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

File formats available for download include comma-separated values (.csv) and Tableau Hyper file (.hyper).

Visit the COVID-19 Data Hub, a free resource page, to learn more about these curated data sources and to access data visualizations, quick-start Tableau dashboards, and other partner-created solutions.

COVID-19 Activity

A global time series of case and death data. This data is sourced from JHU CSSE COVID-19 Data as well as The New York Times.

COVID-19 Case - DEPRECATED AS OF JUNE 5

This dataset was deprecated on June 5. The last update remains for posterity.

​About

• Refreshed daily by 1 p.m. PT
• See the Data dictionary for a description of the column names ​

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

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. (2023). CrisisTransformers: Pre-trained language models and sentence encoders for crisis-related social media texts. arXiv preprint arXiv:2309.05494.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 tweets(2020) July 10 - July 11: 1578 tweets(2020) July 11 - July 12: 1575 tweets(2020) July 12 - July 13: 1346 tweets(2020) July 13 - July 14: 1295 tweets(2020) July 14 - July 15: 1372 tweets(2020) July 15 - July 16: 1213 tweets(2020) July

The World Health Organization reported 766440796 Coronavirus Cases since the epidemic began. In addition, countries reported 6932591 Coronavirus Deaths. This dataset provides - World Coronavirus Cases- actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset contains the number of confirmed cases, recoveries and deaths by province due to the Coronavirus pandemic in Indonesia.

Methodology - Direct Observational Data/Anecdotal Data

Source: https://data.humdata.org/dataset/indonesia-covid-19-cases-recoveries-and-deaths-per-province
Last updated at https://data.humdata.org/organization/ocha-roap : 2020-05-15

License - Open Data Commons Attribution License (ODC-BY)

This dataset gives a cursory glimpse at the overall sentiment trend of the public discourse regarding the COVID-19 pandemic on Twitter. The live scatter plot of this dataset is available as The Overall Trend block at https://live.rlamsal.com.np. The trend graph reveals multiple peaks and drops that need further analysis. The n-grams during those peaks and drops can prove beneficial for better understanding the discourse.Announcement: We 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:Lamsal, R. (2021). Design and analysis of a large-scale COVID-19 tweets dataset. Applied Intelligence, 51(5), 2790-2804.Lamsal, R., Harwood, A., & Read, M. R. (2022). Socially Enhanced Situation Awareness from Microblogs using Artificial Intelligence: A Survey. ACM Computing Surveys.Lamsal, R., Harwood, A., & Read, M. R. (2022). Twitter conversations predict the daily confirmed COVID-19 cases. Applied Soft Computing, 109603.Lamsal, R., Harwood, A., & Read, M. R. (2022). Addressing the location A/B problem on Twitter: the next generation location inference research. In Proceedings of the 6th ACM SIGSPATIAL LocalRec (pp. 1-4).— 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 Developer Policy and (iii) cite the following paper:Lamsal, R. (2020). Design and analysis of a large-scale COVID-19 tweets dataset. Applied Intelligence, 1-15. DOI: https://doi.org/10.1007/s10489-020-02029-zBibTeX:@article{lamsal2020design, title={Design and analysis of a large-scale COVID-19 tweets dataset}, author={Lamsal, Rabindra}, journal={Applied Intelligence}, pages={1--15}, year={2020}, publisher={Springer} }-------------------------------------Related datasets:(a) Coronavirus (COVID-19) Tweets Dataset(b) Coronavirus (COVID-19) Geo-tagged Tweets Dataset(c) Tweets Originating from India During COVID-19 Lockdowns-------------------------------------A quick overview of the datasetNote: This dataset is no longer maintained.The sentiment scores are defined in the range [-1,0), 0, and (0,+1] for negative sentiment, neutral sentiment, and positive sentiment. Since the number of negative sentiment tweets is always less than the combined number of neutral and positive sentiment tweets, the majority of the time, the average sentiment falls pretty close to +0.05. So if we consider +0.05 as the neutral point for average sentiment score, then any score greater than +0.1 (peaks) and smaller than 0 (drops) can be regarded as a point of interest for further scrutinizing. Following are the dates when the Twitter stream (based on the tweets present in the Coronavirus (COVID-19) Tweets Dataset) experienced those peaks and drops:Positive peaks: In 2020 (April 30, May 3, May 23, May 24, May 25, May 26, June 2, June 22, June 28, July 3, July 12, July 26, August 15, August 16, August 18, August 21, August 24, August 31, September 1, September 2, September 4, September 5, September 9, September 21, September 23, October 2, October 9, October 18, October 22, November 4, November 6, November 7, November 8, November 9, November 10, November 16, November 18, November 19, November 23, November 24, November 26, November 30, December 13, December 14, December 15, December 18, December 24, December 25, December 27). In 2021 (January 1, January 3, January 12, January 18, January 22, January 25, January 26, January 28, January 29)Negative Peaks: In 2020 (May 28, May 30, May 31, June 1, June 2, June 7, June 8, June 12, June 13, June 14, June 15, June 21, June 24, June 25, July 6, July 7, July 10, August 26, September 1, September 3, September 13, September 17, September 25, September 26, September 28, October 5, October 9, October 10, October 15, October 26, November 1, November 8, November 9, November 13, November 15, November 21, November 22, December 1, December 6, December 19). In 2021 (January 7)What's inside the dataset files?Tweets collected every 10 minutes are sampled together, and an average sentiment score is computed. This dataset contains TXT files, each with two columns: (i) date/time (in UTC) and (ii) average sentiment. The first column is date/time and is by default in Unix timestamp (in ms). You can use this formula =cell/1000/60/60/24 + DATE(1970,1,1) in Spreadsheets, or this pd.to_datetime(dataframe_name[column],unit='ms') if you're comfortable with Python, to convert the Unix timestamp to human-readable format. Note that there are multiple instances where the average sentiment score is NULL because of technical issues (networking (at cloud service) and API).

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"

The COVID-19 Open Research Dataset is “a free resource of over 29,000 scholarly articles, including over 13,000 with full text, about COVID-19 and the coronavirus family of viruses for use by the global research community.”

in-the-news: On March 16, 2020, the White House issued a “call to action to the tech community” regarding the dataset, asking experts “to develop new text and data mining techniques that can help the science community answer high-priority scientific questions related to COVID-19.”

Included in this dataset: * Commercial use subset (includes PMC content) -- 9000 papers, 186Mb * Non-commercial use subset (includes PMC content) -- 1973 papers, 36Mb * PMC custom license subset -- 1426 papers, 19Mb * bioRxiv/medRxiv subset (pre-prints that are not peer reviewed) -- 803 papers, 13Mb

Each paper is represented as a single JSON object. The schema is available here.

We also provide a comprehensive metadata file of 29,000 coronavirus and COVID-19 research articles with links to PubMed, Microsoft Academic and the WHO COVID-19 database of publications (includes articles without open access full text): * Metadata file (readme) -- 47Mb

Source: https://pages.semanticscholar.org/coronavirus-research Updated: Weekly License: https://data.world/kgarrett/covid-19-open-research-dataset/workspace/file?filename=COVID.DATA.LIC.AGMT.pdf

See more COVID-19 data at data.world's Coronavirus (COVID-19) Data Resource Hub

Original Title: The New York Times Coronavirus (Covid-19) Cases and Deaths in the United States

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. # United States Data Data on cumulative coronavirus cases and deaths can be found in two files for states and counties. Each row of data reports cumulative counts based on our best reporting up to the moment we publish an update. We do our best to revise earlier entries in the data when we receive new information. Both files contain FIPS codes, a standard geographic identifier, to make it easier for an analyst to combine this data with other data sets like a map file or population data. ## State-Level Data State-level data can be found in the us-states.csv file. date,state,fips,cases,deaths 2020-01-21,Washington,53,1,0 ... ## County-Level Data County-level data can be found in the us-counties.csv file. date,county,state,fips,cases,deaths 2020-01-21,Snohomish,Washington,53061,1,0 ... In some cases, the geographies where cases are reported do not map to standard county boundaries. See the list of geographic exceptions for more detail on these. # Github Repository This dataset contains COVID-19 data for the United States of America made available by The New York Times on github at https://github.com/nytimes/covid-19-data

Caveats - If you have questions about the data or licensing conditions, please contact us at:

covid-data@nytimes.com

Methodology - Other

Source: https://data.humdata.org/dataset/nyt-covid-19-data
Last updated at https://data.humdata.org/organization/hdx : 2020-05-18

License - Other

More information about the license

Coronavirus (COVID-19) data published by the Texas Department of State Health Services (DSHS). They have also included a dashboard.

Includes:

• Case numbers by county (positive and fatalities)
• Number of tests (public health labs and commercial labs)

DSHS has also released a COVID-19 dashboard, which includes stats on Texas case counts.

https://media.data.world/dNm5oYkpQZOPahTasMcY_Screen%20Shot%202020-04-02%20at%2010.46.57%20AM.png" alt="https://media.data.world/dNm5oYkpQZOPahTasMcY\_Screen%20Shot%202020-04-02%20at%2010.46.57%20AM.png"> (Screen cap from 2020-04-02 10:30 AM CST. Please see the visualization at the source for updated data.) ​

Source: https://www.dshs.texas.gov/coronavirus/ All data are provisional and subject to change.

Context

Complete COVID-19 dataset is a collection of the COVID-19 data maintained by Our World in Data. It is updated daily and includes data on confirmed cases, deaths, hospitalizations, testing, and vaccinations as well as other variables of potential interest.

Content

The variables represent all data related to confirmed cases, deaths, hospitalizations, and testing, as well as other variables of potential interest.
the columns are: iso_code, continent, location, date, total_cases, new_cases, new_cases_smoothed, total_deaths, new_deaths, new_deaths_smoothed, total_cases_per_million, new_cases_per_million, new_cases_smoothed_per_million, total_deaths_per_million, new_deaths_per_million, new_deaths_smoothed_per_million, reproduction_rate, icu_patients, icu_patients_per_million, hosp_patients, hosp_patients_per_million, weekly_icu_admissions, weekly_icu_admissions_per_million, weekly_hosp_admissions, weekly_hosp_admissions_per_million, total_tests, new_tests, total_tests_per_thousand, new_tests_per_thousand, new_tests_smoothed, new_tests_smoothed_per_thousand, positive_rate, tests_per_case, tests_units, total_vaccinations, people_vaccinated, people_fully_vaccinated, new_vaccinations, new_vaccinations_smoothed, total_vaccinations_per_hundred, people_vaccinated_per_hundred, people_fully_vaccinated_per_hundred, new_vaccinations_smoothed_per_million, stringency_index, population, population_density, median_age, aged_65_older, aged_70_older, gdp_per_capita, extreme_poverty, cardiovasc_death_rate, diabetes_prevalence, female_smokers, male_smokers, handwashing_facilities, hospital_beds_per_thousand, life_expectancy, human_development_index

Acknowledgements/ Data Source

https://systems.jhu.edu/research/public-health/ncov/ https://www.ecdc.europa.eu/en/publications-data/download-data-hospital-and-icu-admission-rates-and-current-occupancy-covid-19 https://coronavirus.data.gov.uk/details/healthcare https://covid19tracker.ca/ https://healthdata.gov/dataset/covid-19-reported-patient-impact-and-hospital-capacity-state-timeseries https://ourworldindata.org/coronavirus-testing#our-checklist-for-covid-19-testing-data

Findings from the Coronavirus (COVID-19) Infection Survey for England.

United States recorded 103436829 Coronavirus Cases since the epidemic began, according to the World Health Organization (WHO). In addition, United States reported 1127152 Coronavirus Deaths. This dataset includes a chart with historical data for the United States Coronavirus Cases.

Indicators from a range of data sources to assess the impact of the coronavirus (COVID-19) pandemic on domestic abuse in England and Wales.

For English, see below

Nederland heeft voor het SARS-CoV-2 virus (coronavirus) een endemische fase bereikt en de GGD teststraten zijn per 17 maart 2023 gesloten. Daardoor wordt de data vanaf 1 april 2023 niet meer bijgewerkt.

Bestand vanaf week 40, 2021: COVID-19_casus_landelijk Bestand tot en met week 39, 2021: COVID-19_casus_landelijk_tm Dit bestand wordt vanaf versie 5 niet meer geüpdatet (zie hieronder)

Beschikbare formaten: .csv en .json Bronsysteem: OSIRIS Algemene Infectieziekten (AIZ)

Beschrijving bestand: Dit bestand bevat de volgende karakteristieken per positief geteste casus in Nederland: Datum voor statistiek, Leeftijdsgroep, Geslacht, Overlijden, Week van overlijden, Provincie, Meldende GGD

Het bestand is als volgt opgebouwd: Een record voor elke laboratorium bevestigde COVID-19 patiënt in Nederland, sinds het begin van de pandemie. Vanaf 11 juli 2022 is deze data opgesplitst (zie beschrijving versie 5). Alleen het bestand vanaf week 40, 2021 wordt iedere dinsdag en vrijdag om 16:00 ververst, op basis van de gegevens zoals op 10:00 uur die dag geregistreerd staan in het landelijk systeem voor meldingsplichtige infectieziekten (Osiris AIZ). Het historische bestand (tot en met week 39, 2021) wordt vanaf 11 juli niet meer geüpdatet.

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 (20 januari 2022): - In versie 2 van deze dataset is de variabele ‘hospital_admission’ niet meer beschikbaar. Voor het aantal ziekenhuisopnames wordt verwezen naar de geregistreerde ziekenhuisopnames van Stichting NICE (data.rivm.nl/covid-19/COVID-19_ziekenhuisopnames.html). Versie 3 update (8 februari 2022) - Vanaf 8 februari 2022 worden de positieve SARS-CoV-2 testuitslagen rechtstreeks vanuit CoronIT aan het RIVM gemeld. Ook worden de testuitslagen van andere testaanbieders (zoals Testen voor Toegang) en zorginstellingen (zoals ziekenhuizen, verpleeghuizen en huisartsen) die hun positieve SARS-CoV-2 testuitslagen via het Meldportaal van GGD GHOR invoeren rechtstreeks aan het RIVM gemeld. Meldingen die onderdeel zijn van de bron- en contactonderzoek steekproef en positieve SARS-CoV-2 testuitslagen van zorginstellingen die via zorgmail aan de GGD worden gemeld worden wel via HPZone aan het RIVM gemeld. Vanaf 8 februari wordt de datum van de positieve testuitslag gebruikt en niet meer de datum van melding aan de GGD Versie 4 update (24 maart 2022): - In versie 4 van deze dataset zijn records samengesteld volgens de gemeente herindeling van 24 maart 2022. Zie beschrijving van de variabele Municipal_health_service voor meer informatie. Versie 5 update (11 juli 2022): - Vanaf 11 juli 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 werkdag geüpdatet. Versie 6 update (1 september 2022): - Vanaf 1 september 2022 wordt het tweede deel van de data (vanaf week 40 2021) niet meer iedere werkdag geüpdatet, maar op dinsdagen en vrijdagen. De data wordt op deze dagen met terugwerkende kracht bijgewerkt voor de andere dagen. Versie 7 update (3 januari 2023): - Per 1 januari 2023 verzamelt het RIVM geen aanvullende informatie meer. Dit heeft als gevolg dat we vanaf 1 januari 2023 geen overlijdens meer rapporteren en worden de kolommen [Deceased] en [Week of Death] niet meer aangevuld.

Date_file: Datum en tijd waarop de gegevens zijn gepubliceerd door het RIVM

Date_statistics: Datum voor statistiek; eerste ziektedag, indien niet bekend, datum lab positief, indien niet bekend, melddatum aan GGD (formaat: jjjj-mm-dd)

Date_statistics_type: Soort datum die beschikbaar was voor datum voor de variabele "Datum voor statistiek", waarbij: DOO = Date of disease onset : Eerste ziektedag zoals gemeld door GGD. Let op: het is niet altijd bekend of deze eerste ziektedag ook echt al Covid-19 betrof. DPL = Date of first Positive Labresult : Datum van de (eerste) positieve labuitslag. DON = Date of Notification : Datum waarop de melding bij de GGD is binnengekomen.

Agegroup: Leeftijdsgroep bij leven; 0-9, 10-19, ..., 90+; bij overlijden <50, 50-59, 60-69, 70-79, 80-89, 90+, Unknown = Onbekend

Sex: Geslacht; Unknown = Onbekend, Male = Man, Female = Vrouw

Province: Naam van de provincie (op basis van de verblijfplaats van de patiënt)

Deceased: Overlijden. Unknown = Onbekend, Yes = Ja, No = Nee. Vanaf 1 januari 2023 is deze kolom leeg.

Week of Death : Week van overlijden. YYYYMM volgens ISO-week notatie (start op maandag t/m zondag). Vanaf 1 januari 2023 is deze kolom leeg.

Municipal_health_service: GGD die de melding heeft gedaan. Vanaf 24 maart 2022 is dit bestand samengesteld volgens de gemeente indeling van 24 maart 2022. Gemeente Weesp is opgegaan in gemeente Amsterdam. Met deze indeling is de veiligheidsregio Gooi- en Vechtstreek kleiner geworden en de veiligheidsregio Amsterdam-Amstelland groter; GGD Amsterdam is groter geworden en GGD Gooi- en Vechtstreek is kleiner geworden (https://www.cbs.nl/nl-nl/onze-diensten/methoden/classificaties/overig/gemeentelijke-indelingen-per-jaar/indeling-per-jaar/gemeentelijke-indeling-op-1-januari-2022).

Covid-19 characteristics per case, nationwide

The Netherlands has reached an endemic phase for the SARS-CoV-2 virus (coronavirus) and the PHS testing facilities will be closed as of March 17, 2023. As a result, the data will no longer be updated from 1 April 2023.

File from week 40, 2021: COVID-19_case_landelijk File up to and including week 39, 2021: COVID-19_casus_landelijk_tm This file will no longer be updated from version 5 (see below)

Available formats: .csv and .json Source system: OSIRIS General Infectious Diseases (AIZ)

File description: This file contains the following characteristics per positively tested case in the Netherlands: Date for statistics, Age group, Gender, Death, Week of death, Province, Notifying PHS

The file is structured as follows: A record for every lab-confirmed COVID-19 patient in the Netherlands since the start of the pandemic. From July 11, 2022, this data has been split (see description version 5). Only the file from week 40, 2021 onwards will be updated every Tuesday and Friday at 4:00 PM, based on the data as registered at 10:00 AM that day in the national system for notifiable infectious diseases (Osiris AIZ). The historical file (up to and including week 39, 2021) will no longer be updated from July 11, 2022.

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 (January 20, 2022): - In version 2 of this dataset, the variable 'hospital_admission' is no longer available. For the number of hospital admissions, reference is made to the registered hospital admissions of the NICE Foundation (data.rivm.nl/covid-19/COVID-19_ziekenhuis Admissions.html). Version 3 update (February 8, 2022) - From 8 February 2022, positive SARS-CoV-2 test results will be reported directly from CoronIT to the RIVM. The test results of other test providers (such as Testing for Access) and healthcare institutions (such as hospitals, nursing homes and general practitioners) that enter their positive SARS-CoV-2 test results via the Reporting Portal of GGD GHOR are also reported directly to the RIVM. Reports that are part of the source and contact investigation sample and positive SARS-CoV-2 test results from healthcare institutions that are reported to the PHS via healthcare email are reported to the RIVM via HPZone. From 8 February 2022, the date of the positive test result is used and no longer the date of notification to the PHS. Version 4 update (March 24, 2022): - In version 4 of this dataset, records are compiled according to the municipality reclassification of March 24, 2022. See description of the Municipal_health_service variable for more information. Version 5 Update (July 11, 2022): - As of July 11, 2022, this dataset is split into two parts. The first part contains the dates from the start of the pandemic to October 3, 2021 (week 39) and contains "tm" in the file name. This data will no longer be updated. The second part contains the data from October 4, 2021 (week 40) and is updated every working day. Version 6 update (September 1, 2022): - From September 1, 2022, the second part of the data (from week 40 2021) will no longer be updated every working day, but on Tuesdays and Fridays. The data is retroactively updated on these days for the other days. Version 7 update (January 3, 2023): - As of 1 January 2023, the RIVM will no longer collect additional information. As a result, we will no longer report deaths from January 1, 2023 and the [Deceased] and [Week of Death] columns will no longer be completed.

Date_file: Date and time when the data was published by the RIVM

Date_statistics: Date for statistics; first day of illness, if not known, date of positive lab result, if not known, reporting date to PHS (format: yyyy-mm-dd)

Date_statistics_type: Type of date that was available for date for the "Date for statistics" variable, where: DOO = Date of disease onset : First day of illness as reported by PHS. Please note: it is not always known whether this first day of illness actually concerned Covid-19. DPL = Date of first Positive Lab result : Date of the (first) positive lab result. DON = Date of

Dataset

This dataset was created by Vadim Lazovskiy

Contents

The weekly, and year to date, provisional number of deaths associated with coronavirus (COVID-19) registered in Scotland.

Monaco recorded 16771 Coronavirus Cases since the epidemic began, according to the World Health Organization (WHO). In addition, Monaco reported 67 Coronavirus Deaths. This dataset includes a chart with historical data for Monaco Coronavirus Cases.