Abstract

JHU Coronavirus COVID-19 Global Cases, by country

Documentation

PHS is updating the Coronavirus Global Cases dataset weekly, Monday, Wednesday and Friday from Cloud Marketplace.

This data comes from the data repository for the 2019 Novel Coronavirus Visual Dashboard operated by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). This database was created in response to the Coronavirus public health emergency to track reported cases in real-time. The data include the location and number of confirmed COVID-19 cases, deaths, and recoveries for all affected countries, aggregated at the appropriate province or state. It was developed to enable researchers, public health authorities and the general public to track the outbreak as it unfolds. Additional information is available in the blog post.

Visual Dashboard (desktop): https://www.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6

Section 2

Included Data Sources are:

• 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.com/cdc.gov.tw/2019ncov/taiwan?authuser=0
• US CDC: https://www.cdc.gov/coronavirus/2019-ncov/index.html
• Government of Canada: https://www.canada.ca/en/public-health/services/diseases/coronavirus.html
• Australia Government Department of Health: https://www.health.gov.au/news/coronavirus-update-at-a-glance
• European Centre for Disease Prevention and Control (ECDC): https://www.ecdc.europa.eu/en/geographical-distribution-2019-ncov-cases
• Ministry of Health Singapore (MOH): https://www.moh.gov.sg/covid-19
• Italy Ministry of Health: http://www.salute.gov.it/nuovocoronavirus
• 1Point3Arces: https://coronavirus.1point3acres.com/en
• WorldoMeters: https://www.worldometers.info/coronavirus/

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Section 3

**Terms of Use: **

This GitHub repo and its contents herein, including all data, mapping, and analysis, copyright 2020 Johns Hopkins University, all rights reserved, is provided to the public strictly for educational and academic research purposes. The Website relies upon publicly available data from multiple sources, that do not always agree. The Johns Hopkins University hereby disclaims any and all representations and warranties with respect to the Website, including accuracy, fitness for use, and merchantability. Reliance on the Website for medical guidance or use of the Website in commerce is strictly prohibited.

Section 4

**U.S. county-level characteristics relevant to COVID-19 **

Chin, Kahn, Krieger, Buckee, Balsari and Kiang (forthcoming) show that counties differ significantly in biological, demographic and socioeconomic factors that are associated with COVID-19 vulnerability. A range of publicly available county-specific data identifying these key factors, guided by international experiences and consideration of epidemiological parameters of importance, have been combined by the authors and are available for use:

https://github.com/mkiang/county_preparedness/

From 20 October 2023, COVID-19 datasets will no longer be updated. Detailed information is available in the fortnightly NSW Respiratory Surveillance Report: https://www.health.nsw.gov.au/Infectious/covid-19/Pages/reports.aspx.
Latest national COVID-19 spread, vaccination and treatment metrics are available on the Australian Government Health website: https://www.health.gov.au/topics/covid-19/reporting?language=und

COVID-19 cases by notification date and postcode, local health district, and local government area. The dataset is updated weekly on Fridays.

The data is for confirmed COVID-19 cases only based on location of usual residence, not necessarily where the virus was contracted.

Case counts reported by NSW Health for a particular notification date may vary over time due to ongoing investigations and the outcome of cases under review thus this dataset and any historical data contained within is subject to change on a daily basis.

The underlying dataset was assessed to measure the risk of identifying an individual and the level of sensitivity of the information gained if it was known that an individual was in the dataset. The dataset was then treated to mitigate these risks, including suppressing and aggregating data.

This dataset does not include cases with missing location information.

This NSW Health site provides detailed weekly reports on COVID-19 in NSW, including information on COVID-19 in specific populations, COVID-19 vaccination status, COVID-19 hospitalisations and deaths, COVID-19 testing in NSW, Variants of Concern, and the NSW Sewage Surveillance Program.

From 20 October 2023, COVID-19 datasets will no longer be updated. Detailed information is available in the fortnightly NSW Respiratory Surveillance Report: https://www.health.nsw.gov.au/Infectious/c…Show full descriptionFrom 20 October 2023, COVID-19 datasets will no longer be updated. Detailed information is available in the fortnightly NSW Respiratory Surveillance Report: https://www.health.nsw.gov.au/Infectious/covid-19/Pages/reports.aspx. Latest national COVID-19 spread, vaccination and treatment metrics are available on the Australian Government Health website: https://www.health.gov.au/topics/covid-19/reporting?language=und As of 10 February 2023, NSW Health will report only positive SARS-CoV-2 test results. Recent changes to the COVID-19 public health orders for COVID-19 means it is no longer necessary for laboratories to provide data on negative PCR test results, in line with other diseases. Positive COVID-19 results, through both PCR tests and notified rapid antigen test results, will continue to be reported. NSW Health uses a wide range of surveillance systems, including hospital data, sewage surveillance, and genomic sequencing, to closely monitor COVID-19 and inform its public health response. COVID-19 tests by date and postcode, local health district, and local government area. The dataset is updated weekly on Fridays. The data is for COVID-19 tests and is based on the Local Health District (LHD) and Local Government Area (LGA) of residence provided by the individual at time of testing. A surge in total number of people tested on a particular day may occur as the test results are updated in batches and new laboratories gain testing capacity. The underlying dataset was assessed to measure the risk of identifying an individual and the level of sensitivity of the information gained if it was known that an individual was in the dataset. The dataset was then treated to mitigate these risks, including suppressing and aggregating data. On 16 September 2021, NSW Health implemented a change in the way testing data is reported. We will discontinue publication of unit record test data file as the data will only be provided as an aggregated file The aggregated data file will only include negative tests. Positive tests (i.e. cases) will not be included. Please note the COVID-19 tests dataset does not include registered positive rapid antigen test (RAT) information.

This is the data for the 2019 Novel Coronavirus Visual Dashboard operated by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). Also, Supported by ESRI Living Atlas Team and the Johns Hopkins University Applied Physics Lab (JHU APL).Data SourcesWorld 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.com/cdc.gov.tw/2019ncov/taiwan?authuser=0 US CDC: https://www.cdc.gov/coronavirus/2019-ncov/index.html Government of Canada: https://www.canada.ca/en/public-health/services/diseases/coronavirus.html Australia Government Department of Health: https://www.health.gov.au/news/coronavirus-update-at-a-glance European Centre for Disease Prevention and Control (ECDC): https://www.ecdc.europa.eu/en/geographical-distribution-2019-ncov-casesMinistry of Health Singapore (MOH): https://www.moh.gov.sg/covid-19Italy Ministry of Health: http://www.salute.gov.it/nuovocoronavirus

Context

Amidst the COVID-19 outbreak, the world is facing great crisis in every way. The value and things we built as a human race are going through tremendous challenges. It is a very small effort to bring curated data set on Novel Corona Virus to accelerate the forecasting and analytical experiments to cope up with this critical situation. It will help to visualize the country level out break and to keep track on regularly added new incidents.

COVID-19 Country Level Timeseries Dataset

This Dataset contains country wise public domain time series information on COVID-19 outbreak. The Data is sorted alphabetically on Country name and Date of Observation.

Column Descriptions

The data set contains the following columns:
ObservationDate: The date on which the incidents are observed country: Country of the Outbreak Confirmed: Number of confirmed cases till observation date Deaths: Number of death cases till observation date Recovered: Number of recovered cases till observation date New Confirmed: Number of new confirmed cases on observation date New Deaths: Number of New death cases on observation date New Recovered: Number of New recovered cases on observation date latitude: Latitude of the affected country longitude: Longitude of the affected country

Acknowledgements

This data set is a cleaner version of the https://www.kaggle.com/sudalairajkumar/novel-corona-virus-2019-dataset data set with added geo location information and regularly added incident counts. I would like to thank this great effort by SRK.

Original Data Source

Johns Hopkins University 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.com/cdc.gov.tw/2019ncov/taiwan?authuser=0 US CDC: https://www.cdc.gov/coronavirus/2019-ncov/index.html Government of Canada: https://www.canada.ca/en/public-health/services/diseases/coronavirus.html Australia Government Department of Health: https://www.health.gov.au/news/coronavirus-update-at-a-glance European Centre for Disease Prevention and Control (ECDC): https://www.ecdc.europa.eu/en/geographical-distribution-2019-ncov-cases Ministry of Health Singapore (MOH): https://www.moh.gov.sg/covid-19 Italy Ministry of Health: http://www.salute.gov.it/nuovocoronavirus

The time it takes for the number of COVID-19 deaths to double varies by country. The doubling rate in the United States was 139 days as of December 13, 2020. In comparison, the number of confirmed deaths in Australia doubled from 450 to 908 in the space of 117 days between August 18 and December 13, 2020.

COVID-19: We are all in this together The commitment of civilians to follow basic hygiene measures and maintain social distancing must continue. The wellbeing of populations cannot be jeopardized, and young people must also engage in the response. In Australia, the 20- to 29-year-old age group accounts for the highest number of COVID-19 cases. With lockdown restrictions lifted, many people have returned to their regular routines and jumped back into socializing. However, there are concerns about complacency and suggestions that young adults could be driving spikes in coronavirus cases.

Receive coronavirus warnings on your smartphone It is of paramount importance that countries keep a vigilant eye on the spread of the coronavirus. One way of doing so is to invest in track and trace surveillance systems. Electronic tools are not essential, but many countries are using contact-tracing smartphone apps to make the tracking of cases more efficient. In June 2020, a contact-tracing app was rolled out across Japan, and it received nearly eight million downloads in the first month. A COVID-19 alert app was also launched in Canada at the end of July 2020. The smartphone software is initially being piloted in Ontario, but it will soon be possible for people in other provinces to use the app and report a diagnosis.

Series of weekly COVID-19 situational assessment reports delivered over the period April 2020 through December 2023.The work was conducted under a series of official orders for the provision of Situational Awareness Modelling for COVID-19 from the Australian Government Department of Health to the University of Melbourne beginning in March 2020. The University of Melbourne was the lead contractor, with contributions delivered under contract from national situational assessment consortium partners.Situational assessment reports for the period from 4th April 2020 through 15th December 2023 were released to the public under agreement with the Commonwealth Government Department of Health on the University of Melbourne website on 18th December 2023.

Ce jeu de données fait un point hebdomadaire de la situation concernant la pandémie COVID-19 au Luxembourg. This dataset contains a weekly report of the COVID-19 situation in Luxembourg.

GlobalData is carrying out weekly consumer surveys in 11 countries between 25th March and 31st May 2020, to track consumer sentiment and shopping behavior during the Coronavirus (COVID19) pandemic. The sample size is 500 respondents per country, per week. The three countries in scope for Asia-Pacific are Australia, China and India. Questions are consistent every week, and cover consumer opinions about COVID-19, buying behavior and product choices and impact of the Coronavirus (COVID19) outbreak on consumers' lifestyle and activities. This report summarizes the key findings from responses in week 9. Read More

This dataset provides a summary of current data, updated weekly, of COVID-19 (coronavirus disease) notified to the department by either Polymerase chain reaction PCR) test or Rapid Antigen Test (RAT) and provides a summary of new cases in the last seven days by local government area

From 20 October 2023, COVID-19 datasets will no longer be updated. Detailed information is available in the fortnightly NSW Respiratory Surveillance Report: https://www.health.nsw.gov.au/Infectious/covid-19/Pages/reports.aspx. Latest national COVID-19 spread, vaccination and treatment metrics are available on the Australian Government Health website: https://www.health.gov.au/topics/covid-19/reporting?language=und

COVID-19 cases by notification date and postcode, local health district, local government area and likely source of infection.

This dataset has been discontinued from 19 November 2021. NSW Health now reports daily COVID-19 cases as a total of local and overseas cases. With quarantine-free international travel, overseas origin of cases can no longer be determined immediately, but will be included in the COVID-19 weekly surveillance reports. The NSW COVID-19 cases by location dataset will continue to be published.

The data is for confirmed COVID-19 cases only based on location of usual residence, not necessarily where the virus was contracted. The case definition of a confirmed case is a person who tests positive to a validated specific SARS-CoV-2 nucleic acid test or has the virus identified by electron microscopy or viral culture, at a reference laboratory. Data reported at 8pm daily.

Case counts reported by NSW Health for a particular notification date may vary over time due to ongoing investigations and the outcome of cases under review thus this dataset and any historical data contained within is subject to change on a daily basis.

The underlying dataset was assessed to measure the risk of identifying an individual and the level of sensitivity of the information gained if it was known that an individual was in the dataset. The dataset was then treated to mitigate these risks, including suppressing and aggregating data.

Objective: To identify gaps among Australian Long COVID support services and guidelines alongside recommendations for future health programs.Methods: Electronic databases and seven government health websites were searched for Long COVID-specific programs or clinics available in Australia as well as international and Australian management guidelines.Results: Five Long COVID specific guidelines and sixteen Australian services were reviewed. The majority of Australian services provided multidisciplinary rehabilitation programs with service models generally consistent with international and national guidelines. Most services included physiotherapists and psychologists. While early investigation at week 4 after contraction of COVID-19 is recommended by the Australian, UK and US guidelines, this was not consistently implemented.Conclusion: Besides Long COVID clinics, future solutions should focus on early identification that can be delivered by General Practitioners and all credentialed allied health professions. Study findings highlight an urgent need for innovative care models that address individual patient needs at an affordable cost. We propose a model that focuses on patient-led self-care with further enhancement via multi-disciplinary care tools.

This project investigated parent perceptions of COVID19 Schooling from home based on a national survey of parents.

Survey questions are listed below:

•What is your usual employment?

•How many hours a week are you currently employed?

•What is your age?

•What is your gender?

•Country of residence

•State

•Postcode

•How many children are currently under your care?

•How many children are you currently schooling at home?

•What is your child’s age?

•What year of school is your child in?

•What is your child’s gender?

•Does your child have any special learning needs, and if so, what are they?

•What type of school does your child attend?

•In what area is your child’s school located?

•What sort of technology or device does your child most often use for schooling at home (e.g. iPad, Chromebook, ACER laptop, Samsung phone, none)?

•Which would best describe the access that your child has to a device or technology in order to undertake schooling at home?

•Approximately how many weeks in total have you schooled your child from home since the beginning of the COVID-19 pandemic?

•Approximately how many hours a week do you personally support your child to undertake schooling at home?

•Approximately how many hours a week does another adult or adults support your child to undertake schooling at home?

•Please rate your agreement with the following questions:

- Schooling at home has been stressful for me.

- Schooling at home has been difficult for my child.

•What has been most stressful and difficult for you and your child about homeschooling, and why?

•What has worked well/has been beneficial for you or your child during homeschooling, and why?

•How many days each week does your child undertake schooling at home?

•On each schooling at home day, approximately how many hours does your child spend schooling at home?

•Are you generally aware of how your child spends their time completing schooling at home?

•Approximately how many minutes each day (on average) would you estimate your child spends completing each of the following schooling-related activities?

- Paper based activities (e.g. printed worksheets)

- Offline tactile activities (e.g., exercise, science experiments)

- Web-conferencing with a teacher (e.g. via Zoom)

- Online learning games (e.g. Mathletics, Reading Eggs)

- Digital worksheets completed online (e.g. fill-in-the-blank)

- Reading online resources (e.g. links to websites)

- Watching videos (teacher created)

- Watching videos (general public domain)

- Digital creativity tasks (e.g. creating essays, videos, posters)

- Other online tasks (e.g. Google Classroom, Moodle chats)

- Other:

•If you could change anything about your child’s online and offline schooling at home activities, what would it be?

•Does your child learn more, the same or less when schooling from home compared to when learning at school?

•How much more or less do you estimate your child is learning during schooling at home compared to their normal learning when at school?

•Please rate your agreement with the following questions:

- My child is able to learn independently using technology

- I am satisfied with the homeschooling support being offered by my child’s school

•Compared to the first time during the pandemic that you had to do schooling at home, how would you rate schooling at home now?

•Please explain the reasons for your answer to the previous question.

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.

Données du "2019 Novel Coronavirus Visual Dashboard, géré par Johns Hopkins University Center for Systems Science and Engineering" (JHU CSSE). Il est également soutenu par l'équipe "ESRI Living Atlas" et "Johns Hopkins University Applied Physics Lab" (JHU APL).Sources de données: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.com/cdc.gov.tw/2019ncov/taiwan?authuser=0 US CDC: https://www.cdc.gov/coronavirus/2019-ncov/index.html Government of Canada: https://www.canada.ca/en/public-health/services/diseases/coronavirus.html Australia Government Department of Health: https://www.health.gov.au/news/coronavirus-update-at-a-glance European Centre for Disease Prevention and Control (ECDC): https://www.ecdc.europa.eu/en/geographical-distribution-2019-ncov-casesMinistry of Health Singapore (MOH): https://www.moh.gov.sg/covid-19Italy Ministry of Health: http://www.salute.gov.it/nuovocoronavirus

Mean number of days of alcohol consumption past week and percentage of heavy episodic drinking of Optimise participants, Victoria, Australia, N = 688.

Abstract

The COVID-19 Preventive Health Survey was designed to help policymakers and health researchers better monitor and understand people’s knowledge, attitudes and practices about COVID-19 to improve communications and their response to the pandemic. This survey is conducted in partnership between Facebook, the Massachusetts Institute of Technology (MIT), and Johns Hopkins University (JHU), with advice from the World Health Organization. Sampled users see the invitation at the top of their News Feed, but the surveys are collected off the Facebook app and the Facebook company does not collect or receive individual survey responses. The survey asks users to self-report their adherence to preventive measures, such as wearing masks and what they know about COVID-19, including symptoms of the disease, risk factors and how their community is handling the pandemic.

Geographic coverage

This survey was fielded in 67 countries and territories.

Wave Countries and Territories: Argentina, Bangladesh, Brazil, Colombia, Egypt, France, Germany, India, Indonesia, Italy, Japan, Malaysia, Mexico, Nigeria, Pakistan, Philippines, Poland, Romania, Thailand, Turkey, United Kingdom, United States, Vietnam

Snapshot Countries and Territories: Afghanistan, Algeria, Angola, Australia, Azerbaijan, Bolivia, Cambodia, Cameroon, Canada, Chile, Cote d’Ivoire, Ecuador, Estonia, Georgia, Ghana, Guatemala, Honduras, Iraq, Jamaica, Kazakhstan, Kenya, Mongolia, Morocco, Mozambique, Myanmar, Nepal, Netherlands, Peru, Portugal, Senegal, Singapore, South Africa, South Korea, Spain, Sri Lanka, Sudan, Taiwan, Tanzania, Trinidad & Tobago, Uganda, Ukraine, United Arab Emirates, Uruguay, Venezuela

Analysis unit

• Public Aggregate Data: Subnational levels
• Microdata through Facebook Data for Good program: Individual level

Universe

The target population consists of active Facebook users. The sampling frame is active Facebook users with ages 18+, which includes users living within 23 countries or territories. The sampling frame is restricted to people who use Facebook in one of the supported locales.

Kind of data

Sample survey data [ssd]

Sampling procedure

The target population consists of active Facebook users. The sampling frame is active Facebook users with ages 18+, which includes users living within 23 countries or territories. The sampling frame is restricted to people who use Facebook in one of the supported locales.

The Facebook app invites a sample of adult users to take an optional, off-Facebook survey through an invitation at the top of their Facebook News Feed. Users who click on the invitation are redirected to a Qualtrics page hosted by MIT where they are informed about the survey and can take the survey. While MIT designs, collects, and analyzes the survey data, Facebook provides assistance with questionnaire translation, survey sampling and recruitment, and statistical bias correction.

Mode of data collection

Internet [int]

Research instrument

The survey includes questions about self-reported preventive behaviors and knowledge and attitudes towards COVID-19 vaccines. The survey instrument is managed by MIT and available in more than 55 languages. Two versions of the survey were fielded across 67 countries and territories. Countries with sufficient sample sizes receive a “Wave Survey” that is fielded every 2 weeks between July 2020 and March 2021. The rest of the countries receive a periodic “Snapshot Survey”. Snapshot and wave surveys were developed based on feedback from global health partners so that information could be collected that is helpful to inform public health responses even in areas with fewer survey respondents. As of Spring 2021, some questions from the survey have been merged with the larger Covid-19 Trends and Impact Survey. The full survey instrument is available here.

The snapshot survey was fielded to 44 countries and territories with a one-time sample over a 2 week period. A follow up sample was done in late 2020 of snapshot countries and territories to provide updated information.

The wave survey was fielded to 23 countries and territories with repeated, bi-monthly cross-sections. Each of the 8 waves is two weeks long. Sampled users may be invited to take the survey again in subsequent weeks, depending on the density of their area. However, the responses of sampled users who participate more than once will not be linked longitudinally.

Response rate

Response rates to online surveys vary widely depending on a number of factors including survey length, region, strength of the relationship with invitees, incentive mechanisms, invite copy, interest of respondents in the topic and survey design.

Sampling error estimates

Any survey data is prone to several forms of error and biases that need to be considered to understand how closely the results reflect the intended population. In particular, the following components of the total survey error are noteworthy:

Sampling error is a natural characteristic of every survey based on samples and reflects the uncertainty in any survey result that is attributable to the fact that not the whole population is surveyed.

Facebook provides MIT (and other researchers) with analytic weights that adjust for non-response and coverage biases. Making adjustments using the weights ensures that the sample more accurately reflects the characteristics of the target population represented.

Data appraisal

Non-Response Bias This means that some sampled users are more likely to respond to the survey than others. To adjust for this, Facebook calculates the inverse probability that sampled users complete the survey using their self-reported age and gender as well as other characteristics known to correlate with nonresponse. Then these inverse probabilities are used to create weights for responses, after which the survey sample reflects the active adult user population on the Facebook app.

Coverage Bias This means not everyone in every country has a Facebook app account or uses their account regularly. To adjust for this, Facebook adjusts the weights created in the first step even further so that the distribution of age, gender, and administrative region of residence in the survey sample reflects that of the general population.

⚠️ Santé publique France met à jour les référentiels géographiques et de population, utilisés pour la production et la diffusion des indicateurs de suivi de l’épidémie de COVID-19. Cette actualisation sera effective le 7 juillet 2022 . Elle est amenée à être renouvelée chaque année.08/06/2022Compte tenu de la tendance actuelle favorable et la baisse des principaux indicateurs, à partir du 11 juin 2022, les indicateurs COVID-19 produits par Santé publique France seront actualisés sur Géodes et data.gouv.fr tous les jours à l’exception des week-end et des jours fériés.Description du jeu de donnéesCe jeu de données comprends l'essentiel des indicateurs de synthèse permettant le suivi de l'épidémie de COVID-19 en France. Un inventaire des données relatives au COVID-19 sur data.gouv.fr est disponible ici.Ces données sont notamment exposées sur l'onglet vue d'ensemble du tableau de bord de suivi de l'épidémie disponible sur gouvernement.fr. Ce dernier présente depuis le 28 mars 2020 les données relatives à l’épidémie de COVID-19 en France.Cet outil dont le code source est libre, a été développé sous l’impulsion d’Etalab et avec la collaboration de la société civile. Il propose une vision consolidée des données officielles disponibles.Description des donnéesLes données contenues dans le jeu de données sont publiées quotidiennement.Données contextuelles :'date' = Date'dep'= Département'reg'= Région'lib_dep'= libellé département'lib_reg'= libellé régionDonnées relatives à la situation hospitalière'hosp'= Nombre de patients actuellement hospitalisés pour COVID-19.'incid_hosp'= Nombre de nouveaux patients hospitalisés au cours des dernières 24h.'rea'= Nombre de patients actuellement en réanimation ou en soins intensifs.'incid_rea'= Nombre de nouveaux patients admis en réanimation au cours des dernières 24h.'rad'= Nombre cumulé de patients ayant été hospitalisés pour COVID-19 et de retour à domicile en raison de l'amélioration de leur état de santé.'incid_rad'= Nouveaux retours à domicile au cours des dernières 24h.Données relatives au décès pour cause de COVID-19'dchosp'= Décès à l’hôpital'incid_dchosp'= Nouveaux patients décédés à l’hôpital au cours des dernières 24h.'esms_dc'= Décès en ESMS'dc_tot'= Cumul des décès (cumul des décès constatés à l'hôpital et en EMS)Données relatives aux tests'conf'= Nombre de cas confirmés'conf_j1'= Nombre de nouveaux cas confirmés (J-1 date de résultats)'pos'= Nombre de personnes déclarées positives (J-3 date de prélèvement)'pos_7j' = Nombre de personnes déclarées positives sur une semaine (J-3 date de prélèvement)'esms_cas' = Cas confirmés en ESMSDonnées relatives aux indicateurs de suivi de l’épidémie de COVID-19'tx_pos'= Taux de positivité des tests virologiques (Le taux de positivité correspond au nombre de personnes testées positives (RT-PCR et test antigénique) pour la première fois depuis plus de 60 jours rapporté au nombre total de personnes testées positives ou négatives sur une période donnée ; et qui n‘ont jamais été testées positive dans les 60 jours précédents.)'tx_incid'= Taux d'incidence (activité épidémique : Le taux d'incidence correspond au nombre de personnes testées positives (RT-PCR et test antigénique) pour la première fois depuis plus de 60 jours rapporté à la taille de la population. Il est exprimé pour 100 000 habitants)'TO'= Taux d'occupation : tension hospitalière sur la capacité en réanimation (Proportion de patients atteints de COVID-19 actuellement en réanimation, en soins intensifs, ou en unité de surveillance continue rapportée au total des lits en capacité initiale, c’est-à-dire avant d’augmenter les capacités de lits de réanimation dans un hôpital).'R'= Facteur de reproduction du virus (évolution du R0 : Le nombre de reproduction du virus : c’est le nombre moyen de personnes qu’une personne infectée peut contaminer. Si le R effectif est supérieur à 1, l’épidémie se développe ; s’il est inférieur à 1, l’épidémie régresse)Points d'attentions :Les méthodes de collecte des données ont évoluées dans le temps ;Au cours de l'été 2020, les données n'ont pas été publiées durant les week-end et jours fériés.RessourcesConsulter le tableau de bordConsulter l'inventaire des données relatives au COVID-19 sur data.gouv.frConsulter les données de Santé publique FranceConsulter les données du ministère des Solidarités et de la Santé

Dans un contexte d’épidémie favorable, à compter du 1er juillet 2023, le cadre juridique actuellement en cours prévoit l’arrêt du traitement des données personnelles issues de SI-DEP. Par conséquent, après une période transitoire d’ajustement de deux semaines, les nouveaux indicateurs de surveillance virologique seront publiés aux niveaux national, régional et départemental) à une fréquence hebdomadaire. Les consignes de saisie spécifiques dans SI-VIC seront levées à partir de cette date, les indicateurs hospitaliers ne seront plus disponibles. Santé publique France maintient la surveillance de l’épidémie à travers son dispositif multi-sources. Les indicateurs relatifs à la surveillance génomique, aux recours aux associations SOS Médecins, aux urgences hospitalières et aux décès resteront disponibles. ### INFORMATION 25/01/2023 Suite à une erreur de saisie, le nombre de décès survenus en semaine 03 en ESMS a été revu à la baisse. Les corrections nécessaires ont été apportées et expliquent la baisse artificielle du nombre de décès total survenus depuis le début de l’épidémie. ⚠️ 15/11/2022 Suite à la suspension de l’activité par une partie des Laboratoires de biologie médicale privés depuis le 14 novembre, le nombre de « Nouveaux cas confirmés depuis la veille » est sous-estimé à compter du mardi 15/11. De même le taux d’incidence et le taux de dépistage seront sous-estimés à compter du jeudi 17/11. Les équipes de Santé publique France restent mobilisées pour assurer le suivi de l’épidémie qui repose sur une surveillance multi-sources. 08/06/2022 Compte tenu de la tendance actuelle favorable et la baisse des principaux indicateurs, à partir du 11 juin 2022, les indicateurs COVID-19 produits par Santé publique France seront actualisés sur Géodes et data.gouv.fr tous les jours à l’exception des week-end et des jours fériés. ### Description du jeu de données Ce jeu de données comprends l'essentiel des indicateurs de synthèse permettant le suivi de l'épidémie de COVID-19 en France. Un inventaire des données relatives au COVID-19 sur data.gouv.fr est disponible ici. Ces données sont notamment exposées sur l'onglet vue d'ensemble du tableau de bord de suivi de l'épidémie disponible sur gouvernement.fr. Ce dernier présente depuis le 28 mars 2020 les données relatives à l’épidémie de COVID-19 en France. Cet outil dont le code source est libre, a été développé sous l’impulsion d’Etalab et avec la collaboration de la société civile. Il propose une vision consolidée des données officielles disponibles. ## Description des données Les données contenues dans le jeu de données sont publiées quotidiennement. ### Données contextuelles : - 'date' = Date - 'dep'= Département - 'reg'= Région - 'lib_dep'= libellé département - 'lib_reg'= libellé région ### Données relatives à la situation hospitalière - 'hosp'= Nombre de patients actuellement hospitalisés pour COVID-19. - 'incid_hosp'= Nombre de nouveaux patients hospitalisés au cours des dernières 24h. - 'rea'= Nombre de patients actuellement en réanimation ou en soins intensifs. - 'incid_rea'= Nombre de nouveaux patients admis en réanimation au cours des dernières 24h. - 'rad'= Nombre cumulé de patients ayant été hospitalisés pour COVID-19 et de retour à domicile en raison de l'amélioration de leur état de santé. - 'incid_rad'= Nouveaux retours à domicile au cours des dernières 24h. ### Données relatives au décès pour cause de COVID-19 - 'dchosp'= Décès à l’hôpital - 'incid_dchosp'= Nouveaux patients décédés à l’hôpital au cours des dernières 24h. - 'esms_dc'= Décès en ESMS - 'dc_tot'= Cumul des décès (cumul des décès constatés à l'hôpital et en EMS) ### Données relatives aux tests - 'conf'= Nombre de cas confirmés - 'conf_j1'= Nombre de nouveaux cas confirmés (J-1 date de résultats) - 'pos'= Nombre de personnes déclarées positives (J-3 date de prélèvement) - 'pos_7j' = Nombre de personnes déclarées positives sur une semaine (J-3 date de prélèvement) - 'esms_cas' = Cas confirmés en ESMS ### Données relatives aux indicateurs de suivi de l’épidémie de COVID-19 - 'tx_pos'= Taux de positivité des tests virologiques (Le taux de positivité correspond au nombre de personnes testées positives (RT-PCR et test antigénique) pour la première fois depuis plus de 60 jours rapporté au nombre total de personnes testées positives ou négatives sur une période donnée ; et qui n‘ont jamais été testées positive dans les 60 jours précédents.) - 'tx_incid'= Taux d'incidence (activité épidémique : Le taux d'incidence correspond au nombre de personnes testées positives (RT-PCR et test antigénique) pour la première fois depuis plus de 60 jours rapporté à la taille de la population. Il est exprimé pour 100 000 habitants) - 'TO'= Taux d'occupation : tension hospitalière sur la capacité en réanimation (Proportion de patients atteints de COVID-19 actuellement en réanimation, en soins intensifs, ou en unité de surveillance continue rapportée au total des lits en capacité initiale, c’est-à-dire avant d’augmenter les capacités de lits de réanimation dans un hôpital). - 'R'= Facteur de reproduction du virus (évolution du R0 : Le nombre de reproduction du virus : c’est le nombre moyen de personnes qu’une personne infectée peut contaminer. Si le R effectif est supérieur à 1, l’épidémie se développe ; s’il est inférieur à 1, l’épidémie régresse) --- Points d'attentions : - Les méthodes de collecte des données ont évoluées dans le temps ; - Au cours de l'été 2020, les données n'ont pas été publiées durant les week-end et jours fériés. ### Ressources - Consulter le tableau de bord - Consulter l'inventaire des données relatives au COVID-19 sur data.gouv.fr - Consulter les données de Santé publique France - Consulter les données du ministère des Solidarités et de la Santé