As of May 2, 2023, the outbreak of the coronavirus disease (COVID-19) had been confirmed in almost every country in the world. The virus had infected over 687 million people worldwide, and the number of deaths had reached almost 6.87 million. The most severely affected countries include the U.S., India, and Brazil.

COVID-19: background information COVID-19 is a novel coronavirus that had not previously been identified in humans. The first case was detected in the Hubei province of China at the end of December 2019. The virus is highly transmissible and coughing and sneezing are the most common forms of transmission, which is similar to the outbreak of the SARS coronavirus that began in 2002 and was thought to have spread via cough and sneeze droplets expelled into the air by infected persons.

Naming the coronavirus disease Coronaviruses are a group of viruses that can be transmitted between animals and people, causing illnesses that may range from the common cold to more severe respiratory syndromes. In February 2020, the International Committee on Taxonomy of Viruses and the World Health Organization announced official names for both the virus and the disease it causes: SARS-CoV-2 and COVID-19, respectively. The name of the disease is derived from the words corona, virus, and disease, while the number 19 represents the year that it emerged.

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.

This dataset provides values for CORONAVIRUS CASES reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

This graph represents the distribution of the dwellings where French people live the lockdown of March 17 due to coronavirus (COVID-19) in March 2020, by surface area in square meters. At that time 34 percent of respondents were confined in dwellings with a surface area varying between 80 and 109 square meters.

For more information on the coronavirus pandemic (COVID-19), please see our page: facts and figures about COVID-19 coronavirus.

Overview

The Marshall Project, the nonprofit investigative newsroom dedicated to the U.S. criminal justice system, has partnered with The Associated Press to compile data on the prevalence of COVID-19 infection in prisons across the country. The Associated Press is sharing this data as the most comprehensive current national source of COVID-19 outbreaks in state and federal prisons.

Lawyers, criminal justice reform advocates and families of the incarcerated have worried about what was happening in prisons across the nation as coronavirus began to take hold in the communities outside. Data collected by The Marshall Project and AP shows that hundreds of thousands of prisoners, workers, correctional officers and staff have caught the illness as prisons became the center of some of the country’s largest outbreaks. And thousands of people — most of them incarcerated — have died.

In December, as COVID-19 cases spiked across the U.S., the news organizations also shared cumulative rates of infection among prison populations, to better gauge the total effects of the pandemic on prison populations. The analysis found that by mid-December, one in five state and federal prisoners in the United States had tested positive for the coronavirus -- a rate more than four times higher than the general population.

This data, which is updated weekly, is an effort to track how those people have been affected and where the crisis has hit the hardest.

Methodology and Caveats

The data tracks the number of COVID-19 tests administered to people incarcerated in all state and federal prisons, as well as the staff in those facilities. It is collected on a weekly basis by Marshall Project and AP reporters who contact each prison agency directly and verify published figures with officials.

Each week, the reporters ask every prison agency for the total number of coronavirus tests administered to its staff members and prisoners, the cumulative number who tested positive among staff and prisoners, and the numbers of deaths for each group.

The time series data is aggregated to the system level; there is one record for each prison agency on each date of collection. Not all departments could provide data for the exact date requested, and the data indicates the date for the figures.

To estimate the rate of infection among prisoners, we collected population data for each prison system before the pandemic, roughly in mid-March, in April, June, July, August, September and October. Beginning the week of July 28, we updated all prisoner population numbers, reflecting the number of incarcerated adults in state or federal prisons. Prior to that, population figures may have included additional populations, such as prisoners housed in other facilities, which were not captured in our COVID-19 data. In states with unified prison and jail systems, we include both detainees awaiting trial and sentenced prisoners.

To estimate the rate of infection among prison employees, we collected staffing numbers for each system. Where current data was not publicly available, we acquired other numbers through our reporting, including calling agencies or from state budget documents. In six states, we were unable to find recent staffing figures: Alaska, Hawaii, Kentucky, Maryland, Montana, Utah.

To calculate the cumulative COVID-19 impact on prisoner and prison worker populations, we aggregated prisoner and staff COVID case and death data up through Dec. 15. Because population snapshots do not account for movement in and out of prisons since March, and because many systems have significantly slowed the number of new people being sent to prison, it’s difficult to estimate the total number of people who have been held in a state system since March. To be conservative, we calculated our rates of infection using the largest prisoner population snapshots we had during this time period.

As with all COVID-19 data, our understanding of the spread and impact of the virus is limited by the availability of testing. Epidemiology and public health experts say that aside from a few states that have recently begun aggressively testing in prisons, it is likely that there are more cases of COVID-19 circulating undetected in facilities. Sixteen prison systems, including the Federal Bureau of Prisons, would not release information about how many prisoners they are testing.

Corrections departments in Indiana, Kansas, Montana, North Dakota and Wisconsin report coronavirus testing and case data for juvenile facilities; West Virginia reports figures for juvenile facilities and jails. For consistency of comparison with other state prison systems, we removed those facilities from our data that had been included prior to July 28. For these states we have also removed staff data. Similarly, Pennsylvania’s coronavirus data includes testing and cases for those who have been released on parole. We removed these tests and cases for prisoners from the data prior to July 28. The staff cases remain.

About the Data

There are four tables in this data:

• covid_prison_cases.csv contains weekly time series data on tests, infections and deaths in prisons. The first dates in the table are on March 26. Any questions that a prison agency could not or would not answer are left blank.

• prison_populations.csv contains snapshots of the population of people incarcerated in each of these prison systems for whom data on COVID testing and cases are available. This varies by state and may not always be the entire number of people incarcerated in each system. In some states, it may include other populations, such as those on parole or held in state-run jails. This data is primarily for use in calculating rates of testing and infection, and we would not recommend using these numbers to compare the change in how many people are being held in each prison system.

• staff_populations.csv contains a one-time, recent snapshot of the headcount of workers for each prison agency, collected as close to April 15 as possible.

• covid_prison_rates.csv contains the rates of cases and deaths for prisoners. There is one row for every state and federal prison system and an additional row with the National totals.

Queries

The Associated Press and The Marshall Project have created several queries to help you use this data:

Get your state's prison COVID data: Provides each week's data from just your state and calculates a cases-per-100000-prisoners rate, a deaths-per-100000-prisoners rate, a cases-per-100000-workers rate and a deaths-per-100000-workers rate here

Rank all systems' most recent data by cases per 100,000 prisoners here

Find what percentage of your state's total cases and deaths -- as reported by Johns Hopkins University -- occurred within the prison system here

Attribution

In stories, attribute this data to: “According to an analysis of state prison cases by The Marshall Project, a nonprofit investigative newsroom dedicated to the U.S. criminal justice system, and The Associated Press.”

Contributors

Many reporters and editors at The Marshall Project and The Associated Press contributed to this data, including: Katie Park, Tom Meagher, Weihua Li, Gabe Isman, Cary Aspinwall, Keri Blakinger, Jake Bleiberg, Andrew R. Calderón, Maurice Chammah, Andrew DeMillo, Eli Hager, Jamiles Lartey, Claudia Lauer, Nicole Lewis, Humera Lodhi, Colleen Long, Joseph Neff, Michelle Pitcher, Alysia Santo, Beth Schwartzapfel, Damini Sharma, Colleen Slevin, Christie Thompson, Abbie VanSickle, Adria Watson, Andrew Welsh-Huggins.

Questions

If you have questions about the data, please email The Marshall Project at info+covidtracker@themarshallproject.org or file a Github issue.

To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

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...

"COVID-19 mortality correlation with cloudiness, sunlight, latitude in European countries"

Dataset for article titled "COVID-19 mortality: positive correlation with cloudiness, sunlight and no correlation with latitude in Europe"

by SECIL OMER, ADRIAN IFTIME, VICTOR BURCEA

Corresponding author: A. Iftime, University of Medicine and Pharmacy "Carol Davila", Biophysics Department, 8 Blvd. Eroii Sanitari, 050474 Bucharest, Romania. Email address: adrian.iftime [at] umfcd.ro.

Preprint corresponding to this dataset: https://doi.org/10.1101/2021.01.27.21250658

===========

Dataset file: 1.0.0.COVID-19_Mortality_Cloudiness_Insolation_EUROPE_March_August_2020.csv

Dataset graphical preview: 1.0.0.INFOGRAFIC_CloudFraction_vs_COVID-19_mortality_Europe_March-August_2020.png

DATASET fields: "Country" : Country name; 37 European countries included.

"Date": Date stamp at the collection time. Data collection was performed in the last day of every month. Date format: YYYY-MM-DD

"Month_Key" : Date stamp at the collection time, formatted for easier monthly time series analysis. Date format: YYYY-MM

"Month_Fct2020" Date stamp at the collection time,formatted for easier graphing, as a string with names of the months (in English).

"Deaths_per_1Mpop" : Monthly mortality from COVID-19 raported in the country, reported as number of COVID-19 deaths per 1 million population of the country, in that particular month / country. NB: it is reported as million population, not patients.

"LogDeaths_per_1Mpop" : Log10 transformation of "Deaths_per_1Mpop"

"Insolation_Average" : Insolation average (solar irradiance at ground level), in that particular month / country. It is expressed in Watt / square meter of the ground surface. Data derived from data avaialble at NASA Langley Research Center, NASA’s Earth Observatory, CERES / FLASHFlux team, 2020, https://neo.sci.gsfc.nasa.gov/view.php?datasetId=CERES_INSOL_M

"Cloud_Fraction" : Cloudiness (also known as cloud fraction, cloud cover, cloud amount or sky cover), as decimal fraction of the sky obscured by clouds, in that particular month / country. Data derived from NASA Goddard Space Flight Center, NASA’s Earth Observatory, MODIS Atmosphere Science Team, 2020, https://neo.sci.gsfc.nasa.gov/view.php?datasetId=MODAL2_M_CLD_FR

"CENTR_latitude" and "CENTR_longitude" : Latitude and Longitude of the country centroid, for each country. Data derived from Google LLC, "Dataset publishing language: country centroids", https://developers.google.com/public-data/docs/canonical/countries_csv
NOTE: This is identical in every month (obviuously); it is redundantly included for easier monthly sectional analysis of the data.

===========

Versioning: 1.0.0.COVID-19_Mortality_Cloudiness_Insolation_EUROPE_March_August_2020.csv

MAJOR: changes yearly; 1 = 2020 MINOR: changes if new monthly data is added in that particular year. PATCH: Changes only if errors or minor edits were performed.

DOI for this version: 10.5281/zenodo.4266758

Dataset file source for this version (internal analysis source file): db_covid_all-ANALYSIS.2020-09-22_r10.csv

Trends in Covid total deaths per million. The latest data for over 100 countries around the world.

Number of social distancing violations regressed on the number of people on the street and each of the other variables.

This graph illustrates the average surface area of the dwellings in which French people live during the containment of March 17 due to the coronavirus (COVID-19) in March 2020, by region and in square meters. At that time in the region of Bourgogne-Franche-Comté, French people were confined in dwellings with an average surface area of 108 square meters.

For more information on the coronavirus pandemic (COVID-19), please see our page: Facts and figures about COVID-19 coronavirus

The size and share of the market is categorized based on Application (Industrial Use, Commercial Use) and Product (Distribution Management Systems (DMS), Demand Response Management Systems (DRM), Meter Data Management Systems (MDMS), Supervisory Control and Data Acquisition (SCADA), Outage Management Systems (OMS), Smart Meter) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

Because of the outbreak of the coronavirus (COVID-19), many countries around the world were put into lockdown. In February, average levels of PM2.5 pollution in the Chinese city of Wuhan were 35.1 micrograms per cubic meter. This was a reduction of approximately 44 percent when compared to the same period in 2019.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Fact and Figures page.

General characteristics of 14 patients with COVID-19 confirmed.

Frequency of the statement related with knowledge level on COVID-19 (KLC-19).

The dataset has three parts; quantitative data, transcripts of Online FGDs and Photovoice Group Discussions, and Photovoice Photographs. Quantitative data includes the outcome variable which consists of nine measures: 1) maintaining a 1-meter distance, 2) avoiding handshakes, 3) avoiding hugs, 4) avoiding public transportation, 5) working/studying from home, 6) avoiding gatherings and crowds, 7) postponing meetings, 8) avoiding visiting elderly people, and 9) praying at home. In addition, other variables in this data set are sociodemographic characteristics; COVID-19-related variables such as COVID-19 testing, knowledge of COVID-19, etc.; and religious and tradition-related activities such as breaking fast during Ramadan, joining Mudik tradition, etc. Qualitative data includes Online FGDs and Photovoice Group Discussions transcripts and Photovoice Photographs. Five Online FGDs transcripts and 10 transcripts for Photovoice. 29 Photographs of Photovoice are also available in a list.

The COVID-19 pandemic lockdown worldwide provided a unique research opportunity for ecologists to investigate the human-wildlife relationship under abrupt changes in human mobility, also known as Anthropause. Here we chose 15 common non-migratory bird species with different levels of synanthrope and we aimed to compare how human mobility changes could influence the occupancy of fully synanthropic species such as House Sparrow (Passer domesticus) versus casual to tangential synanthropic species such as White-breasted Nuthatch (Sitta carolinensis). We extracted data from the eBird citizen science project during three study periods in the spring and summer of 2020 when human mobility changed unevenly across different counties in North Carolina. We used the COVID-19 Community Mobility reports from Google to examine how community mobility changes towards workplaces, an indicator of overall human movements at the county level, could influence bird occupancy., The data source we used for bird data was eBird, a global citizen science project run by the Cornell Lab of Ornithology. We used the COVID-19 Community Mobility Reports by Google to represent the pause of human activities at the county level in North Carolina. These data are publicly available and were last updated on 10/15/2022. We used forest land cover data from NC One Map that has a high resolution (1-meter pixel) raster data from 2016 imagery to represent canopy cover at each eBird checklist location. We also used the raster data of the 2019 National Land Cover Database to represent the degree of development/impervious surface at each eBird checklist location. All three measurements were used for the highest resolution that was available to use. We downloaded the eBird Basic Dataset (EBD) that contains the 15 study species from February to June 2020. We also downloaded the sampling event data that contains the checklist efforts information. First, we used the R package Auk (versio..., , # Processed data for the analysis of human mobility changes on bird occupancy in NC

https://doi.org/10.5061/dryad.gb5mkkwxr

There are 3 types of data here including Google Community Mobility data, and processed data (data after extracting spatial covariates and merging with all covariates for the Occupancy Modeling as well as extracted predicted occupancy data that we used to create figures).

Description of the data and file structure

Google Community Mobility data: This is the dataset downloaded from https://www.google.com/covid19/mobility/ that measures the mobility changes throughout the world during the COVID-19 lockdown. Please visit the above website for more information about the data. Please see the "Anthropause_AMCR_02112024" R file (uploaded to Zenodo) for details on how we processed the raw data.

| Dataset name | Dataset description ...

Clinical characteristics and laboratory results of 87 recovered COVID-19 patients.

In April 2020, the Sakha (Yakutiya) Republic recorded the most significant price drop in real estate prices in Russia with a roughly five percent price fall per square meter. In the Moscow and Leningrad Regions, the price of residential properties dropped by 3.2 and 3 percentage points per square meter over the given period, respectively.

In 2020, 30 percent of patients in oncology centers in Poland during the coronavirus epidemic (COVID-19) claimed that the number of patients in the hospital caused a crowd that made it impossible to maintain a distance of 2 meters.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

Unleash Flow Meters Market Growth Secrets! Discover key trends, CAGR of 6.0%, top applications & players like Honeywell. Download FREE report & gain insights!