As of February 26, 2023, the coronavirus (COVID-19) pandemic in Czechia resulted in 42,426 deaths. However, the distribution of these deaths differed greatly by age. To this day, most deaths occurred in older patients. In Czechia, 65 percent of fatalities occurred in patients aged 75 years and over.

The first cases of coronavirus infection in Czechia were reported on 1 March.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

Context

This dataset has been collected from multiple sources provided by MVCR on their websites and contains daily summarized statistics as well as details statistics up to age & sex level.

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Columns description

Date - Calendar date when data were collected Daily tested - Sum of tests performed Daily infected - Sum of confirmed cases those were positive Daily cured - Sum of cured people that does not have Covid-19 anymore Daily deaths - Sum of people those died on Covid-19 Daily cum tested - Cumulative sum of tests performed Daily infected - Cumulative sum of confirmed cases those were positive Daily cured - Cumulative sum of cured people that does not have Covid-19 anymore Daily deaths - Cumulative sum of people those died on Covid-19 Region - Region of Czech republic Sub-Region - Sub-Region of Czech republic Region accessories qty - Quantity of health care accessories delivered to region for all the time Age - Age of person Sex - Sex of person Infected - Sum of infected people for specific date, region, sub-region, age and sex Cured - Sum of cured people for specific date, region, sub-region, age and sex Death - Sum of people those dies on Covid-19 for specific date, region, sub-region, age and sex

Data granularity

Dataset contains data on different level of granularities. Make sure you do not mix different granularities. Let's suppose you have loaded data into pandas dataframe called df.

Day level

df_daily = df.groupby(['date']).max()[['daily_tested','daily_infected','daily_cured','daily_deaths','daily_cum_tested','daily_cum_infected','daily_cum_cured','daily_cum_deaths']].reset_index()

Region level

df_region = df[df['region'] != ''].groupby(['region']).agg(
  region_accessories_qty=pd.NamedAgg(column='region_accessories_qty', aggfunc='max'), 
  infected=pd.NamedAgg(column='infected', aggfunc='sum'),
  cured=pd.NamedAgg(column='cured', aggfunc='sum'),
  death=pd.NamedAgg(column='death', aggfunc='sum')
).reset_index()

Detail level

df_detail = df[['date','region','sub_region','age','sex','infected','cured','death']].reset_index(drop=True)

Acknowledgements

Thanks to websites of MVCR for sharing such great information.

Inspiration

Can you see relation between health care accessories delivered to region and number of cured/infected in that region? Why Czech Republic belongs to pretty safe countries when talking about Covid-19 Pandemic? Can you find out what is difference of pandemic evolution in Czech Republic comparing to other surrounding coutries, like Germany or Slovakia?

Czech Republic recorded 42789 Coronavirus Deaths since the epidemic began, according to the World Health Organization (WHO). In addition, Czech Republic reported 4641431 Coronavirus Cases. This dataset includes a chart with historical data for Czech Republic Coronavirus Deaths.

Based on a comparison of coronavirus deaths in 210 countries relative to their population, Peru had the most losses to COVID-19 up until July 13, 2022. As of the same date, the virus had infected over 557.8 million people worldwide, and the number of deaths had totaled more than 6.3 million. Note, however, that COVID-19 test rates can vary per country. Additionally, big differences show up between countries when combining the number of deaths against confirmed COVID-19 cases. The source seemingly does not differentiate between "the Wuhan strain" (2019-nCOV) of COVID-19, "the Kent mutation" (B.1.1.7) that appeared in the UK in late 2020, the 2021 Delta variant (B.1.617.2) from India or the Omicron variant (B.1.1.529) from South Africa.

The difficulties of death figures

This table aims to provide a complete picture on the topic, but it very much relies on data that has become more difficult to compare. As the coronavirus pandemic developed across the world, countries already used different methods to count fatalities, and they sometimes changed them during the course of the pandemic. On April 16, for example, the Chinese city of Wuhan added a 50 percent increase in their death figures to account for community deaths. These deaths occurred outside of hospitals and went unaccounted for so far. The state of New York did something similar two days before, revising their figures with 3,700 new deaths as they started to include “assumed” coronavirus victims. The United Kingdom started counting deaths in care homes and private households on April 29, adjusting their number with about 5,000 new deaths (which were corrected lowered again by the same amount on August 18). This makes an already difficult comparison even more difficult. Belgium, for example, counts suspected coronavirus deaths in their figures, whereas other countries have not done that (yet). This means two things. First, it could have a big impact on both current as well as future figures. On April 16 already, UK health experts stated that if their numbers were corrected for community deaths like in Wuhan, the UK number would change from 205 to “above 300”. This is exactly what happened two weeks later. Second, it is difficult to pinpoint exactly which countries already have “revised” numbers (like Belgium, Wuhan or New York) and which ones do not. One work-around could be to look at (freely accessible) timelines that track the reported daily increase of deaths in certain countries. Several of these are available on our platform, such as for Belgium, Italy and Sweden. A sudden large increase might be an indicator that the domestic sources changed their methodology.

Where are these numbers coming from?

The numbers shown here were collected by Johns Hopkins University, a source that manually checks the data with domestic health authorities. For the majority of countries, this is from national authorities. In some cases, like China, the United States, Canada or Australia, city reports or other various state authorities were consulted. In this statistic, these separately reported numbers were put together. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.