MM-COVID is a dataset for fake news detection related to COVID-19. This dataset provides the multilingual fake news and the relevant social context. It contains 3,981 pieces of fake news content and 7,192 trustworthy information from English, Spanish, Portuguese, Hindi, French and Italian, 6 different languages.

The peer-reviewed publication for this dataset has been presented in the 2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), and can be accessed here: https://arxiv.org/abs/2205.02596. Please cite this when using the dataset.

This dataset contains a heterogeneous set of True and False COVID claims and online sources of information for each claim.

The claims have been obtained from online fact-checking sources, existing datasets and research challenges. It combines different data sources with different foci, thus enabling a comprehensive approach that combines different media (Twitter, Facebook, general websites, academia), information domains (health, scholar, media), information types (news, claims) and applications (information retrieval, veracity evaluation).

The processing of the claims included an extensive de-duplication process eliminating repeated or very similar claims. The dataset is presented in a LARGE and a SMALL version, accounting for different degrees of similarity between the remaining claims (excluding respectively claims with a 90% and 99% probability of being similar, as obtained through the MonoT5 model). The similarity of claims was analysed using BM25 (Robertson et al., 1995; Crestani et al., 1998; Robertson and Zaragoza, 2009) with MonoT5 re-ranking (Nogueira et al., 2020), and BERTScore (Zhang et al., 2019).

The processing of the content also involved removing claims making only a direct reference to existing content in other media (audio, video, photos); automatically obtained content not representing claims; and entries with claims or fact-checking sources in languages other than English.

The claims were analysed to identify types of claims that may be of particular interest, either for inclusion or exclusion depending on the type of analysis. The following types were identified: (1) Multimodal; (2) Social media references; (3) Claims including questions; (4) Claims including numerical content; (5) Named entities, including: PERSON − People, including fictional; ORGANIZATION − Companies, agencies, institutions, etc.; GPE − Countries, cities, states; FACILITY − Buildings, highways, etc. These entities have been detected using a RoBERTa base English model (Liu et al., 2019) trained on the OntoNotes Release 5.0 dataset (Weischedel et al., 2013) using Spacy.

The original labels for the claims have been reviewed and homogenised from the different criteria used by each original fact-checker into the final True and False labels.

The data sources used are:

• The CoronaVirusFacts/DatosCoronaVirus Alliance Database. https://www.poynter.org/ifcn-covid-19-misinformation/

• CoAID dataset (Cui and Lee, 2020) https://github.com/cuilimeng/CoAID

• MM-COVID (Li et al., 2020) https://github.com/bigheiniu/MM-COVID

• CovidLies (Hossain et al., 2020) https://github.com/ucinlp/covid19-data

• TREC Health Misinformation track https://trec-health-misinfo.github.io/

• TREC COVID challenge (Voorhees et al., 2021; Roberts et al., 2020) https://ir.nist.gov/covidSubmit/data.html

The LARGE dataset contains 5,143 claims (1,810 False and 3,333 True), and the SMALL version 1,709 claims (477 False and 1,232 True).

The entries in the dataset contain the following information:

• Claim. Text of the claim.

• Claim label. The labels are: False, and True.

• Claim source. The sources include mostly fact-checking websites, health information websites, health clinics, public institutions sites, and peer-reviewed scientific journals.

• Original information source. Information about which general information source was used to obtain the claim.

• Claim type. The different types, previously explained, are: Multimodal, Social Media, Questions, Numerical, and Named Entities.

Funding. This work was supported by the UK Engineering and Physical Sciences Research Council (grant no. EP/V048597/1, EP/T017112/1). ML and YH are supported by Turing AI Fellowships funded by the UK Research and Innovation (grant no. EP/V030302/1, EP/V020579/1).

References

• Arana-Catania M., Kochkina E., Zubiaga A., Liakata M., Procter R., He Y.. Natural Language Inference with Self-Attention for Veracity Assessment of Pandemic Claims. NAACL 2022 https://arxiv.org/abs/2205.02596

• Stephen E Robertson, Steve Walker, Susan Jones, Micheline M Hancock-Beaulieu, Mike Gatford, et al. 1995. Okapi at trec-3. Nist Special Publication Sp,109:109.

• Fabio Crestani, Mounia Lalmas, Cornelis J Van Rijsbergen, and Iain Campbell. 1998. “is this document relevant?. . . probably” a survey of probabilistic models in information retrieval. ACM Computing Surveys (CSUR), 30(4):528–552.

• Stephen Robertson and Hugo Zaragoza. 2009. The probabilistic relevance framework: BM25 and beyond. Now Publishers Inc.

• Rodrigo Nogueira, Zhiying Jiang, Ronak Pradeep, and Jimmy Lin. 2020. Document ranking with a pre-trained sequence-to-sequence model. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings, pages 708–718.

• Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi. 2019. Bertscore: Evaluating text generation with bert. In International Conference on Learning Representations.

• Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692.

• Ralph Weischedel, Martha Palmer, Mitchell Marcus, Eduard Hovy, Sameer Pradhan, Lance Ramshaw, Nianwen Xue, Ann Taylor, Jeff Kaufman, Michelle Franchini, et al. 2013. Ontonotes release 5.0 ldc2013t19. Linguistic Data Consortium, Philadelphia, PA, 23.

• Limeng Cui and Dongwon Lee. 2020. Coaid: Covid-19 healthcare misinformation dataset. arXiv preprint arXiv:2006.00885.

• Yichuan Li, Bohan Jiang, Kai Shu, and Huan Liu. 2020. Mm-covid: A multilingual and multimodal data repository for combating covid-19 disinformation.

• Tamanna Hossain, Robert L. Logan IV, Arjuna Ugarte, Yoshitomo Matsubara, Sean Young, and Sameer Singh. 2020. COVIDLies: Detecting COVID-19 misinformation on social media. In Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020, Online. Association for Computational Linguistics.

• Ellen Voorhees, Tasmeer Alam, Steven Bedrick, Dina Demner-Fushman, William R Hersh, Kyle Lo, Kirk Roberts, Ian Soboroff, and Lucy Lu Wang. 2021. Trec-covid: constructing a pandemic information retrieval test collection. In ACM SIGIR Forum, volume 54, pages 1–12. ACM New York, NY, USA.

A full description of this dataset along with updated information can be found here.

In response to the COVID-19 pandemic, the Allen Institute for AI has partnered with leading research groups to prepare and distribute the COVID-19 Open Research Dataset (CORD-19), a free resource of scholarly articles, including full text content, about COVID-19 and the coronavirus family of viruses for use by the global research community.

This dataset is intended to mobilize researchers to apply recent advances in natural language processing to generate new insights in support of the fight against this infectious disease. The corpus will be updated weekly as new research is published in peer-reviewed publications and archival services like bioRxiv, medRxiv, and others.

By downloading this dataset you are agreeing to the Dataset license. Specific licensing information for individual articles in the dataset is available in the metadata file.

Additional licensing information is available on the PMC website, medRxiv website and bioRxiv website.

Dataset content:

Commercial use subset

Non-commercial use subset

PMC custom license subset

bioRxiv/medRxiv subset (pre-prints that are not peer reviewed)

Metadata file

Readme

Each paper is represented as a single JSON object (see schema file for details).

Description:

The dataset contains all COVID-19 and coronavirus-related research (e.g. SARS, MERS, etc.) from the following sources:

PubMed's PMC open access corpus using this query (COVID-19 and coronavirus research)

Additional COVID-19 research articles from a corpus maintained by the WHO

bioRxiv and medRxiv pre-prints using the same query as PMC (COVID-19 and coronavirus research)

We also provide a comprehensive metadata file of 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).

We recommend using metadata from the comprehensive file when available, instead of parsed metadata in the dataset. Please note the dataset may contain multiple entries for individual PMC IDs in cases when supplementary materials are available.

This repository is linked to the WHO database of publications on coronavirus disease and other resources, such as Microsoft Academic Graph, PubMed, and Semantic Scholar. A coalition including the Chan Zuckerberg Initiative, Georgetown University’s Center for Security and Emerging Technology, Microsoft Research, and the National Library of Medicine of the National Institutes of Health came together to provide this service.

Citation:

When including CORD-19 data in a publication or redistribution, please cite the dataset as follows:

In bibliography:

COVID-19 Open Research Dataset (CORD-19). 2020. Version 2020-MM-DD. Retrieved from https://pages.semanticscholar.org/coronavirus-research. Accessed YYYY-MM-DD. 10.5281/zenodo.3715505

In text:

(CORD-19, 2020)

The Allen Institute for AI and particularly the Semantic Scholar team will continue to provide updates to this dataset as the situation evolves and new research is released.

Ref: https://github.com/CSSEGISandData/COVID-19 Daily reports (csse_covid_19_daily_reports) This folder contains daily case reports. All timestamps are in UTC (GMT+0). File naming convention MM-DD-YYYY.csv in UTC. Field description Province/State: China - province name; US/Canada/Australia/ - city name, state/province name; Others - name of the event (e.g., "Diamond Princess" cruise ship); other countries - blank. Country/Region: country/region name conforming to WHO (will be updated). Last Update: MM/DD/YYYY HH:mm (24 hour format, in UTC). Confirmed: the number of confirmed cases. For Hubei Province: from Feb 13 (GMT +8), we report both clinically diagnosed and lab-confirmed cases. For lab-confirmed cases only (Before Feb 17), please refer to who_covid_19_situation_reports. For Italy, diagnosis standard might be changed since Feb 27 to "slow the growth of new case numbers." (Source) Deaths: the number of deaths. Recovered: the number of recovered cases. Update frequency Files after Feb 1 (UTC): once a day around 23:59 (UTC). Files on and before Feb 1 (UTC): the last updated files before 23:59 (UTC). Sources: archived_data and dashboard. Data sources Refer to the mainpage. Why create this new folder? Unifying all timestamps to UTC, including the file name and the "Last Update" field. Pushing only one file every day. All historic data is archived in archived_data. Time series summary (csse_covid_19_time_series) This folder contains daily time series summary tables, including confirmed, deaths and recovered. All data are from the daily case report. Field descriptioin Province/State: same as above. Country/Region: same as above. Lat and Long: a coordinates reference for the user. Date fields: M/DD/YYYY (UTC), the same data as MM-DD-YYYY.csv file.

Multivariate regression for prediction of severe COVID-19.

COVID-19 mRNA vaccines are highly efficacious in preventing COVID-19 morbidity and mortality in phase 3 clinical studies as well as in real-world settings. Emerging evidence suggests that some individuals with underlying comorbidities may mount suboptimal antibody responses to SARS-CoV-2 immunization (Addeo et al., 2021; Monin et al., 2021; Thakkar et al., 2021). Indeed, patients with multiple myeloma (MM) are immuno-compromised due to defects in humoral and cellular immunity as well as due to immunosuppressive therapy. Preliminary reports indicate that the antibody response in MM after the initial dose of SARS-CoV-2 mRNA vaccine is attenuated and delayed compared to healthy controls (Bird et al., 2021; Terpos et al., 2021). Moreover, MM patients who receive anti-CD38 monoclonal antibodies may have poorer vaccine-induced antibody responses even after completion of the full two-dose mRNA vaccine regimen (Pimpinelli et al., 2021). The kinetics of the vaccine responses in MM patients with prior COVID-19 infection and the impact of treatments, including BCMA-targeting agents, to vaccine response remain unknown.

"COVID-19 mortality correlation with cloudiness, sunlight, latitude in European countries"

Dataset for article titled
"COVID-19 mortality: positive correlation with cloudiness, sunlight and no correlation with latitude in Europe"

by SECIL OMER, ADRIAN IFTIME, VICTOR BURCEA

Corresponding author: A. Iftime, University of Medicine and Pharmacy "Carol Davila", Biophysics Department, 8 Blvd. Eroii Sanitari, 050474 Bucharest, Romania. Email address: adrian.iftime [at] umfcd.ro.

Preprint corresponding to this dataset: https://doi.org/10.1101/2021.01.27.21250658

===========
Dataset file:
1.0.0.COVID-19_Mortality_Cloudiness_Insolation_EUROPE_March_August_2020.csv

Dataset graphical preview:
1.0.0.INFOGRAFIC_CloudFraction_vs_COVID-19_mortality_Europe_March-August_2020.png

DATASET fields:
"Country" :
Country name; 37 European countries included.

"Date":
Date stamp at the collection time.
Data collection was performed in the last day of every month.
Date format: YYYY-MM-DD

"Month_Key" :
Date stamp at the collection time, formatted for easier monthly time series analysis.
Date format: YYYY-MM

"Month_Fct2020"
Date stamp at the collection time,formatted for easier graphing, as a string with names of the months
(in English).

"Deaths_per_1Mpop" :
Monthly mortality from COVID-19 raported in the country,
reported as number of COVID-19 deaths per 1 million population of the country,
in that particular month / country.
NB: it is reported as million population, not patients.

"LogDeaths_per_1Mpop" :
Log10 transformation of "Deaths_per_1Mpop"

"Insolation_Average" :
Insolation average (solar irradiance at ground level),
in that particular month / country.
It is expressed in Watt / square meter of the ground surface.
Data derived from data avaialble at NASA Langley Research Center, NASA’s Earth Observatory,
CERES / FLASHFlux team, 2020,
https://neo.sci.gsfc.nasa.gov/view.php?datasetId=CERES_INSOL_M

"Cloud_Fraction" :
Cloudiness (also known as cloud fraction, cloud cover, cloud amount or sky cover),
as decimal fraction of the sky obscured by clouds,
in that particular month / country.
Data derived from NASA Goddard Space Flight Center, NASA’s Earth Observatory,
MODIS Atmosphere Science Team, 2020,
https://neo.sci.gsfc.nasa.gov/view.php?datasetId=MODAL2_M_CLD_FR

"CENTR_latitude" and
"CENTR_longitude" :
Latitude and Longitude of the country centroid, for each country.
Data derived from Google LLC, "Dataset publishing language: country centroids",
https://developers.google.com/public-data/docs/canonical/countries_csv
NOTE: This is identical in every month (obviuously);
it is redundantly included for easier monthly sectional analysis of the data.

===========
Versioning: 1.0.0.COVID-19_Mortality_Cloudiness_Insolation_EUROPE_March_August_2020.csv

MAJOR: changes yearly; 1 = 2020
MINOR: changes if new monthly data is added in that particular year.
PATCH: Changes only if errors or minor edits were performed.

DOI for this version: 10.5281/zenodo.4266758

Dataset file source for this version (internal analysis source file):
db_covid_all-ANALYSIS.2020-09-22_r10.csv

The rapid spread of COVID-19, caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a worldwide health emergency. Unfortunately, to date, a very small number of remedies have been to be found effective against SARS-CoV-2 infection. Therefore, further research is required to achieve a lasting solution against this deadly disease. Repurposing available drugs and evaluating natural product inhibitors against target proteins of SARS-CoV-2 could be an effective approach to accelerate drug discovery and development. With this strategy in mind, we derived Marine Natural Products (MNP)-based drug-like small molecules and evaluated them against three major target proteins of the SARS-CoV-2 virus replication cycle. A drug-like database from MNP library was generated using Lipinski’s rule of five and ADMET descriptors. A total of 2,033 compounds were obtained and were subsequently subjected to molecular docking with 3CLpro, PLpro, and RdRp. The docking analyses revealed that a total of 14 compounds displayed better docking scores than the reference compounds and have significant molecular interactions with the active site residues of SARS-CoV-2 virus targeted proteins. Furthermore, the stability of docking-derived complexes was analyzed using molecular dynamics simulations and binding free energy calculations. The analyses revealed two hit compounds against each targeted protein displaying stable behavior, binding affinity, and molecular interactions. Our investigation identified two hit compounds against each targeted proteins displaying stable behavior, higher binding affinity and key residual molecular interactions, with good in silico pharmacokinetic properties, therefore can be considered for further in vitro studies.

Context

Late in December 2019, the World Health Organisation (WHO) China Country Office obtained information about severe pneumonia of an unknown cause, detected in the city of Wuhan in Hubei province, China. This later turned out to be the novel coronavirus disease (COVID-19), an infectious disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) of the coronavirus family. The disease causes respiratory illness characterized by primary symptoms like cough, fever, and in more acute cases, difficulty in breathing. WHO later declared COVID-19 as a Pandemic because of its fast rate of spread across the Globe.

Content

The COVID-19 datasets organized by continent contain daily level information about the COVID-19 cases in the different continents of the world. It is a time-series data and the number of cases on any given day is cumulative. The original datasets can be found on this John Hopkins University Github repository. I will be updating the COVID-19 datasets on a regular basis with every update from John Hopkins University. I have also included the World COVID-19 tests data scraped from Worldometer and 2020 world population also scraped from worldometer.

The datasets

COVID-19 cases covid19_world.csv. It contains the cumulative number of COVID-19 cases from around the world since January 22, 2020, as compiled by John Hopkins University. covid19_asia.csv, covid19_africa.csv, covid19_europe.csv, covid19_northamerica.csv, covid19.southamerica.csv, covid19_oceania.csv, and covid19_others.csv. These contain the cumulative number of COVID-19 cases organized by the continent.

Field description - ObservationDate: Date of observation in YY/MM/DD - Country_Region: name of Country or Region - Province_State: name of Province or State - Confirmed: the number of COVID-19 confirmed cases - Deaths: the number of deaths from COVID-19 - Recovered: the number of recovered cases - Active: the number of people still infected with COVID-19 Note: Active = Confirmed - (Deaths + Recovered)

COVID-19 tests covid19_tests.csv. It contains the cumulative number of COVID tests data from worldometer conducted since the onset of the pandemic. Data available from June 01, 2020.

Field description Date: date in YY/MM/DD Country, Other: Country, Region, or dependency TotalTests: cumulative number of tests up till that date Population: population of Country, Region, or dependency Tests/1M pop: tests per 1 million of the population 1 Testevery X ppl: 1 test for every X number of people

2020 world population world_population(2020).csv. It contains the 2020 world population as reported by woldometer.

Field description Country (or dependency): Country or dependency Population (2020): population in 2020 Yearly Change: yearly change in population as a percentage Net Change: the net change in population Density(P/km2): population density Land Area(km2): land area Migrants(net): net number of migrants Fert. Rate: Fertility Rate Med. Age: median age Urban pop: urban population World Share: share of the world population as a percentage

Acknowledgements

1. John Hopkins University for making COVID-19 datasets available to the public: https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_daily_reports
2. John Hopkins University COVID-19 Dashboard: https://coronavirus.jhu.edu/map.html
3. COVID-19 Africa dashboard: http://covid-19-africa.sen.ovh/
4. Worldometer: https://www.worldometers.info/
5. United Nations Department of General Assembly and Conference Management: https://www.un.org/depts/DGACM/RegionalGroups.shtml
6. wallpapercave.com: https://wallpapercave.com/covid-19-wallpapers

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.

This dataset is based on the Github repository maintained by OpenZH. Data has been enriched with geographical data for the cantons, in order to produce visualisations.Field NameDescriptionFormatNote

updateDate and time of notification YYYY-MM-DD-HH-MM

nameName of the reporting cantonTextabbreviation_canton_and_fl Abbreviation of the reporting canton

Text

ncumul_testedReported number of tests performed as of dateNumberIrrespective of canton of residence

ncumul_confReported number of confirmed cases as of dateNumberOnly cases that reside in the current canton

current_hosp (formerly ncumul_hosp) *Reported number of hospitalised patients on dateNumberIrrespective of canton of residencecurrent_icu (formerly ncumul_icu) *Reported number of hospitalised patients in ICUs on dateNumberIrrespective of canton of residencecurrent_vent(formerly ncumul_vent) *Reported number of patients requiring ventilation on dateNumberIrrespective of canton of residencencumul_released Reported number of patients released from hospitals or reported recovered as of date

NumberIrrespective of canton of residence

ncumul_deceasedReported number of deceased as of dateNumberOnly cases that reside in the current cantonnew_hosp *Number of new hospitalisations since last dateNumberIrrespective of canton of residence

sourceSource of the informationURL linkgeo_point_2dGeographical centroid of the cantongeo_point_2dcurrent_isolatedReported number of isolated persons on dateNumberInfected persons, who are not hospitalisedcurrent_quarantinedReported number of quarantined persons on dateNumberPersons, who were in 'close contact' with an infected person, while that person was infectious, and are not hospitalised themselvescurrent_quarantined_riskareatravelReported number of quarantined persons on dateNumberPeople arriving in Switzerland from certain countries and areas, required to go into quarantine (introduced in May 2021)*These variables were affected by the format change on April 9th, 2020, which consists in:- new variable "new_hosp"- variables "ncumul_hosp", "ncumul_icu", "ncumul_vent" have been renamed to "current_hosp", "current_icu", "current_vent", to fit with their nature. To ensure compatibility with already made dashboards or reuses, these fields have been duplicated to avoid errors when their old names are used; but we strongly recommand to replace their old names by the new as soon as possible.

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

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

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

BackgroundMyopic shift had been observed during the COVID-19 lockdown in young school children. It remains unknown whether myopic shift is accompanied with increase in axial length. We aimed to evaluate the impact of the COVID-19 lockdown on myopia and axial length of school children in China by comparing them before, during and after the lockdown.MethodsIn this population-based cross-sectional study, school-based myopia screenings were conducted in the Fall of 2019, 2020, and 2021 (representing before, during and after COVID-19 lockdown respectively) in Chengdu, China. Myopia screenings were performed on 83,132 students aged 6 to 12 years. Non-cycloplegic refractive error was examined using NIDEK auto-refractor (ARK-510A; NIDEK Corp., Tokyo, Japan) and axial length was measured using AL-Scan (NIDEK Corp., Tokyo, Japan). Spherical equivalent (SER, calculated as sphere+ 0.5*cylinder), prevalence of myopia (SER ≤ -0.50 D), and axial length were compared across 3 years stratified by age.ResultsMyopia prevalence rate was 45.0% (95% CI: 44.6–45.5%) in 2019, 48.7% (95% CI: 48.3–49.1%) in 2020, and 47.5% (95% CI: 47.1–47.9%) in 2021 (p < 0.001). The mean non-cycloplegic SER (SD) was −0.70 (1.39) D, −0.78 (1.44) D, and −0.78 (1.47) D respectively (p < 0.001). The mean (SD) axial length was 23.41 (1.01) mm, 23.45 (1.03) mm, and 23.46 (1.03) mm across 3 years respectively (p < 0.001). From the multivariable models, the risk ratio (RR) of myopia was 1.07 (95% CI: 1.06–1.08) times, the SER was 0.05 D (95% CI: 0.04 D to 0.06 D) more myopic and the mean axial length increased by 0.01 mm (95% CI: 0.01 mm to 0.02 mm) in 2020 compared to 2019. In 2021, the risk ratio (RR) of myopia was 1.05 (95% CI: 1.04–1.06), the mean SER was 0.06 D (95% CI: 0.05 D to 0.07 D) more myopic, and the mean axial length increased by 0.03 mm (95% CI: 0.02 mm to 0.04 mm) compared to 2019.ConclusionsThe COVID-19 lockdown had significant impact on myopia development and axial length, and these impacts remained 1 year after the lockdown. Further longitudinal studies following-up with these students are needed to help understand the long-term effects of COVID-19 lockdown on myopia.

This dataset is based on the Github repository maintained by OpenZH. Data has been enriched with geographical data for the cantons, in order to produce visualisations.Field NameDescriptionFormatNote

updateDate and time of notification YYYY-MM-DD-HH-MM

nameName of the reporting cantonTextabbreviation_canton_and_fl Abbreviation of the reporting canton

Text

ncumul_testedReported number of tests performed as of dateNumberIrrespective of canton of residence

ncumul_confReported number of confirmed cases as of dateNumberOnly cases that reside in the current canton

current_hosp (formerly ncumul_hosp) *Reported number of hospitalised patients on dateNumberIrrespective of canton of residencecurrent_icu (formerly ncumul_icu) *Reported number of hospitalised patients in ICUs on dateNumberIrrespective of canton of residencecurrent_vent(formerly ncumul_vent) *Reported number of patients requiring ventilation on dateNumberIrrespective of canton of residencencumul_released Reported number of patients released from hospitals or reported recovered as of date

NumberIrrespective of canton of residence

ncumul_deceasedReported number of deceased as of dateNumberOnly cases that reside in the current cantonnew_hosp *Number of new hospitalisations since last dateNumberIrrespective of canton of residence

sourceSource of the informationURL linkgeo_point_2dGeographical centroid of the cantongeo_point_2dcurrent_isolatedReported number of isolated persons on dateNumberInfected persons, who are not hospitalisedcurrent_quarantinedReported number of quarantined persons on dateNumberPersons, who were in 'close contact' with an infected person, while that person was infectious, and are not hospitalised themselvescurrent_quarantined_riskareatravelReported number of quarantined persons on dateNumberPeople arriving in Switzerland from certain countries and areas, required to go into quarantine (introduced in May 2021)*These variables were affected by the format change on April 9th, 2020, which consists in:- new variable "new_hosp"- variables "ncumul_hosp", "ncumul_icu", "ncumul_vent" have been renamed to "current_hosp", "current_icu", "current_vent", to fit with their nature. To ensure compatibility with already made dashboards or reuses, these fields have been duplicated to avoid errors when their old names are used; but we strongly recommand to replace their old names by the new as soon as possible.

The recently emerged COVID-19 has been declared a pandemic by the World Health Organization as to date; no therapeutic drug/vaccine is available for the treatment. Due to the lack of time and the urgency to contain the pandemic, computational screening appears to be the best tool to find a therapeutic solution. Accumulated evidence suggests that many phyto-compounds possess anti-viral activity. Therefore, we identified possible phyto-compounds that could be developed and used for COVID-19 treatment. In particular, molecular docking was used to prioritize the possible active phyto-compounds against two key targets namely RNA dependent RNA polymerase (RdRp) and main protease (Mpro) of SARS-CoV-2. In this study, an antiviral drug- Remdesivir (RdRp inhibitor) and Darunavir (Mpro inhibitor) are used as reference drugs. This study revealed that phyto-molecules- Mulberroside-A/C/E/F, Emblicanin A, Nimbolide, and Punigluconin showed high binding affinity against RdRp while Andrographolides, Mulberrosides, Anolignans, Chebulic acid, Mimusopic acid, and Punigluconin showed better binding affinity against Mpro as compared with the reference drug. Furthermore, ADME profiles validated the drug-likeness properties of prioritized phyto-compounds. Besides, to assess the stability, MD simulations studies were performed along with reference inhibitors for Mpro (Darunavir) and RdRp (Remdesivir). Binding free energy calculations (MM-PBSA) revealed the estimated value (ΔG) of Mpro_Darunavir; Mpro_Mulberroside E; RdRp_Remdesivir and RdRp_Emblicanin A were −111.62 ± 6.788, −141.443 ± 9.313, 30.782 ± 5.85 and −89.424 ± 3.130 kJmol−1, respectively. Taken together, the study revealed the potential of these phyto-compounds as inhibitors of RdRp and Mpro inhibitor that could be further validated against SARS-CoV-2 for clinical benefits. Communicated by Ramaswamy H. Sarma

For English, see below 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) Variant or 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 germ 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 designated 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 lead 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 last two weeks: these are therefore not reported. Additionally, the germ 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. N.B.: Due to internationally changing tribal 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 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 it is possible that the reported Variant_cases aggregate samples that are already 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 data file was last updated by 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 info 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 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 in the week in question the specific VOC, VOI or VUM was found. Consists of whole numbers only. -------------------------------------------------- --------------------------------------------- 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.

Effective June 7th, 2024, this dataset will no longer be updated.This file contains data for the last 6 weeks on: Weekly counts and rates of Ottawa residents with laboratory-confirmed COVID-19 by episode date (i.e. the earliest of symptom onset, testing or reported date) and age. Weekly counts and rates of Ottawa residents with laboratory-confirmed COVID-19 by reported date. Data are from the Ontario Ministry of Health Public Health Case and Contact Management Solution (CCM).

Accuracy: Points of consideration for interpretation of the data: Data are entered into and extracted by Ottawa Public Health from the Ontario Ministry of Health Public Health Case and Contact Management Solution (CCM). The COD is a dynamic disease reporting system that allows for ongoing updates; data represent a snapshot at the time of extraction and may differ from previous or subsequent reports.As the cases are investigated and more information is available, the dates are updated. A person’s exposure may have occurred up to 14 days prior to onset of symptoms. Symptomatic cases occurring in approximately the last 14 days are likely under-reported due to the time for individuals to seek medical assessment, availability of testing, and receipt of test results.Confirmed cases are those with a confirmed COVID-19 laboratory result as per the Ministry of Health Public health management of cases and contacts of COVID-19 in Ontario. March 25, 2020 version 6.0.Counts will be subject to varying degrees of underreporting due to a variety of factors, such as disease awareness and medical care seeking behaviours, which may depend on severity of illness, clinical practice, changes in laboratory testing, and reporting behaviours.Surveillance testing for COVID-19 began in long term care facilities on April 25, 2020. Update Frequency: Tuesdays and Fridays

Attributes: Data fields: Week – Date of the first day of the episode week (i.e. the week during which the case first developed symptom, got tested or was reported to OPH – whichever was earliest). Date in format YYYY-MM-DD H:MM. Weekly Rate of COVID-19 by 20-year Age Groupings (per 100,000 pop) and Episode Date – The number of Ottawa residents with confirmed COVID-19 within an age group (e.g. 0-9 years) divided by the total Ottawa population for that age group. This fraction is then multiplied by 100,000 to get a rate of COVID-19 per 100,000 population for that age group.Weekly Total of Cases by Episode Date - number of Ottawa residents with laboratory-confirmed COVID-19 by episode date.Weekly Total of Cases by Reported Date – number of Ottawa residents with laboratory-confirmed COVID-19 by reported date.Weekly Rate of COVID-19 (per 100,000 pop) by Reported Date – number of Ottawa residents with laboratory-confirmed COVID-19 by reported date divided by the total Ottawa population and multiplied by 100,000. Contact: OPH Epidemiology Team | Epidemiology & Evidence, Ottawa Public Health

Data Summary

Data is collected from mentioned Sources, and further processed and available here in usable format. This Data is used for Exploratory data analysis ( EDA ), and for various visualizations.

Fixes

1. We tried to fix few major issue with data in Italy, france and spain between 11thmarch to 13th march.

Column Description

• Country : Affected Country
• Date : Date of the observation in YYYY-MM-DD
• Confirmed : Cumulative number of confirmed cases
• Death : Cumulative number of death cases
• Recovered : Cumulative number of recovered cases
• newConfirmed : Number of Confirmed cases per day
• newDeath : Number of Death cases per day
• newRecovered : Number of Recovered cases per day

Acknowledgements / Sources

1. Johns Hopkins University : Fetched from GitHub Source - https://github.com/CSSEGISandData/COVID-19/blob/master/csse_covid_19_data/

2. European Centre for Disease Prevention and Control (ECDC): https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic-distribution-covid-19-cases-worldwide

Inspiration

Insights like following - 1. Changes in number of Confirmed cases over time. 2. Changes in number of Death cases over time. 3. Changes in number of Recovered cases over time.

Data

Tweets_YYYY-MM.tsv.gz:
The first column is the tweet id, the second column is the date and time (JST) when the tweet was posted, the third column is the tweet id of the mention destination, the fourth column is the tweet id of the retweet source, the fifth column is the place id, the sixth column is the country code, the seventh column is the prefecture code if the country code is JP, and the eighth column is the COVID-19-related keyword included in the tweet. Columns with no information are empty. For example, a tweet with an empty eighth column is not a COVID-19-related tweet.
This data was collected using statuses/sample of the Twitter Streaming API, narrowed down by language=ja. Therefore, most of the tweets are Japanese tweets. Also, due to a failure of the data collection server, a large number of tweets on January 22 are missing :(
We have used 肺炎, コロナ and COVID (case insensitive) as keywords related to COVID-19.

Emotions_YYYY-MM.tsv.gz:
The first column is the tweet id, the second and subsequent columns are the number of occurrences of each emotional keyword. Column names (types of emotion) are shown in the first row.
We used mlask43-simple (Perl implementation of ML-Ask) with dictionaries used in pymlask to extract emotional keywords from the tweet.

Publication

This data set was created for my study. If you make use of this data set, please cite:
Mitsuo Yoshida. The State of Social Media During the COVID-19 Pandemic: Japan's Situation, Research Trends and Public Datasets. Journal of Japanese Society for Artificial Intelligence (in Japanese). vol.35, no.5, pp.644-653, 2020.
吉田光男. COVID-19流行下におけるソーシャルメディア ―日本での状況と研究動向・公開データセット―. 人工知能. vol.35, no.5, pp.644-653, 2020.

This dataset contains tweets collected from 09/2019 through 01/2022 (relevant to the COVID-19 pandemic) and located near Chicago. The tweets are stored in separate JSON files for each month with the name "tweets-MM-YYYY.json". Note that the "allData" file does not contain the entirety of the dataset but a sample that was used for our analysis of joy. The snscrape package was used for data collection: https://github.com/JustAnotherArchivist/snscrape