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.