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.

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

Includes a staic and dynamic visualization of sample data of Covid Infections in London. Open: - static visualization of covid infections - dynamicCovidVisualization

Description: The COVID-19 dataset used for this EDA project encompasses comprehensive data on COVID-19 cases, deaths, and recoveries worldwide. It includes information gathered from authoritative sources such as the World Health Organization (WHO), the Centers for Disease Control and Prevention (CDC), and national health agencies. The dataset covers global, regional, and national levels, providing a holistic view of the pandemic's impact.

Purpose: This dataset is instrumental in understanding the multifaceted impact of the COVID-19 pandemic through data exploration. It aligns perfectly with the objectives of the EDA project, aiming to unveil insights, patterns, and trends related to COVID-19. Here are the key objectives: 1. Data Collection and Cleaning: • Gather reliable COVID-19 datasets from authoritative sources (such as WHO, CDC, or national health agencies). • Clean and preprocess the data to ensure accuracy and consistency. 2. Descriptive Statistics: • Summarize key statistics: total cases, recoveries, deaths, and testing rates. • Visualize temporal trends using line charts, bar plots, and heat maps. 3. Geospatial Analysis: • Map COVID-19 cases across countries, regions, or cities. • Identify hotspots and variations in infection rates. 4. Demographic Insights: • Explore how age, gender, and pre-existing conditions impact vulnerability. • Investigate disparities in infection rates among different populations. 5. Healthcare System Impact: • Analyze hospitalization rates, ICU occupancy, and healthcare resource allocation. • Assess the strain on medical facilities. 6. Economic and Social Effects: • Investigate the relationship between lockdown measures, economic indicators, and infection rates. • Explore behavioral changes (e.g., mobility patterns, remote work) during the pandemic. 7. Predictive Modeling (Optional): • If data permits, build simple predictive models (e.g., time series forecasting) to estimate future cases.

Data Sources: The primary sources of the COVID-19 dataset include the Johns Hopkins CSSE COVID-19 Data Repository, Google Health’s COVID-19 Open Data, and the U.S. Economic Development Administration (EDA). These sources provide reliable and up-to-date information on COVID-19 cases, deaths, testing rates, and other relevant variables. Additionally, GitHub repositories and platforms like Medium host supplementary datasets and analyses, enriching the available data resources.

Data Format: The dataset is available in various formats, such as CSV and JSON, facilitating easy access and analysis. Before conducting the EDA, the data underwent preprocessing steps to ensure accuracy and consistency. Data cleaning procedures were performed to address missing values, inconsistencies, and outliers, enhancing the quality and reliability of the dataset.

License: The COVID-19 dataset may be subject to specific usage licenses or restrictions imposed by the original data sources. Proper attribution is essential to acknowledge the contributions of the WHO, CDC, national health agencies, and other entities providing the data. Users should adhere to any licensing terms and usage guidelines associated with the dataset.

Attribution: We acknowledge the invaluable contributions of the World Health Organization (WHO), the Centers for Disease Control and Prevention (CDC), national health agencies, and other authoritative sources in compiling and disseminating the COVID-19 data used for this EDA project. Their efforts in collecting, curating, and sharing data have been instrumental in advancing our understanding of the pandemic and guiding public health responses globally.

City-wise confirmed Covid-19 cases within India and specifically within KeralaFor discussions, please visit and follow the Facebook profile: https://www.facebook.com/viswaprabhaTo see the underlying live data, please visit this Google Sheet

Comprehensive time series data on the global spread of COVID-19, detailing the total number of confirmed cases, recoveries, and deaths by location. Compiled by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE) in collaboration with the ESRI Living Atlas Team and JHU Applied Physics Lab (APL), this dataset offers insights into the pandemic’s progression across diverse regions and time periods.

Features:

• Date: Specific date of recorded data.
• Country/Region: Geographic location, typically at the country or region level.
• Province/State: Subdivision data, where available (e.g., states in the US, provinces in Canada).
• Confirmed Cases: Cumulative number of confirmed COVID-19 cases.
• Deaths: Cumulative number of deaths attributed to COVID-19.
• Recovered: Cumulative number of recoveries (where available).

Usage:

The dataset is useful for: - Statistical analysis of COVID-19 trends and patterns. - Forecasting models to estimate the spread and potential impact of pandemics. - Visualization projects to map and display the pandemic’s impact over time.

Coverage:

Data is provided globally, covering various countries, regions, and some states/provinces where available. The dataset's granularity allows for comprehensive insights into the pandemic’s reach and severity across different locations.

License:

CC BY 4.0 (Creative Commons)

Who can use it:

Epidemiologists, data scientists, public health researchers, and policy-makers can utilize this dataset for research, statistical modeling, and public health strategy development.

How to use it:

• Conduct historical analyses to understand the progression of COVID-19 cases across regions.
• Build predictive models to forecast infection rates and mortality trends.
• Develop visualization tools (e.g., heat maps, time-series graphs) to illustrate the pandemic's evolution over time.

How to Read the map.This map allows you to visualize the trends over time and cases, recoveries, deaths and testing at the regional health unit. The Map shows the relative state of the COVID-19 outbreak in each region. Colour (red to green) shows the time since a new reported case.

7 Day Hot Spots

The map highlights regions with an active outbreak with a "glowing ball". The size of the ball reflects the average number of new cases in the past 7 days as a rate per 100K population.

High

Low

Important InformationNot all data is reported for all regional health units. Data sources are consulted every 24 hours, however not all organizations report on a daily bases. As this data is cumulative, values carry-forward if updates are not provided. Values can go down due to corrected errors as reported. Data SourcesThe source of the data for each regional health unit is listed in the "SourceURL" field.

Looking for the raw data? You can find it here.

FAIR Supplementary Information and Raw Data for Hofer S. et al., 2021, SARS-CoV-2-Laden Respiratory Aerosol Deposition in the Lung Alveolar-Interstitial Region Is a Potential Risk Factor for Severe Disease: A Modeling Study, Journal of Personalized Medicine 11(5):431, DOI: https://doi.org/10.3390/jpm11050431

1. pdf/A of deposition heat maps (incl probability values) for 5 different ARAP modes

2. xls-formatted file of MPPD v3.04-derived deposition raw data sets for 5 different ARAP modes

3.-7. rpt-formatted MPPD v3.04 files of deposition raw data sets for 5 different ARAP modes

8.-12. csv-formatted files of MPPD v3.04-derived deposition raw data sets for 5 different ARAP modes

13. pdf/A of deposition heat maps (incl probability values) for 5 different ERAP modes (upon rehydration of ARAPs)

14. txt-formatted README file for Hofer et al 2021

The COVID-19 dashboard includes data on city/town COVID-19 activity, confirmed and probable cases of COVID-19, confirmed and probable deaths related to COVID-19, and the demographic characteristics of cases and deaths.

Supplementary Information and Raw Data for Hofer S. et al., 2021, SARS-CoV-2-Laden Respiratory Aerosol Deposition in the Lung Alveolar-Interstitial Region Is a Potential Risk Factor for Severe Disease: A Modeling Study, Journal of Personalized Medicine 11(5):431, https://doi.org/10.3390/jpm11050431

1. pdf of deposition heat maps (incl probability values) for 5 different ARAP modes

2. xls-formatted file of MPPD-derived deposition raw data sets for 5 different ARAP modes

3.-7. rpt-formatted MPPD files of deposition raw data sets for 5 different ARAP modes

Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remains a serious pandemic. COVID-19 vaccination is urgent needed for limiting SARS-CoV-2 outbreaks by herd immunity. Simultaneously, post-marketing surveillance to assess vaccine safety is important, and collection of vaccine-related adverse events has been in progress. Vision-threatening ophthalmic adverse events of COVID-19 vaccines are rare but are a matter of concern. We report a 45-year-old Japanese male with positive for HLA-DR4/HLA-DRB1*0405, who developed bilateral panuveitis resembling Vogt-Koyanagi-Harada (VKH) disease after the second dose of Pfizer-BioNTech COVID-19 mRNA (BNT162b2) vaccine. Glucocorticosteroid (GC) therapy combined with cyclosporine A (CsA) readily improved the panuveitis. The immune profile at the time of onset was analyzed using CyTOF technology, which revealed activations of innate immunity mainly consisting of natural killer cells, and acquired immunity predominantly composed of B cells and CD8+ T cells. On the other hand, the immune profile in the remission phase was altered by GC therapy with CsA to a profile composed primarily of CD4+ cells, which was considerably similar to that of the healthy control before the vaccination. Our results indicate that BNT162b2 vaccine may trigger an accidental immune cross-reactivity to melanocyte epitopes in the choroid, resulting in the onset of panuveitis resembling VKH disease.

A story map featuring heat charts relating to coronavirus cases in Montgomery County, Texas.

The data displayed on the NSW vaccination map on nsw.gov.au is an important tool to help encourage the community to see the value of getting vaccinated to keep themselves and their loved ones safe. The Department of Customer Service has presented the data in a way that is easy to read and understand, but the data sources belong to the federal and state health agencies.\r \r This map is updated every Tuesdays and Fridays.

Geostatistics analyzes and predicts the values associated with spatial or spatial-temporal phenomena. It incorporates the spatial (and in some cases temporal) coordinates of the data within the analyses. It is a practical means of describing spatial patterns and interpolating values for locations where samples were not taken (and measures the uncertainty of those values, which is critical to informed decision making). This archive contains results of geostatistical analysis of COVID-19 case counts for all available US counties. Test results were obtained with ArcGIS Pro (ESRI). Sources are state health departments, which are scraped and aggregated by the Johns Hopkins Coronavirus Resource Center and then pre-processed by MappingSupport.com.

This update of the Zenodo dataset (version 6) consists of three compressed archives containing geostatistical analyses of SARS-CoV-2 testing data. This dataset utilizes many of the geostatistical techniques used in previous versions of this Zenodo archive, but has been significantly expanded to include analyses of up-to-date U.S. COVID-19 case data (from March 24th to September 8^th, 2020):

Archive #1: “1.Geostat. Space-Time analysis of SARS-CoV-2 in the US (Mar24-Sept6).zip” – results of a geostatistical analysis of COVID-19 cases incorporating spatially-weighted hotspots that are conserved over one-week timespans. Results are reported starting from when U.S. COVID-19 case data first became available (March 24^th, 2020) for 25 consecutive 1-week intervals (March 24th through to September 6th, 2020). Hotspots, where found, are reported in each individual state, rather than the entire continental United States.

Archive #2: "2.Geostat. Spatial analysis of SARS-CoV-2 in the US (Mar24-Sept8).zip" – the results from geostatistical spatial analyses only of corrected COVID-19 case data for the continental United States, spanning the period from March 24^th through September 8th, 2020. The geostatistical techniques utilized in this archive includes ‘Hot Spot’ analysis and ‘Cluster and Outlier’ analysis.

Archive #3: "3.Kriging and Densification of SARS-CoV-2 in LA and MA.zip" – this dataset provides preliminary kriging and densification analysis of COVID-19 case data for certain dates within the U.S. states of Louisiana and Massachusetts.

These archives consist of map files (as both static images and as animations) and data files (including text files which contain the underlying data of said map files [where applicable]) which were generated when performing the following Geostatistical analyses: Hot Spot analysis (Getis-Ord Gi*) [‘Archive #1’: consecutive weeklong Space-Time Hot Spot analysis; ‘Archive #2’: daily Hot Spot Analysis], Cluster and Outlier analysis (Anselin Local Moran's I) [‘Archive #2’], Spatial Autocorrelation (Global Moran's I) [‘Archive #2’], and point-to-point comparisons with Kriging and Densification analysis [‘Archive #3’].

The Word document provided ("Description-of-Archive.Updated-Geostatistical-Analysis-of-SARS-CoV-2 (version 6).docx") details the contents of each file and folder within these three archives and gives general interpretations of these results.

After entering Italy, the coronavirus (COVID-19) spread fast. The strict lockdown implemented by the government during the Spring 2020 helped to slow down the outbreak. However, the country had to face four new harsh waves of contagion. As of January 1, 2025, the total number of cases reported by the authorities reached over 26.9 million. The north of the country was mostly hit, and the region with the highest number of cases was Lombardy, which registered almost 4.4 million of them. The north-eastern region of Veneto and the southern region of Campania followed in the list. When adjusting these figures for the population size of each region, however, the picture changed, with the region of Veneto being the area where the virus had the highest relative incidence. Coronavirus in Italy Italy has been among the countries most impacted by the coronavirus outbreak. Moreover, the number of deaths due to coronavirus recorded in Italy is significantly high, making it one of the countries with the highest fatality rates worldwide, especially in the first stages of the pandemic. In particular, a very high mortality rate was recorded among patients aged 80 years or older. Impact on the economy The lockdown imposed during the Spring 2020, and other measures taken in the following months to contain the pandemic, forced many businesses to shut their doors and caused industrial production to slow down significantly. As a result, consumption fell, with the sectors most severely hit being hospitality and tourism, air transport, and automotive. Several predictions about the evolution of the global economy were published at the beginning of the pandemic, based on different scenarios about the development of the pandemic. According to the official results, it appeared that the coronavirus outbreak had caused Italy’s GDP to shrink by approximately nine percent in 2020.

The folder contains the heatmap plots obtained from the gene burden analysis considering gene disruptive variants. The plots are provided in a static format (.jpeg) and in a more interactive one (.html). Additionally, tables generating the heatmaps are provided in a .txt file format.

Parallel to the dataset CORD-19 of scholarly articles, we provide the literature graph LG-covid19-HOTP composed of not only articles (graph nodes) that are relevant to the study of coronavirus, but also in and out citation links (directed graph edges) to base navigation and search among the articles. The article records are related and connected, not isolated. The graph has been updated weekly since March 26, 2020. The current graph includes 42,279 hot-off-the-press (HOTP) articles since January 2020. It contains 485,097 articles and 4,259,944 links. The link-to-node ratio is remarkably higher than some other existing literature graphs. In addition to the dataset we provide more functionalities at lg-covid-19-hotp.cs.duke.edu such as new articles, weekly meta-data analysis in terms of publication growth over time, ranking by citation, and statistical near-neighbor embedding maps by similarity in co-citation, and similarity in co-reference. Since April 11, we have enabled a novel functionality - self-navigated surf-search over the maps. At the site we also take courtesy input of COVID-19 articles that are missing from the current collection.

Stamp Out COVID-19An apple a day keeps the doctor away.Linda Angulo LopezDecember 3, 2020https://theconversation.com/coronavirus-where-do-new-viruses-come-from-136105SNAP Participation Rates, was explored and analysed on ArcGIS Pro, the results of which can help decision makers set up further SNAP-D initiatives.In the USA foods are stored in every State and U.S. territory and may be used by state agencies or local disaster relief organizations to provide food to shelters or people who are in need.US Food Stamp Program has been ExtendedThe Supplemental Nutrition Assistance Program, SNAP, is a State Organized Food Stamp Program in the USA and was put in place to help individuals and families during this exceptional time. State agencies may request to operate a Disaster Supplemental Nutrition Assistance Program (D-SNAP) .D-SNAP Interactive DashboardAlmost all States have set up Food Relief Programs, in response to COVID-19.Scroll Down to Learn more about the SNAP Participation Analysis & ResultsSNAP Participation AnalysisInitial results of yearly participation rates to geography show statistically significant trends, to get acquainted with the results, explore the following 3D Time Cube Map:Visualize A Space Time Cube in 3Dhttps://arcg.is/1q8LLPnetCDF ResultsWORKFLOW: a space-time cube was generated as a netCDF structure with the ArcGIS Pro Space-Time Mining Tool : Create a Space Time Cube from Defined Locations, other tools were then used to incorporate the spatial and temporal aspects of the SNAP County Participation Rate Feature to reveal and render statistically significant trends about Nutrition Assistance in the USA.Hot Spot Analysis Explore the results in 2D or 3D.2D Hot Spotshttps://arcg.is/1Pu5WH02D Hot Spot ResultsWORKFLOW: Hot Spot Analysis, with the Hot Spot Analysis Tool shows that there are various trends across the USA for instance the Southeastern States have a mixture of consecutive, intensifying, and oscillating hot spots.3D Hot Spotshttps://arcg.is/1b41T43D Hot Spot ResultsThese trends over time are expanded in the above 3D Map, by inspecting the stacked columns you can see the trends over time which give result to the overall Hot Spot Results.Not all counties have significant trends, symbolized as Never Significant in the Space Time Cubes.Space-Time Pattern Mining AnalysisThe North-central areas of the USA, have mostly diminishing cold spots.2D Space-Time Mininghttps://arcg.is/1PKPj02D Space Time Mining ResultsWORKFLOW: Analysis, with the Emerging Hot Spot Analysis Tool shows that there are various trends across the USA for instance the South-Eastern States have a mixture of consecutive, intensifying, and oscillating hot spots.Results ShowThe USA has counties with persistent malnourished populations, they depend on Food Aide.3D Space-Time Mininghttps://arcg.is/01fTWf3D Space Time Mining ResultsIn addition to obvious planning for consistent Hot-Hot Spot Areas, areas oscillating Hot-Cold and/or Cold-Hot Spots can be identified for further analysis to mitigate the upward trend in food insecurity in the USA, since 2009 which has become even worse since the outbreak of the COVID-19 pandemic.After Notes:(i) The Johns Hopkins University has an Interactive Dashboard of the Evolution of the COVID-19 Pandemic.Coronavirus COVID-19 (2019-nCoV)(ii) Since March 2020 in a Response to COVID-19, SNAP has had to extend its benefits to help people in need. The Food Relief is coordinated within States and by local and voluntary organizations to provide nutrition assistance to those most affected by a disaster or emergency.Visit SNAPs Interactive DashboardFood Relief has been extended, reach out to your state SNAP office, if you are in need.(iii) Follow these Steps to build an ArcGIS Pro StoryMap:Step 1: [Get Data][Open An ArcGIS Pro Project][Run a Hot Spot Analysis][Review analysis parameters][Interpret the results][Run an Outlier Analysis][Interpret the results]Step 2: [Open the Space-Time Pattern Mining 2 Map][Create a space-time cube][Visualize a space-time cube in 2D][Visualize a space-time cube in 3D][Run a Local Outlier Analysis][Visualize a Local Outlier Analysis in 3DStep 3: [Communicate Analysis][Identify your Audience & Takeaways][Create an Outline][Find Images][Prepare Maps & Scenes][Create a New Story][Add Story Elements][Add Maps & Scenes] [Review the Story][Publish & Share]A submission for the Esri MOOCSpatial Data Science: The New Frontier in AnalyticsLinda Angulo LopezLauren Bennett . Shannon Kalisky . Flora Vale . Alberto Nieto . Atma Mani . Kevin Johnston . Orhun Aydin . Ankita Bakshi . Vinay Viswambharan . Jennifer Bell & Nick Giner

The folder contains the heatmap plots obtained from the gene burden analysis considering non-coding variants. The plots are provided in a static format (.jpeg) and in a more interactive one (.html). Additionally, tables generating the heatmaps are provided in a .txt file format.

This feature contain several data layers: 1 depicts the up-to-date COVID-19 cases for Nigeria by states and the 2 shows population density of Nigeria by Local Government Areas; and these were superimposed on each other for easy comparison; 3 is a map of the statistically significant population hot spot and cold spot in Nigeria. All these datasets constitute this well presented COVID-19 dashboard for monitoring Nigeria cases. Data sources include NCDC, WHO, and Africa Geoportal. The COVID-19 data is updated at least once per day, following NCDC update timeline. This layer is created and maintained by DR. NKEKI F. N. and his team (Eugene .A. Atakpiri and Akinde .N. Kolawole) to Support NCDC to fight against the spread of COVID-19 in Nigeria. This layer is opened to the public and free to share. Contact Info: Phone: +23408063131159Email: nkekifndidi@gmail.com