Awesome

COVID-19 Coronavirus Disease Spread Time Series Analyses for German Regions and Selected Countries

Historical data backups

• 2022-01-19: I performed a history cleanup and removed data commit history. See backup repo COVID-19-Coronavirus-German-Regions-OLD-DATA-BACKUP-220119.
• 2021-06-26: I performed a history cleanup and removed data commit history. See backup repo COVID-19-Coronavirus-German-Regions-OLD-DATA-BACKUP-210626.

Here are fetching scripts and resulting data for all charts and reports presented at https://entorb.net/COVID-19-coronavirus/

Scripts

• fetching of updated data
• converting data to common format
• calculating new entities from data
• plotting charts and uploading them to my analyis and report https://entorb.net/COVID-19-coronavirus/
• empowering an interactive country comparison chart
• empowering https://covid19-trends.de
• empowering https://github.com/pschwede/covid19plots

Resulting data in JSON and CSV/TSV format can be browsed here at GitHub.

Data sources

• German districts data is from ArcGIS Covid19_RKI_Sums
• German hospital data is from DIVI-Intensivregister. (Thanks to Mr. Parvu for granting usage permission!)
• International data is from Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE) obtained via pomber/covid19

List of provided/generated data fields

• Date
• Days_Past
• Cases
• Deaths
• Cases_New
• Deaths_New
• Cases_Last_Week
• Deaths_Last_Week
• Cases_Per_Million
• Deaths_Per_Million
• Cases_New_Per_Million
• Deaths_New_Per_Million
• Cases_Last_Week_Per_Million
• Deaths_Last_Week_Per_Million
• Cases_Doubling_Time
• Deaths_Doubling_Time

Nomenclature of data fields

• New = Difference to previous day
• Last_Week = Difference to 7 days past
• Per_Million = Scaled by 1 Million Population
• Doubling_Time = Derived by fitting data with exp. growth function

DIVI

For German districts (Landkreise) I additionally fetch and provide a time series of the DIVI Intensivregister hospital occupation.