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 the first reported coronavirus case in Washington State on Jan. 21, 2020, 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.

The highest R-value is recorded in the following voivodships: Dolnoslaskie - 1.43 and Warminsko-Mazurskie - 1.33. Such R-values indicate a continuous development of the COVID-19 epidemic in these regions. The high R-factor is confirmed by data on the incidence of the disease in these voivodeships.

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

Dataset

This dataset was created by Elif Özcan

Contents

This file contains data regarding a 7-day average of the estimated instantaneous reproduction number, R(t), of COVID-19 in Ottawa. The reproduction number, R, is the average number of secondary cases of disease caused by a single infected individual over his or her infectious period. R(t) values greater than 1 indicate the virus is spreading faster and each case infects more than one contact, and less than 1 indicates the spread is slowing and the epidemic is coming under control.

R(t) was calculated using the EpiEstim package, developed by Cori et al. (2013; DOI: 10.1093/aje/kwt133), in the R software environment for statistical computing and graphics. Accurate episode date was used as the time anchor and cases were assigned as having a local or travel-related source of infection.

Accuracy: Points of consideration for interpretation of the data: Data are entered into and extracted by Ottawa Public Health from la Solution de gestion des cas et des contacts pour la santé publique (Solution GCC). The CCM is a dynamic disease reporting system that allows for ongoing updates; data represent a snapshot at the time of extraction and may differ from previous or subsequent reports.As the cases are investigated and more information is available, the dates are updated.A person’s exposure may have occurred up to 14 days prior to onset of symptoms. Symptomatic cases occurring in approximately the last 14 days are likely under-reported due to the time for individuals to seek medical assessment, availability of testing, and receipt of test results.Confirmed cases are those with a confirmed COVID-19 laboratory result as per the Ministry of Health Public health management of cases and contacts of COVID-19 in Ontario. March 25, 2020 version 6.0.Counts will be subject to varying degrees of underreporting due to a variety of factors, such as disease awareness and medical care seeking behaviours, which may depend on severity of illness, clinical practice, changes in laboratory testing, and reporting behaviours.Surveillance testing for COVID-19 began in long term care facilities on April 25, 2020. Attributes: Data fields: Date – the earliest of symptom onset, test or reported date for cases (YYYY-MM-DD H:MM).Lower Bound - 95% Confidence Interval - lower bound of the 95% confidence interval for the 7-day average of the R(t) estimate. Upper Bound - 95% Confidence Interval - upper bound of the 95% confidence interval for the 7-day average of the R(t) estimate.Estimate of R(t) (7 Day Average) - 7-day average of the estimated instantaneous reproduction number, R(t), of COVID-19 in Ottawa. Nowcasting Adjusted Cases by Episode Date – number of Ottawa residents with confirmed COVID-19 by episode date. Counts for the most recent 14 days represent a nowcasting adjusted estimate developed by R. Imgrund in 2020. The model uses linear regression to estimate the number of future cases expected to have an accurate episode date within that 14-day window. Update Frequency: As of March 2022, the dataset is no longer updated. Historical data only. Contact: OPH Epidemiology Team

As of March 10, 2023, the death rate from COVID-19 in the state of New York was 397 per 100,000 people. New York is one of the states with the highest number of COVID-19 cases.

As global communities responded to COVID-19, we heard from public health officials that the same type of aggregated, anonymized insights we use in products such as Google Maps would be helpful as they made critical decisions to combat COVID-19. These Community Mobility Reports aimed to provide insights into what changed in response to policies aimed at combating COVID-19. The reports charted movement trends over time by geography, across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential.

Identifying changes in the reproduction number, rate of spread, and doubling time during the course of the COVID-19 outbreak whilst accounting for potential biases due to delays in case reporting at the local authority level in the United Kingdom.

Note: The date last updated is 2022-02-28, the dataset is no longer provided.

The R value, also known as the reproduction number, describes whether cases are currently increasing, decreasing or staying the same. It tells us the average number of people that someone with COVID-19 will infect.

For example, if the R value is:

• at 1, then an infected person will infect one other person, on average
• below 1, it means the rate of transmission was decreasing during that period
• above 1, the transmission rate was increasing

COVID-19 R values are updated weekly.

Data from https://www.alberta.ca/covid-19-alberta-data.aspx; updated 2022-03-18 16:08 with data as of end of day 2022-03-17.

Deaths involving coronavirus disease 2019 (COVID-19) by month of death, region, age, place of death, and race and Hispanic origin.

Identifying changes in the reproduction number, rate of spread, and doubling time during the course of the COVID-19 outbreak whilst accounting for potential biases due to delays in case reporting both nationally and subnationally in the United States of America. These results are impacted by changes in testing effort, increases and decreases in testing effort will increase and decrease reproduction number estimates respectively.

Identifying changes in the reproduction number, rate of spread, and doubling time during the course of the COVID-19 outbreak whilst accounting for potential biases due to delays in case reporting both nationally and subnationally in Italy. These results are impacted by changes in testing effort, increases and decreases in testing effort will increase and decrease reproduction number estimates respectively.

Association between FCV-19S score and experiences during COVID-19 epidemic.

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.

For English, see below Het aantal COVID-19 gerelateerde hospitalisaties is al geruime tijd laag en COVID-19 is per 1 juli 2023 geen meldingsplichtige ziekte meer. Daarom wordt de data vanaf 11 juli 2023 niet meer bijgewerkt. Het reproductiegetal R geeft het gemiddeld aantal mensen dat besmet wordt door één persoon met COVID-19. Voor de schatting van dit reproductiegetal gebruiken we het aantal gemelde COVID-19 ziekenhuisopnames per dag in Nederland. Dit aantal ziekenhuisopnames wordt bijgehouden door Stichting NICE (Nationale Intensive Care Evaluatie). Omdat een COVID-19 opname met enige vertraging doorgegeven wordt in het rapportagesysteem, corrigeren we het aantal opnames voor deze vertraging [1]. Voor een groot deel van de gemelde gevallen is de eerste ziektedag bekend. Deze informatie wordt gebruikt om de eerste ziektedag voor de ziekenhuisopnames te schatten. Door het aantal COVID-19 opnames per datum van eerste ziektedag weer te geven is direct te zien of het aantal infecties toeneemt, piekt of afneemt. Voor de berekening van het reproductiegetal is het ook nodig te weten wat de tijdsduur is tussen de eerste ziektedag van een COVID-19 geval en de eerste ziektedag van zijn of haar besmetter. Deze tijdsduur is gemiddeld 4 dagen voor SARS-CoV-2 varianten in 2020 en 2021, en gemiddeld 3.5 dagen voor recentere varianten, berekend op basis van COVID-19 meldingen aan de GGD. Met deze informatie wordt de waarde van het reproductiegetal berekend zoals beschreven in Wallinga & Lipsitch 2007 [2]. Tot 12 juni 2020 werd het reproductiegetal berekend op basis van COVID-19 ziekenhuisopnames, en tot 15 maart 2023 werd het reproductiegetal berekend op basis van COVID-19 meldingen aan de GGD’en. [1] van de Kassteele J, Eilers PHC, Wallinga J. Nowcasting the Number of New Symptomatic Cases During Infectious Disease Outbreaks Using Constrained P-spline Smoothing. Epidemiology. 2019;30(5):737-745. doi:10.1097/EDE.0000000000001050. [2] Wallinga J, Lipsitch M. How generation intervals shape the relationship between growth rates and reproductive numbers. Proc Biol Sci. 2007;274(1609):599-604. doi:10.1098/rspb.2006.3754. Beschrijving van de variabelen: Version: Versienummer van de dataset. Wanneer de inhoud van de dataset structureel wordt gewijzigd (dus niet de dagelijkse update of een correctie op record niveau), zal het versienummer aangepast worden (+1) en ook de corresponderende metadata in RIVMdata (https://data.rivm.nl). Versie 2 update (8 februari 2022): - In de berekening van het reproductiegetal wordt in plaats van de GGD meldingsdatum nu de datum van positieve testuitslag gebruikt. Versie 3 update (17 februari 2022): - In de berekening van het reproductiegetal wordt nu rekening gehouden met verschillende generatietijden voor verschillende varianten. Voor de varianten tot en met Delta is de gemiddelde generatietijd 4 dagen, vanaf Omikron is dat 3.5 dagen. Het hier gepubliceerde reproductiegetal is een gewogen gemiddelde van de reproductiegetallen per variant. Versie 4 update (1 september 2022): - Vanaf 1 september 2022 is deze dataset opgesplitst in twee delen. Het eerste deel bevat de data vanaf het begin van de pandemie tot en met 3 oktober 2021 (week 39) en bevat ‘tm’ in de bestandsnaam. Deze data wordt niet meer geüpdatet. Het tweede deel bevat de data vanaf 4 oktober 2021 (week 40) en wordt iedere dinsdag en vrijdag geüpdatet. - Tot 31 augustus werd het gepubliceerde reproductiegetal berekend met de data van de dag voor publicatie. Vanaf 1 september is het gepubliceerde reproductiegetal berekend met de data van de dag van publicatie. Versie 5 update (31 maart 2023): - Vanaf 15 maart 2023 wordt het reproductiegetal berekend op basis van COVID-19 ziekenhuisopnames volgens de NICE ziekenhuisregistratie. Van 13 juni 2020 t/m 14 maart 2023 werd het reproductiegetal berekend op basis van COVID-19 meldingen aan de GGD. Het aantal meldingen wordt echter sterk bepaald door het testbeleid, en is door het aangepaste testbeleid per 10 maart 2023 en het sluiten van de GGD teststraten per 17 maart 2023 minder geschikt als basis voor het berekenen van het reproductiegetal. Tot en met 12 juni 2020 werd het reproductiegetal ook berekend op basis van ziekenhuisopnames, maar toen zoals gemeld aan de GGD. Date: Datum waarvoor het reproductiegetal is geschat Rt_low: Ondergrens 95% betrouwbaarheidsinterval Rt_avg: Geschat reproductiegetal Rt_up: Bovengrens 95% betrouwbaarheidsinterval population: patiëntpopulatie met waarde “hosp” voor gehospitaliseerde patiënten of “testpos” voor test positieve patiënten Voor recente R schattingen is de betrouwbaarheid niet groot, omdat de betrouwbaarheid afhangt van de tijd tussen infectie en ziek worden en de tijd tussen ziek worden en melden. Daarom is de variabele Rt_avg afwezig in de laatste twee weken. -------------------------------------------------------------------------------- Covid-19 reproduction number The number of COVID-19 related hospitalizations has been low for quite some time and COVID-19 is no longer a notifiable disease as of July 1, 2023. Therefore, the data will no longer be updated from July 11, 2023. The reproduction number R gives the average number of people infected by one person with COVID-19. To estimate this reproduction number, we use the number of reported COVID-19 hospital admissions per day in the Netherlands. This number of hospital admissions is tracked by the NICE Foundation (National Intensive Care Evaluation). Because a COVID-19 admission is reported with some delay in the reporting system, we correct the number of admissions for this delay [1]. The first day of illness is known for a large proportion of the reported cases. This information is used to estimate the first day of illness for hospital admissions. By displaying the number of COVID-19 admissions per date of the first day of illness, it is immediately possible to see whether the number of infections is increasing, peaking or decreasing. To calculate the reproduction number, it is also necessary to know the length of time between the first day of illness of a COVID-19 case and the first day of illness of his or her infector. This duration is an average of 4 days for SARS-CoV-2 variants in 2020 and 2021, and an average of 3.5 days for more recent variants, calculated on the basis of COVID-19 reports to the PHS. With this information, the value of the reproduction number is calculated as described in Wallinga & Lipsitch 2007 [2]. Until June 12, 2020, the reproduction number was calculated on the basis of COVID-19 hospital admissions, and until March 15, 2023, the reproduction number was calculated on the basis of COVID-19 reports to the GGDs. [1] van de Kassteele J, Eilers PHC, Wallinga J. Nowcasting the Number of New Symptomatic Cases During Infectious Disease Outbreaks Using Constrained P-spline Smoothing. Epidemiology. 2019;30(5):737-745. doi:10.1097/EDE.0000000000001050. [2] Wallinga J, Lipsitch M. How generation intervals shape the relationship between growth rates and reproductive numbers. Proc Biol Sci. 2007;274(1609):599-604. doi:10.1098/rspb.2006.3754. Description of the variables: Version: Version number of the dataset. When the content of the dataset is structurally changed (so not the daily update or a correction at record level), the version number will be adjusted (+1) and also the corresponding metadata in RIVMdata (https://data.rivm.nl). Version 2 update (February 8, 2022): - In the calculation of the reproduction number, the date of the positive test result is now used instead of the PHS notification date. Version 3 update (February 17, 2022): - The calculation of the reproduction number now takes into account different generation times for different variants. For the variants up to and including Delta, the average generation time is 4 days, from Omikron it is 3.5 days. The reproduction number published here is a weighted average of the reproduction numbers per variant. Version 4 update (September 1, 2022): - As of September 1, 2022, this dataset is split into two parts. The first part contains the dates from the start of the pandemic till 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. - Until August 31, the published reproduction number was calculated with the data of the day before publication. From September 1, the published reproduction number is calculated with the data of the day of publication. Version 5 update (March 31, 2023): - As of March 15, 2023, the reproduction number is calculated based on COVID-19 hospital admissions according to the NICE hospital registry. From June 13, 2020 to March 14, 2023, the reproduction number was calculated on the basis of COVID-19 reports to the PHS. However, the number of reports is strongly determined by the test policy, and is less suitable as a basis for calculating the reproduction number due to the adjusted test policy as of March 10, 2023 and the closure of the PHS test lanes as of March 17, 2023. Until 12 June 2020, the reproduction number was also calculated on the basis of hospital admissions, but then as reported to the PHS. Date: Date for which the reproduction number was estimated Rt_low: Lower limit 95% confidence interval Rt_avg: Estimated reproduction number Rt_up: Upper bound 95% confidence interval population: patient population with value “hosp” for hospitalized patients or “testpos” for test positive patients For recent R estimates, the reliability is not great, because the reliability depends on the time between infection and becoming ill and the time between becoming ill and reporting. Therefore, the variable Rt_avg is absent in the last two weeks.

Updated Data Regarding COVID-19

This U.S. County COVID-19 Mapping Dashboard shows the county-by-county impact of the coronavirus across the U.S., including percentages of the population infected. https://covid.woolpert.com The link to the desktop version is on the left of this home page, and the mobile version on the right.

By clicking on any state in the left column, state data by county will appear. The map can also be used to navigate to an area of interest and the statistics for all counties within the map will update. There are links to each state’s data and surveillance dashboard and to the Twitter accounts of each state’s department of health.

This information will be refreshed daily as data becomes available.

For additional data, check out the COVID-19 GIS Hub by our partner Esri at https://coronavirus-disasterresponse.hub.arcgis.com/ #covid19

Identifying changes in the reproduction number, rate of spread, and doubling time during the course of the COVID-19 outbreak whilst accounting for potential biases due to delays in case reporting both nationally and subnationally in India. These results are impacted by changes in testing effort, increases and decreases in testing effort will increase and decrease reproduction number estimates respectively.

Introduction into heat maps, non-parametric t-test and GIF (optional) in R using an original dataset on COVID-19 infections from different counties of New Jersey, USA. Suitable for students who have basic experience in R.

The data is for COVID-19 clinics.\r \r From 20 October 2023, COVID-19 datasets will no longer be updated. \r Detailed information is available in the fortnightly NSW Respiratory Surveillance Report: https://www.health.nsw.gov.au/Infectious/covid-19/Pages/reports.aspx. \r Latest national COVID-19 spread, vaccination and treatment metrics are available on the Australian Government Health website: https://www.health.gov.au/topics/covid-19/reporting?language=und \r \r This dataset provides data on COVID-19 testing and assessment clinics by geolocation, address, contact details, services provided and opening hours.\r \r This data is subject to change as clinic locations are changed.\r \r The Government has obligations under the Privacy and Personal Information Protection Act 1998 and the Health Records and Information Privacy Act 2002 in relation to the collection, use and disclosure of the personal, including the health information, of individuals. Information about NSW Privacy laws is available here: https://data.nsw.gov.au/understand-key-data-legislation. \r \r The information published about COVID-19 clinics does not include any information to directly identify individuals, such as their name, date of birth or address.\r \r Other governments and private sector bodies also have legal obligations in relation to the protection of personal, including health, information. The Government does not authorise any reproduction or visualisation of the data on this website which includes any representation or suggestion in relation to the personal or health information of any individual. The Government does not endorse or control any third party websites including products and services offered by, from or through those websites or their content.\r \r For any further enquiries, please contact us at datansw@customerservice.nsw.gov.au

The Washington State Legislature has budgeted $200 million in funds to respond to the 2020 COVID-19 outbreak crisis, through Engrossed House Bill 2965. The link below provides information on COVID-19-related distributions to state agencies and institutions.

This dataset includes three tables with the model-based projections and estimates as shown on CalCAT in 2025 (http://calcat.cdph.ca.gov) for California state, regions, and counties.

(1) COVID-19 Nowcasts includes the R-effective estimates for COVID-19 from the different models available for the past 80 days from the archive date and the median ensemble thereof.

(2) CalCAT Forecasts includes hospital census and admissions forecasts for COVID-19 and Influenza, and the corresponding ensemble metrics for a 4 week horizon from the archive date.

(3) Variant Proportion Nowcasts contains the Integrated Genomic Epidemiology Dataset (IGED)-based and Terra-based estimates of COVID-19 variants circulating over the past 3 months as well as model-based predictions for the proportions of the variants of concern for dates leading up to the archive date. Prediction intervals are included when available.

This dataset provides CalCAT users with programmatic access to the downloadable datasets on CalCAT.

This dataset also includes a zipped file with the historical archives of the COVID-19 Nowcasts, CalCAT Forecasts and Variant Proportion Nowcasts through 2023.