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/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.

WHO: COVID-2019: Number of Patients: Death: New: Australia data was reported at 0.000 Person in 24 Dec 2023. This stayed constant from the previous number of 0.000 Person for 23 Dec 2023. WHO: COVID-2019: Number of Patients: Death: New: Australia data is updated daily, averaging 0.000 Person from Jan 2020 (Median) to 24 Dec 2023, with 1430 observations. The data reached an all-time high of 1,094.000 Person in 31 Dec 2022 and a record low of -76.000 Person in 16 Jul 2023. WHO: COVID-2019: Number of Patients: Death: New: Australia data remains active status in CEIC and is reported by World Health Organization. The data is categorized under High Frequency Database’s Disease Outbreaks – Table WHO.D002: World Health Organization: Coronavirus Disease 2019 (COVID-2019): by Country and Region (Discontinued). Negative data reflects the number of retrospective adjustments made by national authorities due to reconciliation exercises, and consequently deducted to the corresponding “To-Date” series.

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.

In August 2020, consumer spending on gyms and fitness during the coronavirus pandemic in Australia had reached ** percent of the normal weekly spending on this activity. However, this was an improvement on previous months. The effects of lockdown measures and social distancing also saw decreased spending on transport, travel, and pubs and venues. On the other end of the spectrum, spending on food delivery increased remarkably alongside spending on home improvement and online gambling.

 Impact on business

With foot traffic in all capital central business districts greatly reduced, brick and mortar businesses were experiencing a corresponding reduction in physical sales. By July 2020, almost all trade, accommodation, and foodservice businesses were operating under modified conditions. Businesses outside of the service industry were only moderately affected by comparison. Despite support from the Australian government in the form of subsidies and payments to maintain staff, almost a quarter of businesses receiving support indicated that they may close their business once coronavirus support measures end.

A rising unemployment rate

Australia’s unemployment rate has traditionally been quite stable, remaining between **** and *** percent for over a decade. Yet in 2020 the unemployment rate was expected to reach over *** percent with the International Monetary Fund (IMF) indicating that this would continue to rise into 2021. This is unsurprising given the number of people who have lost employment or have been temporarily stood down as a result of business lost due to the coronavirus pandemic.

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

NSW has been hit by the Omicron variant, with skyrocketing cases. This dataset, updated regularly, details the location of positive cases. A prediction of where the most cases could occur can be derived from this dataset and a potential prediction of how many cases there is likely to be.

Content

notification_date: Text, dates to when the positive case was notified of a positive test result. postcode: Text, lists the postcode of the positive case. lhd_2010_code: Text, the code of the local health district of the positive case. lhd_2010_name: Text, the name of the local health district of the positive case. lga_code19: Text, the code of the local government area of the positive case. lga_name19: Text, the name of the local government area of the positive case.

Acknowledgements

Thanks to NSW Health for providing and updating the dataset.

Inspiration

The location of cases is highly important in NSW. In mid-2021, Western Sydney had the highest proportion of COVID-19 cases with many deaths ensuing. Western Sydney is one of Sydney's most diverse areas, with many vulnerable peoples. The virus spread to western NSW, imposing a risk to the Indigenous communities. With location data, a prediction service can be made to forecast the areas at risk of transmission.

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.

Context

The data comes from the dataset maintained and updated by the Johns Hopkins University Center for Systems Science and Engineering. Tableau cleans, reshapes, and makes this data ready for your analysis.

Content

The data represent a point-in-time snapshot of to-date totals of confirmed cases and total deaths.

Here are the fields included:

Case_type: Confirmed Cases and total deaths Cases: Point in time snapshot of to-date totals (i.e., Mar 22 is inclusive of all prior dates) Date: Jan 23, 2020 - Present Country_region: Provided for all countries Province_state: Provided for Australia, Canada, China, Denmark, France, Netherlands, United Kingdom, United States Admin2: US only - County name FIPS: US only - 5-digit Federal Information Processing Standard Combined_Key: US only - Combination of Admin 2, State_Province, and Country_Region Lat Long Location Table Names: The Table Name is used to delineate the specific Johns Hopkins datasets that were used: JHU Timeseries - Country-level data (non-US) is sourced from the current JHU Global Timeseries dataset, provided to the public once per day JHU Timeseries - US data through Mar 23 (state-level) is sourced from the Mar 22 JHU Global Timeseries dataset JHU Daily - US data from Mar 23 (county-level) is sourced from the JHU Daily datasets (e.g., Mar 23 + Mar 24 + Mar 25, etc.)

As global communities responded to COVID-19, we heard from public health officials that the same type of aggregated, anonymized insights we use in products such as Google Maps would be helpful as they made critical decisions to combat COVID-19. These Community Mobility Reports aimed to provide insights into what changed in response to policies aimed at combating COVID-19. The reports charted movement trends over time by geography, across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential.

Tracking Australian Subnational policy outcomes during the COVID-19 pandemic. Drawing on the Oxford COVID-19 Government Response Tracker (OxCGRT) coding system, we provide a systematic and objective account of the strength of Covid-19 response policies that have been instigated by Australia’s federal government and state and territory governments. Currently we provide coding for 16 indicators. These indicators allow the creation of four different indices: the stringency index, the containment and health index, the government response index and economic support index. The dataset is updated continuously in real time.

From 15/08/2020, I am no longer updating these files. Instead, I am directly reading data files from the Covid-19 Repository at John Hopkins University.

Context

I have created these datasets specifically for my analysis notebooks:

https://www.kaggle.com/aiaiaidavid/how-spain-became-leader-in-covid-19-infections

And others I am working on.

Content

These datasets contain covid-19 confirmed, recovered and detah cases time series for the following 10 world countries:

Europe: Spain, Italy, France, Germany and UK
Rest of the world: Australia, Brazil, Canada, Iran and USA

Note the files for 27072020 had two countries (Iran and Australia) removed.

Full data is obtained from the COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University:

https://github.com/CSSEGISandData/COVID-19

Acknowledgements

Thank you to the community of AI Saturdays Spain, which introduced me into Jupyter Notebooks and Kaggle, which has open up a new world of opportunities for me.

This dataset presents the percentage change in weekly payroll jobs between the week ending 04 January 2020 to the week ending 3 October 2020 relative to the week ending 14 March 2020 (the week Australia recorded its 100th confirmed COVID-19 case). The data is aggregated to Statistical Area Level 4 (SA4) from the 2016 Australian Statistical Geography Standard (ASGS).

These weekly estimates are derived from Single Touch Payroll (STP) data, which is provided to the Australian Taxation Office (ATO) by businesses with STP-enabled payroll or accounting software each time the business runs its payroll. STP data includes both business and job level tax information and superannuation information. The data are combined with other administrative data from the Australian taxation system to determine additional classification attributes, such as the age and sex of employees.

This data is sourced from the Australian Bureau of Statistics.

Note:

• The weekly change estimates for the most recent weeks of data contain a higher degree of reporting variability and imputation. The ABS recommends that users exercise caution when focusing on change in the most recent weeks, as these estimates are subject to a greater levels of revision in subsequent releases.

For more information please visit the Data Methodology.

This release presents experimental estimates of weekly payroll jobs and wages for the purpose of assessing the economic impact of COVID-19 on employees and the labour market.

AURIN has restructured and spatially enabled the original dataset.

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.

On March 10, 2023, the Johns Hopkins Coronavirus Resource Center ceased its collecting and reporting of global COVID-19 data. For updated cases, deaths, and vaccine data please visit the following sources: World Health Organization (WHO)For more information, visit the Johns Hopkins Coronavirus Resource Center.-- Esri COVID-19 Trend Report for 3-9-2023 --0 Countries have Emergent trend with more than 10 days of cases: (name : # of active cases) 41 Countries have Spreading trend with over 21 days in new cases curve tail: (name : # of active cases)Monaco : 13, Andorra : 25, Marshall Islands : 52, Kyrgyzstan : 79, Cuba : 82, Saint Lucia : 127, Cote d'Ivoire : 148, Albania : 155, Bosnia and Herzegovina : 172, Iceland : 196, Mali : 198, Suriname : 246, Botswana : 247, Barbados : 274, Dominican Republic : 304, Malta : 306, Venezuela : 334, Micronesia : 346, Uzbekistan : 356, Afghanistan : 371, Jamaica : 390, Latvia : 402, Mozambique : 406, Kosovo : 412, Azerbaijan : 427, Tunisia : 528, Armenia : 594, Kuwait : 716, Thailand : 746, Norway : 768, Croatia : 847, Honduras : 1002, Zimbabwe : 1067, Saudi Arabia : 1098, Bulgaria : 1148, Zambia : 1166, Panama : 1300, Uruguay : 1483, Kazakhstan : 1671, Paraguay : 2080, Ecuador : 53320 Countries may have Spreading trend with under 21 days in new cases curve tail: (name : # of active cases)61 Countries have Epidemic trend with over 21 days in new cases curve tail: (name : # of active cases)Liechtenstein : 48, San Marino : 111, Mauritius : 742, Estonia : 761, Trinidad and Tobago : 1296, Montenegro : 1486, Luxembourg : 1540, Qatar : 1541, Philippines : 1915, Ireland : 1946, Brunei : 2010, United Arab Emirates : 2013, Denmark : 2111, Sweden : 2149, Finland : 2154, Hungary : 2169, Lebanon : 2208, Bolivia : 2838, Colombia : 3250, Switzerland : 3321, Peru : 3328, Slovakia : 3556, Malaysia : 3608, Indonesia : 3793, Portugal : 4049, Cyprus : 4279, Argentina : 5050, Iran : 5135, Lithuania : 5323, Guatemala : 5516, Slovenia : 5689, South Africa : 6604, Georgia : 7938, Moldova : 8082, Israel : 8746, Bahrain : 8932, Netherlands : 9710, Romania : 12375, Costa Rica : 12625, Singapore : 13816, Serbia : 14093, Czechia : 14897, Spain : 17399, Ukraine : 19568, Canada : 24913, New Zealand : 25136, Belgium : 30599, Poland : 38894, Chile : 41055, Australia : 50192, Mexico : 65453, United Kingdom : 65697, France : 68318, Italy : 70391, Austria : 90483, Brazil : 134279, Korea - South : 209145, Russia : 214935, Germany : 257248, Japan : 361884, US : 6440500 Countries may have Epidemic trend with under 21 days in new cases curve tail: (name : # of active cases) 54 Countries have Controlled trend: (name : # of active cases)Palau : 3, Saint Kitts and Nevis : 4, Guinea-Bissau : 7, Cabo Verde : 8, Mongolia : 8, Benin : 9, Maldives : 10, Comoros : 10, Gambia : 12, Bhutan : 14, Cambodia : 14, Syria : 14, Seychelles : 15, Senegal : 16, Libya : 16, Laos : 17, Sri Lanka : 19, Congo (Brazzaville) : 19, Tonga : 21, Liberia : 24, Chad : 25, Fiji : 26, Nepal : 27, Togo : 30, Nicaragua : 32, Madagascar : 37, Sudan : 38, Papua New Guinea : 38, Belize : 59, Egypt : 60, Algeria : 64, Burma : 65, Ghana : 72, Haiti : 74, Eswatini : 75, Guyana : 79, Rwanda : 83, Uganda : 88, Kenya : 92, Burundi : 94, Angola : 98, Congo (Kinshasa) : 125, Morocco : 125, Bangladesh : 127, Tanzania : 128, Nigeria : 135, Malawi : 148, Ethiopia : 248, Vietnam : 269, Namibia : 422, Cameroon : 462, Pakistan : 660, India : 4290 41 Countries have End Stage trend: (name : # of active cases)Sao Tome and Principe : 1, Saint Vincent and the Grenadines : 2, Somalia : 2, Timor-Leste : 2, Kiribati : 8, Mauritania : 12, Oman : 14, Equatorial Guinea : 20, Guinea : 28, Burkina Faso : 32, North Macedonia : 351, Nauru : 479, Samoa : 554, China : 2897, Taiwan* : 249634 -- SPIKING OF NEW CASE COUNTS --20 countries are currently experiencing spikes in new confirmed cases:Armenia, Barbados, Belgium, Brunei, Chile, Costa Rica, Georgia, India, Indonesia, Ireland, Israel, Kuwait, Luxembourg, Malaysia, Mauritius, Portugal, Sweden, Ukraine, United Kingdom, Uzbekistan 20 countries experienced a spike in new confirmed cases 3 to 5 days ago: Argentina, Bulgaria, Croatia, Czechia, Denmark, Estonia, France, Korea - South, Lithuania, Mozambique, New Zealand, Panama, Poland, Qatar, Romania, Slovakia, Slovenia, Switzerland, Trinidad and Tobago, United Arab Emirates 47 countries experienced a spike in new confirmed cases 5 to 14 days ago: Australia, Austria, Bahrain, Bolivia, Brazil, Canada, Colombia, Congo (Kinshasa), Cyprus, Dominican Republic, Ecuador, Finland, Germany, Guatemala, Honduras, Hungary, Iran, Italy, Jamaica, Japan, Kazakhstan, Lebanon, Malta, Mexico, Micronesia, Moldova, Montenegro, Netherlands, Nigeria, Pakistan, Paraguay, Peru, Philippines, Russia, Saint Lucia, Saudi Arabia, Serbia, Singapore, South Africa, Spain, Suriname, Thailand, Tunisia, US, Uruguay, Zambia, Zimbabwe 194 countries experienced a spike in new confirmed cases over 14 days ago: Afghanistan, Albania, Algeria, Andorra, Angola, Antigua and Barbuda, Argentina, Armenia, Australia, Austria, Azerbaijan, Bahamas, Bahrain, Bangladesh, Barbados, Belarus, Belgium, Belize, Benin, Bhutan, Bolivia, Bosnia and Herzegovina, Botswana, Brazil, Brunei, Bulgaria, Burkina Faso, Burma, Burundi, Cabo Verde, Cambodia, Cameroon, Canada, Central African Republic, Chad, Chile, China, Colombia, Comoros, Congo (Brazzaville), Congo (Kinshasa), Costa Rica, Cote d'Ivoire, Croatia, Cuba, Cyprus, Czechia, Denmark, Djibouti, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Eritrea, Estonia, Eswatini, Ethiopia, Fiji, Finland, France, Gabon, Gambia, Georgia, Germany, Ghana, Greece, Grenada, Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, Hungary, Iceland, India, Indonesia, Iran, Iraq, Ireland, Israel, Italy, Jamaica, Japan, Jordan, Kazakhstan, Kenya, Kiribati, Korea - South, Kosovo, Kuwait, Kyrgyzstan, Laos, Latvia, Lebanon, Lesotho, Liberia, Libya, Liechtenstein, Lithuania, Luxembourg, Madagascar, Malawi, Malaysia, Maldives, Mali, Malta, Marshall Islands, Mauritania, Mauritius, Mexico, Micronesia, Moldova, Monaco, Mongolia, Montenegro, Morocco, Mozambique, Namibia, Nauru, Nepal, Netherlands, New Zealand, Nicaragua, Niger, Nigeria, North Macedonia, Norway, Oman, Pakistan, Palau, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russia, Rwanda, Saint Kitts and Nevis, Saint Lucia, Saint Vincent and the Grenadines, Samoa, San Marino, Sao Tome and Principe, Saudi Arabia, Senegal, Serbia, Seychelles, Sierra Leone, Singapore, Slovakia, Slovenia, Solomon Islands, Somalia, South Africa, South Sudan, Spain, Sri Lanka, Sudan, Suriname, Sweden, Switzerland, Syria, Taiwan*, Tajikistan, Tanzania, Thailand, Timor-Leste, Togo, Tonga, Trinidad and Tobago, Tunisia, Turkey, Tuvalu, US, Uganda, Ukraine, United Arab Emirates, United Kingdom, Uruguay, Uzbekistan, Vanuatu, Venezuela, Vietnam, West Bank and Gaza, Yemen, Zambia, Zimbabwe Strongest spike in past two days was in US at 64,861 new cases.Strongest spike in past five days was in US at 64,861 new cases.Strongest spike in outbreak was 424 days ago in US at 1,354,505 new cases. Global Total Confirmed COVID-19 Case Rate of 8620.91 per 100,000Global Active Confirmed COVID-19 Case Rate of 37.24 per 100,000Global COVID-19 Mortality Rate of 87.69 per 100,000 21 countries with over 200 per 100,000 active cases.5 countries with over 500 per 100,000 active cases.3 countries with over 1,000 per 100,000 active cases.1 country with over 2,000 per 100,000 active cases.Nauru is worst at 4,354.54 per 100,000.

Abstract

COVID-19 caused significant disruption to the global education system. A thorough analysis of recorded learning loss evidence documented since the beginning of the school closures between March 2020 and March 2022 finds even evidence of learning loss. Most studies observed increases in inequality where certain demographics of students experienced more significant learning losses than others. But there are also outliers, countries that managed to limit the amount of loss. This review consolidates all the available evidence and documents the empirical findings. Data for 41 countries is included, together with other variables related to the pandemic experience. This data is publicly available and will be updated regularly.

Geographic coverage

The data covers 41 countries.

Analysis unit

Country

Kind of data

Aggregate data [agg]

Mode of data collection

Other [oth]

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

Operating Status: All Businesses: Operating as Normal data was reported at 32.000 % in Sep 2020. This records an increase from the previous number of 25.000 % for Aug 2020. Operating Status: All Businesses: Operating as Normal data is updated monthly, averaging 28.500 % from Aug 2020 (Median) to Sep 2020, with 2 observations. The data reached an all-time high of 32.000 % in Sep 2020 and a record low of 25.000 % in Aug 2020. Operating Status: All Businesses: Operating as Normal data remains active status in CEIC and is reported by Australian Bureau of Statistics. The data is categorized under Global Database’s Australia – Table AU.S001: Business Impacts of COVID-19 Survey.

Australia Expected Number of Employees Over the Next Month: All Businesses: Decrease data was reported at 5.000 % in Sep 2020. This stayed constant from the previous number of 5.000 % for Aug 2020. Australia Expected Number of Employees Over the Next Month: All Businesses: Decrease data is updated monthly, averaging 5.000 % from Aug 2020 (Median) to Sep 2020, with 2 observations. The data reached an all-time high of 5.000 % in Sep 2020 and a record low of 5.000 % in Sep 2020. Australia Expected Number of Employees Over the Next Month: All Businesses: Decrease data remains active status in CEIC and is reported by Australian Bureau of Statistics. The data is categorized under Global Database’s Australia – Table AU.S001: Business Impacts of COVID-19 Survey.

Australia Revenue Over the Last Month: All Businesses: Increased data was reported at 13.000 % in Sep 2020. This records a decrease from the previous number of 16.000 % for Aug 2020. Australia Revenue Over the Last Month: All Businesses: Increased data is updated monthly, averaging 14.500 % from Aug 2020 (Median) to Sep 2020, with 2 observations. The data reached an all-time high of 16.000 % in Aug 2020 and a record low of 13.000 % in Sep 2020. Australia Revenue Over the Last Month: All Businesses: Increased data remains active status in CEIC and is reported by Australian Bureau of Statistics. The data is categorized under Global Database’s Australia – Table AU.S001: Business Impacts of COVID-19 Survey.