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.

In March 2020, "COVID 19" was the fastest-growing coronavirus-related search query on Google worldwide. The search term grew ***** percent in volume compared to the previous months. "Coronavirus tips" was ranked second with a ***** percent month-over-month search volume increase.

This data serves as a Kaggle mirror of the COVID-19 mobility report published by Google. This will be updated regularly as Google pushes out additional reports.

For reference: https://www.google.com/covid19/mobility/

Between February 10 and March 27, 2020 the highest number of Google searches with the terms coronavirus or COVID-19 were recorded in Switzerland. That was the day on which the Swiss Federal Council decided on special measures to contain the virus in the country. After March 16, while Google searches about the disease once again decreased, media reporting about the coronavirus (COVID-19) peaked on March 19.

This dataset contains the stock prices of Google since the COVID-19 pandemic began. There are 7 columns in this dataset:

The chart shows the number of accounts suspended by Google for Coronavirus-related policy violations, by type of account, from January 2020 until April 2022. The data used are those reported by Google under the European Union Code of Practice on Disinformation monitoring programme. Overall, the data shows that Germany has the highest number of accounts suspended (589 accounts), followed by France (328) and Spain (224).

According to data from Pi Datametrics, Google search volumes from January to April 2020 increased across almost all categories compared to the same period in 2019. The coronavirus outbreak may have led to the rise in search volume for some of the categories, including "Things to do at home", "Food & drink", and "Fitness equipment & classes".

I scraped data from Google Trends relating to serach queries of the virus 'Coronavirus' and 'COVID-19'. This is based on several research articles showing that google trends has the potential to help with the prediction and detection of disease outbreaks, such as the 2015 Zika outbreak, Influenza and Dengue fever. Other search queries such as the symptoms of the virus can in turn be used to make predictions as they should be correlated with people feeling unwell and presenting with symptoms as well as physician visits. I have not yet scraped the data for search queries for symptoms keywords, but would highly encourage someone else to do it!

Values are calculated on a scale from 0 to 100, where 100 is the location with the most popularity as a fraction of total searches in that location, a value of 50 indicates a location which is half as popular. A value of 0 indicates a location where there was not enough data for this term. Note: A higher value means a higher proportion of all queries, not a higher absolute query count. So a tiny country where 80% of the queries are for "covid19" will get twice the score of a giant country where only 40% of the queries are for "covid19".

UPDATED: Keywords 'lockdown', 'symptoms of coronavirus', 'social distancing'

The coronavirus (COVID-19) outbreak caused major changes in the everyday lives of people and also in the way they spent their free time in Hungary. Compared to data from March 2019, there had been a 356 percent rise in the interest for watching series in Hungary as of March 2020, based on Google search queries.For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

Data that is collected at the individual-level from mobile phones is typically aggregated to the population-level for privacy reasons. If we are interested in answering questions regarding the mean, or working with groups appropriately modeled by a continuum, then this data is immediately informative. However, coupling such data regarding a population to a model that requires information at the individual-level raises a number of complexities. This is the case if we aim to characterize human mobility and simulate the spatial and geographical spread of a disease by dealing in discrete, absolute numbers. In this work, we highlight the hurdles faced and outline how they can be overcome to effectively leverage the specific dataset: Google COVID-19 Aggregated Mobility Research Dataset (GAMRD). Using a case study of Western Australia, which has many sparsely populated regions with incomplete data, we firstly demonstrate how to overcome these challenges to approximate absolute flow of people around a transport network from the aggregated data. Overlaying this evolving mobility network with a compartmental model for disease that incorporated vaccination status we run simulations and draw meaningful conclusions about the spread of COVID-19 throughout the state without de-anonymizing the data. We can see that towns in the Pilbara region are highly vulnerable to an outbreak originating in Perth. Further, we show that regional restrictions on travel are not enough to stop the spread of the virus from reaching regional Western Australia. The methods explained in this paper can be therefore used to analyze disease outbreaks in similarly sparse populations. We demonstrate that using this data appropriately can be used to inform public health policies and have an impact in pandemic responses.

Context

In partnership with the Harvard Global Health Institute, Google Cloud is releasing the COVID-19 Public Forecasts to serve as an additional resource for first responders in healthcare, the public sector, and other impacted organizations preparing for what lies ahead. These forecasts are available for free and provide a projection of COVID-19 cases, deaths, and other metrics over the next 14 days for US counties and states. For more info, see https://cloud.google.com/blog/products/ai-machine-learning/google-cloud-is-releasing-the-covid-19-public-forecasts and https://storage.googleapis.com/covid-external/COVID-19ForecastWhitePaper.pdf

Content

A projection of COVID-19 cases, deaths, and other metrics over the next 14 days for US counties and states

Acknowledgements

Released on BigQuery by Google Cloud:

https://cloud.google.com/blog/products/ai-machine-learning/google-cloud-is-releasing-the-covid-19-public-forecasts

https://pantheon.corp.google.com/marketplace/product/bigquery-public-datasets/covid19-public-forecasts

This dataset is maintained by the European Centre for Disease Prevention and Control (ECDC) and reports on the geographic distribution of COVID-19 cases worldwide. This data includes COVID-19 reported cases and deaths broken out by country. This data can be visualized via ECDC’s Situation Dashboard . More information on ECDC’s response to COVID-19 is available here . 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 . This dataset is hosted in both the EU and US regions of BigQuery. See the links below for the appropriate dataset copy: US region EU region This dataset has significant public interest in light of the COVID-19 crisis. 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. Users of ECDC public-use data files must comply with data use restrictions to ensure that the information will be used solely for statistical analysis or reporting purposes.

Background: Alongside the COVID-19 pandemic, government authorities around the world have had to face a growing infodemic capable of causing serious damages to public health and economy. In this context, the use of infoveillance tools has become a primary necessity.Objective: The aim of this study is to test the reliability of a widely used infoveillance tool which is Google Trends. In particular, the paper focuses on the analysis of relative search volumes (RSVs) quantifying their dependence on the day they are collected.Methods: RSVs of the query coronavirus + covid during February 1—December 4, 2020 (period 1), and February 20—May 18, 2020 (period 2), were collected daily by Google Trends from December 8 to 27, 2020. The survey covered Italian regions and cities, and countries and cities worldwide. The search category was set to all categories. Each dataset was analyzed to observe any dependencies of RSVs from the day they were gathered. To do this, by calling i the country, region, or city under investigation and j the day its RSV was collected, a Gaussian distribution Xi=X(σi,x¯i) was used to represent the trend of daily variations of xij=RSVsij. When a missing value was revealed (anomaly), the affected country, region or city was excluded from the analysis. When the anomalies exceeded 20% of the sample size, the whole sample was excluded from the statistical analysis. Pearson and Spearman correlations between RSVs and the number of COVID-19 cases were calculated day by day thus to highlight any variations related to the day RSVs were collected. Welch’s t-test was used to assess the statistical significance of the differences between the average RSVs of the various countries, regions, or cities of a given dataset. Two RSVs were considered statistical confident when t

The chart shows the number of Coronavirus-related ads removed or blocked by Google for policy violations, since January 2020 until April 2022. The data used are those reported by Google under the European Union Code of Practice on Disinformation monitoring programme.

Google searches for "Netflix" in Colombia increased 30 percent on March 15, 2020, compared to the average recorded on Sundays from January 19 and March 8 in the same year. But online searches for restaurants and movie theatres in the country decreased 19 and 41 percent respectively. The change was linked to the outbreak of the novel coronavirus (SARS-CoV-2), which causes the COVID-19. The disease also was predicted to lead to a 400 percent increase in the usage of WhatsApp in Colombia.

The MOOD project (MOnitoring Outbreak events for Disease surveillance in a data science context. H2020) has geo-referenced the data Google has published as a series of PDF files presenting reports on national and subnational human mobility levels relative to a baseline data of late January 2020. The details and the PDF files can be found at https://www.google.com/covid19/mobility/.More detail on these files can be found at https://www.moodspatialdata.com/humanmobilityforcovid19 The first set of data were released on April 2 2020 and have been revised weekly since then. The maps now utilise the CSV data released by Google. Please note that the maps figures use a mean of the previous three days, while the Google PDFs use a single days data so there will be differences between values in our maps when compare to the Google PDFs.The authors have extracted the majority of these data into a series of excel spreadsheets. Each worksheet provides the data for % change in numbers of records at various types of location categories illustrated by: retail and recreation, grocery and pharmacy, parks and beaches, transit stations, workplaces and residential (columns f to K). A second set of columns calculates the difference of each value from the mean values for each category (columns L to P) Columns A to E contain geographical details. Column Q contains the names used to link to a mapping file.There are separate worksheets for the date of the data from each dated release (e.g. 2903, 0504 etc.) and separate worksheets calculating the changes between specific dates.A second spreadsheet has been added calculating the 3 day moving mean of each day from the 15th of February. Each day is referenced by the Gregorian calendar day count. So day 48 = Feb 17th.The maps (for EU & Global) display these data. We provide 600 dpi jpegs of the Global (“WD”) and European (“EU”) mapped values at the latest date available, for each of the mobility categories: retail and recreation (“retrec”) , grocery and pharmacy (“grocphar”) , parks (“parks”) , transit stations (“transit”), residential (“resid”) and workplaces (“work”). We also provide maps of the changes from the previous week (“ch”).All data extracting and subsequent processing have been carried out by ERGO (Environmental Research Group Oxford, c/o Dept Zoology, University of Oxford) on behalf of the MOOD H2020 project. Data will be periodically updated. Additional maps can be obtained on request to the authors.

This data from USAFacts provides US COVID-19 case and death counts by state and county. This data is sourced from the CDC, and state and local health agencies. For more information, see the USAFacts site on the Coronavirus. Interactive data visualizations are also available via USAFacts. 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 . This dataset has significant public interest in light of the COVID-19 crisis. 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.

Motivation

This repository includes:
1) Data scraper of Google, Apple and Waze Mobility data
2) Preprocessed mobility reports in different formats
3) Merged mobility reports in summary files

License

See LICENSE.txt

About data

About Google COVID-19 Community Mobility Reports

About Apple COVID-19 Mobility Trends Reports

About Waze COVID-19 local driving trends

Credit

If you use this dataset, please cite original data sources:

1. Google LLC "Google COVID-19 Community Mobility Reports". https://www.google.com/covid19/mobility/ Accessed:

2. Apple Inc. "Apple COVID-19 Mobility Trends Reports". https://www.apple.com/covid19/mobility Accessed:

3. Waze Ltd "Waze COVID-19 Impact Dashboard". https://www.waze.com/covid19 Accessed:

Dataset

This dataset was created by Samar Sengar

Contents

This is the US Coronavirus data repository from The New York Times . This data includes COVID-19 cases and deaths reported by state and county. The New York Times compiled this data based on reports from state and local health agencies. More information on the data repository is available here . For additional reporting and data visualizations, see The New York Times’ U.S. coronavirus interactive site . 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 . This dataset has significant public interest in light of the COVID-19 crisis. 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. Users of The New York Times public-use data files must comply with data use restrictions to ensure that the information will be used solely for noncommercial purposes.