The HCUP Visualization of Inpatient Trends in COVID-19 and Other Conditions displays State-specific monthly trends in inpatient stays related to COVID-19 and other conditions, and facilitates comparisons of the number of hospital discharges, the average length of stays, and in-hospital mortality rates across patient/stay characteristics and States. This information is based on the HCUP State Inpatient Databases (SID), starting with 2018 data, plus newer annual and quarterly inpatient data, if and when available.

The Story behind the dataset

I wanted to Collect all the Covid-19 cases all over the world and make analysis on it

Data is simple but can bring a lot of insights

Data is classified into 4 columns (Country/Region', 'Confirmed', 'Country Abbr 2', 'Country Abbr 3)

1. Country/Region contain all world Countries

2. Confirmed contain all confirmed Covid-19 cases

3. Country Abbr 2 contain every country with the abbreviation of 2 letter

4. Country Abbr 3 contain every country with the abbreviation of 3 letter

This 2 columns are useful to use in visualization of Choropleth with plotly to make the world map Data is collected from many resources to be accurate

Analytics and Data Visualization for COVID-19 Intelligence.An ArcGIS Blog arcticle that explains how to leverage ready-to-use reports and tutorials to gauge COVID-19 pandemic's impact worldwide._Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...

The purpose of this code is to produce a line graph visualization of COVID-19 data. This Jupyter notebook was built and run on Google Colab. This code will serve mostly as a guide and will need to be adapted where necessary to be run locally. The separate COVID-19 datasets uploaded to this Dataverse can be used with this code. This upload is made up of the IPYNB and PDF files of the code.

Data visualization using Python (Pandas, Plotly).

Data was used to visualization of the infection rate and the death rate from 01/20 to 04/22.

The data was made available on Github: https://raw.githubusercontent.com/datasets/covid-19/master/data/countries-aggregated.csv

This dataset contains 3,000 rows and 26 columns of synthetically generated COVID-19 records. It replicates realistic global pandemic data, simulating values for cases, deaths, tests, vaccinations, demographics, and policy measures. The data mimics actual records from sources like Our World in Data, designed specifically for data science experimentation, visualization, and machine learning projects.

It is ideal for:

Practicing exploratory data analysis (EDA)

Creating dashboards

Building predictive models

Teaching or student projects

Kaggle Notebooks without API dependencies

This dataset tracks the updates made on the dataset "HCUP Visualization of Inpatient Trends in COVID-19 and Other Conditions" as a repository for previous versions of the data and metadata.

The coronavirus traffic light acts as a dynamic tool for a consistent, coordinated and transparent approach by the authorities to COVID-19 according to the respective epidemiological situation at regional level. The Corona traffic light serves as a guidance system for informing authorities and the public about the corresponding COVID-19 risk. On the basis of the coronavirus traffic light, the Austrian authorities are taking appropriate measures and guidelines for all social and economic sectors at regional level. In order to contain the COVID-19 crisis, the public is asked to take note of and comply with these requirements on an ongoing basis. The recommendations and guidelines are based on the respective epidemiological situation and are flexibly adapted to the respective COVID-19 situation. The measures may apply to the entire federal territory, individual states or districts.

This dataset is uploaded considering following questions -Is corona deaths/cases are having any correlation with Population -Is corona spread heavily in countries which has done less corona test -Which countries topping the list in terms of more cases & deaths -Which countries are at the bottom of the list in terms of more cases & deaths(what can be reasons) -Data cleaning -Comparison between total cases vs total deaths vs total tests.Any correlation? -Other EDA

Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus.

Most people infected with the virus will experience mild to moderate respiratory illness and recover without requiring special treatment. However, some will become seriously ill and require medical attention. Older people and those with underlying medical conditions like cardiovascular disease, diabetes, chronic respiratory disease, or cancer are more likely to develop serious illness. Anyone can get sick with COVID-19 and become seriously ill or die at any age. Prevention is better than cure!!!

COVID-19 mortality forecasting models provide critical information about the trajectory of the pandemic, which is used by policymakers and public health officials to guide decision-making. However, thousands of published COVID-19 mortality forecasts now exist, many with their own unique methods, assumptions, format, and visualization. As a result, it is difficult to compare models and understand under which circumstances a model performs best. Here, we describe the construction and usability of covidcompare.io, a web tool built to compare numerous forecasts and offer insight into how each has performed over the course of the pandemic. From its launch in December 2020 to June 2021, we have seen 4,600 unique visitors from 85 countries. A study conducted with public health professionals showed high usability overall as formally assessed using a Post-Study System Usability Questionnaire (PSSUQ). We find that covidcompare.io is an impactful tool for the comparison of international COVID-19 mortality forecasting models.

AbstractThe dataset provided here contains the efforts of independent data aggregation, quality control, and visualization of the University of Arizona (UofA) COVID-19 testing programs for the 2019 novel Coronavirus pandemic. The dataset is provided in the form of machine-readable tables in comma-separated value (.csv) and Microsoft Excel (.xlsx) formats.Additional InformationAs part of the UofA response to the 2019-20 Coronavirus pandemic, testing was conducted on students, staff, and faculty prior to start of the academic year and throughout the school year. These testings were done at the UofA Campus Health Center and through their instance program called "Test All Test Smart" (TATS). These tests identify active cases of SARS-nCoV-2 infections using the reverse transcription polymerase chain reaction (RT-PCR) test and the Antigen test. Because the Antigen test provided more rapid diagnosis, it was greatly used three weeks prior to the start of the Fall semester and throughout the academic year.As these tests were occurring, results were provided on the COVID-19 websites. First, beginning in early March, the Campus Health Alerts website reported the total number of positive cases. Later, numbers were provided for the total number of tests (March 12 and thereafter). According to the website, these numbers were updated daily for positive cases and weekly for total tests. These numbers were reported until early September where they were then included in the reporting for the TATS program.For the TATS program, numbers were provided through the UofA COVID-19 Update website. Initially on August 21, the numbers provided were the total number (July 31 and thereafter) of tests and positive cases. Later (August 25), additional information was provided where both PCR and Antigen testings were available. Here, the daily numbers were also included. On September 3, this website then provided both the Campus Health and TATS data. Here, PCR and Antigen were combined and referred to as "Total", and daily and cumulative numbers were provided.At this time, no official data dashboard was available until September 16, and aside from the information provided on these websites, the full dataset was not made publicly available. As such, the authors of this dataset independently aggregated data from multiple sources. These data were made publicly available through a Google Sheet with graphical illustration provided through the spreadsheet and on social media. The goal of providing the data and illustrations publicly was to provide factual information and to understand the infection rate of SARS-nCoV-2 in the UofA community.Because of differences in reported data between Campus Health and the TATS program, the dataset provides Campus Health numbers on September 3 and thereafter. TATS numbers are provided beginning on August 14, 2020.Description of Dataset ContentThe following terms are used in describing the dataset.1. "Report Date" is the date and time in which the website was updated to reflect the new numbers2. "Test Date" is to the date of testing/sample collection3. "Total" is the combination of Campus Health and TATS numbers4. "Daily" is to the new data associated with the Test Date5. "To Date (07/31--)" provides the cumulative numbers from 07/31 and thereafter6. "Sources" provides the source of information. The number prior to the colon refers to the number of sources. Here, "UACU" refers to the UA COVID-19 Update page, and "UARB" refers to the UA Weekly Re-Entry Briefing. "SS" and "WBM" refers to screenshot (manually acquired) and "Wayback Machine" (see Reference section for links) with initials provided to indicate which author recorded the values. These screenshots are available in the records.zip file.The dataset is distinguished where available by the testing program and the methods of testing. Where data are not available, calculations are made to fill in missing data (e.g., extrapolating backwards on the total number of tests based on daily numbers that are deemed reliable). Where errors are found (by comparing to previous numbers), those are reported on the above Google Sheet with specifics noted.For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to data-management@arizona.edu

Data visualizations of the COVID-19 pandemic in the United States often have presented case and death rates by state in separate visualizations making it difficult to discern the temporal relationship between these two epidemiological metrics. By combining the COVID-19 case and death rates into a single visualization we have provided an intuitive format for depicting the relationship between cases and deaths. Moreover, by using animation we have made the temporal lag between cases and subsequent deaths more obvious and apparent. This work helps to inform expectations for the trajectory of death rates in the United States given the recent surge in case rates.

Context

The DATESET is of US-COUNTRIES for COVID19.

Description

1. Covid_Data based on each countystates.csv= Contains Deaths,confirmed_cases,state,county 2.Covid_Data= Contains state,county,country,zipcode,city,Covidimpacted,latitude,longitude,timezone

Prediction can be done for column CovidImpacted by choosing Deaths,confirmed cases by some algo and show the accuracy,performance etc

Content

• The DATASET has city,state,county,Deaths,Confirmed_cases,latitude,longitude,zipcode.
• DATASET can be used to classification based on cases/Deaths
• DATA Analysis,DATA VISUALISATION can be done for DATASET.

Inspiration

As because we are in COVID19 hope this DATA can be used for beginners,intermediate to work in it Hope it Helps!

Visual map at kumu.io/access2perspectives/covid19-resources

Data set doi: 10.5281/zenodo.3732377 // available in different formats (pdf, xls, ods, csv,)

Correspondence: (JH) info@access2perspectives.com

Objectives

Provide citizens with crucial and reliable information

Encourage and facilitate South South collaboration

Bridging language barriers

Provide local governments and cities with lessons learned about COVID-19 crisis response

Facilitate global cooperation and immediate response on all societal levels

Enable LMICs to collaborate and innovate across distances and leverage locally available and context-relevant resources

Methodology

The data feeding the map at kumu.io was compiled from online resources and information shared in various community communication channels.

Kumu.io is a visualization platform for mapping complex systems and to provide a deeper understanding of their intrinsic relationships. It provides blended systems thinking, stakeholder mapping, and social network analysis.

Explore the map // https://kumu.io/access2perspectives/covid19-resources#global

Click on individual nodes and view the information by country

info hotlines

governmental informational websites, Twitter feeds & Facebook pages

fact checking online resources

language indicator

DIY resources

clinical staff capacity building

etc.

With the navigation buttons to the right, you can zoom in and out, select and focus on specific elements.

If you have comments, questions or suggestions for improvements on this map email us at info@access2perspectives.com

Contribute

Please add data to the spreadsheet at https://tinyurl.com/COVID19-global-response

you can add additional information on country, city or neighbourhood level (see e.g. the Cape Town entry)

Related documents

Google Doc: tinyurl.com/COVID19-Africa-Response

A data visualization representing Indonesia's Covid-19 cases have spiked

Additional file 1: Table S1. A list of normalized COVID-19/SARS-CoV-2-related subjects. Table S2. COVID-19 KG data source comparison.

Summary table of the initial T cell subsets test.

Multivariate logistic regression model result using death as the outcome.

As scientific technology and space science progress, remote sensing has emerged as an innovative solution to ease the challenges of the COVID-19 pandemic. To examine the research characteristics and growth trends in using remote sensing for monitoring and managing the COVID-19 research, a bibliometric analysis was conducted on the scientific documents appearing in the Scopus database. A total of 1,509 documents on this study topic were indexed between 2020 and 2022, covering 165 countries, 577 journals, 5239 institutions, and 8,616 authors. The studies related to remote sensing and COVID-19 have a significant increase of 30% with 464 articles. The United States (429 articles, 28.42% of the global output), China (295 articles, 19.54% of the global output), and the United Kingdom (174 articles, 11.53%) appeared as the top three most contributions to the literature related to remote sensing and COVID-19 research. Sustainability, Science of the Total Environment, and International Journal of Environmental Research and Public Health were the three most productive journals in this research field. The utmost predominant themes were COVID-19, remote sensing, spatial analysis, coronavirus, lockdown, and air pollution. The expansion of these topics appears to be associated with cross-sectional research on remote sensing, evidence-based tools, satellite mapping, and geographic information systems (GIS). Global pandemic risks will be monitored and managed much more effectively in the coming years with the use of remote sensing technology.

The dataset data visualization contains information on where COVID-19 is spreading by tracking new cases found each day and the total number of cases and deaths in the US on the county-level. The data can be dowloaded and visualized on the website.