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.

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

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

On March 10, 2023, the Johns Hopkins Coronavirus Resource Center ceased collecting and reporting of global COVID-19 data. For updated cases, deaths, and vaccine data please visit the following sources:Global: World Health Organization (WHO)U.S.: U.S. Centers for Disease Control and Prevention (CDC)For more information, visit the Johns Hopkins Coronavirus Resource Center.This feature layer contains the most up-to-date COVID-19 cases and the latest trend plot. It covers the US (county or state level), China, Canada, Australia (province/state level), and the rest of the world (country/region level, represented by either the country centroids or their capitals). Data sources are WHO, CDC, ECDC, NHC, DXY, 1point3acres, Worldometers.info, BNO, the COVID Tracking Project (testing and hospitalizations), state and national government health departments, and local media reports. This layer is created and maintained by the Center for Systems Science and Engineering (CSSE) at the Johns Hopkins University. This feature layer is supported by Esri Living Atlas team, JHU APL and JHU Data Services. This layer is opened to the public and free to share. Contact us.

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.

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

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.

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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.

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 8th, 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 24th, 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 24th 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.

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.

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.

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.

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

A joint map of resources targeted towards the remedy and recovery during and after the COVID 19 crisis. Information about resources and support services provided by a number of organisations across the city.If you are a provider of services and resources, your information can be added and made public via the form available from here.If you have any questions about this dataset please email smart@leicester.gov.uk or complete the form available from here.

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.

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.

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

This map is designed to work in the new ArcGIS Online Map Viewer. Open in Map Viewer to view map. What does this map show?This map shows the population in the US by race. The map shows this pattern nationwide for states, counties, and tracts. Open the map in the new ArcGIS Online Map Viewer Beta to see the dot density pattern. What is dot density?The density is visualized by randomly placing one dot per a given value for the desired attribute. Unlike choropleth visualizations, dot density can be mapped using total counts since the size of the polygon plays a significant role in the perceived density of the attribute.Where is the data from?The data in this map comes from the most current American Community Survey (ACS) from the U.S. Census Bureau. Table B03002. The layer being used if updated with the most current data each year when the Census releases new estimates. The layer can be found in ArcGIS Living Atlas of the World: ACS Race and Hispanic Origin Variables - Boundaries.What questions does this map answer?Where do people of different races live?Do people of a similar race live close to people of their own race?Which cities have a diverse range of different races? Less diverse?

The following dashboards provide data on contagious respiratory viruses, including acute respiratory diseases, COVID-19, influenza (flu), and respiratory syncytial virus (RSV) in Massachusetts. The data presented here can help track trends in respiratory disease and vaccination activity across Massachusetts.

The folder contains the different heatmaps originating from the variant prioritization analysis in both .jpeg and .html file. The latter are interactive. Moreover, the individuals genotype table originating the heatmaps is provided in .txt format.