Read the associated blogpost for a detailed description of how this dataset was prepared; plus extra code for producing animated maps.

Context

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.

Content

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.

Column Description

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.

Acknowledgements

All the data have been extracted from open data sources which are being gratefully acknowledged:

• The [Robert ...

When asked in what ways their shopping behavior had changed during the coronavirus (COVID-19) pandemic in 2021, about *** in *** consumers in Germany said it had. That year, roughly half of the survey respondents, who said their consumer behavior had changed, specifically indicated that they had started shopping online more, except for groceries. A considerable share of German consumers also reduced their spending or started investing heavily in their homes. Just shy of ** percent of respondents stated that the crisis did not affect their shopping habits.

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.

Between just two weekends in March 2020, footfall in German shopping streets has fallen dramatically in the wake of the coronavirus (COVID-19) outbreak in the country. The German government implemented measures to prevent further spread of the disease. These measures included retail businesses closing for the time being, as well as a contact ban among the population to help ensure social distancing.

The aim of the special survey of the GESIS panel on the outbreak of the corona virus SARS-CoV-2 in Germany was to collect timely data on the effects of the corona crisis on people´s daily lives. The study focused on questions of risk perception, risk minimization measures, evaluation of political measures and their compliance, trust in politics and institutions, changed employment situation, childcare obligations, and media consumption. Due to the need for timely data collection, only the GESIS panel sub-sample of online respondents was invited (about three quarters of the sample). Since, due to time constraints, respondents could only participate in the online survey but not by mail, the results cannot be easily transferred to the overall population. Further longitudinal surveys on Covid-19 with the entire sample of the GESIS panel are planned for 2020.

Topics: Risk perception: Probability of events related to corona infection in the next two months (self, infection of a person from close social surrondings, hospital treatment, quarantine measures regardless of whether infected or not, infecting other people)

Risk minimization: risk minimization measures taken in the last seven days (avoided certain (busy) places, kept minimum distance to other people, adapted school or work situation, quarantine due to symptoms or without symptoms, washed hands more often, used disinfectant, stocks increased, reduced social interactions, worn face mask, other, none of these measures).

Evaluation of the effectiveness of various policy measures to combat the further spread of corona virus (closure of day-care centres, kindergartens and schools, closure of sports facilities, closure of bars, cafés and restaurants, closure of all shops except supermarkets and pharmacies, ban on visiting hospitals, nursing homes and old people´s homes, curfew for persons aged 70 and over or people with health problems or for anyone not working in the health sector or other critical professions (except for basic purchases and urgent medical care).

Curfew compliance or refusal: Willingness to obey a curfew vs. refusal; reasons for the compliance with curfew (social duty, fear of punishment, protection against infection, fear of infecting others (loved ones, infecting others in general, a risk group); reasons for refusal of curfew (restrictions too drastic or not justified, other obligations, does not stop the spread, not affected by the outbreak, boring at home, will not be punished).

Evaluation of the effectiveness of various government measures (medical care, restrictions on social life such as closure of public facilities and businesses, reduction of economic damage, communication with the population).

Trust in politics and institutions with regard to dealing with the coronavirus (physician, local health authority, local and municipal administration, Robert Koch Institute (RKI), Federal Government, German Chancellor, Ministry of Health, World Health Organization (WHO), scientists).

Changed employment situation: employment status at the beginning of March; change in occupational situation since the spread of coronavirus: dependent employees: number of hours reduced, number of hours increased, more home office, leave of absence with/ without continued wage payment , fired, no change; self-employed: working hours reduced, working hours increased, more home office, revenue decreased, revenue increased, company temporarily closed by the authorities, company temporarily voluntarily closed, financial hardship, company permanently closed or insolvent, no change.

Childcare: children under 12 in the household; organisation of childcare during the closure of day-care centres, kindergartens and schools (staying at home, partner stays at home, older siblings take care, grandparents are watching, etc.)

Media consumption on Corona: information sources used for Corona (e.g. nationwide public or private television or radio, local public or private television or radio, national newspapers or local newspapers, Facebook, other social media, personal conversations with friends and family, other, do not inform myself on the subject); frequency of Facebook usage; information about Corona obtained from regional Facebook page or regional Facebook group.

Demography: sex; age (categorized); education (categorized); intention to vote and choice of party (Sunday question); Left-right self-assessment; marital status; size of household.

Additionally coded: Respondent ID;...

View daily updates and historical trends for Germany Coronavirus Full Vaccination Rate. Source: Our World in Data. Track economic data with YCharts analyt…

Feature 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".

View daily updates and historical trends for Germany Coronavirus Cases Per Day. Source: Johns Hopkins Center for Systems Science and Engineering. Track ec…

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.

View daily updates and historical trends for Germany Coronavirus Recoveries. Source: Johns Hopkins Center for Systems Science and Engineering. Track econo…

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.

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.

Germany COVID-2019: Number of Deaths: To Date: CC: Thuringen data was reported at 8,390.000 Person in 21 Apr 2023. This records an increase from the previous number of 8,387.000 Person for 20 Apr 2023. Germany COVID-2019: Number of Deaths: To Date: CC: Thuringen data is updated daily, averaging 4,324.000 Person from Mar 2020 (Median) to 21 Apr 2023, with 933 observations. The data reached an all-time high of 8,390.000 Person in 21 Apr 2023 and a record low of 0.000 Person in 23 Mar 2020. Germany COVID-2019: Number of Deaths: To Date: CC: Thuringen data remains active status in CEIC and is reported by Robert Koch Institute. The data is categorized under High Frequency Database’s Disease Outbreaks – Table DE.D001: Robert Koch Institute: Coronavirus Disease 2019 (COVID-2019) (Discontinued).

Panic buying has been a consistently increasing trend in German food retail since the first confirmed case of coronavirus (COVID-19) in the country in January 2020. Certain products were especially in demand among consumers rushing to stock up in case of extended quarantine.

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.

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.

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)

A recent study measuring the sentiment of German consumers towards sustainability and fashion during the Covid pandemic revealed that a large proportion stated they would be altering their fashion consumption habits; ** percent of those surveyed agreed with the statement “I will be throwing out fashion items less often”, ** percent agreed with the statement “I will be buying more high-quality items that can last longer” and ** percent agreed with “I will be repairing fashion items more rather than buying new ones”. However, ** percent, ** percent and ** percent of those consumers surveyed “disagreed” on the same respective statements.

Italian and German consumer perceptions

Studies also measured the opinions of German and Italian consumers as to how they thought fashion retailers should act in response to the pandemic. Over half of German consumers surveyed stated they believed the fashion industry should “care for the health of its employees”. Whereas ** percent stated fashion industries should “reduce their negative impact on the environment”. The perceptions of consumers in Italy towards the sustainability of fashion industries following the pandemic were noticeably different. ** percent of Italian consumers surveyed stated “my opinions about fashion retailers haven’t changed that much”. Furthermore, ** percent “neither agree* or disagree*” to the statement “coronavirus crisis has made my fashion consumption habits (i.e. purchases, repairs, or disposals of clothing items) more sustainable”.

Online revenues in fashion retail during Coronavirus pandemic

During the coronavirus pandemic, Italy and Germany also had markedly different rates of online ordering of fashion accessories. On April 19th, the rate of online purchases in Italy was *** percent higher than the year prior. Whereas in Germany during the same week, the rate of online purchases was half that of Italy’s and ** percent higher than the year prior.

Germany COVID-2019: Number of Deaths: To Date: CC: Hamburg data was reported at 3,706.000 Person in 21 Apr 2023. This records an increase from the previous number of 3,705.000 Person for 20 Apr 2023. Germany COVID-2019: Number of Deaths: To Date: CC: Hamburg data is updated daily, averaging 1,589.000 Person from Mar 2020 (Median) to 21 Apr 2023, with 933 observations. The data reached an all-time high of 3,706.000 Person in 21 Apr 2023 and a record low of 0.000 Person in 26 Mar 2020. Germany COVID-2019: Number of Deaths: To Date: CC: Hamburg data remains active status in CEIC and is reported by Robert Koch Institute. The data is categorized under High Frequency Database’s Disease Outbreaks – Table DE.D001: Robert Koch Institute: Coronavirus Disease 2019 (COVID-2019) (Discontinued).

The COVID-19 pandemic as a disruptive event was initially considered an opportunity for a transformation towards more sustainable lifestyles. In two telephone surveys with more than 1000 participants each, we explored in October 2020 and May 2021 how people in Germany experienced the COVID-19 related situation, and in particular the lockdown restrictions. Specifically, we asked how they felt their lives had been impaired during the pandemic; which changes they had experienced as particularly bothersome; which ones they perceived to be beneficial; and how these perceptions related to either their urge to return to “normal” or, in contrast, to their openness towards lifestyle changes. Overall, we found that by 2021, the pandemic had impacted people more negatively than in 2020. Most respondents missed social contacts, traveling and cultural events. Some positive changes – e.g., spending less money for useless things – were mentioned as well. A third of the participants agreed that they would like to question their behavior before the pandemic and live more consciously. Apart from slight differences in gender, age and, most importantly, academic background, socio-economic characteristics hardly help explain why some people were more open to change than others. Therefore, we conducted a cluster analysis and found that respondents with stronger pro-environmental attitudes were more open to change, no matter how much they felt impacted by the pandemic. This finding indicates that pro-environmental values and education seem to be key for more ecologically responsible lifestyle choices. We herewith provide the original questionnaires and datasets of the surveys.