In past 24 hours, Switzerland, Europe had N/A new cases, N/A deaths and N/A recoveries.

As of January 2023, members of the Swiss population aged 80 years and older have been most vulnerable to the coronavirus (COVID-19) outbreak, with the highest number of deaths recorded in this age group. Older age groups are believed to be especially at risk.

Switzerland recorded 4404327 Coronavirus Cases since the epidemic began, according to the World Health Organization (WHO). In addition, Switzerland reported 14008 Coronavirus Deaths. This dataset includes a chart with historical data for Switzerland Coronavirus Cases.

This dataset is based on the Github repository maintained by OpenZH. Data has been enriched with geographical data for the cantons, in order to produce visualisations.Field NameDescriptionFormatNote

updateDate and time of notification YYYY-MM-DD-HH-MM

nameName of the reporting cantonTextabbreviation_canton_and_fl Abbreviation of the reporting canton

Text

ncumul_testedReported number of tests performed as of dateNumberIrrespective of canton of residence

ncumul_confReported number of confirmed cases as of dateNumberOnly cases that reside in the current canton

current_hosp (formerly ncumul_hosp) *Reported number of hospitalised patients on dateNumberIrrespective of canton of residencecurrent_icu (formerly ncumul_icu) *Reported number of hospitalised patients in ICUs on dateNumberIrrespective of canton of residencecurrent_vent(formerly ncumul_vent) *Reported number of patients requiring ventilation on dateNumberIrrespective of canton of residencencumul_released Reported number of patients released from hospitals or reported recovered as of date

NumberIrrespective of canton of residence

ncumul_deceasedReported number of deceased as of dateNumberOnly cases that reside in the current cantonnew_hosp *Number of new hospitalisations since last dateNumberIrrespective of canton of residence

sourceSource of the informationURL linkgeo_point_2dGeographical centroid of the cantongeo_point_2dcurrent_isolatedReported number of isolated persons on dateNumberInfected persons, who are not hospitalisedcurrent_quarantinedReported number of quarantined persons on dateNumberPersons, who were in 'close contact' with an infected person, while that person was infectious, and are not hospitalised themselvescurrent_quarantined_riskareatravelReported number of quarantined persons on dateNumberPeople arriving in Switzerland from certain countries and areas, required to go into quarantine (introduced in May 2021)*These variables were affected by the format change on April 9th, 2020, which consists in:- new variable "new_hosp"- variables "ncumul_hosp", "ncumul_icu", "ncumul_vent" have been renamed to "current_hosp", "current_icu", "current_vent", to fit with their nature. To ensure compatibility with already made dashboards or reuses, these fields have been duplicated to avoid errors when their old names are used; but we strongly recommand to replace their old names by the new as soon as possible.

Switzerland has been recording coronavirus (COVID-19) case numbers across the country since the end of February 2020. As of January 2023, there were 4,383,648 confirmed cases.

Switzerland recorded 34400 Coronavirus Recovered since the epidemic began, according to the World Health Organization (WHO). In addition, Switzerland reported 11410 Coronavirus Deaths. This dataset includes a chart with historical data for Switzerland Coronavirus Recovered.

Context:

From World Health Organization - On 31 December 2019, WHO was alerted to several cases of pneumonia in Wuhan City, Hubei Province of China. The virus did not match any other known virus. This raised concern because when a virus is new, we do not know how it affects people.

So daily level information on the affected people can give some interesting insights when it is made available to the broader data science community.

Johns Hopkins University has made an excellent dashboard using the affected cases data. Data is extracted from the google sheets associated and made available here.

Edited:

Now data is available as csv files in the Johns Hopkins Github repository. Please refer to the github repository for the Terms of Use details. Uploading it here for using it in Kaggle kernels and getting insights from the broader DS community.

Content

2019 Novel Coronavirus (2019-nCoV) is a virus (more specifically, a coronavirus) identified as the cause of an outbreak of respiratory illness first detected in Wuhan, China. Early on, many of the patients in the outbreak in Wuhan, China reportedly had some link to a large seafood and animal market, suggesting animal-to-person spread. However, a growing number of patients reportedly have not had exposure to animal markets, indicating person-to-person spread is occurring. At this time, it’s unclear how easily or sustainably this virus is spreading between people - CDC

This dataset has daily level information on the number of affected cases, deaths and recovery from 2019 novel coronavirus. Please note that this is a time series data and so the number of cases on any given day is the cumulative number.

The data is available from 22 Jan, 2020.

Here’s a polished version suitable for a professional Kaggle dataset description:

Dataset Description

This dataset contains time-series and case-level records of the COVID-19 pandemic. The primary file is covid_19_data.csv, with supporting files for earlier records and individual-level line list data.

Files and Columns

1. covid_19_data.csv (Main File)

This is the primary dataset and contains aggregated COVID-19 statistics by location and date.

• Sno – Serial number of the record
• ObservationDate – Date of the observation (MM/DD/YYYY)
• Province/State – Province or state of the observation (may be missing for some entries)
• Country/Region – Country of the observation
• Last Update – Timestamp (UTC) when the record was last updated (not standardized, requires cleaning before use)
• Confirmed – Cumulative number of confirmed cases on that date
• Deaths – Cumulative number of deaths on that date
• Recovered – Cumulative number of recoveries on that date

2. 2019_ncov_data.csv (Legacy File)

This file contains earlier COVID-19 records. It is no longer updated and is provided only for historical reference. For current analysis, please use covid_19_data.csv.

3. COVID_open_line_list_data.csv

This file provides individual-level case information, obtained from an open data source. It includes patient demographics, travel history, and case outcomes.

4. COVID19_line_list_data.csv

Another individual-level case dataset, also obtained from public sources, with detailed patient-level information useful for micro-level epidemiological analysis.

✅ Use covid_19_data.csv for up-to-date aggregated global trends.

✅ Use the line list datasets for detailed, individual-level case analysis.

Country level datasets:

If you are interested in knowing country level data, please refer to the following Kaggle datasets:

India - https://www.kaggle.com/sudalairajkumar/covid19-in-india

South Korea - https://www.kaggle.com/kimjihoo/coronavirusdataset

Italy - https://www.kaggle.com/sudalairajkumar/covid19-in-italy

Brazil - https://www.kaggle.com/unanimad/corona-virus-brazil

USA - https://www.kaggle.com/sudalairajkumar/covid19-in-usa

Switzerland - https://www.kaggle.com/daenuprobst/covid19-cases-switzerland

Indonesia - https://www.kaggle.com/ardisragen/indonesia-coronavirus-cases

Acknowledgements :

Johns Hopkins University for making the data available for educational and academic research purposes

MoBS lab - https://www.mobs-lab.org/2019ncov.html

World Health Organization (WHO): https://www.who.int/

DXY.cn. Pneumonia. 2020. http://3g.dxy.cn/newh5/view/pneumonia.

BNO News: https://bnonews.com/index.php/2020/02/the-latest-coronavirus-cases/

National Health Commission of the People’s Republic of China (NHC): http://www.nhc.gov.cn/xcs/yqtb/list_gzbd.shtml

China CDC (CCDC): http://weekly.chinacdc.cn/news/TrackingtheEpidemic.htm

Hong Kong Department of Health: https://www.chp.gov.hk/en/features/102465.html

Macau Government: https://www.ssm.gov.mo/portal/

Taiwan CDC: https://sites.google....

Key figures on laboratory-confirmed cases, hospitalisations, deaths, tests, vaccinations, relevant virus variants, Re values, contact tracing (isolation and quarantine), hospital capacity and the international situation. ### Documentation - data documentation - release notes - data context API

The number of COVID-19 vaccination doses administered in Switzerland rose to 16939459 as of Oct 27 2023. This dataset includes a chart with historical data for Switzerland Coronavirus Vaccination Total.

The coronavirus (COVID-19) has severely affected Switzerland. Based on current figures from January 2023, of all the Swiss cantons, Zürich has the highest number of confirmed cases, followed by Bern.

The page of a dataset published on the SWISSUbase research data catalogue.

Covid-19 pandemic in Switzerland. Map types: Charts, Choropleths. Spatial extent: Switzerland. Times: February 2020, March 2020, 1.3.2020, April 2020, 1.4.2020, May 2020, 1.5.2020, June 2020, 1.6.2020, July 2020, 1.7.2020, August 2020, 1.8.2020, September 2020, 1.9.2020, October 2020, 1.10.2020, November 2020, 1.11.2020, December 2020, 1.12.2020, January 2021, 1.1.2021, February 2021, 1.2.2021, March 2021, 1.3.2021, April 2021, 1.4.2021, May 2021, 1.5.2021, June 2021, 1.6.2021, July 2021, 1.7.2021, August 2021, 1.8.2021, September 2021, 1.9.2021, October 2021, 1.10.2021, November 2021, 1.11.2021, December 2021, 1.12.2021, January 2022, 1.1.2022, February 2022, 1.2.2022, 1.3.2022, March 2022, April 2022, 1.4.2022, 1.5.2022. Spatial unit: Cantons. Distinction: monthly, cumulative

As of January 2023, there were 13,885 coronavirus (COVID-19) deaths in Switzerland. The most affected canton was Zürich in the German-speaking part of the country.

Il n'y a pas de description pour ce jeu de données.

The Science Barometer Switzerland analyzes through which media, in which form and how often the Swiss come in contact with scientific issues, and whether and how this affects their scientific knowledge as well as their opinions regarding science. The project surveys a representative sample of the language-assimilated resident population of Switzerland every three years (2016, 2019, 2022), interviewing ca. 1000 respondents aged 15 years and older in the German-, French-, and Italian-speaking regions of the country. It is planned to continue the project permanently after 2022. On the one hand, the survey gathers information about the usage of different information sources, asking how often respondents encounter scientific issues in newspapers, radio and television, and how often they look for scientific issues on the internet and in social media. Furthermore, it asks how often the Swiss go to science museums, as well as how often they speak about scientific topics with family and friends. In addition, it assesses how credible, comprehensible and useful respondents judge these different sources of information. On the other hand, the Science Barometer Switzerland measures the Swiss’ scientific knowledge and their attitudes towards science. Based on these dimensions, it connects patterns of information behavior and public opinions about science in explanatory models.

In November 2020, a special Science Barometer survey on COVID-19 was conducted in the form of an online representative survey of Swiss residents aged 15 or older. It was financed by the Swiss Academies of Arts and Sciences.

This Project Tycho dataset includes a CSV file with COVID-19 data reported in SWITZERLAND: 2019-12-30 - 2021-07-31. It contains counts of cases and deaths. Data for this Project Tycho dataset comes from: "COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University", "European Centre for Disease Prevention and Control Website", "World Health Organization COVID-19 Dashboard". The data have been pre-processed into the standard Project Tycho data format v1.1.

The COVID-19 pandemic generated (and keeps generating) a huge corpus of news articles, easily retrievable in Factiva with very targeted queries.This dataset, generated with an ad-hoc parser and NLP pipeline, analyzes the frequency of lemmas and named entities in news articles (in German, French, Italian and English ) regarding Switzerland and COVID-19. The analysis of large bodies of grey literature via text mining and computational linguistics is an increasingly frequent approach to understand the large-scale trends of specific topics. We used Factiva, a news monitoring and search engine developed and owned by Dow Jones, to gather and download all the news articles published between January and July 2020 on Covid-19 and Switzerland.Due to Factiva's copyright policy, it is not possible to share the original dataset with the exports of the articles' text; however, we can share the results of our work on the corpus. All the information relevant to reproduce the results is provided.Factiva allows a very granular definition of the queries, and moreover has access to full text articles published by the major media outlet of the world. The query has been defined as follows (syntax in bold, explanation in italics): ((coronavirus or Wuhan virus or corvid19 or corvid 19 or covid19 or covid 19 or ncov or novel coronavirus or sars) and (atleast3 coronavirus or atleast3 wuhan or atleast3 corvid* or atleast3 covid* or atleast3 ncov or atleast3 novel or atleast3 corona*))Keywords for covid19; must appear at least 3 times in the textand ns=(gsars or gout)Subject is “novel coronaviruses” or “outbreaks and epidemics” and “general news”and la=XLanguage is X (DE, FR, IT, EN)and rst=tmnbRestrict to TMNB (major news and business publications)and wc>300At least 300 wordsand date from 20191001 to 20200801Date intervaland re=SWITZRegion is Switzerland It is important to specify some details that characterize the query. The query is not limited to articles published by Swiss media, but to articles regarding Switzerland. The reason is simple: a Swiss user googling for “Schweiz Coronavirus” or for “Coronavirus Ticino” can easily find and read articles published by foreign media outlets (namely, German or Italian) on that topic. If the objective is capturing and describing the information trends to which people are exposed, this approach makes much more sense than limiting the analysis to articles published by Swiss media.Factiva’s field “NS” is a descriptor for the content of the article. “gsars” is defined in Factiva’s documentation as “All news on Severe Acute Respiratory Syndrome”, and “gout” as “The widespread occurrence of an infectious disease affecting many people or animals in a given population at the same time”; however, the way these descriptors are assigned to articles is not specified in the documentation.Finally, the query has been restricted to major news and business publications of at least 300 words. Duplicate check is performed by Factiva. Given the incredibly large amount of articles published on COVID-19, this (absolutely arbitrary) restriction allows retrieving a corpus that is both meaningful and manageable.metadata.xlsx contains information about the articles retrieved (strategy, amount)The PDF files document the execution of the Jupyter notebooks. The zip file contains the lemma and NE frequencies data, divided by language. The "Lemmas" folder contains a CSV file per month and a general timeseries; the "Entities" folder contains a CSV file per month, a general timeseries, plus subsets that are category-specific. For a comprehensive explanation about categories, you can check the PDF files. This work is part of the PubliCo research project.

Online survey among an online sample of people living in Switzerland, on how the Swiss population was affected by the COVID-19 pandemic and how it thinks about the government's responses

Two data files in csv and sav formats, codebook and metadata along DDI Standard

The page of a dataset published on the SWISSUbase research data catalogue.

Excerpt from the data collected by the cantonal authorities Basel-Stadt, Switzerland on COVID-19 relevant for the study "Systematic screening on admission for SARS-CoV-2 to detect asymptomatic infections".Official website: https://data.bs.ch/explore/dataset/100073/table/?sort=timestampReference: Präsidialdepartement, Fachstelle für OGD Basel-Stadt, https://github.com/openZH/covid_19