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.

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

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 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.

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

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.

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 age range. Data is available from 29th of June 2021.

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

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. Age ranges have been combined to minimise these risks.

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.

The COVID-19 Search Trends symptoms dataset shows aggregated, anonymized trends in Google searches for a broad set of health symptoms, signs, and conditions. The dataset provides a daily or weekly time series for each region showing the relative volume of searches for each symptom. This dataset is intended to help researchers to better understand the impact of COVID-19. It shouldn't be used for medical diagnostic, prognostic, or treatment purposes. It also isn't intended to be used for guidance on personal travel plans. To learn more about the dataset, how we generate it and preserve privacy, read the data documentation . To visualize the data, try exploring these interactive charts and map of symptom search trends . As of Dec. 15, 2020, the dataset was expanded to include trends for Australia, Ireland, New Zealand, Singapore, and the United Kingdom. This expanded data is available in new tables that provide data at country and two subregional levels. We will not be updating existing state/county tables going forward. All bytes processed in queries against this dataset will be zeroed out, making this part of the query free. Data joined with the dataset will be billed at the normal rate to prevent abuse. After September 15, queries over these datasets will revert to the normal billing rate. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

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.

The data is for COVID-19 clinics.

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

This dataset provides data on COVID-19 testing and assessment clinics by geolocation, address, contact details, services provided and opening hours.

This data is subject to change as clinic locations are changed.

The Government has obligations under the Privacy and Personal Information Protection Act 1998 and the Health Records and Information Privacy Act 2002 in relation to the collection, use and disclosure of the personal, including the health information, of individuals. Information about NSW Privacy laws is available here: https://data.nsw.gov.au/understand-key-data-legislation.

The information published about COVID-19 clinics does not include any information to directly identify individuals, such as their name, date of birth or address.

Other governments and private sector bodies also have legal obligations in relation to the protection of personal, including health, information. The Government does not authorise any reproduction or visualisation of the data on this website which includes any representation or suggestion in relation to the personal or health information of any individual. The Government does not endorse or control any third party websites including products and services offered by, from or through those websites or their content.

For any further enquiries, please contact us at datansw@customerservice.nsw.gov.au

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.

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.

The data is for locations associated with confirmed COVID-19 cases that have been classified by NSW Health for action. Refer to the latest COVID-19 news and updates for information on action advice provided by NSW Health.

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]

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.

DATA NSW has recently made available three separate COVID-19 data sources relating to cases in New South Wales. They can be found in the following locations.

https://data.nsw.gov.au/data/dataset/covid-19-cases-by-location https://data.nsw.gov.au/data/dataset/nsw-covid-19-cases-by-likely-source-of-infection https://data.nsw.gov.au/data/dataset/nsw-covid-19-cases-by-age-range

As things currently stand, the datasets are not linked.

This set was last updated on 4 April 2020 at 6pm AEST.

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.