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.

2019 Novel Coronavirus COVID-19 (2019-nCoV) Visual Dashboard and Map:
https://www.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6

• Confirmed Cases by Country/Region/Sovereignty
• Confirmed Cases by Province/State/Dependency
• Deaths
• Recovered

Downloadable data:
https://github.com/CSSEGISandData/COVID-19

Additional Information about the Visual Dashboard:
https://systems.jhu.edu/research/public-health/ncov

JHU Has Stopped Collecting Data As Of 03/10/2023

After three years of around-the-clock tracking of COVID-19 data from around the world, Johns Hopkins has discontinued the Coronavirus Resource Center’s operations.

The site’s two raw data repositories will remain accessible for information collected from 1/22/20 to 3/10/23 on cases, deaths, vaccines, testing and demographics.

Novel Corona Virus (COVID-19) epidemiological data since 22 January 2020. The data is compiled by the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) from various sources including the World Health Organization (WHO), DXY.cn, BNO News, National Health Commission of the People’s Republic of China (NHC), China CDC (CCDC), Hong Kong Department of Health, Macau Government, Taiwan CDC, US CDC, Government of Canada, Australia Government Department of Health, European Centre for Disease Prevention and Control (ECDC), Ministry of Health Singapore (MOH), and others. JHU CCSE maintains the data on the 2019 Novel Coronavirus COVID-19 (2019-nCoV) Data Repository on Github.

Fields available in the data include Province/State, Country/Region, Last Update, Confirmed, Suspected, Recovered, Deaths.

On 23/03/2020, a new data structure was released. The current resources for the latest time series data are:

• time_series_covid19_confirmed_global.csv
• time_series_covid19_deaths_global.csv
• time_series_covid19_recovered_global.csv

---DEPRECATION WARNING---
The resources below ceased being updated on 22/03/2020 and were removed on 26/03/2020:

• time_series_19-covid-Confirmed.csv
• time_series_19-covid-Deaths.csv
• time_series_19-covid-Recovered.csv

This dataset (COV19Tweets) includes CSV files that contain IDs and sentiment scores of the tweets related to the COVID-19 pandemic. The real-time Twitter feed is monitored for coronavirus-related tweets using 90+ different keywords and hashtags that are commonly used while referencing the pandemic. The oldest tweets in this dataset date back to October 01, 2019. This dataset has been wholly re-designed on March 20, 2020, to comply with the content redistribution policy set by Twitter. Twitter's policy restricts the sharing of Twitter data other than IDs; therefore, only the tweet IDs are released through this dataset. You need to hydrate the tweet IDs in order to get complete data. For detailed instructions on the hydration of tweet IDs, please read this article.Announcements: We release CrisisTransformers (https://huggingface.co/crisistransformers), a family of pre-trained language models and sentence encoders introduced in the paper "CrisisTransformers: Pre-trained language models and sentence encoders for crisis-related social media texts". The models were trained based on the RoBERTa pre-training procedure on a massive corpus of over 15 billion word tokens sourced from tweets associated with 30+ crisis events such as disease outbreaks, natural disasters, conflicts, etc. CrisisTransformers were evaluated on 18 public crisis-specific datasets against strong baselines such as BERT, RoBERTa, BERTweet, etc. Our pre-trained models outperform the baselines across all 18 datasets in classification tasks, and our best-performing sentence-encoder outperforms the state-of-the-art by more than 17% in sentence encoding tasks. Please refer to the associated paper for more details.MegaGeoCOV Extended — an extended version of MegaGeoCOV has been released. The dataset is introduced in the paper "A Twitter narrative of the COVID-19 pandemic in Australia".We have released BillionCOV — a billion-scale COVID-19 tweets dataset for efficient hydration. Hydration takes time due to limits placed by Twitter on its tweet lookup endpoint. We re-hydrated the tweets present in this dataset (COV19Tweets) and found that more than 500 million tweet identifiers point to either deleted or protected tweets. If we avoid hydrating those tweet identifiers alone, it saves almost two months in a single hydration task. BillionCOV will receive quarterly updates, while this dataset (COV19Tweets) will continue to receive updates every day. Learn more about BillionCOV on its page: https://dx.doi.org/10.21227/871g-yp65. Related publications:Rabindra Lamsal. (2021). Design and analysis of a large-scale COVID-19 tweets dataset. Applied Intelligence, 51(5), 2790-2804.Rabindra Lamsal, Aaron Harwood, Maria Rodriguez Read. (2022). Socially Enhanced Situation Awareness from Microblogs using Artificial Intelligence: A Survey. ACM Computing Surveys, 55(4), 1-38. (arXiv)Rabindra Lamsal, Aaron Harwood, Maria Rodriguez Read. (2022). Twitter conversations predict the daily confirmed COVID-19 cases. Applied Soft Computing, 129, 109603. (arXiv)Rabindra Lamsal, Aaron Harwood, Maria Rodriguez Read. (2022). Addressing the location A/B problem on Twitter: the next generation location inference research. In 2022 ACM SIGSPATIAL LocalRec (pp. 1-4).Rabindra Lamsal, Aaron Harwood, Maria Rodriguez Read. (2022). Where did you tweet from? Inferring the origin locations of tweets based on contextual information. In 2022 IEEE International Conference on Big Data (pp. 3935-3944). (arXiv)Rabindra Lamsal, Maria Rodriguez Read, Shanika Karunasekera. (2023). BillionCOV: An Enriched Billion-scale Collection of COVID-19 tweets for Efficient Hydration. Data in Brief, 48, 109229. (arXiv)Rabindra Lamsal, Maria Rodriguez Read, Shanika Karunasekera. (2023). A Twitter narrative of the COVID-19 pandemic in Australia. In 20th International ISCRAM Conference (pp. 353-370). (arXiv)Rabindra Lamsal, Maria Rodriguez Read, Shanika Karunasekera. (2024). CrisisTransformers: Pre-trained language models and sentence encoders for crisis-related social media texts. Knowledge-Based Systems, 296, 111916. (arXiv)Rabindra Lamsal, Maria Rodriguez Read, Shanika Karunasekera. (2024). Semantically Enriched Cross-Lingual Sentence Embeddings for Crisis-related Social Media Texts. In 21st International ISCRAM Conference (in press). (arXiv)An Open access Billion-scale COVID-19 Tweets Dataset (COV19Tweets)— Dataset name: COV19Tweets Dataset— Number of tweets : 2,263,729,117 tweets— Coverage : Global— Language : English (EN)— Dataset usage terms : By using this dataset, you agree to (i) use the content of this dataset and the data generated from the content of this dataset for non-commercial research only, (ii) remain in compliance with Twitter's Policy and (iii) cite the following paper:Lamsal, R. (2021). Design and analysis of a large-scale COVID-19 tweets dataset. Applied Intelligence, 51, 2790-2804. https://doi.org/10.1007/s10489-020-02029-zBibTeX entry:@article{lamsal2021design, title={Design and analysis of a large-scale COVID-19 tweets dataset}, author={Lamsal, Rabindra}, journal={Applied Intelligence}, volume={51}, number={5}, pages={2790--2804}, year={2021}, publisher={Springer} }— Geo-tagged Version: Coronavirus (COVID-19) Geo-tagged Tweets Dataset (GeoCOV19Tweets Dataset)— Dataset updates : Everyday— Active keywords and hashtags (archive: keywords.tsv) : corona, #corona, coronavirus, #coronavirus, covid, #covid, covid19, #covid19, covid-19, #covid-19, sarscov2, #sarscov2, sars cov2, sars cov 2, covid_19, #covid_19, #ncov, ncov, #ncov2019, ncov2019, 2019-ncov, #2019-ncov, pandemic, #pandemic #2019ncov, 2019ncov, quarantine, #quarantine, flatten the curve, flattening the curve, #flatteningthecurve, #flattenthecurve, hand sanitizer, #handsanitizer, #lockdown, lockdown, social distancing, #socialdistancing, work from home, #workfromhome, working from home, #workingfromhome, ppe, n95, #ppe, #n95, #covidiots, covidiots, herd immunity, #herdimmunity, pneumonia, #pneumonia, chinese virus, #chinesevirus, wuhan virus, #wuhanvirus, kung flu, #kungflu, wearamask, #wearamask, wear a mask, vaccine, vaccines, #vaccine, #vaccines, corona vaccine, corona vaccines, #coronavaccine, #coronavaccines, face shield, #faceshield, face shields, #faceshields, health worker, #healthworker, health workers, #healthworkers, #stayhomestaysafe, #coronaupdate, #frontlineheroes, #coronawarriors, #homeschool, #homeschooling, #hometasking, #masks4all, #wfh, wash ur hands, wash your hands, #washurhands, #washyourhands, #stayathome, #stayhome, #selfisolating, self isolating Important Notes:> Dataset files are published in chronological order.> Twitter's content redistribution policy restricts the sharing of tweet information other than tweet IDs and/or user IDs. Twitter wants researchers to always pull fresh data. It is because a user might delete a tweet or make his/her profile protected.> Retweets are excluded in the files corona_tweets_chi.csv and earlier.> Only the tweet IDs are available (sentiment scores are not available) for the tweets present in the files: corona_tweets_11b.csv, corona_tweets_223.csv, corona_tweets_297.csv, corona_tweets_395.csv and the files containing tweets from before March 20, 2020.> March 29, 2020 04:02 PM - March 30, 2020 02:00 PM -- Some technical fault has occurred. Preventive measures have been taken. Tweets for this session won't be available. [update: the tweets for this session are now available in the corona_tweets_11b.csv file; retweets are excluded though]> Please go through the Dataset Files section for specific notes.> There's a Combined_Files section (at the bottom of the dataset files list) if you want to download dataset files in bulk.> The naming convention for the later added CSVs (tweets from before March 20, 2020) will have a greek alphabet name instead of a numeric counter. I'll start with the last greek alphabet name "omega" and proceed up towards "alpha".> If you want access to tweets older than October 01, 2019, feel free to reach out to me at rlamsal [at] student.unimelb.edu.au using your academic/research institution email.Dataset Files (GMT+5:45)--------- tweets from before March 20, 2020 ---------corona_tweets_theta.csv: 418,625 tweets (October 01, 2019 12:00 AM - October 18, 2019, 07:51 AM)corona_tweets_iota.csv: 1,000,000 tweets (October 18, 2019, 07:51 AM - December 01, 2019 01:25 AM)corona_tweets_kappa.csv: 1,000,000 tweets (December 01, 2019 01:25 AM - January 09, 2020, 10:20 PM)corona_tweets_lambda.csv: 1,000,000 tweets (January 09, 2020, 10:20 PM - January 26, 2020, 05:14 PM)corona_tweets_mu.csv: 1,000,000 tweets (January 26, 2020, 05:14 PM - January 31, 2020, 07:18 AM)corona_tweets_nu.csv: 1,000,000 tweets (January 31, 2020, 07:18 AM - February 05, 2020 03:38 PM)corona_tweets_xi.csv: 4,003,032 tweets (February 05, 2020 03:38 PM - February 28, 2020 04:27 AM)corona_tweets_omicron.csv: 3,000,000 tweets (February 28, 2020 04:27 AM - March 04, 2020 03:36 PM)corona_tweets_pi.csv: 3,000,000 tweets (March 04, 2020 03:36 PM - March 09, 2020 07:58 AM)corona_tweets_rho.csv: 3,990,232 tweets (March 09, 2020 07:58 AM - March 12, 2020 12:01 PM)corona_tweets_sigma.csv: 3,000,000 tweets (March 12, 2020 12:01 PM - March 13, 2020 07:13 PM)corona_tweets_tau.csv: 3,000,000 tweets (March 13, 2020 07:13 PM - March 15, 2020 04:03 AM)corona_tweets_upsilon.csv: 3,999,408 tweets (March 15, 2020 04:03 AM - March 17, 2020 03:25 AM)corona_tweets_phi.csv: 3,000,000 tweets (March 17, 2020 03:25 AM - March 18, 2020 06:51 AM)corona_tweets_chi.csv: 3,000,000 tweets (March 18, 2020 06:51 AM - March 19, 2020 10:57 AM)corona_tweets_psi.csv: 3,878,586 tweets (March 19, 2020 10:57 AM - March 19, 2020 08:04 PM)corona_tweets_omega.csv: 4,000,000 tweets (March 19, 2020 08:04 PM - March 20, 2020 01:37 AM)----------------------------------corona_tweets_01.csv + corona_tweets_02.csv + corona_tweets_03.csv: 2,475,980 tweets (March 20, 2020 01:37 AM - March 21, 2020 09:25 AM)corona_tweets_04.csv: 1,233,340

This dataset (GeoCOV19Tweets) contains IDs and sentiment scores of geo-tagged tweets related to the COVID-19 pandemic. The real-time Twitter feed is monitored for coronavirus-related tweets using 90+ different keywords and hashtags that are commonly used while referencing the pandemic. Complying with Twitter's content redistribution policy, only the tweet IDs are shared. The tweet IDs in this dataset belong to the tweets created providing an exact location. You can reconstruct the dataset by hydrating these IDs. For detailed instructions on the hydration of tweet IDs, please read this article.Announcements: We release CrisisTransformers (https://huggingface.co/crisistransformers), a family of pre-trained language models and sentence encoders introduced in the paper "CrisisTransformers: Pre-trained language models and sentence encoders for crisis-related social media texts". The models were trained based on the RoBERTa pre-training procedure on a massive corpus of over 15 billion word tokens sourced from tweets associated with 30+ crisis events such as disease outbreaks, natural disasters, conflicts, etc. CrisisTransformers were evaluated on 18 public crisis-specific datasets against strong baselines such as BERT, RoBERTa, BERTweet, etc. Our pre-trained models outperform the baselines across all 18 datasets in classification tasks, and our best-performing sentence-encoder outperforms the state-of-the-art by more than 17% in sentence encoding tasks. Please refer to the associated paper for more details.MegaGeoCOV Extended — an extended version of MegaGeoCOV has been released. The dataset is introduced in the paper "A Twitter narrative of the COVID-19 pandemic in Australia".We have released BillionCOV — a billion-scale COVID-19 tweets dataset for efficient hydration. Hydration takes time due to limits placed by Twitter on its tweet lookup endpoint. We re-hydrated the tweets present in COV19Tweets and found that more than 500 million tweet identifiers point to either deleted or protected tweets. If we avoid hydrating those tweet identifiers alone, it saves almost two months in a single hydration task. BillionCOV will receive quarterly updates, while COV19Tweets will continue to receive updates every day. Learn more about BillionCOV on its page: https://dx.doi.org/10.21227/871g-yp65We also release a million-scale COVID-19-specific geotagged tweets dataset — MegaGeoCOV (on GitHub). The dataset is introduced in the paper "Twitter conversations predict the daily confirmed COVID-19 cases". Related publications:Rabindra Lamsal. (2021). Design and analysis of a large-scale COVID-19 tweets dataset. Applied Intelligence, 51(5), 2790-2804.Rabindra Lamsal, Aaron Harwood, Maria Rodriguez Read. (2022). Socially Enhanced Situation Awareness from Microblogs using Artificial Intelligence: A Survey. ACM Computing Surveys, 55(4), 1-38. (arXiv)Rabindra Lamsal, Aaron Harwood, Maria Rodriguez Read. (2022). Twitter conversations predict the daily confirmed COVID-19 cases. Applied Soft Computing, 129, 109603. (arXiv)Rabindra Lamsal, Aaron Harwood, Maria Rodriguez Read. (2022). Addressing the location A/B problem on Twitter: the next generation location inference research. In 2022 ACM SIGSPATIAL LocalRec (pp. 1-4).Rabindra Lamsal, Aaron Harwood, Maria Rodriguez Read. (2022). Where did you tweet from? Inferring the origin locations of tweets based on contextual information. In 2022 IEEE International Conference on Big Data (pp. 3935-3944). (arXiv)Rabindra Lamsal, Maria Rodriguez Read, Shanika Karunasekera. (2023). BillionCOV: An Enriched Billion-scale Collection of COVID-19 tweets for Efficient Hydration. Data in Brief, 48, 109229. (arXiv)Rabindra Lamsal, Maria Rodriguez Read, Shanika Karunasekera. (2023). A Twitter narrative of the COVID-19 pandemic in Australia. In 20th International ISCRAM Conference (pp. 353-370). (arXiv)Rabindra Lamsal, Maria Rodriguez Read, Shanika Karunasekera. (2024). CrisisTransformers: Pre-trained language models and sentence encoders for crisis-related social media texts. Knowledge-Based Systems, 296, 111916. (arXiv)Rabindra Lamsal, Maria Rodriguez Read, Shanika Karunasekera. (2024). Semantically Enriched Cross-Lingual Sentence Embeddings for Crisis-related Social Media Texts. In 21st International ISCRAM Conference (in press). (arXiv)Below is a quick overview of this dataset.— Dataset name: GeoCOV19Tweets Dataset— Number of tweets : 502,067 tweets— Coverage : Global— Language : English (EN)— Dataset usage terms : By using this dataset, you agree to (i) use the content of this dataset and the data generated from the content of this dataset for non-commercial research only, (ii) remain in compliance with Twitter's Policy and (iii) cite the following paper:Lamsal, R. (2021). Design and analysis of a large-scale COVID-19 tweets dataset. Applied Intelligence, 51, 2790-2804. https://doi.org/10.1007/s10489-020-02029-zBibTeX entry:@article{lamsal2021design, title={Design and analysis of a large-scale COVID-19 tweets dataset}, author={Lamsal, Rabindra}, journal={Applied Intelligence}, volume={51}, number={5}, pages={2790--2804}, year={2021}, publisher={Springer} }— Primary dataset : Coronavirus (COVID-19) Tweets Dataset (COV19Tweets Dataset)— Dataset updates : Everyday— Keywords and hashtags: keywords.tsvPlease visit this page (primary dataset) for more details.Collection date & Number of tweets(2020) March 20 - March 21: 1290 tweets(2020) March 21 - March 22: 1020 tweets(2020) March 22 - March 23: 1069 tweets(2020) March 23 - March 24: 1072 tweets(2020) March 24 - March 25: 949 tweets(2020) March 25 - March 26: 913 tweets(2020) March 26 - March 27: 810 tweets(2020) March 27 - March 28: 855 tweets(2020) March 28 - March 29: 828 tweets(2020) March 29 - March 30: 5318 tweets (this file was added on June 29, 2021; its primary file corona_tweets_11b.csv was created while excluding retweets right at the API level; compared to other days the geo-tagged tweets are significantly higher for this day; Reason: Twitter's full-search endpoint was asked to create a corpus while excluding retweets; retweets have NULL geo and place objects, and since they were excluded I was able to come up with 5318 geo-tagged tweets out of 1,677,362 tweets collected for this day; this was quite an interesting observation to note)(2020) March 30 - March 31: 538 tweets(2020) March 31 - April 1: 636 tweets(2020) April 1 - April 2: 608 tweets(2020) April 2 - April 3: 661 tweets(2020) April 3 - April 4: 592 tweets(2020) April 4 - April 5: 661 tweets(2020) April 5 - April 6: 709 tweets(2020) April 6 - April 7: 549 tweets(2020) April 7 - April 8: 593 tweets(2020) April 8 - April 9: 491 tweets(2020) April 9 - April 10: 507 tweets(2020) April 10 - April 11: 534 tweets(2020) April 11 - April 12: 539 tweets(2020) April 12- April 13: 543 tweets(2020) April 13 - April 14: 510 tweets(2020) April 14 - April 15: 387 tweets(2020) April 15 - April 16: 321 tweets(2020) April 16 - April 17: 443 tweets(2020) April 17 - April 18: 373 tweets(2020) April 18 - April 19: 1020 tweets(2020) April 19 - April 20: 884 tweets(2020) April 20 - April 21: 869 tweets(2020) April 21 - April 22: 878 tweets(2020) April 22 - April 23: 831 tweets(2020) April 23 - April 24: 818 tweets(2020) April 24 - April 25: 747 tweets(2020) April 25- April 26: 693 tweets(2020) April 26 - April 27: 939 tweets(2020) April 27 - April 28: 744 tweets(2020) April 28 - April 29: 1408 tweets(2020) April 29 - April 30: 1751 tweets(2020) April 30 - May 1: 1637 tweets(2020) May 1 - May 2: 1866 tweets(2020) May 2 - May 3: 1839 tweets(2020) May 3 - May 4: 1566 tweets(2020) May 4 - May 5: 1615 tweets(2020) May 5 - May 6: 1635 tweets(2020) May 6 - May 7: 1571 tweets(2020) May 7 - May 8: 1621 tweets(2020) May 8 - May 9: 1684 tweets(2020) May 9 - May 10: 1474 tweets(2020) May 10 - May 11: 1130 tweets(2020) May 11 - May 12: 1281 tweets(2020) May 12- May 13: 1630 tweets(2020) May 13 - May 14: 1480 tweets(2020) May 14 - May 15: 1652 tweets(2020) May 15 - May 16: 1583 tweets(2020) May 16 - May 17: 1487 tweets(2020) May 17 - May 18: 1341 tweets(2020) May 18 - May 19: 1398 tweets(2020) May 19 - May 20: 1389 tweets(2020) May 20 - May 21: 1397 tweets(2020) May 21 - May 22: 1562 tweets(2020) May 22 - May 23: 1558 tweets(2020) May 23 - May 24: 1299 tweets(2020) May 24 - May 25: 1297 tweets(2020) May 25- May 26: 1190 tweets(2020) May 26 - May 27: 1184 tweets(2020) May 27 - May 28: 1257 tweets(2020) May 28 - May 29: 1277 tweets(2020) May 29 - May 30: 1202 tweets(2020) May 30 - May 31: 1209 tweets(2020) May 31 - June 1: 1080 tweets(2020) June 1 - June 2: 1233 tweets(2020) June 2 - June 3: 917 tweets(2020) June 3 - June 4: 1055 tweets(2020) June 4 - June 5: 1117 tweets(2020) June 5 - June 6: 1184 tweets(2020) June 6 - June 7: 1093 tweets(2020) June 7 - June 8: 1054 tweets(2020) June 8 - June 9: 1180 tweets(2020) June 9 - June 10: 1155 tweets(2020) June 10 - June 11: 1131 tweets(2020) June 11 - June 12: 1148 tweets(2020) June 12- June 13: 1189 tweets(2020) June 13 - June 14: 1045 tweets(2020) June 14 - June 15: 1024 tweets(2020) June 15 - June 16: 1663 tweets(2020) June 16 - June 17: 1692 tweets(2020) June 17 - June 18: 1634 tweets(2020) June 18 - June 19: 1610 tweets(2020) June 19 - June 20: 1698 tweets(2020) June 20 - June 21: 1613 tweets(2020) June 21 - June 22: 1419 tweets(2020) June 22 - June 23: 1524 tweets(2020) June 23 - June 24: 1431 tweets(2020) June 24 - June 25: 1454 tweets(2020) June 25- June 26: 1539 tweets(2020) June 26 - June 27: 1403 tweets(2020) June 27 - June 28: 1766 tweets(2020) June 28 - June 29: 1405 tweets(2020) June 29 - June 30: 1534 tweets(2020) June 30 - June 31: 1519 tweets(2020) July 1 - July 2: 1841 tweets(2020) July 2 - July 3: 1434 tweets(2020) July 3 - July 4: 1475 tweets(2020) July 4 - July 5: 2028 tweets(2020) July 5 - July 6: 1491 tweets(2020) July 6 - July 7: 1275 tweets(2020) July 7 - July 8: 1336 tweets(2020) July 8 - July 9: 1428 tweets(2020) July 9 - July 10: 1831

Abstract

JHU Coronavirus COVID-19 Global Cases, by country

Documentation

PHS is updating the Coronavirus Global Cases dataset weekly, Monday, Wednesday and Friday from Cloud Marketplace.

This data comes from the data repository for the 2019 Novel Coronavirus Visual Dashboard operated by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). This database was created in response to the Coronavirus public health emergency to track reported cases in real-time. The data include the location and number of confirmed COVID-19 cases, deaths, and recoveries for all affected countries, aggregated at the appropriate province or state. It was developed to enable researchers, public health authorities and the general public to track the outbreak as it unfolds. Additional information is available in the blog post.

Visual Dashboard (desktop): https://www.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6

Section 2

Included Data Sources are:

• World Health Organization (WHO): https://www.who.int/
• DXY.cn. Pneumonia. 2020. http://3g.dxy.cn/newh5/view/pneumonia.
• BNO News: https://bnonews.com/index.php/2020/02/the-latest-coronavirus-cases/
• National Health Commission of the People’s Republic of China (NHC): http://www.nhc.gov.cn/xcs/yqtb/list_gzbd.shtml
• China CDC (CCDC): http://weekly.chinacdc.cn/news/TrackingtheEpidemic.htm
• Hong Kong Department of Health: https://www.chp.gov.hk/en/features/102465.html
• Macau Government: https://www.ssm.gov.mo/portal/
• Taiwan CDC: https://sites.google.com/cdc.gov.tw/2019ncov/taiwan?authuser=0
• US CDC: https://www.cdc.gov/coronavirus/2019-ncov/index.html
• Government of Canada: https://www.canada.ca/en/public-health/services/diseases/coronavirus.html
• Australia Government Department of Health: https://www.health.gov.au/news/coronavirus-update-at-a-glance
• European Centre for Disease Prevention and Control (ECDC): https://www.ecdc.europa.eu/en/geographical-distribution-2019-ncov-cases
• Ministry of Health Singapore (MOH): https://www.moh.gov.sg/covid-19
• Italy Ministry of Health: http://www.salute.gov.it/nuovocoronavirus
• 1Point3Arces: https://coronavirus.1point3acres.com/en
• WorldoMeters: https://www.worldometers.info/coronavirus/

%3C!-- --%3E

Section 3

**Terms of Use: **

This GitHub repo and its contents herein, including all data, mapping, and analysis, copyright 2020 Johns Hopkins University, all rights reserved, is provided to the public strictly for educational and academic research purposes. The Website relies upon publicly available data from multiple sources, that do not always agree. The Johns Hopkins University hereby disclaims any and all representations and warranties with respect to the Website, including accuracy, fitness for use, and merchantability. Reliance on the Website for medical guidance or use of the Website in commerce is strictly prohibited.

Section 4

**U.S. county-level characteristics relevant to COVID-19 **

Chin, Kahn, Krieger, Buckee, Balsari and Kiang (forthcoming) show that counties differ significantly in biological, demographic and socioeconomic factors that are associated with COVID-19 vulnerability. A range of publicly available county-specific data identifying these key factors, guided by international experiences and consideration of epidemiological parameters of importance, have been combined by the authors and are available for use:

https://github.com/mkiang/county_preparedness/

This dataset shows the cases of Coronavirus (COVID-19) in Vietnam. The dataset information will be updated according to the announcements from the ministry of health in Vietnam. The data is updated frenquently along with the data of Ministry of Vietnam. Note: The first case of COVID-19 in Vietnam was first announced on January 22, 2020, including a 66-year-old Chinese man (#1) traveling from Wuhan to Hanoi to visit his son living in Vietnam, and his 28-year-old son (# 2), who is believed to have contracted the disease from his father when they met in Nha Trang. This dataset is updated as the case progresses, thus requiring the public to understand and verify the data that ODV has published.

This story map explores the partnership between the City of Tempe and Arizona State University to study city wastewater for Coronavirus/COVID-19. Featured sections include:What is Coronavirus/COVID-19Analyzing Wastewater DataData-Driven Decision MakingWhat You Can DoFrequently Asked Questions Important ContactsPlease also see the Spanish language version.

As of November 11, 2022, almost 96.8 million confirmed cases of COVID-19 had been reported by the World Health Organization (WHO) for the United States. The pandemic has impacted all 50 states, with vast numbers of cases recorded in California, Texas, and Florida.

The coronavirus in the U.S. The coronavirus hit the United States in mid-March 2020, and cases started to soar at an alarming rate. The country has performed a high number of COVID-19 tests, which is a necessary step to manage the outbreak, but new coronavirus cases in the U.S. have spiked several times since the pandemic began, most notably at the end of 2022. However, restrictions in many states have been eased as new cases have declined.

The origin of the coronavirus In December 2019, officials in Wuhan, China, were the first to report cases of pneumonia with an unknown cause. A new human coronavirus – SARS-CoV-2 – has since been discovered, and COVID-19 is the infectious disease it causes. All available evidence to date suggests that COVID-19 is a zoonotic disease, which means it can spread from animals to humans. The WHO says transmission is likely to have happened through an animal that is handled by humans. Researchers do not support the theory that the virus was developed in a laboratory.

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 latest trend plot. It covers China, Canada, Australia (at province/state level), and the rest of the world (at country level, represented by either the country centroids or their capitals)and the US at county-level. Data sources: WHO, CDC, ECDC, NHC, DXY, 1point3acres, Worldometers.info, BNO, state and national government health departments, and local media reports. . The China data is automatically updating at least once per hour, and non-China data is updating hourly. 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 and JHU Data Services. This layer is opened to the public and free to share. Contact us.

Replaced by http://data.europa.eu/88u/dataset/covid-19-coronavirus-data-daily-up-to-14-december-2020

On January 21, 2020, the U.S. Centers for Disease Control and Prevention (CDC) and Washington State Department of Health (DOH) announced the first case of 2019 Novel Coronavirus (COVID-19) in the United States, in Washington state. The link below provides access to DOH daily updates of confirmed Washington State COVID-19 cases and deaths, along with essential information about the virus and guidance on prevention and risk management. The link includes Frequently Asked Questions, as well as resources for specific groups such as parents, caregivers, employers, schools and health care providers.

Note: Reporting of new COVID-19 Case Surveillance data will be discontinued July 1, 2024, to align with the process of removing SARS-CoV-2 infections (COVID-19 cases) from the list of nationally notifiable diseases. Although these data will continue to be publicly available, the dataset will no longer be updated.

Authorizations to collect certain public health data expired at the end of the U.S. public health emergency declaration on May 11, 2023. The following jurisdictions discontinued COVID-19 case notifications to CDC: Iowa (11/8/21), Kansas (5/12/23), Kentucky (1/1/24), Louisiana (10/31/23), New Hampshire (5/23/23), and Oklahoma (5/2/23). Please note that these jurisdictions will not routinely send new case data after the dates indicated. As of 7/13/23, case notifications from Oregon will only include pediatric cases resulting in death.

This case surveillance public use dataset has 12 elements for all COVID-19 cases shared with CDC and includes demographics, any exposure history, disease severity indicators and outcomes, presence of any underlying medical conditions and risk behaviors, and no geographic data.

CDC has three COVID-19 case surveillance datasets:

• COVID-19 Case Surveillance Public Use Data with Geography: Public use, patient-level dataset with clinical data (including symptoms), demographics, and county and state of residence. (19 data elements)
• COVID-19 Case Surveillance Public Use Data: Public use, patient-level dataset with clinical and symptom data and demographics, with no geographic data. (12 data elements)
• COVID-19 Case Surveillance Restricted Access Detailed Data: Restricted access, patient-level dataset with clinical and symptom data, demographics, and state and county of residence. Access requires a registration process and a data use agreement. (33 data elements)

The following apply to all three datasets:

• Data elements can be found on the COVID-19 case report form located at www.cdc.gov/coronavirus/2019-ncov/downloads/pui-form.pdf.
• Data are considered provisional by CDC and are subject to change until the data are reconciled and verified with the state and territorial data providers.
• Some data cells are suppressed to protect individual privacy.
• The datasets will include all cases with the earliest date available in each record (date received by CDC or date related to illness/specimen collection) at least 14 days prior to the creation of the current datasets. This 14-day lag allows case reporting to be stabilized and ensures that time-dependent outcome data are accurately captured.
• Datasets are updated monthly.
• Datasets are created using CDC’s Policy on Public Health Research and Nonresearch Data Management and Access and include protections designed to protect individual privacy.
• For more information about data collection and reporting, please see https://www.cdc.gov/coronavirus/2019-ncov/covid-data/about-us-cases-deaths.html.
• For more information about the COVID-19 case surveillance data, please see https://www.cdc.gov/coronavirus/2019-ncov/covid-data/faq-surveillance.html
  

Overview

The COVID-19 case surveillance database includes individual-level data reported to U.S. states and autonomous reporting entities, including New York City and the District of Columbia (D.C.), as well as U.S. territories and affiliates. On April 5, 2020, COVID-19 was added to the Nationally Notifiable Condition List and classified as “immediately notifiable, urgent (within 24 hours)” by a Council of State and Territorial Epidemiologists (CSTE) Interim Position Statement (Interim-20-ID-01). CSTE updated the position statement on August 5, 2020, to clarify the interpretation of antigen detection tests and serologic test results within the case classification (Interim-20-ID-02). The statement also recommended that all states and territories enact laws to make COVID-19 reportable in their jurisdiction, and that jurisdictions conducting surveillance should submit case notifications to CDC. COVID-19 case surveillance data are collected by jurisdictions and reported voluntarily to CDC.

For more information: NNDSS Supports the COVID-19 Response | CDC.

The deidentified data in the “COVID-19 Case Surveillance Public Use Data” include demographic characteristics, any exposure history, disease severity indicators and outcomes, clinical data, laboratory diagnostic test results, and presence of any underlying medical conditions and risk behaviors. All data elements can be found on the COVID-19 case report form located at www.cdc.gov/coronavirus/2019-ncov/downloads/pui-form.pdf.

COVID-19 Case Reports

COVID-19 case reports have been routinely submitted using nationally standardized case reporting forms. On April 5, 2020, CSTE released an Interim Position Statement with national surveillance case definitions for COVID-19 included. Current versions of these case definitions are available here: https://ndc.services.cdc.gov/case-definitions/coronavirus-disease-2019-2021/.

All cases reported on or after were requested to be shared by public health departments to CDC using the standardized case definitions for laboratory-confirmed or probable cases. On May 5, 2020, the standardized case reporting form was revised. Case reporting using this new form is ongoing among U.S. states and territories.

Data are Considered Provisional

• The COVID-19 case surveillance data are dynamic; case reports can be modified at any time by the jurisdictions sharing COVID-19 data with CDC. CDC may update prior cases shared with CDC based on any updated information from jurisdictions. For instance, as new information is gathered about previously reported cases, health departments provide updated data to CDC. As more information and data become available, analyses might find changes in surveillance data and trends during a previously reported time window. Data may also be shared late with CDC due to the volume of COVID-19 cases.
• Annual finalized data: To create the final NNDSS data used in the annual tables, CDC works carefully with the reporting jurisdictions to reconcile the data received during the year until each state or territorial epidemiologist confirms that the data from their area are correct.
• Access Addressing Gaps in Public Health Reporting of Race and Ethnicity for COVID-19, a report from the Council of State and Territorial Epidemiologists, to better understand the challenges in completing race and ethnicity data for COVID-19 and recommendations for improvement.

Data Limitations

To learn more about the limitations in using case surveillance data, visit FAQ: COVID-19 Data and Surveillance.

Data Quality Assurance Procedures

CDC’s Case Surveillance Section routinely performs data quality assurance procedures (i.e., ongoing corrections and logic checks to address data errors). To date, the following data cleaning steps have been implemented:

• Questions that have been left unanswered (blank) on the case report form are reclassified to a Missing value, if applicable to the question. For example, in the question “Was the individual hospitalized?” where the possible answer choices include “Yes,” “No,” or “Unknown,” the blank value is recoded to Missing because the case report form did not include a response to the question.
• Logic checks are performed for date data. If an illogical date has been provided, CDC reviews the data with the reporting jurisdiction. For example, if a symptom onset date in the future is reported to CDC, this value is set to null until the reporting jurisdiction updates the date appropriately.
• Additional data quality processing to recode free text data is ongoing. Data on symptoms, race and ethnicity, and healthcare worker status have been prioritized.

Data Suppression

To prevent release of data that could be used to identify people, data cells are suppressed for low frequency (<5) records and indirect identifiers (e.g., date of first positive specimen). Suppression includes rare combinations of demographic characteristics (sex, age group, race/ethnicity). Suppressed values are re-coded to the NA answer option; records with data suppression are never removed.

For questions, please contact Ask SRRG (eocevent394@cdc.gov).

Additional COVID-19 Data

COVID-19 data are available to the public as summary or aggregate count files, including total counts of cases and deaths by state and by county. These

The table covid19_jhu_csse_summary is part of the dataset Coronavirus COVID-19 Global Cases, available at https://redivis.com/datasets/rxta-4v35cgyzf. It contains 390476 rows across 13 variables.

Updates

• Notice of data discontinuation: Since the start of the pandemic, AP has reported case and death counts from data provided by Johns Hopkins University. Johns Hopkins University has announced that they will stop their daily data collection efforts after March 10. As Johns Hopkins stops providing data, the AP will also stop collecting daily numbers for COVID cases and deaths. The HHS and CDC now collect and visualize key metrics for the pandemic. AP advises using those resources when reporting on the pandemic going forward.

  • CDC Weekly case and death counts (national and state level)
  • CDC County level cases and deaths
  • HHS New hospital admissions
  • CDC NowCast COVID variant proportions (national and regional level)

• April 9, 2020

  • The population estimate data for New York County, NY has been updated to include all five New York City counties (Kings County, Queens County, Bronx County, Richmond County and New York County). This has been done to match the Johns Hopkins COVID-19 data, which aggregates counts for the five New York City counties to New York County.

• April 20, 2020

  • Johns Hopkins death totals in the US now include confirmed and probable deaths in accordance with CDC guidelines as of April 14. One significant result of this change was an increase of more than 3,700 deaths in the New York City count. This change will likely result in increases for death counts elsewhere as well. The AP does not alter the Johns Hopkins source data, so probable deaths are included in this dataset as well.

• April 29, 2020

  • The AP is now providing timeseries data for counts of COVID-19 cases and deaths. The raw counts are provided here unaltered, along with a population column with Census ACS-5 estimates and calculated daily case and death rates per 100,000 people. Please read the updated caveats section for more information.

• September 1st, 2020

  • Johns Hopkins is now providing counts for the five New York City counties individually.

• February 12, 2021

  • The Ohio Department of Health recently announced that as many as 4,000 COVID-19 deaths may have been underreported through the state’s reporting system, and that the "daily reported death counts will be high for a two to three-day period."
  • Because deaths data will be anomalous for consecutive days, we have chosen to freeze Ohio's rolling average for daily deaths at the last valid measure until Johns Hopkins is able to back-distribute the data. The raw daily death counts, as reported by Johns Hopkins and including the backlogged death data, will still be present in the new_deaths column.

• February 16, 2021

  - Johns Hopkins has reconciled Ohio's historical deaths data with the state.

  Overview

The AP is using data collected by the Johns Hopkins University Center for Systems Science and Engineering as our source for outbreak caseloads and death counts for the United States and globally.

The Hopkins data is available at the county level in the United States. The AP has paired this data with population figures and county rural/urban designations, and has calculated caseload and death rates per 100,000 people. Be aware that caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.

This data is from the Hopkins dashboard that is updated regularly throughout the day. Like all organizations dealing with data, Hopkins is constantly refining and cleaning up their feed, so there may be brief moments where data does not appear correctly. At this link, you’ll find the Hopkins daily data reports, and a clean version of their feed.

The AP is updating this dataset hourly at 45 minutes past the hour.

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

Queries

Use AP's queries to filter the data or to join to other datasets we've made available to help cover the coronavirus pandemic

• Filter cases by state here

• Rank states by their status as current hotspots. Calculates the 7-day rolling average of new cases per capita in each state: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=481e82a4-1b2f-41c2-9ea1-d91aa4b3b1ac

• Find recent hotspots within your state by running a query to calculate the 7-day rolling average of new cases by capita in each county: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=b566f1db-3231-40fe-8099-311909b7b687&showTemplatePreview=true

• Join county-level case data to an earlier dataset released by AP on local hospital capacity here. To find out more about the hospital capacity dataset, see the full details.

• Pull the 100 counties with the highest per-capita confirmed cases here

• Rank all the counties by the highest per-capita rate of new cases in the past 7 days here. Be aware that because this ranks per-capita caseloads, very small counties may rise to the very top, so take into account raw caseload figures as well.

Interactive

The AP has designed an interactive map to track COVID-19 cases reported by Johns Hopkins.

@(https://datawrapper.dwcdn.net/nRyaf/15/)

Interactive Embed Code

<iframe title="USA counties (2018) choropleth map Mapping COVID-19 cases by county" aria-describedby="" id="datawrapper-chart-nRyaf" src="https://datawrapper.dwcdn.net/nRyaf/10/" scrolling="no" frameborder="0" style="width: 0; min-width: 100% !important;" height="400"></iframe><script type="text/javascript">(function() {'use strict';window.addEventListener('message', function(event) {if (typeof event.data['datawrapper-height'] !== 'undefined') {for (var chartId in event.data['datawrapper-height']) {var iframe = document.getElementById('datawrapper-chart-' + chartId) || document.querySelector("iframe[src*='" + chartId + "']");if (!iframe) {continue;}iframe.style.height = event.data['datawrapper-height'][chartId] + 'px';}}});})();</script>

Caveats

• This data represents the number of cases and deaths reported by each state and has been collected by Johns Hopkins from a number of sources cited on their website.
• In some cases, deaths or cases of people who've crossed state lines -- either to receive treatment or because they became sick and couldn't return home while traveling -- are reported in a state they aren't currently in, because of state reporting rules.
• In some states, there are a number of cases not assigned to a specific county -- for those cases, the county name is "unassigned to a single county"
• This data should be credited to Johns Hopkins University's COVID-19 tracking project. The AP is simply making it available here for ease of use for reporters and members.
• Caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.
• Population estimates at the county level are drawn from 2014-18 5-year estimates from the American Community Survey.
• The Urban/Rural classification scheme is from the Center for Disease Control and Preventions's National Center for Health Statistics. It puts each county into one of six categories -- from Large Central Metro to Non-Core -- according to population and other characteristics. More details about the classifications can be found here.

Johns Hopkins timeseries data - Johns Hopkins pulls data regularly to update their dashboard. Once a day, around 8pm EDT, Johns Hopkins adds the counts for all areas they cover to the timeseries file. These counts are snapshots of the latest cumulative counts provided by the source on that day. This can lead to inconsistencies if a source updates their historical data for accuracy, either increasing or decreasing the latest cumulative count. - Johns Hopkins periodically edits their historical timeseries data for accuracy. They provide a file documenting all errors in their timeseries files that they have identified and fixed here

Attribution

This data should be credited to Johns Hopkins University COVID-19 tracking project

Dataset

This dataset was created by vmahawar

Released under Other (specified in description)

Contents

This dataset shows the cases of Coronavirus (COVID-19) in Laos. The dataset information will be updated according to the information from publicly available sources (official website and news). This dataset is updated as the case progresses, thus requiring the public to understand and verify the data that ODL has published.

This is the data for the 2019 Novel Coronavirus Visual Dashboard operated by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). Also, Supported by ESRI Living Atlas Team and the Johns Hopkins University Applied Physics Lab (JHU APL).Data SourcesWorld Health Organization (WHO): https://www.who.int/ DXY.cn. Pneumonia. 2020. http://3g.dxy.cn/newh5/view/pneumonia. BNO News: https://bnonews.com/index.php/2020/02/the-latest-coronavirus-cases/ National Health Commission of the People’s Republic of China (NHC): http://www.nhc.gov.cn/xcs/yqtb/list_gzbd.shtml China CDC (CCDC): http://weekly.chinacdc.cn/news/TrackingtheEpidemic.htm Hong Kong Department of Health: https://www.chp.gov.hk/en/features/102465.html Macau Government: https://www.ssm.gov.mo/portal/ Taiwan CDC: https://sites.google.com/cdc.gov.tw/2019ncov/taiwan?authuser=0 US CDC: https://www.cdc.gov/coronavirus/2019-ncov/index.html Government of Canada: https://www.canada.ca/en/public-health/services/diseases/coronavirus.html Australia Government Department of Health: https://www.health.gov.au/news/coronavirus-update-at-a-glance European Centre for Disease Prevention and Control (ECDC): https://www.ecdc.europa.eu/en/geographical-distribution-2019-ncov-casesMinistry of Health Singapore (MOH): https://www.moh.gov.sg/covid-19Italy Ministry of Health: http://www.salute.gov.it/nuovocoronavirus

This is the USA counties data extracted from the 2019 Coronavirus data hub operated by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). Also, Supported by ESRI Living Atlas Team and the Johns Hopkins University Applied Physics Lab (JHU APL).Sources:1Point3Arces: https://coronavirus.1point3acres.com/enUS CDC: https://www.cdc.gov/coronavirus/2019-ncov/index.html Enrichmentthe official FIPS codes are available and should be used for joins or geojoins needs.Terms of Use:This data set is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) by the Johns Hopkins University on behalf of its Center for Systems Science in Engineering. Copyright Johns Hopkins University 2020.Attribute the data as the "COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University" or "JHU CSSE COVID-19 Data" for short, and the url: https://github.com/CSSEGISandData/COVID-19.For publications that use the data, please cite the following publication: "Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Inf Dis. 20(5):533-534. doi: 10.1016/S1473-3099(20)30120-1"

Reporting of new Aggregate Case and Death Count data was discontinued May 11, 2023, with the expiration of the COVID-19 public health emergency declaration. This dataset will receive a final update on June 1, 2023, to reconcile historical data through May 10, 2023, and will remain publicly available.

Aggregate Data Collection Process Since the start of the COVID-19 pandemic, data have been gathered through a robust process with the following steps:

• A CDC data team reviews and validates the information obtained from jurisdictions’ state and local websites via an overnight data review process.
• If more than one official county data source exists, CDC uses a comprehensive data selection process comparing each official county data source, and takes the highest case and death counts respectively, unless otherwise specified by the state.
• CDC compiles these data and posts the finalized information on COVID Data Tracker.
• County level data is aggregated to obtain state and territory specific totals.

This process is collaborative, with CDC and jurisdictions working together to ensure the accuracy of COVID-19 case and death numbers. County counts provide the most up-to-date numbers on cases and deaths by report date. CDC may retrospectively update counts to correct data quality issues.

Methodology Changes Several differences exist between the current, weekly-updated dataset and the archived version:

• Source: The current Weekly-Updated Version is based on county-level aggregate count data, while the Archived Version is based on State-level aggregate count data.
• Confirmed/Probable Cases/Death breakdown:  While the probable cases and deaths are included in the total case and total death counts in both versions (if applicable), they were reported separately from the confirmed cases and deaths by jurisdiction in the Archived Version.  In the current Weekly-Updated Version, the counts by jurisdiction are not reported by confirmed or probable status (See Confirmed and Probable Counts section for more detail).
• Time Series Frequency: The current Weekly-Updated Version contains weekly time series data (i.e., one record per week per jurisdiction), while the Archived Version contains daily time series data (i.e., one record per day per jurisdiction).
• Update Frequency: The current Weekly-Updated Version is updated weekly, while the Archived Version was updated twice daily up to October 20, 2022.

Important note: The counts reflected during a given time period in this dataset may not match the counts reflected for the same time period in the archived dataset noted above. Discrepancies may exist due to differences between county and state COVID-19 case surveillance and reconciliation efforts.

Confirmed and Probable Counts In this dataset, counts by jurisdiction are not displayed by confirmed or probable status. Instead, confirmed and probable cases and deaths are included in the Total Cases and Total Deaths columns, when available. Not all jurisdictions report probable cases and deaths to CDC.* Confirmed and probable case definition criteria are described here:

Council of State and Territorial Epidemiologists (ymaws.com).

Deaths CDC reports death data on other sections of the website: CDC COVID Data Tracker: Home, CDC COVID Data Tracker: Cases, Deaths, and Testing, and NCHS Provisional Death Counts. Information presented on the COVID Data Tracker pages is based on the same source (total case counts) as the present dataset; however, NCHS Death Counts are based on death certificates that use information reported by physicians, medical examiners, or coroners in the cause-of-death section of each certificate. Data from each of these pages are considered provisional (not complete and pending verification) and are therefore subject to change. Counts from previous weeks are continually revised as more records are received and processed.

Number of Jurisdictions Reporting There are currently 60 public health jurisdictions reporting cases of COVID-19. This includes the 50 states, the District of Columbia, New York City, the U.S. territories of American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, Puerto Rico, and the U.S Virgin Islands as well as three independent countries in compacts of free association with the United States, Federated States of Micronesia, Republic of the Marshall Islands, and Republic of Palau. New York State’s reported case and death counts do not include New York City’s counts as they separately report nationally notifiable conditions to CDC.

CDC COVID-19 data are available to the public as summary or aggregate count files, including total counts of cases and deaths, available by state and by county. These and other data on COVID-19 are available from multiple public locations, such as:

https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/cases-in-us.html

https://www.cdc.gov/covid-data-tracker/index.html

https://www.cdc.gov/coronavirus/2019-ncov/covid-data/covidview/index.html

https://www.cdc.gov/coronavirus/2019-ncov/php/open-america/surveillance-data-analytics.html

Additional COVID-19 public use datasets, include line-level (patient-level) data, are available at: https://data.cdc.gov/browse?tags=covid-19.

Archived Data Notes:

November 3, 2022: Due to a reporting cadence issue, case rates for Missouri counties are calculated based on 11 days’ worth of case count data in the Weekly United States COVID-19 Cases and Deaths by State data released on November 3, 2022, instead of the customary 7 days’ worth of data.

November 10, 2022: Due to a reporting cadence change, case rates for Alabama counties are calculated based on 13 days’ worth of case count data in the Weekly United States COVID-19 Cases and Deaths by State data released on November 10, 2022, instead of the customary 7 days’ worth of data.

November 10, 2022: Per the request of the jurisdiction, cases and deaths among non-residents have been removed from all Hawaii county totals throughout the entire time series. Cumulative case and death counts reported by CDC will no longer match Hawaii’s COVID-19 Dashboard, which still includes non-resident cases and deaths. 

November 17, 2022: Two new columns, weekly historic cases and weekly historic deaths, were added to this dataset on November 17, 2022. These columns reflect case and death counts that were reported that week but were historical in nature and not reflective of the current burden within the jurisdiction. These historical cases and deaths are not included in the new weekly case and new weekly death columns; however, they are reflected in the cumulative totals provided for each jurisdiction. These data are used to account for artificial increases in case and death totals due to batched reporting of historical data.

December 1, 2022: Due to cadence changes over the Thanksgiving holiday, case rates for all Ohio counties are reported as 0 in the data released on December 1, 2022.

January 5, 2023: Due to North Carolina’s holiday reporting cadence, aggregate case and death data will contain 14 days’ worth of data instead of the customary 7 days. As a result, case and death metrics will appear higher than expected in the January 5, 2023, weekly release.

January 12, 2023: Due to data processing delays, Mississippi’s aggregate case and death data will be reported as 0. As a result, case and death metrics will appear lower than expected in the January 12, 2023, weekly release.

January 19, 2023: Due to a reporting cadence issue, Mississippi’s aggregate case and death data will be calculated based on 14 days’ worth of data instead of the customary 7 days in the January 19, 2023, weekly release.

January 26, 2023: Due to a reporting backlog of historic COVID-19 cases, case rates for two Michigan counties (Livingston and Washtenaw) were higher than expected in the January 19, 2023 weekly release.

January 26, 2023: Due to a backlog of historic COVID-19 cases being reported this week, aggregate case and death counts in Charlotte County and Sarasota County, Florida, will appear higher than expected in the January 26, 2023 weekly release.

January 26, 2023: Due to data processing delays, Mississippi’s aggregate case and death data will be reported as 0 in the weekly release posted on January 26, 2023.

February 2, 2023: As of the data collection deadline, CDC observed an abnormally large increase in aggregate COVID-19 cases and deaths reported for Washington State. In response, totals for new cases and new deaths released on February 2, 2023, have been displayed as zero at the state level until the issue is addressed with state officials. CDC is working with state officials to address the issue.

February 2, 2023: Due to a decrease reported in cumulative case counts by Wyoming, case rates will be reported as 0 in the February 2, 2023, weekly release. CDC is working with state officials to verify the data submitted.

February 16, 2023: Due to data processing delays, Utah’s aggregate case and death data will be reported as 0 in the weekly release posted on February 16, 2023. As a result, case and death metrics will appear lower than expected and should be interpreted with caution.

February 16, 2023: Due to a reporting cadence change, Maine’s