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 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 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.

Dataset

This dataset was created by Dhrumil Gohel

Contents

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

Dataset

This dataset was created by Aditya301112

Contents

It contains the following files:

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.

Daily confirmed new cases, rolling 7-day average

The datasets used for this manuscript were derived from multiple sources: Denver Public Health, Esri, Google, and SafeGraph. Any reuse or redistribution of the datasets are subjected to the restrictions of the data providers: Denver Public Health, Esri, Google, and SafeGraph and should consult relevant parties for permissions.1. COVID-19 case dataset were retrieved from Denver Public Health (Link: https://storymaps.arcgis.com/stories/50dbb5e7dfb6495292b71b7d8df56d0a )2. Point of Interests (POIs) data were retrieved from Esri and SafeGraph (Link: https://coronavirus-disasterresponse.hub.arcgis.com/datasets/6c8c635b1ea94001a52bf28179d1e32b/data?selectedAttribute=naics_code) and verified with Google Places Service (Link: https://developers.google.com/maps/documentation/javascript/reference/places-service)3. The activity risk information is accessible from Texas Medical Association (TMA) (Link: https://www.texmed.org/TexasMedicineDetail.aspx?id=54216 )The datasets for risk assessment and mapping are included in a geodatabase. Per SafeGraph data sharing guidelines, raw data cannot be shared publicly. To view the content of the geodatabase, users should have installed ArcGIS Pro 2.7. The geodatabase includes the following:1. POI. Major attributes are locations, name, and daily popularity.2. Denver neighborhood with weekly COVID-19 cases and computed regional risk levels.3. Simulated four travel logs with anchor points provided. Each is a separate point layer.

This dataset sets out to clarify the statistics of how the pandemic spread all over the world and its impact on a global scale.These statistics help people predict plan for future incident.This dataset dives deep into the statistics on a global scale .

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.

This case study includes multiple workflows, visualizing global countries' COVID-19 cases as dynamic maps, such as HTML, GIF, and MP4.

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.

Visualizers Market Outlook

In 2023, the global visualizers market size was estimated to be valued at approximately $1.8 billion, and it is projected to reach around $4.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 10.5% during the forecast period. This robust growth is driven by technological advancements, increasing demand for immersive visualization tools, and the rising importance of data visualization in various sectors.

One of the primary growth factors for the visualizers market is the rapid technological advancements in hardware and software solutions. The development of high-resolution screens, 3D rendering capabilities, and powerful data processing tools have revolutionized the way information is visualized and interpreted. These advancements enable more comprehensive and detailed visualizations, which are crucial for applications ranging from education to scientific research. The proliferation of these technologies is driven by the increasing demand for more sophisticated and interactive visualization methods, which in turn, fuels the market growth.

Another significant factor contributing to the market growth is the increasing need for data visualization in decision-making processes. In a data-driven world, businesses across various sectors are recognizing the importance of visualizing data to uncover insights, make informed decisions, and communicate complex information effectively. As a result, the adoption of data visualization tools has seen substantial growth. Enterprises are investing heavily in these tools to enhance their data analytics capabilities, which is expected to drive the market for visualizers in the coming years.

The growing emphasis on remote and online education has also played a crucial role in the expansion of the visualizers market. The COVID-19 pandemic accelerated the shift towards online learning, and educational institutions worldwide have increasingly adopted visualizers to improve the quality of virtual learning environments. These tools help educators create engaging and interactive content, making complex subjects easier to understand for students. As educational institutions continue to invest in digital learning solutions, the demand for visualizers is expected to rise significantly.

The rise of Portable Visual Presenter devices has significantly impacted the education sector, especially in the realm of remote and online learning. These compact and versatile tools allow educators to project documents, images, and even live demonstrations with ease, enhancing the virtual classroom experience. Unlike traditional visualizers, portable visual presenters are designed for flexibility and mobility, making them ideal for educators who need to adapt to various teaching environments. Their integration into digital learning platforms has been seamless, offering educators the ability to engage students more effectively by providing clear and interactive visual aids. As the demand for innovative educational tools continues to grow, the role of portable visual presenters is expected to expand, further driving the visualizers market.

Regionally, North America currently holds the largest share of the visualizers market, driven by the presence of major technology companies, high adoption rates of advanced technologies, and significant investments in research and development. However, the Asia Pacific region is anticipated to witness the highest growth rate during the forecast period. The rapid economic growth, increasing penetration of digital technologies, and the expansion of the education and healthcare sectors in countries such as China and India are key factors contributing to this growth. Europe, Latin America, and the Middle East & Africa are also expected to see substantial growth, albeit at a slightly slower pace, due to varying degrees of technological adoption and economic development.

Product Type Analysis

The product type segment of the visualizers market includes 3D visualizers, data visualizers, architectural visualizers, scientific visualizers, and others. Each of these product types caters to specific needs and applications, offering unique features and capabilities that drive their adoption in different sectors.

3D visualizers are increasingly being adopted due to their ability to create highly detailed and realistic representations of objects and environments. These tools are widely used in industries such as architecture, engi

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!

Note: Authorizations to collect certain public health data expired at the end of the U.S. public health emergency declaration on May 11, 2023. The following jurisdictions discontinued COVID-19 case notifications to CDC: Iowa (11/8/21), Kansas (5/12/23), Louisiana (10/31/23), New Hampshire (5/23/23), and Oklahoma (5/2/23). Please note that these jurisdictions will not routinely send new case data after the dates indicated. As of 7/13/23, case notifications from Oregon will only include pediatric cases resulting in death.

This table summarizes COVID-19 case and death data submitted to CDC as case reports for the line-level dataset. Case and death counts are stratified according to sex, age, and race and ethnicity at regional and national levels. Data for US territories are included in case and death counts, but not population counts. Weekly cumulative counts with five or fewer cases or deaths are not reported to protect confidentiality of patients. Records with unknown or missing sex, age, or race and ethnicity and of multiple, non-Hispanic race and ethnicity are included in case and death totals. COVID-19 case and death data are provisional and are subject to change. Visualization of COVID-19 case and death rate trends by demographic variables may be viewed on COVID Data Tracker (https://covid.cdc.gov/covid-data-tracker/#demographicsovertime).

Analysis of ‘COVID-19 India dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/dhamur/covid19-india-dataset on 28 January 2022.

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

 This data set contains the data of covid-19 Conformed, Recovered and Deaths in India. This data is took from the non-governmental organization. 

Website

COVID-19 Daily updates

My Github

Profile Data Set

Data collected from

COVID19-India - The data from 31-Jan-2020 to 31-Oct-2021. Remaining data from

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

Estimates of excess deaths can provide information about the burden of mortality potentially related to the COVID-19 pandemic, including deaths that are directly or indirectly attributed to COVID-19. Excess deaths are typically defined as the difference between the observed numbers of deaths in specific time periods and expected numbers of deaths in the same time periods. This visualization provides weekly estimates of excess deaths by the jurisdiction in which the death occurred. Weekly counts of deaths are compared with historical trends to determine whether the number of deaths is significantly higher than expected.Counts of deaths from all causes of death, including COVID-19, are presented. As some deaths due to COVID-19 may be assigned to other causes of deaths (for example, if COVID-19 was not diagnosed or not mentioned on the death certificate), tracking all-cause mortality can provide information about whether an excess number of deaths is observed, even when COVID-19 mortality may be undercounted. Additionally, deaths from all causes excluding COVID-19 were also estimated. Comparing these two sets of estimates — excess deaths with and without COVID-19 — can provide insight about how many excess deaths are identified as due to COVID-19, and how many excess deaths are reported as due to other causes of death. These deaths could represent misclassified COVID-19 deaths, or potentially could be indirectly related to the COVID-19 pandemic (e.g., deaths from other causes occurring in the context of health care shortages or overburdened health care systems).Estimates of excess deaths can be calculated in a variety of ways, and will vary depending on the methodology and assumptions about how many deaths are expected to occur. Estimates of excess deaths presented in this webpage were calculated using Farrington surveillance algorithms (1). A range of values for the number of excess deaths was calculated as the difference between the observed count and one of two thresholds (either the average expected count or the upper bound of the 95% prediction interval), by week and jurisdiction.Provisional death counts are weighted to account for incomplete data. However, data for the most recent week(s) are still likely to be incomplete. Weights are based on completeness of provisional data in prior years, but the timeliness of data may have changed in 2020 relative to prior years, so the resulting weighted estimates may be too high in some jurisdictions and too low in others. As more information about the accuracy of the weighted estimates is obtained, further refinements to the weights may be made, which will impact the estimates. Any changes to the methods or weighting algorithm will be noted in the Technical Notes when they occur. More detail about the methods, weighting, data, and limitations can be found in the Technical Notes.This visualization includes several different estimates:Number of excess deaths: A range of estimates for the number of excess deaths was calculated as the difference between the observed count and one of two thresholds (either the average expected count or the upper bound threshold), by week and jurisdiction. Negative values, where the observed count fell below the threshold, were set to zero.Percent excess: The percent excess was defined as the number of excess deaths divided by the threshold.Total number of excess deaths: The total number of excess deaths in each jurisdiction was calculated by summing the excess deaths in each week, from February 1, 2020 to present. Similarly, the total number of excess deaths for the US overall was computed as a sum of jurisdiction-specific numbers of excess deaths (with negative values set to zero), and not directly estimated using the Farrington surveillance algorithms.Select a dashboard from the menu, then click on “Update Dashboard” to navigate through the different graphics.The first dashboard shows the weekly predicted counts of deaths from all causes, and the threshold for the expected number of deaths. Select a jurisdiction from the drop-down menu to show data for that jurisdiction.The second dashboard shows the weekly predicted counts of deaths from all causes and the weekly count of deaths from all causes excluding COVID-19. Select a jurisdiction from the drop-down menu to show data for that jurisdiction.The th

Dataset Card for BEIR Benchmark

  Dataset Summary

BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:

Fact-checking: FEVER, Climate-FEVER, SciFact Question-Answering: NQ, HotpotQA, FiQA-2018 Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus News Retrieval: TREC-NEWS, Robust04 Argument Retrieval: Touche-2020, ArguAna Duplicate Question Retrieval: Quora, CqaDupstack Citation-Prediction: SCIDOCS Tweet… See the full description on the dataset page: https://huggingface.co/datasets/BeIR/trec-covid.

Materials for reproducibility of results in manuscript Rapid on-site pathology visualization of COVID-19 characteristics using higher harmonic generation microscopy.