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.

On March 10, 2023, the Johns Hopkins Coronavirus Resource Center ceased its collecting and reporting of global COVID-19 data. For updated cases, deaths, and vaccine data please visit: World Health Organization (WHO)For more information, visit the Johns Hopkins Coronavirus Resource Center.COVID-19 Trends MethodologyOur goal is to analyze and present daily updates in the form of recent trends within countries, states, or counties during the COVID-19 global pandemic. The data we are analyzing is taken directly from the Johns Hopkins University Coronavirus COVID-19 Global Cases Dashboard, though we expect to be one day behind the dashboard’s live feeds to allow for quality assurance of the data.DOI: https://doi.org/10.6084/m9.figshare.125529863/7/2022 - Adjusted the rate of active cases calculation in the U.S. to reflect the rates of serious and severe cases due nearly completely dominant Omicron variant.6/24/2020 - Expanded Case Rates discussion to include fix on 6/23 for calculating active cases.6/22/2020 - Added Executive Summary and Subsequent Outbreaks sectionsRevisions on 6/10/2020 based on updated CDC reporting. This affects the estimate of active cases by revising the average duration of cases with hospital stays downward from 30 days to 25 days. The result shifted 76 U.S. counties out of Epidemic to Spreading trend and no change for national level trends.Methodology update on 6/2/2020: This sets the length of the tail of new cases to 6 to a maximum of 14 days, rather than 21 days as determined by the last 1/3 of cases. This was done to align trends and criteria for them with U.S. CDC guidance. The impact is areas transition into Controlled trend sooner for not bearing the burden of new case 15-21 days earlier.Correction on 6/1/2020Discussion of our assertion of an abundance of caution in assigning trends in rural counties added 5/7/2020. Revisions added on 4/30/2020 are highlighted.Revisions added on 4/23/2020 are highlighted.Executive SummaryCOVID-19 Trends is a methodology for characterizing the current trend for places during the COVID-19 global pandemic. Each day we assign one of five trends: Emergent, Spreading, Epidemic, Controlled, or End Stage to geographic areas to geographic areas based on the number of new cases, the number of active cases, the total population, and an algorithm (described below) that contextualize the most recent fourteen days with the overall COVID-19 case history. Currently we analyze the countries of the world and the U.S. Counties. The purpose is to give policymakers, citizens, and analysts a fact-based data driven sense for the direction each place is currently going. When a place has the initial cases, they are assigned Emergent, and if that place controls the rate of new cases, they can move directly to Controlled, and even to End Stage in a short time. However, if the reporting or measures to curtail spread are not adequate and significant numbers of new cases continue, they are assigned to Spreading, and in cases where the spread is clearly uncontrolled, Epidemic trend.We analyze the data reported by Johns Hopkins University to produce the trends, and we report the rates of cases, spikes of new cases, the number of days since the last reported case, and number of deaths. We also make adjustments to the assignments based on population so rural areas are not assigned trends based solely on case rates, which can be quite high relative to local populations.Two key factors are not consistently known or available and should be taken into consideration with the assigned trend. First is the amount of resources, e.g., hospital beds, physicians, etc.that are currently available in each area. Second is the number of recoveries, which are often not tested or reported. On the latter, we provide a probable number of active cases based on CDC guidance for the typical duration of mild to severe cases.Reasons for undertaking this work in March of 2020:The popular online maps and dashboards show counts of confirmed cases, deaths, and recoveries by country or administrative sub-region. Comparing the counts of one country to another can only provide a basis for comparison during the initial stages of the outbreak when counts were low and the number of local outbreaks in each country was low. By late March 2020, countries with small populations were being left out of the mainstream news because it was not easy to recognize they had high per capita rates of cases (Switzerland, Luxembourg, Iceland, etc.). Additionally, comparing countries that have had confirmed COVID-19 cases for high numbers of days to countries where the outbreak occurred recently is also a poor basis for comparison.The graphs of confirmed cases and daily increases in cases were fit into a standard size rectangle, though the Y-axis for one country had a maximum value of 50, and for another country 100,000, which potentially misled people interpreting the slope of the curve. Such misleading circumstances affected comparing large population countries to small population counties or countries with low numbers of cases to China which had a large count of cases in the early part of the outbreak. These challenges for interpreting and comparing these graphs represent work each reader must do based on their experience and ability. Thus, we felt it would be a service to attempt to automate the thought process experts would use when visually analyzing these graphs, particularly the most recent tail of the graph, and provide readers with an a resulting synthesis to characterize the state of the pandemic in that country, state, or county.The lack of reliable data for confirmed recoveries and therefore active cases. Merely subtracting deaths from total cases to arrive at this figure progressively loses accuracy after two weeks. The reason is 81% of cases recover after experiencing mild symptoms in 10 to 14 days. Severe cases are 14% and last 15-30 days (based on average days with symptoms of 11 when admitted to hospital plus 12 days median stay, and plus of one week to include a full range of severely affected people who recover). Critical cases are 5% and last 31-56 days. Sources:U.S. CDC. April 3, 2020 Interim Clinical Guidance for Management of Patients with Confirmed Coronavirus Disease (COVID-19). Accessed online. Initial older guidance was also obtained online. Additionally, many people who recover may not be tested, and many who are, may not be tracked due to privacy laws. Thus, the formula used to compute an estimate of active cases is: Active Cases = 100% of new cases in past 14 days + 19% from past 15-25 days + 5% from past 26-49 days - total deaths. On 3/17/2022, the U.S. calculation was adjusted to: Active Cases = 100% of new cases in past 14 days + 6% from past 15-25 days + 3% from past 26-49 days - total deaths. Sources: https://www.cdc.gov/mmwr/volumes/71/wr/mm7104e4.htm https://covid.cdc.gov/covid-data-tracker/#variant-proportions If a new variant arrives and appears to cause higher rates of serious cases, we will roll back this adjustment. We’ve never been inside a pandemic with the ability to learn of new cases as they are confirmed anywhere in the world. After reviewing epidemiological and pandemic scientific literature, three needs arose. We need to specify which portions of the pandemic lifecycle this map cover. The World Health Organization (WHO) specifies six phases. The source data for this map begins just after the beginning of Phase 5: human to human spread and encompasses Phase 6: pandemic phase. Phase six is only characterized in terms of pre- and post-peak. However, these two phases are after-the-fact analyses and cannot ascertained during the event. Instead, we describe (below) a series of five trends for Phase 6 of the COVID-19 pandemic.Choosing terms to describe the five trends was informed by the scientific literature, particularly the use of epidemic, which signifies uncontrolled spread. The five trends are: Emergent, Spreading, Epidemic, Controlled, and End Stage. Not every locale will experience all five, but all will experience at least three: emergent, controlled, and end stage.This layer presents the current trends for the COVID-19 pandemic by country (or appropriate level). There are five trends:Emergent: Early stages of outbreak. Spreading: Early stages and depending on an administrative area’s capacity, this may represent a manageable rate of spread. Epidemic: Uncontrolled spread. Controlled: Very low levels of new casesEnd Stage: No New cases These trends can be applied at several levels of administration: Local: Ex., City, District or County – a.k.a. Admin level 2State: Ex., State or Province – a.k.a. Admin level 1National: Country – a.k.a. Admin level 0Recommend that at least 100,000 persons be represented by a unit; granted this may not be possible, and then the case rate per 100,000 will become more important.Key Concepts and Basis for Methodology: 10 Total Cases minimum threshold: Empirically, there must be enough cases to constitute an outbreak. Ideally, this would be 5.0 per 100,000, but not every area has a population of 100,000 or more. Ten, or fewer, cases are also relatively less difficult to track and trace to sources. 21 Days of Cases minimum threshold: Empirically based on COVID-19 and would need to be adjusted for any other event. 21 days is also the minimum threshold for analyzing the “tail” of the new cases curve, providing seven cases as the basis for a likely trend (note that 21 days in the tail is preferred). This is the minimum needed to encompass the onset and duration of a normal case (5-7 days plus 10-14 days). Specifically, a median of 5.1 days incubation time, and 11.2 days for 97.5% of cases to incubate. This is also driven by pressure to understand trends and could easily be adjusted to 28 days. Source

This layer has been DEPRECATED (last updated12/1/2021). This was formerly a weekly update.

Summary The Outbreak-Associated Cases in Congregate Living data dashboard on coronavirus.maryland.gov was redesigned on 11/17/21 to align with other outbreak reporting. Visit https://opendata.maryland.gov/dataset/MD-COVID-19-Congregate-Outbreak/ey5n-qn5s to view Outbreak-Associated Cases in Congregate Living data as reported after 11/17/21.

Confirmed COVID-19 deaths among Maryland residents within a single Maryland jurisdiction who live and work in congregate living facilities for the reporting period.

Description The MD COVID-19 - Total Deaths in Congregate Facility Settings data layer is a total of deaths confirmed by a positive COVID-19 test result that have been reported to MDH in nursing homes, assisted living facilities, group homes of 10 or more and state and local facilities in each Maryland jurisdiction for the reporting period. Data are reported to MDH by local health departments, the Department of Public Safety and Correctional Services and the Department of Juvenile Services. To appear on the list, facilities report at least one confirmed case of COVID-19 over the prior 14 days. Facilities are removed from the list when health officials determine 14 days have passed with no new cases and no tests pending. The list provides a point-in-time picture of COVID-19 case activity among these facilities. Numbers reported for each facility listed reflect totals ever reported for deaths. Data are updated once weekly.

Terms of Use The Spatial Data, and the information therein, (collectively the "Data") is provided "as is" without warranty of any kind, either expressed, implied, or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted, nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct, indirect, incidental, consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data, nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

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 Tracking Project collects information from 50 US states, the District of Columbia, and 5 other US territories to provide the most comprehensive testing data we can collect for the novel coronavirus, SARS-CoV-2. We attempt to include positive and negative results, pending tests, and total people tested for each state or district currently reporting that data.

Testing is a crucial part of any public health response, and sharing test data is essential to understanding this outbreak. The CDC is currently not publishing complete testing data, so we’re doing our best to collect it from each state and provide it to the public. The information is patchy and inconsistent, so we’re being transparent about what we find and how we handle it—the spreadsheet includes our live comments about changing data and how we’re working with incomplete information.

From here, you can also learn about our methodology, see who makes this, and find out what information states provide and how we handle it.

As of May 2, 2023, the outbreak of the coronavirus disease (COVID-19) had spread to almost every country in the world, and more than 6.86 million people had died after contracting the respiratory virus. Over 1.16 million of these deaths occurred in the United States.

Waves of infections Almost every country and territory worldwide have been affected by the COVID-19 disease. At the end of 2021 the virus was once again circulating at very high rates, even in countries with relatively high vaccination rates such as the United States and Germany. As rates of new infections increased, some countries in Europe, like Germany and Austria, tightened restrictions once again, specifically targeting those who were not yet vaccinated. However, by spring 2022, rates of new infections had decreased in many countries and restrictions were once again lifted.

What are the symptoms of the virus? It can take up to 14 days for symptoms of the illness to start being noticed. The most commonly reported symptoms are a fever and a dry cough, leading to shortness of breath. The early symptoms are similar to other common viruses such as the common cold and flu. These illnesses spread more during cold months, but there is no conclusive evidence to suggest that temperature impacts the spread of the SARS-CoV-2 virus. Medical advice should be sought if you are experiencing any of these symptoms.

At the height of the coronavirus pandemic, on the last day of March 2020, Wikipedia in all languages broke a record for most traffic in a single day. Since the breakout of the Covid-19 pandemic at the start of January, tens if not hundreds of millions of people have come to Wikipedia to read - and in some cases also contribute - knowledge, information and data about the virus to an ever-growing pool of articles. Our study focuses on the scientific backbone behind the content people across the world read: which sources informed Wikipedia’s coronavirus content, and how was the scientific research on this field represented on Wikipedia. Using citation as readout we try to map how COVID-19 related research was used in Wikipedia and analyse what happened to it before and during the pandemic. Understanding how scientific and medical information was integrated into Wikipedia, and what were the different sources that informed the Covid-19 content, is key to understanding the digital knowledge echosphere during the pandemic. To delimitate the corpus of Wikipedia articles containing Digital Object Identifier (DOI), we applied two different strategies. First we scraped every Wikipedia pages form the COVID-19 Wikipedia project (about 3000 pages) and we filtered them to keep only page containing DOI citations. For our second strategy, we made a search with EuroPMC on Covid-19, SARS-CoV2, SARS-nCoV19 (30’000 sci papers, reviews and preprints) and a selection on scientific papers form 2019 onwards that we compared to the Wikipedia extracted citations from the english Wikipedia dump of May 2020 (2’000’000 DOIs). This search led to 231 Wikipedia articles containing at least one citation of the EuroPMC search or part of the wikipedia COVID-19 project pages containing DOIs. Next, from our 231 Wikipedia articles corpus we extracted DOIs, PMIDs, ISBNs, websites and URLs using a set of regular expressions. Subsequently, we computed several statistics for each wikipedia article and we retrive Atmetics, CrossRef and EuroPMC infromations for each DOI. Finally, our method allowed to produce tables of citations annotated and extracted infromations in each wikipadia articles such as books, websites, newspapers.Files used as input and extracted information on Wikipedia's COVID-19 sources are presented in this archive.See the WikiCitationHistoRy Github repository for the R codes, and other bash/python scripts utilities related to this project.

From before to during the coronavirus pandemic, the share of respondents who made purchases via livestream increased by an average of ** percentage points worldwide. Of the regions included in the study, Europe saw the highest growth during this period, with livestream shoppers growing by ** percentage points. The Middle East followed with **, while North America recorded a usage spike of about ** percentage points.

To make it easy to see what content is being shared on social media about the virus, we’ve built a set of CrowdTangle Live Displays and made them public so everyone can have access. Use them to keep track of some of the biggest content about coronavirus on Facebook and Instagram from local news outlets, regional World Health Organization Pages, government agencies, local politicians, and more.

Click the Download button to access these Live Displays, or go here: https://apps.crowdtangle.com/public-hub/covid19

The COVID-19 pandemic that spread across the world at the beginning of 2020 was not only a big threat to public health, but also to the entire sports industry. Many professional leagues closed their doors to spectators or postponed their seasons entirely. During an ********** survey in the United States, some ** percent of respondents expected to be able to watch live professional and college sports again in August or **************.

For English, see below As of 1 January 2023, RIVM will no longer collect additional information. As a result, from January 1, 2023, we will no longer report data on infections among people over 70 living at home . File description: - This file contains the following numbers: (number of newly reported) positively tested individuals aged 70 and older living at home*, by safety region, per date of the positive test result. - (number of newly reported) deceased individuals aged 70 and older living at home who tested positive*, by safety region, by date on which the patient died. The numbers concern COVID-19 reports since the registration of the (residential) institution in OSIRIS with effect from questionnaire 5 (01-07-2020). * For reports from 01-07-2020, it is recorded whether the patient lives in an institution. Reports from 01-07-2020 are regarded as individuals aged 70 and older living at home if, according to the information known to the GGD, they: • Do not live in an institution AND • Are aged 70 or older AND • The person is not employed and is not a healthcare worker Persons whose residential facility/institution is not listed can still be excluded as individuals aged 70 and older living at home if they: • Can be linked to a known location of a disability care institution or nursing home on the basis of their 6-digit zip code OR • Have 'Disabled care institution' or 'Nursing home' as the location of the contamination mentioned. OR • Based on the content of free text fields, can be linked to a disability care institution or nursing home. The file is structured as follows: A set of records per date of with for each date: • A record for each security region (including 'Unknown') in the Netherlands, even if there are no reports for the relevant security region. The numbers are then 0 (zero). • Security region is unknown when a record cannot be assigned to one unique security region. A date 01-01-1900 is also included in this file for statistics whose associated date is unknown. The following describes how the variables are defined. Description of the variables: Version: Version number of the dataset. This version number is adjusted (+1) when the content of the dataset is structurally changed (so not the daily update or a correction at record level. The corresponding metadata in RIVMdata (https://data.rivm.nl) is also changed. Version 2 update (January 25, 2022): • An updated list of known nursing or care home locations and private residential care centers was received from the umbrella organization Patient Federation of the Netherlands on 03-12-2021. taken to determine whether individuals live in an institution Version 3 update (February 8, 2022) • From February 8, 2022, positive SARS-CoV-2 test results will be reported directly from CoronIT to RIVM. such as Testing for Access) and healthcare institutions (such as hospitals, nursing homes and general practitioners) that enter their positive SARS-CoV-2 test results via the Reporting Portal of GGD GHOR directly to RIVM. Reports that are part of the source and contact investigation sample and positive SARS-CoV-2 test results from healthcare institutions that are reported to the GGD via healthcare email are reported to RIVM via HPZone. From 8 February, the date of the positive test result is used and no longer the date of notification to the GGD. Version 4 update (March 24, 2022): • In version 4 of this dataset, records have been compiled according to the municipality reclassification of March 24, 2022. See description of the variable security_region_code for more information. Version 5 update (August 2, 2022): • The classification of persons aged 70 years and parents living independently has not been applied to reports that have only been received by RIVM since February 8, 2022 via an alternative reporting route. From 8 February to 1 August 2022, the number of reports from independently living persons aged 70 and parents was therefore underestimated by approximately 14%. As of August 2, 2022, this format will be retroactively updated. Version 6 update (September 1, 2022): - From September 1, 2022, the data will no longer be updated every working day, but on Tuesdays and Fridays. The data is retroactively updated on these days for the other days. - As of September 1, 2022, this dataset is split into two parts. The first part contains the dates from the start of the pandemic to October 3, 2021 (week 39) and contains "tm" in the file name. This data will no longer be updated. The second part contains the data from October 4, 2021 (week 40) and is updated every Tuesday and Friday. Date_of_report: Date and time on which the data file was created by RIVM. Date_of_statistic_reported: The date used for reporting the 70plus statistic living at home. This can be different for each reported statistic, namely: • For [Total_cases_reported] this is the date of the positive test result. • For [Total_deceased_reported] this is the date on which the patients died. Security_region_code: Security region code. The code of the security region based on the patient's place of residence. If the place of residence is not known, the safety region is based on the GGD that submitted the report, except for the Central and West Brabant and Brabant-Noord safety regions, since the GGD and safety region are not comparable for these regions. See also: https://www.cbs.nl/nl-nl/figures/detail/84721ENG?q=Veiliteiten From March 24, 2022, this file has been compiled according to the municipality classification of March 24, 2022. The municipality of Weesp has been merged into the municipality of Amsterdam . With this division, the Gooi- en Vechtstreek safety region has become smaller and the Amsterdam-Amstelland safety region larger; GGD Amsterdam has become larger and GGD Gooi- en Vechtstreek has become smaller (Municipal division on 1 January 2022 (cbs.nl). Security_region_name: Security region name. Security region name is based on the Security Region Code. See also: https://www.rijksoverheid.nl /topics/safety-regions-and-crisis-management/safety-regions Total_cases_reported: The number of new COVID-19 infected over-70s living at home reported to the GGD on [Date_of_statistic_reported].The actual number of COVID-19 infected over-70s living at home is higher than the number of reports in surveillance, because not everyone with a possible infection is tested. In addition, it is not known for every report whether this concerns a person over 70 living at home. Date_of_statistic_reported] The actual number of deceased people over 70 living at home who died of COVID-19 is higher than the number of reports in the surveillance, because not all deceased patients are tested and deaths are not legally reportable. Moreover, it is not known for every report whether this concerns a person over 70 living at home. Corrections made to reports in the OSIRIS source system can also lead to corrections in this database. In that case, numbers published by RIVM in the past may deviate from the numbers in this database. This file therefore always contains the numbers based on the most up-to-date data in the OSIRIS source system. The CSV file uses a semicolon as a separator. There are no empty lines in the file. Below are the column names and the types of values ​​in the CSV file: • Version: Consisting of a single whole number (integer). Is always filled for each row. Example: 2. • Date_of_report: Written in format YYYY-MM-DD HH:MM. Is always filled for each row. Example: 2020-10-16 10:00 AM. • Date_of_statistic_reported: Written in format YYYY-MM-DD. Is always filled for each row. Example: 2020-10-09. • Security_region_code: Consisting of 'VR' followed by two digits. Can also be empty if the region is unknown. Example: VR01. • Security_region_name: Consisting of a character string. Is always filled for each row. Example: Central and West Brabant. • Total_cases_reported: Consisting of only whole numbers (integer). Is always filled for each row. Example: 12. • Total_deceased_reported: Consisting of only whole numbers (integer). Is always filled for each row. Example: 8. ---------------------------------------------- ---------------------------------- Covid-19 statistics for persons aged 70 and older living outside an institution, by security region and date As of 1 January 2023, the RIVM will no longer collect additional information. As a result, from January 1, 2023, we will no longer report data on infections among people over 70 living at home. File description: This file contains the following numbers: - Number of newly reported persons aged 70 and older living at home who tested positive*, by security region, by date of the positive test result. - Number of newly reported deceased persons aged 70 and older living at home who tested positive*, by security region, by date on which the patient died. The numbers concern COVID-19 reports since the registration of the (residential) institution in OSIRIS with effect from questionnaire 5 (01-07-2020). * For reports from 01-07-2020, it is recorded whether the patient lives in an institution. For reports from 01-07-2020 persons aged 70 and older are considered to be living at home if, according to the information known to the PHS, they: • were not living in an institution AND • Are aged 70 years or older AND • The person is not employed and is not a healthcare worker Persons whose residential facility/institution is not listed can still be excluded as being an persons aged 70 and older living at home if they: • Based on their 6-digit zip code, can be linked to a known location of a care institution for the disabled or a nursing home OR • Have 'Disability care institution' or 'Nursing home' as the stated location of transmission. OR • Based on the content of free text fields, links can be made to a care institution for the disabled or a nursing home. The file is structured as follows: A set of records by date, with for

The Listening to Young Lives at Work: COVID-19 Phone Survey, First Call, Second Call and Third Call, 2020 is an adapted version of the Round 6 survey with additional questions to directly assess the impact of COVID-19. The survey consists of three phone calls with each of our Young Lives respondents, across both the younger and older cohorts, and in all four study countries (reaching an estimated total of around 11,000 young people).

The Phone Survey will enable Young Lives to inform policy makers on the short-term effects of the COVID-19 pandemic. Subsequently, and together with data collected in further survey rounds, Young Lives will be able to assess the medium and long term implications of the crisis. Further information is available on the Young Lives at Work webpage.

The Listening to Young Lives at Work: COVID-19 Phone Survey, First Call, Second Call and Third Call, 2020 is held at the UK Data Archive under SN 8678 and the Listening to Young Lives at Work: COVID-19 Phone Survey Calls 1-5 Constructed Files, 2020-2021 is held under SN 9070.

Latest edition information:
For the fourth edition (July 2022), region and cluster location variables have been added to the main survey datasets for all four countries, across the three phone surveys. Food security variables have also been added to the Second and Third Call datasets. A small inconsistency in the labelling of the typesite variable (urban/rural) has also been corrected. Additionally, documents related to copyright and survey references have been added, as well as a technical note related to the food security variables.

These data show the number of assisted living facility residents and employees who were reported to DC Health as having any type of symptom or COVID-19 exposure that prompted a healthcare provider to order a test to determine if they had COVID-19; many of these people were tested when DC Health approval was required for ordering a test through the DC Public Health Laboratory. Resident and personnel loss of life that was associated with a positive SARS-CoV-2 test has been documented since mid-March 2020; DC Health relies on assisted living residences to be forthcoming about this information in order for it to be properly documented in public reports. A resident is determined to be "cleared from isolation for COVID-19" if they are still alive and it has been at least 21 days since their initial symptom onset date or first positive specimen collection date for this COVID-19 infection.

Florida COVID-19 Case Line data, exported from the Florida Department of Health GIS Layer on date seen in file name. Archived by the University of South Florida Libraries, Digital Heritage and Humanities Collections. Contact: LibraryGIS@usf.edu.

Please Cite Our GIS HUB. If you are a researcher or other utilizing our Florida COVID-19 HUB as a tool or accessing and utilizing the data provided herein, please provide an acknowledgement of such in any publication or re-publication. The following citation is suggested: University of South Florida Libraries, Digital Heritage and Humanities Collections. 2020. Florida COVID-19 Hub. Available at https://covid19-usflibrary.hub.arcgis.com/. https://doi.org/10.5038/USF-COVID-19-GIS

Live FDOH Data Source: https://services1.arcgis.com/CY1LXxl9zlJeBuRZ/arcgis/rest/services/Florida_COVID19_Case_Line_Data_NEW/FeatureServer

Archives for this data layer begin on 5/11/2020. Archived data was exported directly from the live FDOH layer into the archive by the University of South Florida Libraries - Digital Heritage and Humanities Collection.

For data definitions please visit the following box folder: https://usf.box.com/s/vfjwbczkj73ucj19yvwz53at6v6w614h Data definition files names include the relative date they were published.

The below information was taken from ancillary documents associated with the original layer from the Florida Department of Health.

This data table represents all laboratory-confirmed cases of COVID-19 in Florida tabulated from the previous day's totals by the Florida Department of Health.

Persons Under Investigation/Surveillance (PUI): Essentially, PUIs are any person who has been or is waiting to be tested. This includes: persons who are considered high-risk for COVID-19 due to recent travel, contact with a known case, exhibiting symptoms of COVID-19 as determined by a healthcare professional, or some combination thereof. PUI’s also include people who meet laboratory testing criteria based on symptoms and exposure, as well as confirmed cases with positive test results. PUIs include any person who is or was being tested, including those with negative and pending results.

All PUIs fit into one of three residency types:

1. Florida residents tested in Florida

2. Non-Florida residents tested in Florida

3. Florida residents tested outside of Florida

Florida Residents Tested Elsewhere: The total number of Florida residents with positive COVID-19 test results who were tested outside of Florida, and were not exposed/infectious in Florida.

Non-Florida Residents Tested in Florida: The total number of people with positive COVID-19 test results who were tested, exposed, and/or infectious while in Florida, but are legal residents of another state.

Table Guide for Records of Confirmed Positive Cases of COVID-19

"County": The Florida county where the individual with COVID-19's case has been processed.

"Jurisdiction" of the case:

"FL resident" -- a resident of Florida "Non-FL resident" -- someone who resides outside of Florida "Travel_Related": Whether or not the positive case of COVID-19 is designated as related to recent travel by the individual. "No" -- Case designated as not being a risk related to recent travel "Unknown" -- Case designated where a travel-related designation has not yet been made. "Yes" -- Case is designated as travel-related for a person who recently traveled overseas or to an area with community "Origin": Where the person likely contracted the virus before arriving / returning to Florida.

"EDvisit": Whether or not an individual who tested positive for coronavirus visited and was admitted to an Emergency Department related to health conditions surrounding COVID-19. "No" -- Individual was not admitted to an emergency department relating to health conditions surrounding the contraction of COVID-19 "Unknown" -- It is unknown whether the individual was admitted to an emergency department relating to health conditions surrounding the contraction of COVID-19 "Yes" -- Individual was admitted to an emergency department relating to health conditions surrounding the contraction of COVID-19 “Hospitalized”: Whether or not a patient who receives a positive laboratory confirmed test for COVID-19 receives inpatient care at a hospital at any time during illness. These people may no longer be hospitalized. This information does not indicate that a COVID-19 positive person is currently hospitalized, only that they have been hospitalized for health conditions relating to COVID-19 at some point during their illness.

"No" -- Individual was not admitted for inpatient care at a hospital at any time during illness "Unknown" -- It is unknown whether the individual was admitted for inpatient care at a hospital at any time during illness "Yes" -- Individual was admitted for inpatient care at a hospital at some point during the illness "Died": Whether or not the individual who tested positive for COVID-19 died as a result of health complications from the viral infection. "NA" -- Not applicable / resident has not died "Yes" -- Individual died of a health complication resulting from COVID-19 "Contact": Whether the person contracted COVID-19 from contact with current or previously confirmed cases. "No" -- Case with no known contact with current or previously confirmed cases "Yes" -- Case with known contact with current or previously confirmed cases "Unknown" -- Case where contact with current or previous confirmed cases is not known or under investigation "Case_": The date the positive laboratory result was received in the Department of Health’s database system and became a “confirmed case.” This is not the date a person contracted the virus, became symptomatic, or was treated. Florida does not create a case or count suspected/probable cases in the case counts without a confirmed-positive lab result.

"EventDate": When the individual reported likely first experiencing symptoms related to COVID-19.

"ChartDate": Also the date the positive laboratory result for an individual was received in the Department of Health’s database system and became a recorded, “confirmed case” of COVID-19 in the state.

Data definitions updated by the FDOH on 5/13/2020.

As of April 26, 2023, the number of both confirmed and presumptive positive cases of the COVID-19 disease reported in the United States had reached over 104 million with over 1.1 million deaths reported among these cases.

Coronavirus deaths by age in the U.S. Daily new cases of COVID-19 hit record highs in the United States at the beginning of 2022. Underlying health conditions can worsen cases of coronavirus, and case fatality rates among confirmed COVID-19 patients increase with age. The highest number of deaths from COVID-19 have been among those aged 85 years and older, with this age group accounting for over 300 thousand deaths.

Where has this coronavirus come from? Coronaviruses are a large group of viruses transmitted between animals and people that cause illnesses ranging from the common cold to more severe diseases. The novel coronavirus that is currently infecting humans was already circulating among certain animal species. The first human case of this new coronavirus strain was reported in China at the end of December 2019. The coronavirus was named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and its associated disease is known as COVID-19.

Note: This dataset is no longer being maintained and will not be updated going forward.

The weekly and cumulative number of residents with confirmed COVID-19 and with COVID-19 associated deaths is obtained from data self-reported by individual assisted living facilities to the Long Term Care Mutual Aid Plan web-based reporting system (www.mutualaidplan.org/ct). Both confirmed and suspect deaths are included.

Confirmed deaths include those among persons who tested positive for COVID-19. Suspected deaths include those among persons with signs and symptoms suggestive of COVID-19 but who did not have a laboratory positive COVID-19 test. Due to differing data collection and processing methods between LTC-MAP and the death data sources used previously, cumulative death data for residents was re-baselined on July 14, 2020. The resident death data before and after July 14, 2020 should not be added due to the differing definitions of COVID-19 associated deaths used and the possibility of duplication of deaths among prior and current data.

The cumulative number of deaths among assisted living residents is based upon data reported by the Office of the Chief Medical Examiner. For public health surveillance, COVID-19-associated deaths include persons who tested positive for COVID-19 around the time of death (laboratory-confirmed) and persons whose death certificate lists COVID-19 disease as a cause of death or a significant condition contributing to death (probable). As of 7/15/20 deaths reported by the Office of the Chief Medical Examiner are no longer being updated on a weekly basis.

Attendees wearing masks and the availability of hand sanitizer were rated as two of the most important factors for attendees when determining if they would attend an event at the time of the COVID-19 pandemic. According to the survey, conducted with event professionals in July 2020, 90 percent thought that from their attendees perspective it would be important to have hand sanitizer available everywhere at events.

This data set includes data reported to VDH on outbreaks in Nursing Homes, Assisted Living Facilities and Multicare Facilities. The data included is the name of the facility, the date VDH was notified and the number of cases and deaths as well as the status of the outbreak. This data set was first published on July 03, 2020. The data set increases in size daily and as a result, the dataset may take longer to update; however, it is expected to be available by 12:00 noon weekly. When you download the data set, the dates will be sorted in ascending order, meaning that the earliest date will be at the top. To see data for the most recent date, please scroll down to the bottom of the data set. Starting November 02, 2020, the data set update schedule has changed from daily to weekly.

A. SUMMARY This archived dataset includes data for population characteristics that are no longer being reported publicly. The date on which each population characteristic type was archived can be found in the field “data_loaded_at”.

B. HOW THE DATASET IS CREATED Data on the population characteristics of COVID-19 cases are from:  * Case interviews  * Laboratories  * Medical providers    These multiple streams of data are merged, deduplicated, and undergo data verification processes.  

Race/ethnicity * We include all race/ethnicity categories that are collected for COVID-19 cases. * The population estimates for the "Other" or “Multi-racial” groups should be considered with caution. The Census definition is likely not exactly aligned with how the City collects this data. For that reason, we do not recommend calculating population rates for these groups.

Gender * The City collects information on gender identity using these guidelines.

Skilled Nursing Facility (SNF) occupancy * A Skilled Nursing Facility (SNF) is a type of long-term care facility that provides care to individuals, generally in their 60s and older, who need functional assistance in their daily lives.  * This dataset includes data for COVID-19 cases reported in Skilled Nursing Facilities (SNFs) through 12/31/2022, archived on 1/5/2023. These data were identified where “Characteristic_Type” = ‘Skilled Nursing Facility Occupancy’.

Sexual orientation * The City began asking adults 18 years old or older for their sexual orientation identification during case interviews as of April 28, 2020. Sexual orientation data prior to this date is unavailable. * The City doesn’t collect or report information about sexual orientation for persons under 12 years of age. * Case investigation interviews transitioned to the California Department of Public Health, Virtual Assistant information gathering beginning December 2021. The Virtual Assistant is only sent to adults who are 18+ years old. https://www.sfdph.org/dph/files/PoliciesProcedures/COM9_SexualOrientationGuidelines.pdf">Learn more about our data collection guidelines pertaining to sexual orientation.

Comorbidities * Underlying conditions are reported when a person has one or more underlying health conditions at the time of diagnosis or death.

Homelessness Persons are identified as homeless based on several data sources: * self-reported living situation * the location at the time of testing * Department of Public Health homelessness and health databases * Residents in Single-Room Occupancy hotels are not included in these figures. These methods serve as an estimate of persons experiencing homelessness. They may not meet other homelessness definitions.

Single Room Occupancy (SRO) tenancy * SRO buildings are defined by the San Francisco Housing Code as having six or more "residential guest rooms" which may be attached to shared bathrooms, kitchens, and living spaces. * The details of a person's living arrangements are verified during case interviews.

Transmission Type * Information on transmission of COVID-19 is based on case interviews with individuals who have a confirmed positive test. Individuals are asked if they have been in close contact with a known COVID-19 case. If they answer yes, transmission category is recorded as contact with a known case. If they report no contact with a known case, transmission category is recorded as community transmission. If the case is not interviewed or was not asked the question, they are counted as unknown.

C. UPDATE PROCESS This dataset has been archived and will no longer update as of 9/11/2023.

D. HOW TO USE THIS DATASET Population estimates are only available for age groups and race/ethnicity categories. San Francisco po

Important: This dataset is updated regularly and the latest version for download can be found here: https://www.semanticscholar.org/cord19/download. In response to the COVID-19 pandemic, the Allen Institute for AI has partnered with leading research groups to prepare and distribute the COVID-19 Open Research Dataset (CORD-19), a free resource of scholarly articles, including full text content, about COVID-19 and the coronavirus family of viruses for use by the global research community. This dataset is intended to mobilize researchers to apply recent advances in natural language processing to generate new insights in support of the fight against this infectious disease. The corpus will be updated weekly as new research is published in peer-reviewed publications and archival services like bioRxiv, medRxiv, and others. By downloading this dataset you are agreeing to the Dataset license. Specific licensing information for individual articles in the dataset is available in the metadata file. Additional licensing information is available on the PMC website, medRxiv website and bioRxiv website. Dataset content: Commercial use subset Non-commercial use subset PMC custom license subset bioRxiv/medRxiv subset (pre-prints that are not peer reviewed) Metadata file Readme Each paper is represented as a single JSON object (see schema file for details). Description: The dataset contains all COVID-19 and coronavirus-related research (e.g. SARS, MERS, etc.) from the following sources: PubMed's PMC open access corpus using this query (COVID-19 and coronavirus research) Additional COVID-19 research articles from a corpus maintained by the WHO bioRxiv and medRxiv pre-prints using the same query as PMC (COVID-19 and coronavirus research) We also provide a comprehensive metadata file of coronavirus and COVID-19 research articles with links to PubMed, Microsoft Academic and the WHO COVID-19 database of publications (includes articles without open access full text). We recommend using metadata from the comprehensive file when available, instead of parsed metadata in the dataset. Please note the dataset may contain multiple entries for individual PMC IDs in cases when supplementary materials are available. This repository is linked to the WHO database of publications on coronavirus disease and other resources, such as Microsoft Academic Graph, PubMed, and Semantic Scholar. A coalition including the Chan Zuckerberg Initiative, Georgetown University’s Center for Security and Emerging Technology, Microsoft Research, and the National Library of Medicine of the National Institutes of Health came together to provide this service. Citation: When including CORD-19 data in a publication or redistribution, please cite our arXiv pre-print. The Allen Institute for AI and particularly the Semantic Scholar team will continue to provide updates to this dataset as the situation evolves and new research is released.