This dataset is no longer being updated as of 7/1/2021. It is being retained on the Open Data Portal for its potential historical interest.

A list of retail stores, restaurants, personal services and other businesses open and closed during the COVID-19 pandemic. Also indicates if business is offering delivery, pick up or on-line sales.

Updated at least biweekly during Covid-19 Pandemic.

Local authorities have received and distributed funding to support small and medium businesses in England during coronavirus. The datasets cover schemes managed by local authorities: Additional Restrictions Support Grant (ARG) Restart Grant - closed June 2021 Local Restrictions Support Grants (LRSG) and Christmas support payments - closed 2021 Small Business Grants Fund (SBGF) - closed August 2020 Retail, Hospitality and Leisure Business Grants Fund (RHLGF) - closed August 2020 Local Authority Discretionary Grants Fund (LADGF) - closed August 2020 The spreadsheets show the total amount of money that each local authority in England: received from central government distributed to SMEs 20 December 2021 update We have published the latest estimates by local authorities for payments made under this grant programme: Additional Restrictions Grants (up to and including 28 November 2021) The number of grants paid out is not necessarily the same as the number of businesses paid. The data has not received full verification.

Context

Coronavirus infection is currently the most important health topic. It surely tested and continues to test to the fullest extent the healthcare systems around the world. Although big progress is made in handling this pandemic, a tremendous number of questions are needed to be answered. I hereby present to you the local Bulgarian COVID-19 dataset with some context. It could be used as a comparator because it stands out compared to other countries and deserves analysis.

Context for Bulgarian population: Population - 6 948 445 Median age - 44.7 years Aged >65 - 20.801 % Aged >70 - 13.272%

Summary of the results: - first pandemic wave was weak, probably because of the early state of emergency (5 days after the first confirmed case). Whether this was a good decision or it was too early and just postpone the inevitable is debatable. -healthcare system collapses (probably due to delayed measures) in the second and third waves which resulted in Bulgaria gaining the top ranks for mortality and morbidity tables worldwide and in the EU. - low percentage of vaccinated people results in a prolonged epidemic and delaying the lifting of the preventive measures.

Some of the important moments that should be considered when interpreting the data: 08.03.2020 - Bulgaria confirmed its first two cases. The government issued a nationwide ban on closed-door public events (first lockdown); 13.03.2020- after 16 reported cases in one day, Bulgaria declared a state of emergency for one month until 13.04.2020. Schools, shopping centres, cinemas, restaurants, and other places of business were closed. All sports events were suspended. Only supermarkets, food markets, pharmacies, banks, and gas stations remain open. 03.04.2020 - The National Assembly approved the government's proposal to extend the state of emergency by one month until 13.05.2020; 14.05.2020 - the national emergency was lifted, and in its place was declared a state of an emergency epidemic situation. Schools and daycares remain closed, as well as shopping centers and indoor restaurants; 18.05.2020 - Shopping malls and fitness centers opened; 01.06.2020 - Restaurants and gaming halls opened; 10.07.2020 - discos and bars are closed, the sports events are without an audience; 29.10.2020 - High school and college students are transitioning to online learning; 27.11.2020 - the whole education is online, restaurants, nightclubs, bars, and discos are closed (second lockdown 27.11 - 21.12); 05.12.2020 - the 14-day mortality rate is the highest in the world; 16.01.2021 - some of the students went back to school; 01.03.2021 - restaurants and casinos opened; 22.03.2021 - restaurants, shopping malls, fitness centers, and schools are closed (third lockdown for 10 days - 22.03 - 31.03); 19.04.2021 - children daycare facilities, fitness centers, and nightclubs are opened;

Content

This dataset consists of 447 rows with 29 columns and covers the period 08.03.2020 - 28.05.2021. In the beginning, there are some missing values until the proper statistical report was established.

Inspiration

A publication proposal is sent to anyone who wishes to collaborate. Based on the results and the value of the findings and the relevance of the topic it is expected to publish: - in a local journal (guaranteed); - in a SCOPUS journal (highly probable); - in an IF journal (if the results are really insightful).

The topics could be, but not limited to: - descriptive analysis of the pandemic outbreak in the country; - prediction of the pandemic or the vaccination rate; - discussion about the numbers compared to other countries/world; - discussion about the government decisions; - estimating cut-off values for step-down or step-up of the restrictions.

Error or query reporting

If you find an error, have a question, or wish to make a suggestion, I encourage you to reach me.

The Project involved getting photos of closed due to COVID signs in shops and businesses in York UK.

One column of "text" includes all the transcripts of the signs with phone numbers removed.

The global economy has been hard hit by the COVID-19 pandemic. Many countries are experiencing a severe and destructive recession. A significant number of firms and businesses have gone bankrupt or been scaled down, and many individuals have lost their jobs. The main goal of this study is to support policy- and decision-makers with additional and real-time information about the labor market flow using Twitter data. We leverage the data to trace and nowcast the unemployment rate of South Africa during the COVID-19 pandemic. First, we create a dataset of unemployment-related tweets using certain keywords. Principal Component Regression (PCR) is then applied to nowcast the unemployment rate using the gathered tweets and their sentiment scores. Numerical results indicate that the volume of the tweets has a positive correlation, and the sentiments of the tweets have a negative correlation with the unemployment rate during and before the COVID-19 pandemic. Moreover, the now-casted unemployment rate using PCR has an outstanding evaluation result with a low Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Symmetric MAPE (SMAPE) of 0.921, 0.018, 0.018, respectively and a high R2-score of 0.929.

Context

Please upvote the dataset if you find it useful!

NOTE: I have update the data set to match the country &province names to match the country names used in the forecasting competition https://www.kaggle.com/c/covid19-global-forecasting-week-3.

It is assumed that this data will be useful to help predict or forecast the total numbers of confirmed cases and deaths. A lockdown is started to help retard the spread of the virus.

Let me know if you find any mistakes so I can correct them.

Content

The data was acquired by going through each country that had at least 1 confirmed case. Searching for news articles, wikipedia and government websites to identify when the lockdown was started. A lockdown is assumed when schools/universities and any non-essential businesses are closed.

Inspiration

There are three main questions this dataset hopes to help try and solve: 1. provide context to help forecast/predict number of cases 2. provide context to help forecast/predict number of deaths 3. identify the effectiveness of a lockdown

Red Flags for Companies using Sentiment Score

There are multiple companies that are on the verge of bankruptcy due to the ongoing pandemic of COVID-19. But a company going bankrupt doesn't happen overnight. There is a chain of sequence that leads it to Bankruptcy. This chain of sequences is called Red Flags for the company.

Data

The below-given data consists of 1400+ Companies with their sentiment score for each quarter for the past 4 years. Companies with the highest negative sentiment score are assigned a score of -100 and the ones with the highest positive sentiment score are assigned a score of +100. The goal of this exercise is to find the top 100 companies which are consistently having a negative sentiment score for multiple quarters of the year and help us find those early signals for those companies (Red Flags) using Machine Learning algorithms.

Here is the data of five major airlines in the USA. I have not uploaded the entire dataset here because it is easily gettable from yahoofinance.com. It contains data from Jun 1, 2016, to Jun 1, 2020. Anyone can explore this dataset and could find out the impact of COVID-19 on these airlines. For instance, how fast the market went down, changes in the volume of stock, etc.

DAL = Delta Airlines AAL = American Airlines LUV = Southwest Airlines SAVE = Spirit Airlines UAL = United Airlines

Columns: Date: Shows date except for the day when the market is closed. Open: Open price for a stock during the market hours. High: Highest price during the market hours. Low: Lowest price during the market hours. Close: Price at the time when the market is closed. Volume: Number of traded shares.

Monthly enquiry figures by branch library for April 2008 to present. Additional information In 2020, all libraries closed from 19 March included due to the coronavirus outbreak. Until 8 April 2018 an enquiry was defined as any question asked of library staff by a member of the public.What we record as an enquiry was first redefined in a narrower way on 9 April 2018, then reviewed several times over the following months. From 1 September 2018 only questions regarding the following are counted as enquiries: assistance with choosing books; assistance with photocopying and printing; catalogue enquiry; check in, check out and renewal; CSC closed; e-books/magazines and other online resources; event enquiry; fax enquiries; heritage enquiry; family history; heritage organisations; local history maps; local history newspapers; heritage online resources support; local history photographs; heritage stack/strongroom; holds and ILL; Home Delivery Service; Internet Taster Session; IT support; job search; lost and found property; membership enquiries; opening times enquiry; PC bookings and PC additional time requests; reading groups; room bookings; sales; stack enquiry; Summer Reading Challenge; tourist information/local directions; Universal Credit support booking; view Electoral Register; visa application support; business enquiry; IP enquiry; email enquiries to Business & IP Centre Newcastle; social media enquiries to Business & IP Centre Newcastle; email enquiries to Newcastle Libraries; social media enquiries to Newcastle Libraries; signposting to partners within the building; building control; complaints; FOI; Council website issues; adult social care; arts and culture; benefits; children social care; council tax; crisis support; democratic services; economic development; education and skills services; electoral registration; Envirocall; finance; garden waste; hospitality services; HR and payroll; income and recovery; insurance services; job shop; legal services; leisure services; planning and development; private rented service; property services; registration services; regulatory services; strategic housing; street wardens; Your Homes Newcastle. Mobile Library (decommissioned April 2012). BIPC is the Business & IP Centre based at City Library; data from April 2013. Blakelaw temporary closure, relocation inside Leisure United Blakelaw 12/07/2024. Temporary location for the 6 week's holiday, no enquiry figures collected from this date. Blank means no data available.