As of May 2, 2023, there were roughly 687 million global cases of COVID-19. Around 660 million people had recovered from the disease, while there had been almost 6.87 million deaths. The United States, India, and Brazil have been among the countries hardest hit by the pandemic.

The various types of human coronavirus The SARS-CoV-2 virus is the seventh known coronavirus to infect humans. Its emergence makes it the third in recent years to cause widespread infectious disease following the viruses responsible for SARS and MERS. A continual problem is that viruses naturally mutate as they attempt to survive. Notable new variants of SARS-CoV-2 were first identified in the UK, South Africa, and Brazil. Variants are of particular interest because they are associated with increased transmission.

Vaccination campaigns Common human coronaviruses typically cause mild symptoms such as a cough or a cold, but the novel coronavirus SARS-CoV-2 has led to more severe respiratory illnesses and deaths worldwide. Several COVID-19 vaccines have now been approved and are being used around the world.

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. (2023). CrisisTransformers: Pre-trained language models and sentence encoders for crisis-related social media texts. arXiv preprint arXiv:2309.05494.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 tweets (March 21, 2020 09:27 AM - March 22, 2020 07:46 AM)corona_tweets_05.csv: 1,782,157 tweets (March 22, 2020 07:50 AM - March 23, 2020 09:08 AM)corona_tweets_06.csv: 1,771,295 tweets (March 23, 2020 09:11 AM - March 24, 2020 11:35

Tentative de classement du nombre de cas confirmés du virus SARS-CoV-2 sur le territoire français par régions. Les données sont issues des médias et des sites de santé de l'état. Le but du document soit d'être le plus précis possible. Sources : https://solidarites-sante.gouv.fr/soins-et-maladies/maladies/maladies-infectieuses/coronavirus/article/points-de-situation-coronavirus-covid-19 https://www.santepubliquefrance.fr/maladies-et-traumatismes/maladies-et-infections-respiratoires/infection-a-coronavirus/articles/infection-au-nouveau-coronavirus-sars-cov-2-covid-19-france-et-monde https://france3-regions.francetvinfo.fr https://www.ars.sante.fr/ https://www.facebook.com/MinSoliSante/ https://geodes.santepubliquefrance.fr/#c=home Autres sources sur data.gouv.fr intéressantes : https://www.data.gouv.fr/fr/datasets/chiffres-cles-concernant-lepidemie-de-covid19-en-france/ https://www.data.gouv.fr/fr/reuses/visualisation-et-analyse-covid-19-monde-france-regions-francaises/ https://www.data.gouv.fr/fr/reuses/tableau-de-bord-de-suivi-de-lepidemie-de-covid19/ Autres : https://www.arcgis.com/apps/opsdashboard/index.html#/3a278da2d7ab4a8a8e1b4ea8bea7121b https://www.esrifrance.fr/coronavirus-ressources.aspx https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6 https://nextstrain.org/ncov

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.

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

Rationale: The World Health Organization (WHO) has declared the current coronavirus disease (COVID-19) outbreak, caused by the SARS-CoV-2 virus, to be a pandemic and, therefore, a Public Health Emergency of International Concern. The COVID-19 outbreak has a huge impact on health care, but also on economic and social costs. Track-and-trace programs are important measures to control the virus, but they have their limitations such as delays in the test results (e.g., it takes time to develop symptoms after infection, to access a test, receive the test result, and for close contacts to be traced). Early traceability of the virus may help in the track-and-trace programs to control the virus. It is currently thought that most – but not all – infected individuals develop symptoms, but that the infectious period starts on average two days before the first overt symptoms appear. It is estimated that pre- and asymptomatic individuals are responsible for up to half of all transmissions. By detecting infected individuals before they have overt symptoms, the proportion of transmissions by pre-symptomatic individuals could potentially be significantly reduced. Primary Objective: Using laboratory-confirmed SARS-CoV-2 infections (detected via serology, PCR and/or antigen tests) as the gold standard, we will determine the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for each of the following two algorithms to detect first time SARS-CoV-2 infection including early or asymptomatic infection: the algorithm using Ava bracelet data when coupled with self-reported Daily Symptom Diary data, and the algorithm using self-reported Daily Symptom Diary data alone. In addition, we will determine which of the two algorithms has superior performance characteristics for detecting SARS-CoV-2 infection including early or asymptomatic infection as confirmed by SARS-CoV-2 virus testing. Study design: Randomized, single-blinded, two-period, two-sequence crossover trial. The study will start with an initial Learning Phase (maximum 3 months), followed by a 3-month Period 1 and a 3-month Period 2. Each subject will undergo the experimental condition (=algorithm uses data from app and bracelet) in one of these periods and the control condition (=algorithm uses data from the app only) in the other period, but the order will be randomly assigned, resulting in Sequence 1 (experimental condition first) and Sequence 2 (control condition first). Study population: A target of 20,000 subjects will be enrolled in this study. Subjects will be recruited from previously studied cohorts as well as via public campaigns. They will be invited to visit the COVID-RED web portal. When they have successfully completed the survey questions in the COVID-RED web portal, are considered eligible and have indicated interest in joining the study, then they will receive the subject information sheet and consent form. Subjects can be enrolled when they comply with the following inclusion and exclusion criteria: Key Inclusion criteria: • Resident of the Netherlands • At least 18 years old • Must have a smartphone that runs at least Android 8.0 or iOS 13.0 operating systems and is active for the duration of the study (in the case of a change of mobile number, study team should be notified) • Be able to read, understand and write Dutch Key Exclusion criteria • Previous positive SARS-CoV-2 test result (confirmed either through PCR/antigen or antibody tests) (self-reported) • Current suspected (e.g., waiting for test result) coronavirus infection or symptoms of a coronavirus infection (self-reported) • Electronic implanted device (such as a pacemaker) • Suffering from cholinergic urticaria Intervention: All subjects will be instructed to complete the Daily Symptom Diary in the Ava COVID-RED app, wear their Ava bracelet each night and synchronise it with the app each day, during the entire period of study participation. The experimental condition (=algorithm uses app and bracelet data) will be compared to the control condition (=algorithm uses app data only). Main study parameters/endpoints: The primary endpoint for this study for each subject is the daily indication of potential SARS-CoV-2 infection as provided by the algorithm of the Ava COVID-RED app with or without using data from the Ava bracelet. This daily endpoint will be compared with actual SARS-CoV-2 test results (PCR/antigen and/or serology) collected before, during and at the end of study participation. For the primary comparison, this daily endpoint will be summarized over each trial period per subject to determine (1) whether a subject was ever judged to have had a high risk for a potential SARS-Cov-2 infection, and (2) whether a subject was ever confirmed to have had a SARS-CoV-2 infection by PCR/antigen and/or serology testing. For this comparison, parameters such as sensitivity, specificity, positive predictive value, and negative predictive value will be calculated. Nature and extent of the burden and risks associated with participation, benefit and group relatedness: Subjects wearing the Ava bracelet may experience skin irritation or sensitization due to rubbing and friction. Subjects are instructed to only wear the device at night to allow the skin to dry and breath during the day. They will be instructed to discontinue wearing the Ava bracelet and contact the study team in case they experience any signs of allergic reaction, feel soreness, tingling, numbness, burning or stiffness in their hands or wrists while or after wearing the Ava bracelet. Subjects may feel uncomfortable answering health questions in the Ava COVID-RED app, but they have the choice of not responding to the questions in the app. Subjects will be asked to donate fingerprick blood for SARS-CoV-2 antibody testing at up to 4 different timepoints, which may cause minor discomfort. This study will use the existing testing infrastructure in the Netherlands provided by the Municipal Health services (GGD) for SARS-CoV-2 infection, and, only when this is not possible, PCR testing in the central study laboratory will be arranged. Recruitment and follow-up will be completely remote and take place via post, email, phone and electronic web portals. In this way, risk of SARS-CoV-2 infection is minimized as much as possible for those wanting to participate in the trial and for the staff conducting the trial. Another risk for the subject is the potential breach of data security. The study team will implement security measures to prevent loss of data or unauthorised access to the data and we will follow the General Data Protection Regulation (GDPR). Data will be pseudo-anonymized within the platforms where data analysis will be performed. Data transfers will use a trial-specific identifier which is not linked to any external participant identifiers. Overall, the burden for the subjects is assessed as small and is justified given the importance of assessing a potential method in early detection of COVID-19. The expected benefit is large as the algorithms trained on the obtained data recordings from the Ava bracelet are expected to recognize COVID-19 earlier than the presentation of clinical symptoms. The latter would allow for earlier isolation and stratification as well as monitoring of SARS-CoV-2 infected persons preventing further spread and, if applicable, allowing for appropriate healthcare.

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

An outbreak of a novel coronavirus (SARS-CoV-2) infection has recently emerged and rapidly spreading in humans causing a significant threat to international health and the economy. Rapid assessment and warning are crucial for an outbreak analysis in response to serious public health. SARS-CoV-2 shares highly homological sequences with SARS-CoVs causing highly lethal pneumonia with respiratory distress and clinical symptoms similar to those reported for SARS-CoV and MERS-CoV infections. Notably, some COVID-19 patients also expressed neurologic signs like nausea, headache, and vomiting. Several studies have reported that coronaviruses are not only causing respiratory illness but also invade the central nervous system through a synapse-connected route. SARS-CoV infections are reported in both patients and experimental animals' brains. Interestingly, some COVID-19 patients have shown the presence of SARS-CoV-2 virus in their cerebrospinal fluid. Considering the similarities between SARS-CoV and SARS-CoV-2 in various aspects, it remains to clarify whether the potent invasion of SARS-CoV-2 may affect in COVID-19 patients. All these indicate that more detailed criteria are needed for the treatment and the prevention of SARS-CoV-2 infected patients. In the absence of potential interventions for COVID-19, there is an urgent need for an alternative strategy to control the spread of this disease.

Context

What Is COVID-19?

A coronavirus is a kind of common virus that causes an infection in your nose, sinuses, or upper throat. Most coronaviruses aren't dangerous.

COVID-19 is a disease that can cause what doctors call a respiratory tract infection. It can affect your upper respiratory tract (sinuses, nose, and throat) or lower respiratory tract (windpipe and lungs). It's caused by a coronavirus named SARS-CoV-2.

It spreads the same way other coronaviruses do, mainly through person-to-person contact. Infections range from mild to serious.

SARS-CoV-2 is one of seven types of coronavirus, including the ones that cause severe diseases like Middle East respiratory syndrome (MERS) and sudden acute respiratory syndrome (SARS). The other coronaviruses cause most of the colds that affect us during the year but aren’t a serious threat for otherwise healthy people.

In early 2020, after a December 2019 outbreak in China, the World Health Organization identified SARS-CoV-2 as a new type of coronavirus. The outbreak quickly spread around the world.

Is there more than one strain of SARS-CoV-2?

It’s normal for a virus to change, or mutate, as it infects people. A Chinese study of 103 COVID-19 cases suggests the virus that causes it has done just that. They found two strains, which they named L and S. The S type is older, but the L type was more common in early stages of the outbreak. They think one may cause more cases of the disease than the other, but they’re still working on what it all means.

How long will the coronavirus last?

It’s too soon to tell how long the pandemic will continue. It depends on many things, including researchers’ work to learn more about the virus, their search for a treatment and a vaccine, and the public’s efforts to slow the spread.

Dozens of vaccine candidates are in various stages of development and testing. This process usually takes years. Researchers are speeding it up as much as they can, but it still might take 12 to 18 months to find a vaccine that works and is safe.

Symptoms of COVID-19

The main symptoms include:

• Fever
• Coughing
• Shortness of breath
• Fatigue
• Chills, sometimes with shaking
• Body aches
• Headache
• Sore throat
• Loss of smell or taste
• Nausea
• Diarrhea

The virus can lead to pneumonia, respiratory failure, septic shock, and death. Many COVID-19 complications may be caused by a condition known as cytokine release syndrome or a cytokine storm. This is when an infection triggers your immune system to flood your bloodstream with inflammatory proteins called cytokines. They can kill tissue and damage your organs.

STAY HOME. STAY SAFE !

Content

ALL DATASETS HAVE BEEN CLEANED FOR DIRECT USE.

Total_World_covid-19.csv : This dataset contains the worldwide data country-wise such as total cases , total active, deaths, etc. along with testing data.

Total_India_covid-19.csv : This dataset contains India level data statewise such as confirmed cases , active cases, deaths, etc.

Total_US_covid-19.csv : This dataset contains India level data statewise such as confirmed cases , active cases, deaths, etc.

Daily_States_India.csv : This dataset contains daily statewise data of India such as daily confirmed , daily active , daily deaths and daily recovered.

Total_Maharshtra_covid-19.csv : This dataset contains Maharashtra's district wise data such as confirmed cases , active cases, deaths, etc.

Acknowledgements

1. World and US data has been collected from Worldometer . Thanks a lot.

2. India and State level along with Maharashtra district data has been collected from Covid19India. Special thanks to them for providing updated and such wonderful data .

Inspiration

1) What has been the Covid-19 trend across the world, Is it declining? Is it increasing? 2) Which countries have been able to sustain and control the virus spread? 3) How is India coping up with the virus? Have they been able to control it at the given cost of 2 months nationwide lockdown?

Note: 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)

• 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 and other COVID-19 data are available from multiple public locations: COVID Data Tracker; United States COVID-19 Cases and Deaths by State; <a

For English, see below

Dit bestand bevat, naast een kolom met het versienummer en een kolom met de datum van aanmaken van het bestand, de volgende karakteristieken per bemonsterde rioolwaterzuiveringsinstallatie (RWZI) in Nederland: Datum van monster, RWZI code, RWZI naam, Virusvracht per 100,000 inwoners

Het bestand is als volgt opgebouwd: Per zuiveringsinstallatie wordt er 24 uur lang een monster genomen van het rioolwater. Deze monsters worden door onderzoekers van het RIVM geanalyseerd op het aantal aanwezige virusdeeltjes. Een record bevat voor elke bemonsterde afval-/rioolwaterzuiveringsinstallatie (AWZI/RWZI) het gemiddelde aantal virusdeeltjes in het rioolwater, gecorrigeerd voor de dagelijkse hoeveelheid rioolwater (debiet) en weergegeven per 100.000 inwoners. Het bestand wordt van maandag tot en met vrijdag ververst (voor 14:00 uur). De informatie over inwonersaantallen per RWZI kunt u vinden in een omzet-tabel, die wordt aangeleverd door het Centraal Bureau voor de Statistiek (CBS). (De versie voor 2021:) (https://www.cbs.nl/nl-nl/maatwerk/2021/06/inwoners-per-rioolwaterzuiveringsinstallatie-1-1-2021) (De versie voor 2022:) (https://www.cbs.nl/nl-nl/maatwerk/2022/42/inwoners-per-rioolwaterzuiveringsinstallatie-1-1-2022)

Per 4 maart 2021 zijn een aantal wijzigingen doorgevoerd voor onderstaande RWZI’s. - Per 8 oktober 2020 is RWZI Aalst opgeheven. Het bijbehorende verzorgingsgebied is toegevoegd aan dat van RWZI Zaltbommel. De waarden voor de RNA_flow_per_100000 voor Zaltbommel zijn in het databestand vanaf 4 maart 2021 met terugwerkende kracht gewijzigd tot aan de bovengenoemde opheffingsdatum. Voor de waarden die voor de opheffingsdatum zijn gerapporteerd, zijn voor RWZI Aalst en RWZI Zaltbommel de individuele inwonersaantallen gebruikt die golden voor de opheffing van RWZI Aalst. - Per 9 december 2020 is RWZI Lienden opgeheven. Het bijbehorende verzorgingsgebied is toegevoegd aan dat van RWZI Tiel. De waarden voor RNA_flow_per_100000 voor RWZI Tiel zijn in het databestand vanaf 4 maart 2021 met terugwerkende kracht gewijzigd tot aan de bovengenoemde opheffingsdatum. Voor de waarden die voor de opheffingsdatum zijn gerapporteerd, zijn voor RWZI Lienden en RWZI Tiel de individuele inwonersaantallen gebruikt die golden voor de opheffing van RWZI Lienden. Wijzigingen vanaf 1 januari 2021 zijn verwerkt in de CBS omzet-tabel. Vanaf 30 september 2021 worden wijzigingen in de CBS omzet-tabel verwerkt, zodra ze bekend worden. Vanaf 30 september is de kolom RNA_per_ml uit het open data bestand verwijderd. Waarden die in deze kolom vermeld stonden, zijn omgerekend naar RNA_flow_per_100000 en in die kolom vermeld, voor zover dat mogelijk was. Daarnaast zijn alle waarden van 2021 én 2020 op 30 september 2021 met terugwerkende kracht herberekend met de inwonersaantallen in de CBS tabel die op 30 september 2021 is gepubliceerd. Alle waarden van 2022 zijn op 30 december 2022 met terugwerkende kracht herberekend met de CBS tabel die op 19 oktober 2022 is gepubliceerd.

Beschrijving van de variabelen: Version: Versienummer van de dataset. Wanneer de inhoud van de dataset structureel word 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 (data.rivm.nl).

Date_of_report: Datum waarop het bestand aangemaakt is. (formaat: jjjj-mm-dd)

Date_measurement: Datum waarop de monstername van het 24-uurs influent (ongezuiverd afval-/rioolwater) monster is gestart (formaat: jjjj-mm-dd).

RWZI_AWZI_code: Code van rioolwaterzuiveringsinstallatie (RWZI) of afvalwaterzuiveringsinstallatie (AWZI).

RWZI_AWZI_name: Naam van rioolwaterzuiveringsinstallatie (RWZI) of afvalwaterzuiveringsinstallatie (AWZI).

RNA_flow_per_100000: De gemiddelde concentratie SARS-CoV-2 RNA, omgerekend naar dagelijkse hoeveelheid rioolwater (debiet) en weergegeven per 100.000 inwoners.

Covid-19 National SARS-CoV-2 sewage surveillance

Please note that from 15-01-2024 onwards, the National Sewage Surveillance will no longer update the open data sets on a daily basis. Until further notice, data will be published on Mondays, Wednesdays and Fridays.

This file contains, in addition to a column with the version number and a column with the date of creation of the file, the following characteristics per sampled sewage treatment plant (STP) in the Netherlands: Sample date, STP code, STP name, Virus load per 100,000 inhabitants

The file is structured as follows: A sample of the sewage water is taken for 24 hours per treatment plant. These samples are analyzed by RIVM researchers for the number of virus particles present. A record contains the average number of virus particles in the sewage water for each waste/sewage treatment plant (STP) sampled, corrected for the daily amount of sewage water (flow rate) and shown per 100,000 inhabitants. The file is refreshed from Monday to Friday (before 2:00 PM). The information on population numbers per STP can be found in a conversion table, which is supplied by Statistics Netherlands (CBS). (The version for 2021:) (https://www.cbs.nl/nl-nl/maatwerk/2021/06/inwoners-per-rioolwaterzuiveringsinstallatie-1-1-2021) (The version for 2022:) (https://www.cbs.nl/nl-nl/maatwerk/2022/42/inwoners-per-rioolwaterzuiveringsinstallatie-1-1-2022)

As of March 4, 2021, a number of changes have been implemented for the STPs below. - As of October 8, 2020, STP Aalst has been closed. The associated catchment area has been added to the STP of Zaltbommel. The values for the RNA_flow_per_100000 for Zaltbommel have been changed in the database from March 4, 2021 retroactively to the aforementioned date of suspension. For the values reported before the closure date, the individual population numbers for STP Aalst and STP Zaltbommel that applied before the closure of STP Aalst were used. - As of December 9, 2020, STP Lienden has been closed. The associated catchment area has been added to the STP of Tiel. The values for RNA_flow_per_100000 for STP Tiel have been changed in the database from March 4, 2021 retroactively to the aforementioned date of suspension. For the values reported before the closure date, the individual population numbers for STP Lienden and STP Tiel that applied before the closure of STP Lienden were used. Changes from 1 January 2021 have been incorporated in the CBS conversion table. From September 30, 2021, changes in the CBS conversion table will be processed as soon as they become known. As of September 30, the column RNA_per_ml has been removed from the open data file. Values reported in this column have been converted to RNA_flow_per_100000 and reported in that column where possible. In addition, all values for 2021 and 2020 were retroactively recalculated on September 30, 2021 with the population numbers in the CBS table published on September 30, 2021. All values for 2022 have been retroactively recalculated on December 30, 2022 using the CBS table published on October 19, 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 (data.rivm.nl).

Date_of_report: Date on which the file was created. (format: yyyy-mm-dd)

Date_measurement: Date on which the sampling of the 24-hour influent (raw waste/sewage) sample started (format: yyyy-mm-dd).

RWZI_AWZI_code: Code of sewage treatment plant (RWZI in Dutch abbreviation) or waste water treatment plant (AWZI in Dutch abbreviation).

RWZI_AWZI_name: Name of sewage treatment plant (RWZI in Dutch abbreviation) or waste water treatment plant (AWZI in Dutch abbreviation).

RNA_flow_per_100000: The average concentration of SARS-CoV-2 RNA, converted to daily amount of sewage (flow rate) and displayed per 100,000 inhabitants.

This repository within the ACTIV TRACE initiative houses a comprehensive collection of datasets related to SARS-CoV-2. The processing of SARS-CoV-2 Sequence Read Archive (SRA) files has been optimized to identify genetic variations in viral samples. This information is then presented in the Variant Call Format (VCF). Each VCF file corresponds to the SRA parent-run's accession ID. Additionally, the data is available in the parquet format, making it easier to search and filter using the Amazon Athena Service. The SARS-CoV-2 Variant Calling Pipeline is designed to handle new data every six hours, with updates to the AWS ODP bucket occurring daily.

For English, see below

Dit bestand bevat de volgende aantallen: - Aantal per VOC, VOI en VUM gedetecteerd per week - Totaal aantal metingen, de noemer, per wekelijkse steekproef

Dit is gesplitst in de door de WHO (https://www.who.int/en/activities/tracking-SARS-CoV-2-variants/) en/of ECDC (https://www.ecdc.europa.eu/en/covid-19/variants-concern) aangemerkte Variant of Concern (VOC), Variant of Interest (VOI) en Variant Under Monitoring (VUM). De week waartoe een monster behoord is gebaseerd op de datum van bemonstering. De aantallen zijn gebaseerd op de aselecte steekproef afkomstig uit de kiemsurveillance, dit betekent dat samples behorend tot uitbraken niet geïncludeerd zijn in de data.

Het bestand is als volgt opgebouwd: - Een record per VOC, VOI en VUM aangemerkte SARS-CoV-2 variant per week. Dit bestand wordt wekelijks op de vrijdag geüpdatet. De manier waarop deze informatie gegenereerd wordt is anders dan de snel-testen en PCR-testen. Er worden geavanceerdere machines gebruikt die een langere doorlooptijd hebben dan, bijvoorbeeld, de machines gebruikt voor PCR-testen. Door alle logistieke processen is het daarom niet haalbaar om een representatief beeld te vormen van de laatste twee weken: deze worden dan ook niet gerapporteerd. Aanvullend, het kiemsurveillance project is sinds oktober 2020 operationeel met een toenemend aantal wekelijkse monsters tot medio begin Januari 2021, daarom zijn oudere data niet beschikbaar. Bij alle gerapporteerde data zijn de instructies, definities en voetnoten zoals vermeld op https://www.rivm.nl/coronavirus-covid-19/virus/varianten leidend. N.B.: Door internationaal veranderende stamnaam definities op basis van voortschrijdend wetenschappelijk inzicht kunnen de records in de hier gepresenteerde data aangepast worden.

Changelog: Versie 2 update (29 oktober 2021): - Een kolom WHO_category is toegevoegd met de actuele variant categorie (VOC/VOI/VUM) zoals toegewezen door de WHO. - Naast de categorieën VOC en VOI wordt nu ook de categorie VUM geïncludeerd in het bestand. Versie 3 update (10 december 2021): - Een kolom May_include_samples_listed_before is toegevoegd met daarin een waarde TRUE het mogelijk is dat de gerapporteerde Variant_cases samples aggregeert die al in een eerdere variant in de tabel zijn geïncludeerd. Wanneer dit niet mogelijk is, is de waarde FALSE. Versie 4 update (8 juli 2022): - De kolom May_include_samples_listed_before is vervangen door een kolom Is_subvariant_of. Indien deze variant een subvariant is van een andere variant die is genoemd, bevat deze kolom een waarde die correspondeert met de Variant_code van de andere variant. De aantallen (Variant_cases) van deze subvariant zijn een subset van die van de andere variant.

Beschrijving van de variabelen: Version: Versienummer van de dataset. Wanneer de inhoud van de dataset structureel word gewijzigd (dus niet de wekelijkse update of een correctie op record niveau), zal het versienummer aangepast worden (+1) en ook de corresponderende metadata in RIVM data (data.rivm.nl).

Date_of_report: Datum en tijd waarop het databestand voor het laatst is bijgewerkt door het RIVM. Schrijfwijze: YYYY-MM-DD hh:mm:ss.

Date_of_statistics_week_start: De datum van de maandag - eerste dag van die week - waarvoor de aantallen per week worden gepresenteerd. De laatste dag van de week is de zondag. Schrijfwijze: YYYY-MM-DD.

Variant_code: Wetenschappelijke naam van SARS-CoV-2 variant op basis van Pangolin nomenclature. Kan letters, cijfers en punten bevatten.

Variant_name: Actuele WHO label van SARS-CoV-2 variant. Bestaat enkel uit letters.

ECDC_category: Geeft aan of het een Variant of Concern (VOC), Variant of Interest (VOI), Variant under Monitoring (VUM), of De-escalated Variant (DEV) is volgens de actuele definities van ECDC. Voor meer info zie ook: https://www.ecdc.europa.eu/en/covid-19/variants-concern.

WHO_category: Geeft aan of het een Variant of Concern (VOC), Variant of Interest (VOI) of Variant under Monitoring (VUM) is volgens de actuele definities van WHO. Voor meer info zie ook: https://www.who.int/en/activities/tracking-SARS-CoV-2-variants/

Is_subvariant_of: Indien deze variant een subvariant is van een andere variant die is genoemd, bevat deze kolom een waarde die correspondeert met de Variant_code van de andere variant. De aantallen (Variant_cases) van deze subvariant zijn een subset van die van de andere variant.

Sample_size: Toont de totale steekproefgrootte in de betreffende week aan. Bestaat enkel uit hele getallen.

Variant_cases: Toont aan voor hoeveel gevallen uit de steekproef in de betreffende week de specifieke VOC, VOI of VUM gevonden is. Bestaat enkel uit hele getallen.

Covid-19 reporting of SARS-CoV-2 variants in the Netherlands through the random sample of RT-PCR positive samples in the national surveillance of virus variants.

This file contains the following numbers: - Number per VOC, VOI and VUM detected per week - Total number of measurements, the denominator, per weekly sample

This is split into the WHO (https://www.who.int/en/activities/tracking-SARS-CoV-2-variants/) and/or ECDC (https://www.ecdc.europa.eu/en/covid-19/variants-concern) designated Variant of Concern (VOC), Variant of Interest (VOI) and Variant Under Monitoring (VUM). The week to which a sample belongs is based on the date of sampling. The numbers are based on the random sample from the virus variant surveillance, which means that samples belonging to outbreaks are not included in the data.

The file is structured as follows: - One record per VOC, VOI and VUM noted SARS-CoV-2 variant per week. This file is updated weekly on Fridays. The way this information is generated is different from the rapid tests and PCR tests. More advanced machines are used that have a longer run time than, for example, the machines used for PCR testing. Due to all the logistics processes, it is therefore not feasible to form a representative picture of the most recent two weeks: these are not reported for that reason. Additionally, the virus variant surveillance project has been operational since October 2020 with an increasing number of weekly samples until mid-early January 2021, therefore older data is not available. For all reported data, the instructions, definitions and footnotes as stated on https://www.rivm.nl/coronavirus-covid-19/virus/varianten are leading. Please note, due to internationally changing variant name definitions based on advancing scientific insight, the records in the data presented here can be adjusted.

Changelog: Version 2 update (October 29, 2021): - A WHO_category column has been added with the current variant category (VOC/VOI/VUM) as assigned by the WHO. - In addition to the VOC and VOI categories, the VUM category is now also included in the file. Version 3 update (December 10, 2021): - A column May_include_samples_listed_before has been added with a value TRUE whenever it is possible for the reported Variant_cases to aggregate samples that have already been included in a previous variant in the table. When this is not possible, the value is FALSE. Version 4 update (July 8, 2022): - The May_include_samples_listed_before column has been replaced by an Is_subvariant_of column. If this variant is a subvariant of another variant mentioned, this column contains a value that corresponds to the Variant_code of the other variant. The numbers (Variant_cases) of this subvariant are a subset of those of the other variant.

Description of the variables: Version: Version number of the dataset. When the content of the dataset is structurally changed (so not the weekly update or a correction at record level), the version number will be adjusted (+1) and also the corresponding metadata in RIVM data (data.rivm.nl).

Date_of_report: Date and time when the database was last updated by the RIVM. Notation: YYYY-MM-DD hh:mm:ss.

Date_of_statistics_week_start: The date of the Monday - first day of that week - for which the numbers per week are presented. The last day of the week is Sunday. Notation: YYYY-MM-DD.

Variant_code: Scientific name of SARS-CoV-2 variant based on Pangolin nomenclature. Can contain letters, numbers and periods.

Variant_name: Current WHO label of SARS-CoV-2 variant. Consists of letters only.

ECDC_category: Indicates whether it is a Variant of Concern (VOC), Variant of Interest (VOI), Variant under Monitoring (VUM), or De-escalated Variant (DEV) according to ECDC's current definitions. For more information see also: https://www.ecdc.europa.eu/en/covid-19/variants-concern.

WHO_category: Indicates whether it is a Variant of Concern (VOC), Variant of Interest (VOI) or Variant under Monitoring (VUM) according to the current WHO definitions. For more information see also: https://www.who.int/en/activities/tracking-SARS-CoV-2-variants/

Is_subvariant_of: If this variant is a subvariant of another variant that has been mentioned, this column contains a value that corresponds to the Variant_code of the other variant. The numbers (Variant_cases) of this subvariant are a subset of those of the other variant.

Sample_size: Shows the total sample size in that week. Consists of whole numbers only.

Variant_cases: Shows for how many cases from the sample from that week the specific VOC, VOI or VUM was found. Consists of whole numbers only.

Datasets in this publication report the number of diagnoses with coronavirus disease (COVID-19) as reported by the Department of Health in Ireland. This includes new cases diagnosed per day and cumulative cases, as well as cases across age groups. The latter also include population estimates by age group for 2019 from Ireland's Central Statistics Office, in order to express cases per million population.

For the files YYYYMMDD_covid_ie_age_groups.csv, variable descriptions are as follows:

• age_group: Age groups, in years
• cases: Total cases of COVID-19 diagnosed in Ireland by age group, as per the Department of Health
• pop_estimate: National population estimates by age group for 2019 in Ireland, as per the Central Statistics Office (Table 7 https://www.cso.ie/en/releasesandpublications/er/pme/populationandmigrationestimatesapril2019/), expressed in thousands.
• cases_per_million: Cases of COVID-19 diagnosed in Ireland by age group, expressed per 1 million individuals

For the files YYYYMMDD_covid_ie_daily_cases, variable descriptions are as follows:

• date: Date, in DD-MM-YYYY format
• daily_cases: New cases of COVID-19 diagnosed per day in Ireland, as per the Department of Health (https://www.gov.ie/en/news/7e0924-latest-updates-on-covid-19-coronavirus/)
• cumulative_cases: Cumulative number of COVID-19 cases in Ireland
• percent_daily_increase: New cases of COVID-19 diagnosed per day in Ireland as a percentage of cumulative number of cases up to that date.

Datasets in this publication report the number of diagnoses with coronavirus disease (COVID-19) as reported by the Department of Health in Ireland. This includes new cases diagnosed per day and cumulative cases, hospitalisations, ICU admissions, deaths, number of healthcare workers, number of clusters, gender of cases, age groups of cases, mode of transmission, age groups of those hospitalised, and cases per county. To aid standardisation of age groups and cases per county, the population estimates by age group for 2019 and the actual county population in the 2016 Census from Ireland's Central Statistics Office are also included as separate datasets, to allow expression of cases per million population.

These are

1. doh_covid_ie_cases_analysis.csv, where data from Ireland's Health Protection Surveillance Centre is included up to midnight on each included date (currently up to 12-Jun-2020).
2. age_population_cso_2019.csv
3. counties_population_cso_2016.csv

age_population_cso_2019.csv has been updated to include separate population estimates for those aged 65-74 years, 75-84 years, and 85 years and over. This is in response to the HSPC releasing case and hospitalisation data for these groups rather than a combined 65 years and over group.

counties_population_cso_2016.csv has been updated to remove trailing spaces in the 'county' column.

doh_covid_ie_cases_analysis.csv is regularly updated at https://github.com/frankmoriarty/covid_ie/blob/master/doh_covid_ie_cases_analysis.csv

This dataset contains information regarding both applications and granted patents on COVID-19 disease

Derwent Innovation database was used for data mining (accessed on Nov. 20, 2021).

The patent search was carried out on by means of a precise set of keywords and performed in the title/abstract/claims search field.

6,148 Inpadoc patent families were retrieved.

The XLS file contains information related to Title, Abstract - DWPI , First Claim, Priority Number, Priority Date, Application Number, Application Date, Publication Number, Publication Date, IPC – Current, CPC – Current, Assignee/Applicant, Optimized Assignee, INPADOC Family Members.

The top countries/regions are China (3,271), WO (1,057), India (487), United States (455).

The top IPC codes are listed in the following table:

Value of the dataset: prior art searches; patent landscape analysis

Steps to reproduce data:

CTB=("covid-19" OR "covid 19" OR "covid19" ADJ "SARS-CoV-2" OR "SARS-CoV2" OR "sarscov2" ADJ "2019 ncov" OR "2019-nCoV" OR "2019nCoV" ADJ "covid-2019" OR "covid 2019" OR "COVID2019" OR "severe acute respiratory syndrome coronavirus 2" OR "2019 novel coronavirus" OR "coronavirus disease 2019" OR "novel corona virus" OR "novel coronavirus" OR "new corona virus" OR "new coronavirus" OR "Wuhan coronavirus")

CTB=title/abstract/claims

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/

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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/

Real-time tracking of pathogen evolution Nextstrain is an open-source project to harness the scientific and public health potential of pathogen genome data. It provides a continually-updated view of publicly available data alongside powerful analytic and visualization tools for use by the community. Its goal is to aid epidemiological understanding and improve outbreak response. With regards to the Novel coronavirus (2019-nCoV), nCoV genomes are incorporated as soon as they are shared, providing analyses and situation reports.