NOTE: This dataset is no longer updated as of 1/31/2024.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

COVID-19 testing sites in the District of Columbia. Individuals are encouraged to get tested through their own health care provider so that when the test results come back the patient is already connected to the health care they need. If an individual needs a COVID-19 test and they do not have a provider, there are a number of options to obtain a test and a provider. If an individual needs a test and their provider is unable to give them a test, that individual should come to one of the District’s walk-up or drive-thru sites. More information at https://coronavirus.dc.gov/testing.

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

The dataset contain lung x-ray image including:

1. Normal - 3,270 images
2. Covid-19 - 3,017 images
3. Viral-pneumonia - 3,013 images
4. Bacterial-pneumonia - 3,000 images
5. Emphysema - 2,550 images

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15315323%2F8041ddd2485bfe9cdf2ba1f9d96bd7e5%2F6_Class_Img.jpg?generation=1741951756137022&alt=media" alt="">

The dataset we use is compiled from many reputable sources including: Dataset 1 [1]: This dataset includes four classes of diseases: COVID-19, viral pneumonia, bacterial pneumonia, and normal. It has multiple versions, and we are currently using the latest version (version 4). Previous studies, such as those by Hariri et al. [18] and Ahmad et al. [20], have also utilized earlier versions of this dataset. Dataset 2 [2]: This dataset is from the National Institutes of Health (NIH) Chest X-Ray Dataset, which contains over 100,000 chest X-ray images from over 30,000 patients. It includes 14 disease classes, including conditions like atelectasis, consolidation, and infiltration. For this study, we have selected 2,550 chest X-ray images specifically from the Emphysema class. Dataset 3 [3]: This is the COVQU dataset, which we have extended to include two additional classes: COVID-19 and viral pneumonia. This dataset has been widely used in previous studies by M.E.H. Chowdhury et al. [4] and Rahman T et al. [5], establishing its reputation as a reliable resource.

In addition, we also publish a modified dataset that aims to remove image regions that do not contain lungs (abdomen, arms, etc.).

References: [1] U. Sait, K. G. Lal, S. P. Prajapati, R. Bhaumik, T. Kumar, S. Shivakumar, K. Bhalla, Curated dataset for covid-19 posterior-anterior chest radiography images (x-rays)., Mendeley Data V4 (2022). doi:10.17632/9xkhgts2s6.4. [2] X. Wang, Y. Peng, L. Lu, Z. Lu, M. Bagheri, R. M. Summers, Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases (2017) 3462–3471. doi:10.1109/CVPR.2017.369. [3] A. M. Tahir, M. E. Chowdhury, A. Khandakar, T. Rahman, Y. Qiblawey, U. Khurshid, S. Kiranyaz, N. Ibtehaz, M. S. Rahman, S. Al-Maadeed,S. Mahmud, M. Ezeddin, K. Hameed, T. Hamid, Covid-19 infection localization and severity grading from chest x-ray images, Computers in Biology and Medicine 139 (2021) 105002. URL: https://www.sciencedirect.com/science/article/pii/S0010482521007964. doi:https://doi.org/10.1016/j.compbiomed.2021.105002. [4] M. E. Chowdhury, T. Rahman, A. Khandakar, R. Mazhar, M. A. Kadir, Z. B. Mahbub, K. R. Islam, M. S. Khan, A. Iqbal, N. A. Emadi, M. B. I. Reaz, M. T. Islam, Can ai help in screening viral and covid-19 pneumonia?, IEEE Access 8 (2020) 132665–132676. doi:10.1109/ACCESS.2020.3010287. [5] T. Rahman, A. Khandakar, Y. Qiblawey, A. Tahir, S. Kiranyaz, S. B. A. Kashem, M. T. Islam, S. A. Maadeed, S. M. Zughaier, M. S. Khan, M. E. Chowdhury, Exploring the effect of image enhancement techniques on covid-19 detection using chest x-ray images, Computers in Biology and Medicine 132 (2021). doi:10.1016/j.compbiomed.2021.104319.

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.).

Business Analyst Report Template. This infographic contains data provided by American Community Survey (ACS), Esri, Esri and Infogroup. The vintage of the data is 2013-2017, 2019, 2024.

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).

Information on Medical Facilities in Various Countries

1. Hospital beds (per 10 000 population) 2. Hospital beds (per 10 000 population) 3. Country has national policy or strategy on the use of social media by government organizations 4. Total density per 100 000 population: District/rural hospitals 5. Total density per 100 000 population: Provincial hospitals 6. Total density per 100 000 population, Specialized hospitals 7. Community health workers density (per 10 000 population)

Let me know if there are any other factors we can use, and I will update the column accordingly.

Let's keep the fight up against COVID-19

Data Sources: Global Health Observatory API: https://www.who.int/data/gho

Tasks to do: Append Proper list of Countries as per SpatialDim

Background: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) can be triggered by infectious agents including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, the impact of the coronavirus disease 2019 (COVID-19) pandemic on ME/CFS prevalence is not well characterized. Methods: In this population-based cross-sectional study, we enrolled a stratified random sample of 9,825 adult participants in the Kaiser Permanente Northern California (KPNC) integrated health system from July to October 2022 to assess overall ME/CFS-like illness prevalence and the proportion that were identified following COVID-19 illness. We used medical record and survey data to estimate the prevalence of ME/CFS-like illness based on self-reported symptoms congruent with the 2015 Institute of Medicine ME/CFS criteria. History of COVID-19 was based on a positive SARS-CoV-2 nucleic acid amplification test or ICD-10 diagnosis code in the medical record, or self-report of prior COVID-19 on a survey. Results: Of 2,745,374 adults in the eligible population, an estimated 45,892 (95% confidence interval [CI]: 32,869, 58,914) or 1.67% (CI 1.20%, 2.15%) had ME/CFS-like illness. Among those with ME/CFS-like illness, an estimated 14.12% (CI 3.64%, 24.6%) developed the illness after COVID-19. Among persons who had COVID-19, those with ME/CFS-like illness after COVID-19 were more likely to be unvaccinated and to have had COVID-19 before June 1, 2021. All persons with ME/CFS-like illness had significant impairment in physical, mental, emotional, social, and occupational functioning compared to persons without ME/CFS-like illness. Conclusions: In a large, integrated health system, 1.67% of adults had ME/CFS-like illness and 14.12% of all persons with ME/CFS-like illness developed it after COVID-19. Though COVID-19 did not substantially increase ME/CFS-like illness in the KPNC population during the study time period, ME/CFS-like illness nevertheless affects a notable portion of this population and is consistent with estimates of ME/CFS prevalence in other populations. Additional attention is needed to improve awareness, diagnosis, and treatment of ME/CFS.

View daily updates and historical trends for Maine Coronavirus Full Vaccination Rate. Source: Our World in Data. Track economic data with YCharts analytic…

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.

BackgroundThe global prevalence of PASC is estimated to be present in 0·43 and based on the WHO estimation of 470 million worldwide COVID-19 infections, corresponds to around 200 million people experiencing long COVID symptoms. Despite this, its clinical features are not well-defined.MethodsWe collected retrospective data from 140 patients with PASC in a post-COVID-19 clinic on demographics, risk factors, illness severity (graded as one-mild to five-severe), functional status, and 29 symptoms and principal component symptoms cluster analysis. The Institute of Medicine (IOM) 2015 criteria were used to determine the Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) phenotype.FindingsThe median age was 47 years, 59.0% were female; 49.3% White, 17.2% Hispanic, 14.9% Asian, and 6.7% Black. Only 12.7% required hospitalization. Seventy-two (53.5%) patients had no known comorbid conditions. Forty-five (33.9%) were significantly debilitated. The median duration of symptoms was 285.5 days, and the number of symptoms was 12. The most common symptoms were fatigue (86.5%), post-exertional malaise (82.8%), brain fog (81.2%), unrefreshing sleep (76.7%), and lethargy (74.6%). Forty-three percent fit the criteria for ME/CFS, majority were female, and obesity (BMI > 30 Kg/m2) (P = 0.00377895) and worse functional status (P = 0.0110474) were significantly associated with ME/CFS.InterpretationsMost PASC patients evaluated at our clinic had no comorbid condition and were not hospitalized for acute COVID-19. One-third of patients experienced a severe decline in their functional status. About 43% had the ME/CFS subtype.

Sociodemographic factors of outbreak Near Me respondents and COVID-19 cases in Ontario based on geographic region of dwelling.

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.

View daily updates and historical trends for Maine Coronavirus Tests Administered. Source: US Department of Health & Human Services. Track economic data w…