Introduction

The dataset contains COVID-19 statistics for the top countries currently affected by the virus. The data was scraped from two popular sites maintaining daily updates on the spread of COVID-19 - https://www.worldometers.info/ and https://en.wikipedia.org/wiki/COVID-19_pandemic

Contents

There are two kinds of csv files. One type of files are country wise daily statistics on COVID-19 spread. The data for the following countries is available:-

• United States
• Russia
• Brazil
• Pakistan
• Germany
• Peru
• Spain
• Belgium
• Italy
• Belarus
• India
• Qatar
• Mexico
• Turkey
• Sweden
• Saudi Arabia
• Iran
• Canada
• Chile
• China
• France
• Ecuador
• Bangladesh

For each of these countries, the dataset contains the following columns:-

• Date
• total cases
• daily cases
• active cases
• total deaths
• daily deaths

The second type of file is the overall statistics which contains statistics for all the countries affected in the world. This dataset contains the following columns:-

• country name
• total cases
• total recoveries
• total deaths

This dataset provides values for CORONAVIRUS DEATHS reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

This dataset is daily time series data of all the COVID 19 confirmed, recovered and death cases across different nations in the world. It consists of columns:

Date <- From 22/01/2020 to the current date Country/Region <- Countries affected by Covid-19 Province/State <- Specific states in the Country Lat <- Latitude of the Country Long <- Longitude of the Country Confirmed <- Total Number of Confirmed Cases per day Recovered <- Total Number of Recovery Cases per day Deaths <- Total Number of Death Cases per day

Original Data Source <- https://raw.githubusercontent.com/datasets/covid-19/master/data/time-series-19-covid-combined.csv

Overview

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). 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, Mapping 2019-nCoV (https://systems.jhu.edu/research/public-health/ncov/), and included data sources are listed here: https://github.com/CSSEGISandData/COVID-19

Sample Query 1

How many confirmed COVID-19 cases were there in the US, by state? This query determines the total number of cases by province in February. A "province_state" can refer to any subset of the US in this particular dataset, including a county or state. SELECT province_state, confirmed AS feb_confirmed_cases, FROM bigquery-public-data.covid19_jhu_csse.summary WHERE country_region = "US" AND date = '2020-02-29' ORDER BY feb_confirmed_cases desc

Sample Query 2

Which countries with the highest number of confirmed cases have the most per capita? This query joins the Johns Hopkins dataset with the World Bank's global population data to determine which countries among those with the highest total number of confirmed cases have the most confirmed cases per capita.

with country_pop AS( SELECT IF(country = "United States","US",IF(country="Iran, Islamic Rep.","Iran",country)) AS country, year_2018 FROM bigquery-public-data.world_bank_global_population.population_by_country)

SELECT cases.date AS date, cases.country_region AS country_region, SUM(cases.confirmed) AS total_confirmed_cases, SUM(cases.confirmed)/AVG(country_pop.year_2018) * 100000 AS confirmed_cases_per_100000 FROM bigquery-public-data.covid19_jhu_csse.summary cases JOIN country_pop ON cases.country_region LIKE CONCAT('%',country_pop.country,'%') WHERE cases.country_region = "US" AND country_pop.country = "US" AND cases.date = DATE_SUB(current_date(),INTERVAL 1 day) GROUP BY country_region, date

UNION ALL

SELECT cases.date AS date, cases.country_region AS country_region, SUM(cases.confirmed) AS total_confirmed_cases, SUM(cases.confirmed)/AVG(country_pop.year_2018) * 100000 AS confirmed_cases_per_100000 FROM bigquery-public-data.covid19_jhu_csse.summary cases JOIN country_pop ON cases.country_region LIKE CONCAT('%',country_pop.country,'%') WHERE cases.country_region = "France" AND country_pop.country = "France" AND cases.date = DATE_SUB(current_date(),INTERVAL 1 day) GROUP BY country_region, date

UNION ALL

SELECT cases.date AS date, cases.country_region AS country_region, SUM(cases.confirmed) AS total_confirmed_cases, SUM(cases.confirmed)/AVG(country_pop.year_2018) * 100000 AS confirmed_cases_per_100000 FROM bigquery-public-data.covid19_jhu_csse.summary cases JOIN country_pop ON cases.country_region LIKE CONCAT('%',country_pop.country,'%') WHERE cases.country_region = "China" AND country_pop.country = "China" AND cases.date = DATE_SUB(current_date(),INTERVAL 1 day)

GROUP BY country_region, date

UNION ALL

SELECT cases.date AS date, cases.country_region AS country_region, cases.confirmed AS total_confirmed_cases, cases.confirmed/country_pop.year_2018 * 100000 AS confirmed_cases_per_100000 FROM bigquery-public-data.covid19_jhu_csse.summary cases JOIN country_pop ON cases.country_region LIKE CONCAT('%',country_pop.country,'%') WHERE cases.country_region IN ("Italy", "Spain", "Germany", "Iran") AND cases.date = DATE_SUB(current_date(),INTERVAL 1 day) ORDER BY confirmed_cases_per_100000 desc

Dataset source

JHU CSSE

Update frequency

Daily

The World Health Organization reported 766440796 Coronavirus Cases since the epidemic began. In addition, countries reported 6932591 Coronavirus Deaths. This dataset provides - World Coronavirus Cases- actual values, historical data, forecast, chart, statistics, economic calendar and news.

The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.

Since late January, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.

We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak.

The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository.

The Novel Coronavirus (COVID-19) daily data of confirmed cases for affected countries and provinces of China reported between 31st December 2019 and 31st May 2020. The data was collected from the European Centre for Disease Prevention and Control (ECDC), and John Hopkin CSSA.

The monthly mean temperature of February to May 2020 of capital cities for the various nations.

Context

COVID19 is spreading across the globe and this data set help in analyzing to what extent the pandemic has affected different countries.

Content

This data set contains the total number of COVID 19 cases reported per country till 26 June 2020 There are 6 columns. date, location, total_cases, new_cases, total_deaths, new_deaths

Acknowledgements

The data was collected from https://ourworldindata.org/coronavirus-data

Inspiration

Lets try to create a bar chart race from this data for the total cases reported by country.

Background : Substantial differences between countries were observed in terms of Covid-19 death tolls during the past two years. It was of interest to find out how the epidemiologic and/or demographic history of the population may have had a role in the high prevalence of the Covid-19 in some countries. Objective : This observational study aimed to investigate possible relations between Covid-19 death numbers in 39 countries and the prepandemic history of epidemiologic and demographic conditions. Methods : We sought the Covid-19 death toll in 39 countries in Europe, America, Africa, and Asia. Records (2019) of epidemiologic (Cancer, Alzheimer's disease) and demographic (natality, mortality, and fetility rates, percentage of people aged 65 and over) parameters as well as data on alcohol intake per capita were retrieved from official web pages. Data was analysed by simple linear or polynomial regression by the mean of Microsoft Excell software (2016). Results : When Covid-19 death numbers were plotted against the geographic latitude of each country, a bell-shaped curve was obtained for both the first and second years (coefficient of determination R2=0.38) of the pandemic. In a similar manner, bell-shaped curves were obtained when latitudes were plotted against the scores of (cancer plus Alzheimer's disease, R² = 0,65,), the percentage of advanced age (R² = 0,52,) and the alcohol intake level (R² = 0,64,). Covid-19 death numbers were positively correlated to the scores of (cancer plus Alzheimer's disease) (R2= 0.41, P= 1.61x10-5), advanced age (R2= 0.38, P= 4.09x10-5) and alcohol intake (R2= 0.48, P= 1.55x10-6). Instead, inverted bell-shaped curves were obtained when latitudes were plotted against the birth rate/mortality rate ratio (R² = 0,51) and the fetility rate (R² = 0,33). In addition, Covid-19 deaths were negatively correlated with the birth rate/mortality rate ratio (R2= 0.67) and fertility rate (R2= 0.50). Conclusion : The results show that the 39 countries in both hemisphers in this study have different patterns of epidemiologic and demographic factors, and that the negative history of epidemiologic and demographic factors of the northern hemisphere countries, as well as their high alcohol intake, were very correlated with their Covid-19 death tolls. Hence, also nutritional habits may have had a role in the general health status of people in regard to their immunity against the coronavirus.

Data Overview

This repository contains spatiotemporal data from many official sources for 2019-Novel Coronavirus beginning 2019 in Hubei, China ("nCoV_2019") You may not use this data for commercial purposes. If there is a need for commercial use of the data, please contact Ginkgo Biosecurity, the biosecurity and public health unit of Ginkgo Bioworks at help-epi-modeling@ginkgobioworks.com to obtain a commercial use license.

The incidence data are in a CSV file format. One row in an incidence file contains a piece of epidemiological data extracted from the specified source.

The file contains data from multiple sources at multiple spatial resolutions in cumulative and non-cumulative formats by confirmation status. To select a single time series of case or death data, filter the incidence dataset by source, spatial resolution, location, confirmation status, and cumulative flag.

Data are collected, structured, and validated by Ginkgo's digital surveillance experts. The data structuring process is designed to produce the most reliable estimates of reported cases and deaths over space and time. The data are cleaned and provided in a uniform format such that information can be compared across multiple sources. Data are collected at the time of publication in the highest geographic and temporal resolutions available in the original report.

This repository is intended to provide a single access point for data from a wide range of data sources. Data will be updated periodically with the latest epidemiological data. Ginkgo Biosecurity maintains a database of epidemiological information for over three thousand high-priority infectious disease events (please note: this database was previously maintained by Metabiota; the team responsible joined Ginkgo Biosecurity in August 2022. When using the database, please cite Ginkgo Biosecurity and refer to this repository). Please contact us (help-epi-modeling@ginkgobioworks.com) if you are interested in licensing the complete dataset.

Cumulative vs. Non-Cumulative Incidence

Reporting sources provide either cumulative incidence, non-cumulative incidence, or both. If the source only provides a non-cumulative incidence value, the cumulative values are inferred using prior reports from the same source. Use the CUMULATIVE FLAG variable to subset the data to cumulative (TRUE) or non-cumulative (FALSE) values.

Case Confirmation Status

The incidence datasets include the confirmation status of cases and deaths when this information is provided by the reporting source. Subset the data by the CONFIRMATION_STATUS variable to either TOTAL, CONFIRMED, SUSPECTED, or PROBABLE to obtain the data of your choice.

Total incidence values include confirmed, suspected, and probable incidence values. If a source only provides suspected, probable, or confirmed incidence, the total incidence is inferred to be the sum of the provided values. If the report does not specify confirmation status, the value is included in the "total" confirmation status value.

The data provided under the "Multisource Fusion" often does not include suspected incidence due to inconsistencies in reporting cases and deaths with this confirmation status.

Outcome - Cases vs. Deaths

The incidence datasets include cases and deaths. Subset the data to either CASE or DEATH using the OUTCOME variable. It should be noted that deaths are included in case counts.

Spatial Resolution

Data are provided at multiple spatial resolutions. Data should be subset to a single spatial resolution of interest using the SPATIAL_RESOLUTION variable.

Information is included at the finest spatial resolution provided to the original epidemic report. We also aggregate incidence to coarser geographic resolutions. For example, if a source only provides data at the province-level, then province-level data are included in the dataset as well as country-level totals. Users should avoid summing all cases or deaths in a given country for a given date without specifying the SPATIAL_RESOLUTION value. For example, subset the data to SPATIAL_RESOLUTION equal to "AL0” in order to view only the aggregated country level data.

There are differences in administrative division naming practices by country. Administrative levels in this dataset are defined using the Google Geolocation API (https://developers.google.com/maps/documentation/geolocation/). For example, the data for the 2019-nCoV from one source provides information for the city of Beijing, which Google Geolocations indicates is a "locality.” Beijing is also the name of the municipality where the city Beijing is located. Thus, the 2019-nCoV dataset includes rows of data for both the city Beijing, as well as the municipality of the same name. If additional cities in the Beijing municipality reported data, those data would be aggregated with the city Beijing data to form the municipality Beijing data.

Sources

Data sources in this repository were selected to provide comprehensive spatiotemporal data for each outbreak. Data from a specific source can be selected using the SOURCE variable.

In addition to the original reporting sources, Ginkgo Biosecurity compiles multiple sources to generate the most comprehensive view of an outbreak. This compilation is stored in the database under the source name "Multisource Fusion". The purpose of generating this new view of the outbreak is to provide the most accurate and precise spatiotemporal data for the outbreak. At this time, Ginkgo Biosecurity does not incorporate unofficial - including media - sources into the "Multisource Fusion" dataset.

Quality Assurance

Data are collected by a team of digital surveillance experts and undergo many quality assurance tests. After data are collected, they are independently verified by at least one additional analyst. The data also pass an automated validation program to ensure data consistency and integrity.

NonCommercial Use License

• Creative Commons License Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)

• This is a human-readable summary of the Legal Code.

• You are free:

  to Share — to copy, distribute and transmit the work to Remix — to adapt the work

• Under the following conditions:

  Attribution — You must attribute the work in the manner specified by the author or licensor (but not in any way that suggests that they endorse you or your use of the work).

  Noncommercial — You may not use this work for commercial purposes.

  Share Alike — If you alter, transform, or build upon this work, you may distribute the resulting work only under the same or similar license to this one.

• With the understanding that:

  Waiver — Any of the above conditions can be waived if you get permission from the copyright holder.

  Public Domain — Where the work or any of its elements is in the public domain under applicable law, that status is in no way affected by the license.

  Other Rights — In no way are any of the following rights affected by the license: Your fair dealing or fair use rights, or other applicable copyright exceptions and limitations; The author's moral rights; Rights other persons may have either in the work itself or in how the work is used, such as publicity or privacy rights. Notice — For any reuse or distribution, you must make clear to others the license terms of this work. The best way to do this is with a link to this web page.

For details and the full license text, see http://creativecommons.org/licenses/by-nc-sa/3.0/

Liability

The information is provided “AS IS” and Concentric makes no representations or warranties, express or implied, of any type whatsoever including, without limitation, title, noninfringement, accuracy, completeness, merchantability, or fitness for any particular purpose. Use of proprietary information shall be at the user’s own risk, and Concentric assumes no liability or obligation to the user as a result of use.

Ginkgo Biosecurity shall in no event be liable for any decision taken by the user based on the data made available. Under no circumstances, shall Ginkgo Biosecurity be liable for any damages (whatsoever) arising out of the use or inability to use the database. The entire risk arising out of the use of the database remains with the user.

Context

Coronaviruses are a large family of viruses which may cause illness in animals or humans. In humans, several coronaviruses are known to cause respiratory infections ranging from the common cold to more severe diseases such as Middle East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS). The most recently discovered coronavirus causes coronavirus disease COVID-19 - WHO

People can catch COVID-19 from others who have the virus. This has been spreading rapidly around the world and Italy is one of the most affected country.

On March 8, 2020 - Italy’s prime minister announced a sweeping coronavirus quarantine early Sunday, restricting the movements of about a quarter of the country’s population in a bid to limit contagions at the epicenter of Europe’s outbreak. - TIME

Content

This dataset is from https://github.com/pcm-dpc/COVID-19 collected by Sito del Dipartimento della Protezione Civile - Emergenza Coronavirus: la risposta nazionale

This dataset has two files

• covid19_italy_province.csv - Province level data of COVID-19 cases
• covid_italy_region.csv - Region level data of COVID-19 cases

Acknowledgements

Data is collected by Sito del Dipartimento della Protezione Civile - Emergenza Coronavirus: la risposta nazionale and is uploaded into this github repo.

Dashboard on the data can be seen here. Picture courtesy is from the dashboard.

Inspiration

Insights on * Spread to various regions over time * Try to predict the spread of COVID-19 ahead of time to take preventive measures

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). The data include the location and number of confirmed COVID-19 cases, deaths, and recoveries for all affected countries, aggregated at the appropriate province/state. It was developed to enable researchers, public health authorities and the general public to track the outbreak. Additional information is available in the blog post, Mapping 2019-nCoV , and included data sources are listed here . For publications that use the data, please cite the following publication Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Inf Dis. 20(5):533-534. doi: 10.1016/S1473-3099(20)30120-1" 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.

Analysis of ‘Indonesia-Coronavirus’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/ardisragen/indonesia-coronavirus-cases on 30 September 2021.

--- Dataset description provided by original source is as follows ---

Context

COVID-19 has infected many people in Indonesia, and the number of confirmed cases is increasing exponentially. Indonesia has raised its coronavirus alert to the "Darurat Nasional (National Emergency)" until 29 May 2020. The Java island, especially Jakarta, the capital city of Indonesia, is the most affected region by the coronavirus.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2849532%2Ff46e130bad5d4e74a8835ca057dd05ca%2Facc.png?generation=1584939612835429&alt=media" alt="">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2849532%2F93b53d1b6601da74041f41ea4ba227f6%2Fcases.png?generation=1584938551413887&alt=media" alt="">

Following are the list of available online portals announce the information of COVID-19, from the public community and provincial (regional) government website in Indonesia.

1. https://www.covid19.go.id/situasi-virus-corona/, by Indonesian National Board for Disaster Management.
2. https://kawalcovid19.id/, by Kawal Covid-19 Indonesia community.
3. https://corona.jakarta.go.id/, Jakarta tanggap Covid-19 by Pemda DKI Jakarta.
4. https://pikobar.jabarprov.go.id/, Pusat Informasi & Koordinasi COVID-19, by Pemprov Jawa Barat.
5. https://corona.jatengprov.go.id/, Jawa Tengah Tanggap COVID-19, by Pemprov Jawa Tengah.
6. https://corona.sumbarprov.go.id/, Sumbar Tanggap Corona, by Pemprov Sumatera Barat.
7. http://corona.jogjaprov.go.id/, Yogyakarta Tanggap Covid-19, by Pemprov DIY.
8. https://covid19.bandung.go.id/. Pusat Informasi & Koordinasi COVID-19 Kota Bandung.

We make a structured dataset based on the report materials in these portals. Thus, the research community can apply recent AI and statistical techniques to generate new insights in support of the ongoing fight against this infectious disease in Indonesia.

Current State

Dataset 1) Total Confirmed Positive Cases 2) Google Trend Related keywords 3) Patient Epidemiological Data 4) Daily Case Statistics 5) Case per Province 6) Case in Jakarta Capital City 7) Daily New Confirmed Cases in Each Province (Timeline)

Kernel 1) Predicting Coronavirus Positive Cases in Indonesia 2) Visualization & Analysis of Covid-19 in Indonesia 3) Logistic Model for Indonesia COVID-19 4) DataSet Characteristics of Corona patients in several countries, including Indonesia 5) Novel Corona Virus (Covid-19) Indonesia EDA 6) Simple Visualization and Forecasting 7) Characteristics of Corona patients DS

Related Publication 1) Response to Covid-19: Data Analytics and Transparency, Koderea Talks, 18 March 2020, https://www.researchgate.net/publication/340003505_Response_to_Covid-19_Data_Analytics_and_Transparency 2) Covid-19 Data Science, ID Institute Obrolin Data Coronavirus, 24 March 2020, https://www.researchgate.net/publication/340116231_IDInstitute_Covid-19_Data_Science

Other Country Level Datasets

• South Korea - https://www.kaggle.com/kimjihoo/coronavirusdataset
• Italy - https://www.kaggle.com/sudalairajkumar/covid19-in-italy
• Brazil - https://www.kaggle.com/unanimad/corona-virus-brazil
• USA - https://www.kaggle.com/sudalairajkumar/covid19-in-usa
• France - https://www.kaggle.com/lperez/coronavirus-france-dataset
• Tunisia - https://www.kaggle.com/ghassen1302/coronavirus-tunisia
• Japan - https://www.kaggle.com/tsubasatwi/close-contact-status-of-corona-in-japan

Acknowledgements

Thanks sincerely to all the members of the DSCI Team, KawalCovid19.id, Pemda DKI Jakarta, Pemprov Jawa Barat, Pemprov Jawa Tengah, Pemprov Sumatera Barat, and Pemprov DIY.

DSCI Team

1. Ardiansyah (ardisragen)
2. Tri A Sundara (trilabs)
3. Thomhert (thomhert)
4. Epsi Sayidina (epsisayidina)
5. Teuku Hashrul (hahasrul)
6. Naufal Hakim (hakimbazol)

Invitation

We welcome anyone to join us as collaborators! Join WAG Chat: https://s.id/fgPoP For more information please contact ardi@ejnu.net or WA +8210-4297-0504

Working with https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2849532%2Fd56eaf0a5d770d756a54cec0d09c87ff%2Fkoderea.png?generation=1584539195622597&alt=media" alt="">

--- Original source retains full ownership of the source dataset ---

The unprecedented travel bans introduced in response to the COVID-19 pandemic is a pertinent phenomenon of interest to scholars across the globe. Quantifying the timing and content of policy changes affecting travel and immigration is key to future research on the spread of SARS-CoV-2 and the socioeconomic impacts of these policies. The COVID Border Accountability Project (COBAP) provides a systematized dataset of >1000 policies, reflecting a timeline of new country-level restrictions on movement across international borders during the 2020 year. Using a 20-question survey, trained research assistants (RAs) sourced and documented for each new border policy: start and end dates, whether the closure constitutes a "complete closure" or "partial closure", which exceptions are made, which countries are banned, and which borders are closed, among other variables. In addition, the full text of each policy was included in the database. We maintain and update the data monthly. For public use, we visualize the data in an interactive map tool visualization: covidborderaccountability.org. For ongoing and future pandemic research, the dataset will be useful to policymakers, social and biomedical scientists, and public health experts alike.

Project Summary This dataset contains all qualitative and quantitative data collected in the first phase of the Pandemic Journaling Project (PJP). PJP is a combined journaling platform and interdisciplinary, mixed-methods research study developed by two anthropologists, with support from a team of colleagues and students across the social sciences, humanities, and health fields. PJP launched in Spring 2020 as the COVID-19 pandemic was emerging in the United States. PJP was created in order to “pre-design an archive” of COVID-19 narratives and experiences open to anyone around the world. The project is rooted in a commitment to democratizing knowledge production, in the spirit of “archival activism” and using methods of “grassroots collaborative ethnography” (Willen et al. 2022; Wurtz et al. 2022; Zhang et al 2020; see also Carney 2021). The motto on the PJP website encapsulates these commitments: “Usually, history is written only by the powerful. When the history of COVID-19 is written, let’s make sure that doesn’t happen.” (A version of this Project Summary with links to the PJP website and other relevant sites is included in the public documentation of the project at QDR.) In PJP’s first phase (PJP-1), the project provided a digital space where participants could create weekly journals of their COVID-19 experiences using a smartphone or computer. The platform was designed to be accessible to as wide a range of potential participants as possible. Anyone aged 15 or older, living anywhere in the world, could create journal entries using their choice of text, images, and/or audio recordings. The interface was accessible in English and Spanish, but participants could submit text and audio in any language. PJP-1 ran on a weekly basis from May 2020 to May 2022. Data Overview This Qualitative Data Repository (QDR) project contains all journal entries and closed-ended survey responses submitted during PJP-1, along with accompanying descriptive and explanatory materials. The dataset includes individual journal entries and accompanying quantitative survey responses from more than 1,800 participants in 55 countries. Of nearly 27,000 journal entries in total, over 2,700 included images and over 300 are audio files. All data were collected via the Qualtrics survey platform. PJP-1 was approved as a research study by the Institutional Review Board (IRB) at the University of Connecticut. Participants were introduced to the project in a variety of ways, including through the PJP website as well as professional networks, PJP’s social media accounts (on Facebook, Instagram, and Twitter) , and media coverage of the project. Participants provided a single piece of contact information — an email address or mobile phone number — which was used to distribute weekly invitations to participate. This contact information has been stripped from the dataset and will not be accessible to researchers. PJP uses a mixed-methods research approach and a dynamic cohort design. After enrolling in PJP-1 via the project’s website, participants received weekly invitations to contribute to their journals via their choice of email or SMS (text message). Each weekly invitation included a link to that week’s journaling prompts and accompanying survey questions. Participants could join at any point, and they could stop participating at any point as well. They also could stop participating and later restart. Retention was encouraged with a monthly raffle of three $100 gift cards. All individuals who had contributed that month were eligible. Regardless of when they joined, all participants received the project’s narrative prompts and accompanying survey questions in the same order. In Week 1, before contributing their first journal entries, participants were presented with a baseline survey that collected demographic information, including political leanings, as well as self-reported data about COVID-19 exposure and physical and mental health status. Some of these survey questions were repeated at periodic intervals in subsequent weeks, providing quantitative measures of change over time that can be analyzed in conjunction with participants' qualitative entries. Surveys employed validated questions where possible. The core of PJP-1 involved two weekly opportunities to create journal entries in the format of their choice (text, image, and/or audio). Each week, journalers received a link with an invitation to create one entry in response to a recurring narrative prompt (“How has the COVID-19 pandemic affected your life in the past week?”) and a second journal entry in response to their choice of two more tightly focused prompts. Typically the pair of prompts included one focusing on subjective experience (e.g., the impact of the pandemic on relationships, sense of social connectedness, or mental health) and another with an external focus (e.g., key sources of scientific information, trust in government, or COVID-19’s economic impact). Each week,...

2019 Novel Coronavirus COVID-19 (2019-nCoV) Visual Dashboard and Map:
https://www.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6

• Confirmed Cases by Country/Region/Sovereignty
• Confirmed Cases by Province/State/Dependency
• Deaths
• Recovered

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

Additional Information about the Visual Dashboard:
https://systems.jhu.edu/research/public-health/ncov

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

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.

The European Centre for Disease Prevention and Control released historical data (to 14 December 2020) on the daily number of new reported COVID-19 cases and deaths worldwide.

Content

The attributes of the dataset are the following: 1) dateRep: Date of the report

2) year_week: Week of the year

3) cases_weekly: Number of cases during the week

4) deaths_weekly: Number of deaths during the week

5) countriesAndTerritories: Country/Territory where the data was reported

6) geoId: Country/Territory id

7) countryterritoryCode: Country/Territory code

8) popData2019: Population data of the Country/Territory in 2019

9) continentExp: Continent of the Country/Territory

10) notification_rate_per_100000_population_14-days: 14-day cumulative number of reported COVID-19 cases per 100 000 population

Disclaimer: Population data in the database is taken from Eurostat for Europe and the World Bank for the rest of the world. Disclaimer: Countries that are not listed in these databases have reported no cases to WHO and no cases were identified in the public domain. The formula to calculate the 14-day cumulative number of reported COVID-19 cases per 100 000 population is (New cases over 14 day period)/Population)*100 000.

Acknowledgements

Data obtained from the European Centre for Disease Prevention and Control, an agency of the European Union

Load data into R

#these libraries need to be loaded library(utils) #read the Dataset sheet into “R”. The dataset will be called "data". data <- read.csv("data.csv", na.strings = "", fileEncoding = "UTF-8-BOM")

This dataset by The COVID Tracking Project at The Atlantic captures the virus’s transmission in 65 cities and counties across the country. Many of these metropolitan areas only report the current day’s totals and remove older data from their public health dashboards so that no historical archive is available. As a result, it’s often impossible to see the impact of the virus on a particular geography over time. Our dataset captures this historical information. It is the only available metropolitan dataset that includes race and ethnicity, which allows us to improve our understanding of how COVID-19 disproportionately affects communities of color.

We have completed our data collection on this project and want to share what we’ve learned from viewing COVID-19 at the local level. Five months in, we’ve seen that local data tells a vastly different story than state-level data. Not only do trends emerge in city and county data before appearing at the state level, but state-level data also o...

Context

The 2019–20 coronavirus pandemic is an ongoing global pandemic of coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The virus first emerged in Wuhan, Hubei, China, in December 2019. On 11 March 2020, the World Health Organization declared the outbreak a pandemic. As of 11 March 2020, over 126,000 cases have been confirmed in more than 110 countries and territories, with major outbreaks in mainland China, Italy, South Korea, and Iran. More than 4,600 have died from the disease and 67,000 have recovered.

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 information on the number of affected cases, deaths and recovery from 2019 novel coronavirus. Please note that this data was scrapped from https://www.worldometers.info/coronavirus/.This data is solely for education purposes only.

More - Find More Exciting🙀 Datasets Here - An Upvote👍 A Dayᕙ(`▿´)ᕗ , Keeps Aman Hurray Hurray..... ٩(˘◡˘)۶Hehe

Acknowledgements

This data is solely belongs to https://www.worldometers.info/coronavirus/. for licensing visit https://www.worldometers.info/licensing/