access to an online forum for asking questions and sharing ideas. Each change in policy and corresponding date is documented at the first-level administrative unit (e.g., states, districts) and nationally for all countries with more detailed geographic resolution in some locations (e.g., counties in the US).
Data are entered into a structured questionnaire with a source document(s) required for each record. Source documents from official government sources are preferred, but other sources are permitted when official sources are unavailable. For each intervention, HIT-COVID captures a suite of additional data including whether interventions are required or recommended and the particular subpopulation to which policies apply. To ensure data quality, contributors are asked to complete weekly self-audit reports, have the ability to submit corrections on past entries, and the management team performs geographic or intervention-specific audits as issues arise.
This package can be used to easily access, filter, and visualize the HIT-COVID database. The full database including detailed documentation is available in the hit-covid repository and more details can be found on the website. The creation of the database is also described in detail in this paper.
You can install hitRcovid in R using the following command:
devtools::install_github('https://github.com/HopkinsIDD/hitRcovid.git')
Before using any of the filtering or visualization functions you first need to pull the HIT-COVID database using hit_pull(). The default behavior of this function will use the covidregionaldata package to include the date of the first case of COVID-19 and the first death from COVID-19 for each country.
hit_data <- hit_pull()
You can then filter the database by location and/or intervention type using hit_filter() function. The documentation provides details on all of the options available for this function. Here is an example looking at the mask policies across Asia:
asia_masks <- hit_filter(hit_data, continent = "Asia", intervention_group = "mask")
The country and admin codes to be used for filtering can be found in the geo_lookup dataframe provided in the package. Additionally, the admin1 codes that have data in the database for a specified country can be printed by using get_admin1. The intervention group codes and names can be found in the intervention_lookup dataframe provided in the package or all options can be printed using the function get_interventions().
The package contains a plotting function, intervention_timeline(), to display a timeline of all of the intervention updates in the database using hit_filter() to focus on the locations and/or intervention types specified. The documentation provides details about all of the filtering and visualization options. Here is an example of a plot of the interventions from India and New Zealand:
intervention_timeline(hit_data, country = c("IND", "NZL"), facet_by = "country")
The package contains a plotting function, intervention_map(), to display a world map showing the current status of a one intervention group at a date specified. The documentation provides details about all of the filtering and visualization options. Here is an example of a map of the status of school closures on September 1, 2020:
intervention_map(hit_data, intervention_group = "school_closed", time_point = "9/01/2020")
The package contains a plotting funciton, intervention_epi(), to display a barplot of daily case counts (the epi-curve) along with a timeline showing the status of selected interventions (border closures, household confinement, universal mask mandates, restaurant closures, primary school closures, and retail store closures) over time. The main usage would be to plot the epi-curves at the country level, but admin1 level case counts are available for 11 countries (Afghanistan, Belgium, Brazil, Canada, Columbia, Germany, India, Italy, Russia, UK, and USA).
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 interpreting these plots without knowledge of the completeness of the intervention data of the location of interest. The check_completeness() function can be used to get a sense of the completeness of a given country or admin1 unit
Here is an example plotting the epi-curve and intervention status for India starting from when the country had 100 total cases:
intervention_epi(hit_data, country = "IND", case_threshold = 100)
The HIT-COVID database is a managed crowd sourcing effort supported by numerous volunteers over the past eight months. Because of this data collection effort, the database is not complete for all countries and admin1 units. This should be taken into account when using this database. To get a sense of the completeness of a given location (country or admin1 unit) the check_completeness() function can be used. It prints information about the date the location was previously updated, what percentage of the entries have been verified by contributors, and the status of each intervention group (complete, incomplete, unsure). Here is an example:
See also the list of data contributors who participated in this project.
This dataset is licensed under the GNU General Public License v3.0 - see the LICENSE.md file for details
Contact: Andrew Azman (azman@jhu.edu) or hit-covid@jhu.edu