NOTE: This dataset is no longer updated as of 1/31/2024. Please use COVID-19 Treatments. Locations of publicly available COVID-19 Therapeutics. Dataset only includes locations for Paxlovid (oral antiviral), Lagevrio (oral antiviral), and outpatient Veklury (intravenous antiviral infusion). COVID-19 therapeutics require a prescription to obtain. Limitations: public contact information. To filter, click 'View Data' below, then 'Filter.' To save your view, click 'Save as,' and this configuration will be saved in your profile under 'My Assets.' Please try not to publish dataset publicly, unless necessary. On 1/3/2022 - The following changes were made to this dataset. - Dropped the Expected Deliver Date column - This was a derived field set to 3 days after the Last Order Date field. - Added the following fields Last Date Delivered Total Courses Courses Available Courses Available Date On 1/4/2022 - Added Geocoded Address On 1/11/2022 - Added NPI - National Provider Identifier On 2/18/2022 - Added new therapeutics, Bebtelovimab & Sotrovimab. On 3/16/2022 - Dropped the following columns - last_order_date - last_date_delivered - total_courses - courses_available_date Added the following columns - facility_id - last_report_date - grantee_code - provider_pin - state_provider_pin On 3/31/2022 - Dropped the following columns - facility_id - grantee_code - provider_pin - state_provider_pin Added the following columns - provider_status - provider_note

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.

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.

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.

After over two years of public reporting, the State Profile Report will no longer be produced and distributed after February 2023. The final release was on February 23, 2023. We want to thank everyone who contributed to the design, production, and review of this report and we hope that it provided insight into the data trends throughout the COVID-19 pandemic. Data about COVID-19 will continue to be updated at CDC’s COVID Data Tracker.

The State Profile Report (SPR) is generated by the Data Strategy and Execution Workgroup in the Joint Coordination Cell, in collaboration with the White House. It is managed by an interagency team with representatives from multiple agencies and offices (including the United States Department of Health and Human Services (HHS), the Centers for Disease Control and Prevention, the HHS Assistant Secretary for Preparedness and Response, and the Indian Health Service). The SPR provides easily interpretable information on key indicators for each state, down to the county level.

It is a weekly snapshot in time that:

• Focuses on recent outcomes in the last seven days and changes relative to the month prior
• Provides additional contextual information at the county level for each state, and includes national level information
• Supports rapid visual interpretation of results with color thresholds

As of March 10, 2023, the state with the highest rate of COVID-19 cases was Rhode Island followed by Alaska. Around 103.9 million cases have been reported across the United States, with the states of California, Texas, and Florida reporting the highest numbers of infections.

From an epidemic to a pandemic The World Health Organization declared the COVID-19 outbreak as a pandemic on March 11, 2020. The term pandemic refers to multiple outbreaks of an infectious illness threatening multiple parts of the world at the same time; when the transmission is this widespread, it can no longer be traced back to the country where it originated. The number of COVID-19 cases worldwide is roughly 683 million, and it has affected almost every country in the world.

The symptoms and those who are most at risk Most people who contract the virus will suffer only mild symptoms, such as a cough, a cold, or a high temperature. However, in more severe cases, the infection can cause breathing difficulties and even pneumonia. Those at higher risk include older persons and people with pre-existing medical conditions, including diabetes, heart disease, and lung disease. Those aged 85 years and older have accounted for around 27 percent of all COVID deaths in the United States, although this age group makes up just two percent of the total population

This file contains COVID-19 death counts, death rates, and percent of total deaths by jurisdiction of residence. The data is grouped by different time periods including 3-month period, weekly, and total (cumulative since January 1, 2020). United States death counts and rates include the 50 states, plus the District of Columbia and New York City. New York state estimates exclude New York City. Puerto Rico is included in HHS Region 2 estimates. Deaths with confirmed or presumed COVID-19, coded to ICD–10 code U07.1. Number of deaths reported in this file are the total number of COVID-19 deaths received and coded as of the date of analysis and may not represent all deaths that occurred in that period. Counts of deaths occurring before or after the reporting period are not included in the file. Data during recent periods are incomplete because of the lag in time between when the death occurred and when the death certificate is completed, submitted to NCHS and processed for reporting purposes. This delay can range from 1 week to 8 weeks or more, depending on the jurisdiction and cause of death. Death counts should not be compared across states. Data timeliness varies by state. Some states report deaths on a daily basis, while other states report deaths weekly or monthly. The ten (10) United States Department of Health and Human Services (HHS) regions include the following jurisdictions. Region 1: Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, Vermont; Region 2: New Jersey, New York, New York City, Puerto Rico; Region 3: Delaware, District of Columbia, Maryland, Pennsylvania, Virginia, West Virginia; Region 4: Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South Carolina, Tennessee; Region 5: Illinois, Indiana, Michigan, Minnesota, Ohio, Wisconsin; Region 6: Arkansas, Louisiana, New Mexico, Oklahoma, Texas; Region 7: Iowa, Kansas, Missouri, Nebraska; Region 8: Colorado, Montana, North Dakota, South Dakota, Utah, Wyoming; Region 9: Arizona, California, Hawaii, Nevada; Region 10: Alaska, Idaho, Oregon, Washington. Rates were calculated using the population estimates for 2021, which are estimated as of July 1, 2021 based on the Blended Base produced by the US Census Bureau in lieu of the April 1, 2020 decennial population count. The Blended Base consists of the blend of Vintage 2020 postcensal population estimates, 2020 Demographic Analysis Estimates, and 2020 Census PL 94-171 Redistricting File (see https://www2.census.gov/programs-surveys/popest/technical-documentation/methodology/2020-2021/methods-statement-v2021.pdf). Rates are based on deaths occurring in the specified week/month and are age-adjusted to the 2000 standard population using the direct method (see https://www.cdc.gov/nchs/data/nvsr/nvsr70/nvsr70-08-508.pdf). These rates differ from annual age-adjusted rates, typically presented in NCHS publications based on a full year of data and annualized weekly/monthly age-adjusted rates which have been adjusted to allow comparison with annual rates. Annualization rates presents deaths per year per 100,000 population that would be expected in a year if the observed period specific (weekly/monthly) rate prevailed for a full year. Sub-national death counts between 1-9 are suppressed in accordance with NCHS data confidentiality standards. Rates based on death counts less than 20 are suppressed in accordance with NCHS standards of reliability as specified in NCHS Data Presentation Standards for Proportions (available from: https://www.cdc.gov/nchs/data/series/sr_02/sr02_175.pdf.).

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.

A. SUMMARY A list of testing locations including address and coordinates for mapping.

B. HOW THE DATASET IS CREATED Dataset is manually compiled by staff from the various testing providers.

C. UPDATE PROCESS Data is updated as needed. If you have a site you'd like added to the dataset and map, please email DPH.DOC.Ops.Testing@sfdph.org.

D. HOW TO USE THIS DATASET Dataset can be used to map locations of test sites and understand which require insurance and which do not. It is the base data used to power the user-facing map of testing locations at https://datasf.org/covid19-testing-locations

Project Tycho datasets contain case counts for reported disease conditions for countries around the world. The Project Tycho data curation team extracts these case counts from various reputable sources, typically from national or international health authorities, such as the US Centers for Disease Control or the World Health Organization. These original data sources include both open- and restricted-access sources. For restricted-access sources, the Project Tycho team has obtained permission for redistribution from data contributors. All datasets contain case count data that are identical to counts published in the original source and no counts have been modified in any way by the Project Tycho team, except for aggregation of individual case count data into daily counts when that was the best data available for a disease and location. The Project Tycho team has pre-processed datasets by adding new variables, such as standard disease and location identifiers, that improve data interpretability. We also formatted the data into a standard data format. All geographic locations at the country and admin1 level have been represented at the same geographic level as in the data source, provided an ISO code or codes could be identified, unless the data source specifies that the location is listed at an inaccurate geographical level. For more information about decisions made by the curation team, recommended data processing steps, and the data sources used, please see the README that is included in the dataset download ZIP file.

United States SB: ME: COVID Test/Vaccine: Negative COVID Test: N/A data was reported at 14.500 % in 11 Apr 2022. This records a decrease from the previous number of 17.100 % for 04 Apr 2022. United States SB: ME: COVID Test/Vaccine: Negative COVID Test: N/A data is updated weekly, averaging 15.100 % from Nov 2021 (Median) to 11 Apr 2022, with 17 observations. The data reached an all-time high of 27.100 % in 27 Dec 2021 and a record low of 10.200 % in 14 Mar 2022. United States SB: ME: COVID Test/Vaccine: Negative COVID Test: N/A data remains active status in CEIC and is reported by U.S. Census Bureau. The data is categorized under Global Database’s United States – Table US.S049: Small Business Pulse Survey: by State: Northeast Region: Weekly, Beg Monday (Discontinued).

This dashboard displays reported Maine COVID-19 confirmed positive cases, recovered cases, and deaths throughout the state at the county level. The dashboard is updated daily with data compiled from the Maine CDC Novel Coronavirus 2019 (COVID-19) status web page: https://www.maine.gov/dhhs/mecdc/infectious-disease/epi/airborne/coronavirus.shtml.

United States Excess Death excl COVID: Predicted: Single Estimate: Maine data was reported at 0.000 Number in 16 Sep 2023. This stayed constant from the previous number of 0.000 Number for 09 Sep 2023. United States Excess Death excl COVID: Predicted: Single Estimate: Maine data is updated weekly, averaging 0.000 Number from Jan 2017 (Median) to 16 Sep 2023, with 350 observations. The data reached an all-time high of 54.000 Number in 06 Nov 2021 and a record low of 0.000 Number in 16 Sep 2023. United States Excess Death excl COVID: Predicted: Single Estimate: Maine data remains active status in CEIC and is reported by Centers for Disease Control and Prevention. The data is categorized under Global Database’s United States – Table US.G012: Number of Excess Deaths: by States: All Causes excluding COVID-19: Predicted (Discontinued).

What being vaccinated against COVID-19 means for me. By getting vaccinated, you benefit from the protection you get against COVID-19 and the easing of restrictive measures in your community.

This dataset's answers referes to 281 employees (fixed employees and public servants), both genders. We have information about marital status, level of education, income and also questions to better understand how populous the teleworking was, considering the second pandemic pick. It means that data was collected between May and July/2021. About the constructs, we publish in this database the follow constructs. - Work as Meaning Inventory WAMI: is a 10-item measure assessing search for Greater Good Motivation (3 items; e.g. My work helps me make sense of the world around me), positive meaning (4 items; e.g. I have found a meaningful career”) and contribution to meaning-making (3 items; e.g. I view my work as contributing to my personal growth”). The items of the inventory are rated from 1 (absolutely untrue) to 5 (absolutely true); - Emotions: Scale composed by positive emotions (N= 4 items) and reverse score of negative emotions (5 items) (item of positive emotion example "over the past six months my work made me feel happy"; item of negative emotion example "over the past six months my work made me feel upset". The response scale is a 5-point agreement scale. - Performance. Short Version of General Self-Assessment Scale of Job Performance has 10 items to measure Task and Context performance (e.g. I take initiatives to improve my results at work). Items are rated from 1 (absolutely false) to 5 (absolutely true). - I- deal. The HR flexibility and development i-deal is a measure with 6 items (HR i-deal e.g. I try to negotiate my job conditions with the company). We use the scale is a single factor just considering flexibility aspect. Items rated from 1 (Never) to 5 (Always). - Recovery Experience Questionnaire is composed of four types of recovery experience (psychological detachment, relaxation, mastery and control). We reduce 1 item of each scale based on the factor loading. Participants indicated on a 5-point scale (1 = never to 5 = always) the extent to which each recovery experience was de-scribing the psychological detachment (3-item), relaxation (3-item), mastery (3-item), and control (3-item).. We keep in this database all the factors about the constructs measured and also the clusters we found considering Belasos Model. All these information was collected in a first moment (T1 386_COVID-19 BR.csv)

An application used by the public to locate the nearest coronavirus testing location.

Context

This is a dataset of the most highly populated city (if applicable) in a form easy to join with the COVID19 Global Forecasting (Week 1) dataset. You can see how to use it in this kernel

Content

There are four columns. The first two correspond to the columns from the original COVID19 Global Forecasting (Week 1) dataset. The other two is the highest population density, at city level, for the given country/state. Note that some countries are very small and in those cases the population density reflects the entire country. Since the original dataset has a few cruise ships as well, I've added them there.

Acknowledgements

Thanks a lot to Kaggle for this competition that gave me the opportunity to look closely at some data and understand this problem better.

Inspiration

Summary: I believe that the square root of the population density should relate to the logistic growth factor of the SIR model. I think the SEIR model isn't applicable due to any intervention being too late for a fast-spreading virus like this, especially in places with dense populations.

After playing with the data provided in COVID19 Global Forecasting (Week 1) (and everything else online or media) a bit, one thing becomes clear. They have nothing to do with epidemiology. They reflect sociopolitical characteristics of a country/state and, more specifically, the reactivity and attitude towards testing.

The testing method used (PCR tests) means that what we measure could potentially be a proxy for the number of people infected during the last 3 weeks, i.e the growth (with lag). It's not how many people have been infected and recovered. Antibody or serology tests would measure that, and by using them, we could go back to normality faster... but those will arrive too late. Way earlier, China will have experimentally shown that it's safe to go back to normal as soon as your number of newly infected per day is close to zero.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F197482%2F429e0fdd7f1ce86eba882857ac7a735e%2Fcovid-summary.png?generation=1585072438685236&alt=media" alt="">

My view, as a person living in NYC, about this virus, is that by the time governments react to media pressure, to lockdown or even test, it's too late. In dense areas, everyone susceptible has already amble opportunities to be infected. Especially for a virus with 5-14 days lag between infections and symptoms, a period during which hosts spread it all over on subway, the conditions are hopeless. Active populations have already been exposed, mostly asymptomatic and recovered. Sensitive/older populations are more self-isolated/careful in affluent societies (maybe this isn't the case in North Italy). As the virus finishes exploring the active population, it starts penetrating the more isolated ones. At this point in time, the first fatalities happen. Then testing starts. Then the media and the lockdown. Lockdown seems overly effective because it coincides with the tail of the disease spread. It helps slow down the virus exploring the long-tail of sensitive population, and we should all contribute by doing it, but it doesn't cause the end of the disease. If it did, then as soon as people were back in the streets (see China), there would be repeated outbreaks.

Smart politicians will test a lot because it will make their condition look worse. It helps them demand more resources. At the same time, they will have a low rate of fatalities due to large denominator. They can take credit for managing well a disproportionally major crisis - in contrast to people who didn't test.

We were lucky this time. We, Westerners, have woken up to the potential of a pandemic. I'm sure we will give further resources for prevention. Additionally, we will be more open-minded, helping politicians to have more direct responses. We will also require them to be more responsible in their messages and reactions.

[Edit 12/09/2020] You will now find in the files below the last 30 days, too many people do not respect the request not to recover too often the dataset (no interest in recovering every minute while the file changes 4 or 5 times a day) If you want access to the entire history, contact me [Edit 31/03/2020] Since yesterday, I made sure to have the data of the day since the ESSC, so the data of the same day are now available and updated several times a day (about every hour) as the new figures fall all over the world. The data of the previous day is always consolidated around 2am (it is no longer 1h since the time change). If you only want to have the complete data, just don't take into account the last day (today’s date) Here I share the data that I compile with the famous coronavirus infection world map created and maintained by The Johns Hopkins University and which serve me to display ** CoronaVirus statistics worldwide and by country** They share the day’s data each night on a GitHub deposit. My tools compile this new data as soon as they are available and I share the result here. This data is used to display tables and graphs on the CoronaVirus website (Covid19) of Politologue.com https://coronavirus.politologue.com/ This data will allow you to make your own graphs and analyses if you look at the subject. I do not oblige you to do it, but if my compilation allows you to do something about it and saved you time, a link to https://coronavirus.politologue.com/ will be appreciable. Information in files (csv and json) — Number of cases — Number of deaths — Number of healing — Death rate (percentage) — Healing rate (percentage) — Infection rate (persons still infected, not deceased or cured) (percentage) — And for data by country, you will find a field “country” If you integrate the client-side json or csv on a site or application, please keep a cache on your servers without risking an unexpected load on my servers.

This dashboard serves as a single-point resource for Maine COVID-19 case data. Included in the application is information about the development of COVID-19 in Maine; frequently asked questions from the Maine CDC; resources for Mainers; case data at the county level (including confirmed positive cases, hospitalizations, recovered cases, and deaths); age distribution of tested positive patients; and data about hospital resources such as the number of COVID-19 positive patients in critical care and on ventilators.

Feature point layer showing locations of distribution sites that currently have COVID-19 rapid antigen test kits available in Manitoba. The main purpose of this data is to provide the public with the hours of operation for these sites. Distributors include pharmacies, retail stores, and community libraries. This data is populated by Manitoba Shared Health and is updated as needed.This feature layer is used in the Manitoba COVID-19 Rapid Antigen Test Kit Availability Map and Manitoba COVID-19 Rapid Antigen Test Kit Availability application.For more information on Manitoba’s response to COVID-19, please visit the following site: https://www.gov.mb.ca/covid19/index.htmlFields included [Alias (Field Name): Field description]Site Category (Site_Category): Indicates whether the site is a retail business, pharmacy, or community librarySite Name (Site_Name): Official business name of the site Address (Address): Street address of the site City or Town (City_or_Town): City or town in which the site is locatedPostal Code (Postal_Code): Postal code that corresponds with the street address of the site Phone (Phone): Telephone number for the siteFrench Service (French_Service): Indicates whether a site self-identified as having services available in French; if unknown, value is null Health Authority (Health_Authority): Name of the regional health authority where the site is locatedHours of Operation (Hours_of_Operation): Listing of days and times that the site is open to the publicAlt Hours Start (Alt_Hours_Start): Start date of when alternate hours are in effectAlt Hours End (Alt_Hours_End): End date of when alternate hours are in effectAlternate Hours of Operation (Alternate_Hours_of_Operation): Listing of alternate days and times that the site is open to the publicLatitude (Latitude): Latitudinal coordinate of the siteLongitude (Longitude): Longitudinal coordinate of the site