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TwitterRead the associated blogpost for a detailed description of how this dataset was prepared; plus extra code for producing animated maps.
The 2019 Novel Coronavirus (COVID-19) continues to spread in countries around the world. This dataset provides daily updated number of reported cases & deaths in Germany on the federal state (Bundesland) and county (Landkreis/Stadtkreis) level. In April 2021 I added a dataset on vaccination progress. In addition, I provide geospatial shape files and general state-level population demographics to aid the analysis.
The dataset consists of thre main csv files: covid_de.csv, demgraphics_de.csv, and covid_de_vaccines.csv. The geospatial shapes are included in the de_state.* files. See the column descriptions below for more detailed information.
covid_de.csv: COVID-19 cases and deaths which will be updated daily. The original data are being collected by Germany's Robert Koch Institute and can be download through the National Platform for Geographic Data (the latter site also hosts an interactive dashboard). I reshaped and translated the data (using R tidyverse tools) to make it better accessible. This blogpost explains how I prepared the data, and describes how to produces animated maps.
demographics_de.csv: General Demographic Data about Germany on the federal state level. Those have been downloaded from Germany's Federal Office for Statistics (Statistisches Bundesamt) through their Open Data platform GENESIS. The data reflect the (most recent available) estimates on 2018-12-31. You can find the corresponding table here.
covid_de_vaccines.csv: In April 2021 I added this file that contains the Covid-19 vaccination progress for Germany as a whole. It details daily doses, broken down cumulatively by manufacturer, as well as the cumulative number of people having received their first and full vaccination. The earliest data are from 2020-12-27.
de_state.*: Geospatial shape files for Germany's 16 federal states. Downloaded via Germany's Federal Agency for Cartography and Geodesy . Specifically, the shape file was obtained from this link.
COVID-19 dataset covid_de.csv:
state: Name of the German federal state. Germany has 16 federal states. I removed converted special characters from the original data.
county: The name of the German Landkreis (LK) or Stadtkreis (SK), which correspond roughly to US counties.
age_group: The COVID-19 data is being reported for 6 age groups: 0-4, 5-14, 15-34, 35-59, 60-79, and above 80 years old. As a shortcut the last category I'm using "80-99", but there might well be persons above 99 years old in this dataset. This column has a few NA entries.
gender: Reported as male (M) or female (F). This column has a few NA entries.
date: The calendar date of when a case or death were reported. There might be delays that will be corrected by retroactively assigning cases to earlier dates.
cases: COVID-19 cases that have been confirmed through laboratory work. This and the following 2 columns are counts per day, not cumulative counts.
deaths: COVID-19 related deaths.
recovered: Recovered cases.
Demographic dataset demographics_de.csv:
state, gender, age_group: same as above. The demographic data is available in higher age resolution, but I have binned it here to match the corresponding age groups in the covid_de.csv file.
population: Population counts for the respective categories. These numbers reflect the (most recent available) estimates on 2018-12-31.
Vaccination progress dataset covid_de_vaccines.csv:
date: calendar date of vaccination
doses, doses_first, doses_second: Daily count of administered doses: total, 1st shot, 2nd shot.
pfizer_cumul, moderna_cumul, astrazeneca_cumul: Daily cumulative number of administered vaccinations by manufacturer.
persons_first_cumul, persons_full_cumul: Daily cumulative number of people having received their 1st shot and full vaccination, respectively.
All the data have been extracted from open data sources which are being gratefully acknowledged:
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TwitterThe coronavirus (COVID-19) epidemic in Germany began in March 2020, with high new daily case numbers still being recorded during 2023. The pandemic is ongoing.
Staying home
The coronavirus (COVID-19) outbreak was declared a pandemic by the World Health Organisation on March 11, 2020. This declaration immediately impacted life in Germany on all levels. Rising coronavirus (COVID-19) case numbers in March-April led to the swift implementation of nationwide distancing and crowd control measures to stop further spread of the virus, which primarily transferred most easily from person to person. From a large-scale economic shutdown, venue, school, daycare and university closures, to social distancing and the contact ban officially implemented by the German government, seemingly in the space of days life as the population knew it came to a standstill in the whole country.
Unlockdown
Later in April 2020, Germany began easing some of the restrictions related to the coronavirus (COVID-19) outbreak as case numbers began to drop. Elements of uncertainty remain and touch on various aspects, for example, regarding national mental and physical health, both among adults and children, the possibility of long-term effects from the virus, immunity. A rising worry among European nations was economic recovery.
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TwitterThe coronavirus (COVID-19) has spread through Germany between 2020 and 2024. As of April 2024, there were over 38.8 million cases recorded in the country. . Click here for more statistical data and facts on the coronavirus.
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TwitterData licence Germany – Attribution – Version 2.0https://www.govdata.de/dl-de/by-2-0
License information was derived automatically
This dataset contains data regarding COVID-19 cases in Germany by Landkreise (district). It was originally published by the Robert Koch-Institut (RKI).For each Landkreis, data is available about: number of cases (cumulative), number of cases per 100 000 persons (cumulative or only the last seven days), percentage of cases (cumulative number of cases among the Landkreis population), number of deaths (cumulative) and death rate (percentage of deaths among the cases).The dataset also contains various geo-administrative information, such as populations, geographical shapes and administrative codes.Enrichment:Dates given in German format have been converted to ISO datetime.
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Germany recorded 3366432 Coronavirus Recovered since the epidemic began, according to the World Health Organization (WHO). In addition, Germany reported 106680 Coronavirus Deaths. This dataset includes a chart with historical data for Germany Coronavirus Recovered.
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License information was derived automatically
Germany recorded 38418899 Coronavirus Cases since the epidemic began, according to the World Health Organization (WHO). In addition, Germany reported 173834 Coronavirus Deaths. This dataset includes a chart with historical data for Germany Coronavirus Cases.
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Twitterhttps://github.com/disease-sh/API/blob/master/LICENSEhttps://github.com/disease-sh/API/blob/master/LICENSE
In past 24 hours, Germany, Europe had 73 new cases, 16 deaths and N/A recoveries.
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TwitterThe coronavirus (COVID-19) has led to over 183,000 deaths in Germany, as of 2024. When looking at the distribution of deaths by age, based on the figures currently available, most death occurred in the age group 80 years and older at approximately 118,938 deaths.
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TwitterAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
The number of COVID-19 vaccination doses administered in Germany rose to 192221468 as of Oct 27 2023. This dataset includes a chart with historical data for Germany Coronavirus Vaccination Total.
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Twitterhttps://elrc-share.eu/terms/openUnderPSI.htmlhttps://elrc-share.eu/terms/openUnderPSI.html
Bilingual (EN, DE) COVID-19-related corpus acquired from the website (https://www.bundesgesundheitsministerium.de/) of Federal Ministry of Health (Germany) (29th April 2020). It contains 90 TUs in total.
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TwitterCoronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. Most people who fall sick with COVID-19 will experience mild to moderate symptoms and recover without special treatment. The virus that causes COVID-19 is mainly transmitted through droplets generated when an infected person coughs, sneezes, or exhales. These droplets are too heavy to hang in the air and quickly fall on floors or surfaces. You can be infected by breathing in the virus if you are within close proximity of someone who has COVID-19, or by touching a contaminated surface and then your eyes, nose o or mouth.
The dataset contains data related to COVID-19 in Germany only. The dataset contains the date and the number of confirmed patients recovered patients, and deaths found on that particular date.
The data is provided by John Hopkins University, Baltimore, Maryland.
You can perform data analysis and visualization to discover trends and patterns in the data. Also, one can predict the forecast for next 15 days.
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License information was derived automatically
The datasets included in this repository represent a pandemic severity indicator for the COVID-19 pandemic in Germany based on a composite indicator for the years 2020 and 2021. The pandemic severity index consists of three indicators: the incidence of patients tested positive for COVID-19, the incidence of patients with COVID-19 in intensive care, and the incidence of registered deaths due to COVID-19. The datasets have been developed within the CODIFF project (Socio-Spatial Diffusion of COVID-19 in Germany) at Leibniz Insitute for Research on Society and Space. The project received funding by Deutsche Forschungsgemeinschaft (DFG, project number 492338717). The datasets have been used in the following publications, in which further methodological details on the indicator can be found:
Stabler, M., & Kuebart, A. (2023). Tempo-spatial dynamics of COVID-19 in Germany: A phase model based on a pandemic severity indicator. medRxiv, 2023-02.
Kuebart, A., & Stabler, M. (2023). Waves in time, but not in space – An analysis of pandemic severity of COVID-19 in Germany. Spatial and Spatio-temporal Epidemiology, 2023.
This repository consists of two files:
pandemic_severity_germany
This table contains the composite indicator for daily pandemic severity for Germany on the national scale as well as the three sub-indicators for each day between 2020-03-01 and 2021-12-31. The sub-indicators were sourced from the Robert Koch Institute, the German government agency responsible for disease control and prevention.
pandemic_severity_counties
This table contains the composite indicator for daily pandemic severity for Germany on the level of the 400 individual counties, as well as the three sub-indicators for each day between 2020-03-01 and 2021-12-31. The sub-indicators were sourced from the Robert Koch Institute, the German government agency responsible for disease control and prevention. The counties can be identified by name (kreis) or by county identification number (ags5)
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TwitterFeature service with the current Covid-19 infections per 100,000 inhabitants on the German districts. The service is updated daily with the current case numbers of the Robert Koch Institute.
Data source: Robert Koch Institute Terms of Use: Robert Koch Institute; German Federal Agency for Cartography and Geodesy Source note: Robert Koch-Institute (RKI), dl-en/by-2-0 Disclaimer: "The content made available on the Internet pages of the Robert Koch-Institute is intended solely for the general information of the public, primarily the specialist public". Data protection declaration: "The use of the RKI website is generally possible without disclosing personal data".
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The COVID-19 pandemic has led to temporary changes in human-animal interactions due to changes in human activities. Here we report on a surge in hedgehog observations during the first COVID-19 lockdown in Germany in 2020, on the citizen science web portal ‘Igel in Bayern’ (Hedgehogs in Bavaria) in Germany. This increase in comparison to previous years could be attributed to an increase in the number of people reporting hedgehog observations, rather than an increase in the number of hedgehog observations done by each observer. Additionally, in contrast to other studies on the effects of a COVID-19 lockdown on observations recorded by Citizen Science projects, the share of observations made in more urbanized areas during the lockdown time was not higher than the change observed in less urbanized areas. This is possibly a result of the differences in COVID-19 measures between Germany and other countries where preceding studies were carried out, in particular the lack of measures limiting outdoor activities for citizens.
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License information was derived automatically
Case data from 02-29-2020 to 05-31-2020, this data repository stores COVID-19 virus case data for Germany, including daily case data, summary data, and base map. Each zip file contains weekly case data from Monday to Sunday.
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TwitterIn 2023, the coronavirus (COVID-19) is still present in Germany, affecting all of its federal states. Case numbers vary across age groups and genders. Based on current figures, among men, the most affected age group was 35-59 years. The same was true for women. These figures confirm that the virus can also affect younger age groups.
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TwitterIn a crisis such as the current outbreak of the newly emerged coronavirus, it is of utmost importance to monitor public perceptions of risk, protective and preparedness behaviours, public trust, as well as knowledge and misinformation to enable government spokespeople, the media, and health organizations to implement adequate responses (WHO Europe, 2017; World Health Organization, 2017). The purpose of this serial cross-sectional study COSMO is to allow rapid and adaptive monitoring of these variables over time and to assess the relations between risk perceptions, knowledge and misinformation to preparedness and protective behaviour regarding COVID-19 in Germany.
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Twitterhttps://www.globaldata.com/privacy-policy/https://www.globaldata.com/privacy-policy/
The Coronavirus (SARS-CoV-2) outbreak, dubbed COVID-19, is first and foremost a human tragedy, affecting millions of people globally. The contagious Coronavirus, which broke out at the close of 2019, has led to a medical emergency across the world, with the World Health Organization officially declaring the novel Coronavirus a pandemic on March 11, 2020. Read More
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Official COVID19 data for Germany publicized by Robert Koch Institute Offizieller Datensatz des Rober-Koch-Instituts zu COVID19-Fällen in Deutschland
I'm just linking the official upload location to Kaggle.
There already is a COVID19 dashboard with a map for Germany, based on that data: https://npgeo-corona-npgeo-de.hub.arcgis.com/ But there certainly are more statistical questions to be answered.
I also started gathering and adding some additional data (not by RKI).
As for the columns labels in two of the three sets: they are very confusing and they are not even explained on the official upload website. Fortunately @sebastianhelm put some work into researching them: https://www.kaggle.com/mreverybody/covid19-data-germany-robert-koch-institute/discussion/142140#808487
RKI data is uploaded here (For the actual download link for the CSV seed download button on the site): - https://npgeo-corona-npgeo-de.hub.arcgis.com/datasets/dd4580c810204019a7b8eb3e0b329dd6_0?selectedAttribute=Datenstand - https://npgeo-corona-npgeo-de.hub.arcgis.com/datasets/ef4b445a53c1406892257fe63129a8ea_0?geometry=-19.734%2C46.270%2C35.989%2C55.886 - https://npgeo-corona-npgeo-de.hub.arcgis.com/datasets/917fc37a709542548cc3be077a786c17_0
Additional data: - Political measures taken and events / incidents: https://github.com/mafleischer/covid19-robert-koch-data/blob/master/additional_data/covid19_events_measures.csv Sources: https://www.deutschland.de/de/news/coronavirus-in-deutschland-informationen#
Rober Koch Institute for making the data public https://www.rki.de/
There are only few official and neutral sources concerning COVID19 cases in Germany, but many false claims and panic going around in the public. Although the RKI data is publically available it is not propagated well and it is a bit hard to come across.
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License information was derived automatically
Responses to the survey asking a nationally representative sample of public in the US and Germany about fair distribution of COVID-19 Vaccines across the world including codebook, variable labels, raw data, and questionnaire
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TwitterRead the associated blogpost for a detailed description of how this dataset was prepared; plus extra code for producing animated maps.
The 2019 Novel Coronavirus (COVID-19) continues to spread in countries around the world. This dataset provides daily updated number of reported cases & deaths in Germany on the federal state (Bundesland) and county (Landkreis/Stadtkreis) level. In April 2021 I added a dataset on vaccination progress. In addition, I provide geospatial shape files and general state-level population demographics to aid the analysis.
The dataset consists of thre main csv files: covid_de.csv, demgraphics_de.csv, and covid_de_vaccines.csv. The geospatial shapes are included in the de_state.* files. See the column descriptions below for more detailed information.
covid_de.csv: COVID-19 cases and deaths which will be updated daily. The original data are being collected by Germany's Robert Koch Institute and can be download through the National Platform for Geographic Data (the latter site also hosts an interactive dashboard). I reshaped and translated the data (using R tidyverse tools) to make it better accessible. This blogpost explains how I prepared the data, and describes how to produces animated maps.
demographics_de.csv: General Demographic Data about Germany on the federal state level. Those have been downloaded from Germany's Federal Office for Statistics (Statistisches Bundesamt) through their Open Data platform GENESIS. The data reflect the (most recent available) estimates on 2018-12-31. You can find the corresponding table here.
covid_de_vaccines.csv: In April 2021 I added this file that contains the Covid-19 vaccination progress for Germany as a whole. It details daily doses, broken down cumulatively by manufacturer, as well as the cumulative number of people having received their first and full vaccination. The earliest data are from 2020-12-27.
de_state.*: Geospatial shape files for Germany's 16 federal states. Downloaded via Germany's Federal Agency for Cartography and Geodesy . Specifically, the shape file was obtained from this link.
COVID-19 dataset covid_de.csv:
state: Name of the German federal state. Germany has 16 federal states. I removed converted special characters from the original data.
county: The name of the German Landkreis (LK) or Stadtkreis (SK), which correspond roughly to US counties.
age_group: The COVID-19 data is being reported for 6 age groups: 0-4, 5-14, 15-34, 35-59, 60-79, and above 80 years old. As a shortcut the last category I'm using "80-99", but there might well be persons above 99 years old in this dataset. This column has a few NA entries.
gender: Reported as male (M) or female (F). This column has a few NA entries.
date: The calendar date of when a case or death were reported. There might be delays that will be corrected by retroactively assigning cases to earlier dates.
cases: COVID-19 cases that have been confirmed through laboratory work. This and the following 2 columns are counts per day, not cumulative counts.
deaths: COVID-19 related deaths.
recovered: Recovered cases.
Demographic dataset demographics_de.csv:
state, gender, age_group: same as above. The demographic data is available in higher age resolution, but I have binned it here to match the corresponding age groups in the covid_de.csv file.
population: Population counts for the respective categories. These numbers reflect the (most recent available) estimates on 2018-12-31.
Vaccination progress dataset covid_de_vaccines.csv:
date: calendar date of vaccination
doses, doses_first, doses_second: Daily count of administered doses: total, 1st shot, 2nd shot.
pfizer_cumul, moderna_cumul, astrazeneca_cumul: Daily cumulative number of administered vaccinations by manufacturer.
persons_first_cumul, persons_full_cumul: Daily cumulative number of people having received their 1st shot and full vaccination, respectively.
All the data have been extracted from open data sources which are being gratefully acknowledged: