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.

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

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.

Context

My first post on Kaggle! This needs to reach people, especially the beginners who are are just starting with their journey in data visualisation.

Content

A beginner's guide to plot Covid-19 visualisation plots.

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

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

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.

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.

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

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!

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

Multivariate logistic regression model result using death as the outcome.

Objectives: Develop a tool for applying various COVID-19 re-opening guidelines to the more than 120 U.S. Environmental Protection Agency (EPA) facilities.Methods: A geographic information system boundary was created for each EPA facility encompassing the county where the EPA facility is located and the counties where employees commuted from. This commuting area is used for display in the Dashboard and to summarize population and COVID-19 health data for analysis.Results: Scientists in EPA’s Office of Research and Development developed the EPA Facility Status Dashboard, an easy-to-use web application that displays data and statistical analyses on COVID-19 cases, testing, hospitalizations, and vaccination rates.Conclusion: The Dashboard was designed to provide readily accessible information for EPA management and staff to view and understand the COVID-19 risk surrounding each facility. It has been modified several times based on user feedback, availability of new data sources, and updated guidance. The views expressed in this article are those of the authors and do not necessarily represent the views or the policies of the U.S. Environmental Protection Agency.

Summary table of the initial T cell subsets test.

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.

The Court provides data on public aid between March 2020 and the end of June 2021. The respective sums were added up and provided with interactive visualization in the form of a bar chart. The visualization shows the area, the name, the sum of the aid measures and the external recipient. The X-axis is logarithmically scaled.

Dataset

This dataset was created by PavanSai2545

Contents

Epidemics and pandemics are geographical in nature and constitute spatial, temporal, and thematic phenomena across large ranges of scales: local infections with a global spread; short-term decisions by governments and institutions with long-term effects; and diverse effects of the disease on many aspects of our lives. Pandemics pose particular challenges to their visual representation by cartographic means. This article briefly summarizes some of these challenges and outlines ways to approach these. We discuss how to use the information usually available for telling the story of an epidemic, illustrated by the example of the 2019–2020 COVID-19 pandemic. The maps attached to this article demonstrate the discussed cartographic means.