As of November 25, 2022 the number of COVID-19 cases in the Australian state of Victoria was at 40,482 people per 100,000 of the population. Since mid-2021, uncontained outbreaks in NSW and Victoria caused the government to move away from its former 'Covid zero' approach.

The economic impact of lockdown measures

In March of 2020, one survey showed that over 70 percent of Australians expected the economic outlook in Australia to get worse in the next three months. For most industries this prediction was correct, with the worst hit industries being hospitality, tourism, and gyms and fitness. However, some businesses flourished under the shift in pandemic consumer behavior with food delivery services, homewares and online gambling showing significant increases in consumption.

As at January 31, 2022 there had been a total of 2,580,386 COVID-19 cases confirmed in Australia. After maintaining a 'COVID zero' infection control policy from the beginning of the outbreak and much of 2021, subsequent outbreaks in the second half of 2021 saw the Australian government shift its policy away from trying to eradicate domestic cases of COVID-19 to a staged reopening of state and international boarders with infection control measures.

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.

As of September 5, 2022, the number of male 20 to 29 year olds diagnosed with COVID-19 in Australia had reached around 23,164 cases per 100,000 people. At the time, people 70-79 years of age had the lowest share of confirmed cases across males and females.

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.

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

This dataset is derived from the list of cases that is published at:

https://discover.data.vic.gov.au/dataset/all-victorian-sars-cov-2-cases-by-local-government-area-postcode-and-acquired-source

It is joined with the following two sources to get postcode and population data.

https://datapacks.censusdata.abs.gov.au/datapacks/ https://www.matthewproctor.com/australian_postcodes

Content

The dataset is expanded to include 0 days for every suburb (hence the size). I have attempted to replicate some relevant statistics, such as estimated replication number using formulas found here:

https://www.hbs.edu/ris/Publication%20Files/20-112_4278525d-ccf2-4f8a-b564-2e95d0e7ca5b.pdf

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

Australia’s first coronavirus case was discovered on ****************. The infected person was a man from the coronavirus epicenter, Wuhan, who had flown into Melbourne on the **** of January. Although some of the first infections in Australia can be attributed to travelers from China, by ********, infections attributed to people who had visited the United States and Italy had overtaken China.

Travel restrictions

With the rate of infection in China climbing steadily in early *************, the Australian government began to implement measures to slow the spread of the coronavirus. These measures involved social distancing and broad travel restrictions, including the closing of boarders to all foreign nationals arriving from China on **********. Overall, the number of travelers moving through airports across Australia had already begun to drop noticeably and Chinese students were one of the largest groups to be affected. By March, well after the 2020 school year had begun, over ** percent of Chinese university students with visas to study in Australia had not entered the country. This also added to economic concerns, with Chinese students representing just over ** billion Australian dollars in education export income in 2019.

Cruise ships

During the COVID-19 pandemic a number of cruise ships were hit by the virus, which spread amongst passengers and staff in the closed environments. The Diamond Princess, which was quarantined in Yokohama, Japan, had around *** Australians on board, of which at least a quarter contracted the coronavirus. The Ruby Princess was another cruise ship attributed to the spread of COVID-19 within Australia. On **************, the ship docked in Sydney harbor and ***** passengers disembarked. By ******** it was confirmed that *** passengers had contracted COVID-19 on the Ruby Princess.

WHO: COVID-2019: Number of Patients: Death: To-Date: Australia data was reported at 23,915.000 Person in 24 Dec 2023. This stayed constant from the previous number of 23,915.000 Person for 23 Dec 2023. WHO: COVID-2019: Number of Patients: Death: To-Date: Australia data is updated daily, averaging 2,674.500 Person from Jan 2020 (Median) to 24 Dec 2023, with 1430 observations. The data reached an all-time high of 23,915.000 Person in 24 Dec 2023 and a record low of 0.000 Person in 01 Mar 2020. WHO: COVID-2019: Number of Patients: Death: To-Date: 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). Due to some inclusions and exclusions of cases that are not properly reflected in WHO report, which are the result of the retrospective adjustments of national authorities, some current day “To-date” figures will not tally to the sum of previous day “To-date” cases and current day new reported cases. Figures with excluded cases are relatively lower compared to the previous day.

On September 30, 2020, there were 17 new reported confirmed cases of COVID-19 in Australia. Australia's daily new confirmed coronavirus cases peaked on July 30 with 746 new cases on that day. This was considered to be the second wave of coronavirus infections in Australia, with the first wave peaking at the end of March at 460 cases before dropping to less than 20 cases per day throughout May and most of June.

 A second wave

Australia’s second wave of coronavirus found its epicenter in Melbourne, after over a month of recording low numbers of national daily cases. Despite being primarily focused within a single state, clusters of coronavirus cases in Victoria soon pushed the daily number of recorded cases over that of the first wave, with well over double the number of deaths. As a result, the Victorian Government once again increased lockdown measures to limit movement and social interaction. At the same time the other states and territories closed or restricted movement across borders, with some of the strictest border closures taking place in Western Australian.

 Is Australia entering into a recession?

After narrowly avoiding a recession during the global financial crisis, by September 2020 Australia had recorded two consecutive quarters of economic decline, hailing the country’s first recession since 1991. This did not necessarily come as a surprise for many Australians who had already witnessed a rising unemployment rate throughout the second quarter of 2020 alongside ongoing restrictions on retail and hospitality trading. However, thanks to welfare initiatives like JobKeeper and a government stimulus payment supplementing many household incomes, the economic situation could have been much worse at this point.

Australia recorded 20553 Coronavirus Deaths since the epidemic began, according to the World Health Organization (WHO). In addition, Australia reported 11299954 Coronavirus Cases. This dataset includes a chart with historical data for Australia Coronavirus Deaths.

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/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

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.

From Monday 15 November 2021, NSW Health will no longer list case locations that a COVID-19 positive person has attended. This is due to a number of reasons, including high vaccination rates in the community. If you are told to self-isolate by NSW Health or get tested for COVID-19 at any time you must follow this advice.

This dataset provides COVID-19 case locations by date of known outbreak, location, address and action. This data is subject to change as further locations are identified. Locations are removed when 14 days have passed since the last known date that a confirmed case was associated with the location.

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 collected about confirmed case locations 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 on datansw@customerservice.nsw.gov.au

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.

On March 10, 2023, the Johns Hopkins Coronavirus Resource Center ceased collecting and reporting of global COVID-19 data. For updated cases, deaths, and vaccine data please visit the following sources:Global: World Health Organization (WHO)U.S.: U.S. Centers for Disease Control and Prevention (CDC)For more information, visit the Johns Hopkins Coronavirus Resource Center.This feature layer contains the most up-to-date COVID-19 cases and latest trend plot. It covers China, Canada, Australia (at province/state level), and the rest of the world (at country level, represented by either the country centroids or their capitals)and the US at county-level. Data sources: WHO, CDC, ECDC, NHC, DXY, 1point3acres, Worldometers.info, BNO, state and national government health departments, and local media reports. . The China data is automatically updating at least once per hour, and non-China data is updating hourly. This layer is created and maintained by the Center for Systems Science and Engineering (CSSE) at the Johns Hopkins University. This feature layer is supported by Esri Living Atlas team and JHU Data Services. This layer is opened to the public and free to share. Contact us.

This feature layer contains the most up-to-date COVID-19 cases and latest trend plot. It covers China, the US, Canada, Australia (at province/state level), and the rest of the world (at country level, represented by either the country centroids or their capitals). Data sources are WHO, US CDC, China NHC, ECDC, and DXY. The China data is automatically updating at least once per hour, and non China data is updating manually. This layer is created and maintained by the Center for Systems Science and Engineering (CSSE) at the Johns Hopkins University. This feature layer is supported by Esri Living Atlas team and JHU Data Services. This layer is opened to the public and free to share. Contact us.The data is processed from JHU Services and filtered for the Middle East and Africa Region.

Differences in COVID-19 testing and tracing across countries, as well as changes in testing within each country over time, make it difficult to estimate the true (population) infection rate based on the confirmed number of cases obtained through RNA viral testing. We applied a backcasting approach to estimate a distribution for the true (population) cumulative number of infections (infected and recovered) for 15 developed countries. Our sample comprised countries with similar levels of medical care and with populations that have similar age distributions. Monte Carlo methods were used to robustly sample parameter uncertainty. We found a strong and statistically significant negative relationship between the proportion of the population who test positive and the implied true detection rate. Despite an overall improvement in detection rates as the pandemic has progressed, our estimates showed that, as at 31 August 2020, the true number of people to have been infected across our sample of 15 countries was 6.2 (95% CI: 4.3–10.9) times greater than the reported number of cases. In individual countries, the true number of cases exceeded the reported figure by factors that range from 2.6 (95% CI: 1.8–4.5) for South Korea to 17.5 (95% CI: 12.2–30.7) for Italy.

Context

This is the data repository 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 Source

• 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/
• COVID Tracking Project: https://covidtracking.com/data. (US Testing and Hospitalization Data. We use the maximum reported value from "Currently" and - - - "Cumulative" Hospitalized for our hospitalization number reported for each state.)
• French Government: https://dashboard.covid19.data.gouv.fr/

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.

Context

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

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

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

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

Content

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

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

The data is available from 22 Jan, 2020.

Column Description

Main file in this dataset is covid_19_data.csv and the detailed descriptions are below.

covid_19_data.csv

• Sno - Serial number
• ObservationDate - Date of the observation in MM/DD/YYYY
• Province/State - Province or state of the observation (Could be empty when missing)
• Country/Region - Country of observation
• Last Update - Time in UTC at which the row is updated for the given province or country. (Not standardised and so please clean before using it)
• Confirmed - Cumulative number of confirmed cases till that date
• Deaths - Cumulative number of of deaths till that date
• Recovered - Cumulative number of recovered cases till that date

Apart from that these two files have individual level information

COVID_open_line_list_data.csv This file is originally obtained from this link

COVID19_line_list_data.csv This files is originally obtained from this link

Country level datasets If you are interested in knowing country level data, please refer to the following Kaggle datasets: South Korea - https://www.kaggle.com/kimjihoo/coronavirusdataset Italy -
https://www.kaggle.com/sudalairajkumar/covid19-in-italy

Acknowledgements

• Johns Hopkins University for making the data available for educational and academic research purposes
• MoBS lab - https://www.mobs-lab.org/2019ncov.html World Health Organization (WHO): https://www.who.int/
• DXY.cn. Pneumonia. 2020. http://3g.dxy.cn/newh5/view/pneumonia.
• BNO News: https://bnonews.com/index.php/2020/02/the-latest-coronavirus-cases/
• National Health Commission of the People’s Republic of China (NHC): http://www.nhc.gov.cn/xcs/yqtb/list_gzbd.shtml
• China CDC (CCDC): http://weekly.chinacdc.cn/news/TrackingtheEpidemic.htm
• Hong Kong Department of Health: https://www.chp.gov.hk/en/features/102465.html
• Macau Government: https://www.ssm.gov.mo/portal/ Taiwan CDC: https://sites.google.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

Inspiration

Some useful insi...

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.