Context

On January 30, 2020, the International Health Regulations Emergency Committee of the World Health Organization declared the outbreak a public health emergency of international concern (PHEIC). On January 31, 2020, Health and Human Services Secretary Alex M. Azar II declared a public health emergency (PHE) for the United States to aid the nation’s healthcare community in responding to COVID-19. On March 11, 2020 WHO publicly characterized COVID-19 as a pandemic.

Content

The data files present the total confirmed cases, total deaths and daily new cases and deaths by country. This data is sourced from the World Health Organization (WHO) Situation Reports (which you find here). The WHO Situation Reports are published daily [reporting data as of 10am (CET; Geneva time)]. The main section of the Situations Reports are long tables of the latest number of confirmed cases and confirmed deaths by country.

This dataset has five files : - total_cases.csv : Total confirmed cases - total_deaths.csv : Total deaths - new_cases.csv : New confirmed cases - new_deathes.csv : New deaths - full_data.csv : put it all files together

Acknowledgements

This dataset is sourced from WHO and confirmed by OurworldInData Special Thank to Hannah Ritchie that did a great reports explaining those datasets.

Inspiration

Insights on - Confirmed cases is what we do know - Confirmed COVID-19 cases by country - How we can make preventive measures - Growth of cases: How long did it take for the number of confirmed cases to double? - Understanding exponential growth - Try to predict the spread of COVID-19 ahead of time .

AbstractThe dataset provided here contains the efforts of independent data aggregation, quality control, and visualization of the University of Arizona (UofA) COVID-19 testing programs for the 2019 novel Coronavirus pandemic. The dataset is provided in the form of machine-readable tables in comma-separated value (.csv) and Microsoft Excel (.xlsx) formats.Additional InformationAs part of the UofA response to the 2019-20 Coronavirus pandemic, testing was conducted on students, staff, and faculty prior to start of the academic year and throughout the school year. These testings were done at the UofA Campus Health Center and through their instance program called "Test All Test Smart" (TATS). These tests identify active cases of SARS-nCoV-2 infections using the reverse transcription polymerase chain reaction (RT-PCR) test and the Antigen test. Because the Antigen test provided more rapid diagnosis, it was greatly used three weeks prior to the start of the Fall semester and throughout the academic year.As these tests were occurring, results were provided on the COVID-19 websites. First, beginning in early March, the Campus Health Alerts website reported the total number of positive cases. Later, numbers were provided for the total number of tests (March 12 and thereafter). According to the website, these numbers were updated daily for positive cases and weekly for total tests. These numbers were reported until early September where they were then included in the reporting for the TATS program.For the TATS program, numbers were provided through the UofA COVID-19 Update website. Initially on August 21, the numbers provided were the total number (July 31 and thereafter) of tests and positive cases. Later (August 25), additional information was provided where both PCR and Antigen testings were available. Here, the daily numbers were also included. On September 3, this website then provided both the Campus Health and TATS data. Here, PCR and Antigen were combined and referred to as "Total", and daily and cumulative numbers were provided.At this time, no official data dashboard was available until September 16, and aside from the information provided on these websites, the full dataset was not made publicly available. As such, the authors of this dataset independently aggregated data from multiple sources. These data were made publicly available through a Google Sheet with graphical illustration provided through the spreadsheet and on social media. The goal of providing the data and illustrations publicly was to provide factual information and to understand the infection rate of SARS-nCoV-2 in the UofA community.Because of differences in reported data between Campus Health and the TATS program, the dataset provides Campus Health numbers on September 3 and thereafter. TATS numbers are provided beginning on August 14, 2020.Description of Dataset ContentThe following terms are used in describing the dataset.1. "Report Date" is the date and time in which the website was updated to reflect the new numbers2. "Test Date" is to the date of testing/sample collection3. "Total" is the combination of Campus Health and TATS numbers4. "Daily" is to the new data associated with the Test Date5. "To Date (07/31--)" provides the cumulative numbers from 07/31 and thereafter6. "Sources" provides the source of information. The number prior to the colon refers to the number of sources. Here, "UACU" refers to the UA COVID-19 Update page, and "UARB" refers to the UA Weekly Re-Entry Briefing. "SS" and "WBM" refers to screenshot (manually acquired) and "Wayback Machine" (see Reference section for links) with initials provided to indicate which author recorded the values. These screenshots are available in the records.zip file.The dataset is distinguished where available by the testing program and the methods of testing. Where data are not available, calculations are made to fill in missing data (e.g., extrapolating backwards on the total number of tests based on daily numbers that are deemed reliable). Where errors are found (by comparing to previous numbers), those are reported on the above Google Sheet with specifics noted.For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to data-management@arizona.edu

Content

COVID-19 data for Mexico, consist of two main datasets: time_series_confirmed_MX: time series of confirmed cases by state. time_series_deaths_MX: time series of deaths by state The data will be updated every day at the start of Secretaría de Salud conference (18:00), with last information recived at 13:00.
If you want the data in github form: https://github.com/carloscerlira/COVIDMX.

Source

https://www.gob.mx/salud/archivo/documentos?idiom=es&filter_id=395&filter_origin=archive

Submitted to The ISSI 2021 Conference. The conference is organised by KU Leuven in close collaboration with the university of Antwerp under the auspices of ISSI – the International Society for Informetrics and Scientometrics (http://www.issi-society.org/).

We present a forecasting analysis on the growth of scientific literature related to COVID-19 expected for 2021. Considering the paramount scientific and financial efforts made by the research community to find solutions to end the COVID-19 pandemic, an unprecedented volume of scientific outputs is being produced. This questions the capacity of scientists, politicians and citizens to maintain infrastructure, digest content and take scientifically informed decisions. A crucial aspect is to make predictions to prepare for such a large corpus of scientific literature. Here we base our predictions on the ARIMA model and use two different data sources: the Dimensions and World Health Organization COVID-19 databases. These two sources have the particularity of including in the metadata information on the date in which papers were indexed. We present global predictions, plus predictions in three specific settings: by type of access (Open Access), by NLM source (PubMed and PMC), and by domain-specific repository (SSRN and MedRxiv). We conclude by discussing our findings.

• Project video: https://youtu.be/KRuv86R887c
• Project demo: http://qatacov.live/

QaTa-COV19 Dataset (Extended)🦠🏥

The researchers of Qatar University and Tampere University have compiled the QaTa-COV19 dataset that consists of 9258 COVID-19 chest X-ray images. For the first time, the dataset includes the ground-truth segmentation masks for the COVID-19 pneumonia segmentation task. For more details, please check the file -> readme.txt

COVID-19 at Early Stages 😷

Early-QaTa-COV19 dataset is a subset of QaTa-COV19 dataset, which consists of 1065 chest X-rays including no or limited sign of COVID-19 pneumonia cases for early COVID-19 detection. This version of the dataset is compiled from cases of COVID-19, where doctors could not specify pneumonia traces of COVID-19 on the CXRs.

If you use the dataset, please consider citing:

[P1] A. Degerli, S. Kiranyaz, M. E. H. Chowdhury and M. Gabbouj, "Osegnet: Operational Segmentation Network for Covid-19 Detection Using Chest X-Ray Images," 2022 IEEE International Conference on Image Processing (ICIP), pp. 2306-2310, 2022, https://doi.org/10.1109/ICIP46576.2022.9897412. -> presentation video available at https://www.youtube.com/watch?v=_SHHwXLz13s

[P2] M. Ahishali, A. Degerli, M. Yamac, S. Kiranyaz, M. E. H. Chowdhury, K. Hameed, T. Hamid, R. Mazhar, and M. Gabbouj, "Advance warning methodologies for covid-19 using chest x-ray images," IEEE Access, vol. 9, pp. 41052-41065, 2021, https://doi.org/10.1109/ACCESS.2021.3064927.

[P3] A. Degerli, M. Ahishali, M. Yamac, S. Kiranyaz, M. E. H. Chowdhury, K. Hameed, T. Hamid, R. Mazhar, and M. Gabbouj, "Covid-19 infection map generation and detection from chest X-ray images," Health Inf Sci Syst 9, 15 (2021). https://doi.org/10.1007/s13755-021-00146-8.

[P4] A. Degerli, M. Ahishali, S. Kiranyaz, M. E. H. Chowdhury, and M. Gabbouj, "Reliable Covid-19 detection using chest x-ray images," 2021 IEEE International Conference on Image Processing (ICIP), pp. 185-189, 2021, https://doi.org/10.1109/ICIP42928.2021.9506442. -> presentation video available at https://www.youtube.com/watch?v=rUXo9rKwFQg

[P5] M. Yamac, M. Ahishali, A. Degerli, S. Kiranyaz, M. E. H. Chowdhury, and M. Gabbouj, "Convolutional sparse support estimator based covid-19 recognition from x-ray images," IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 5, pp. 1810-1820, 2021, https://doi.org/10.1109/TNNLS.2021.3070467.

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Dataset Description: Infected and Death Cases of Covid-19 in Bangladesh This dataset contains detailed information on Covid-19 cases in Bangladesh, focusing on the number of new cases and deaths reported. The data spans from September 27, 2020, to November 19, 2021. The dataset is structured with three primary columns:

Date: The date when the data was recorded, formatted as YYYY-MM-DD. New Cases: The number of new Covid-19 cases reported on the corresponding date. Deaths: The number of deaths attributed to Covid-19 on the corresponding date. Key Features: Time Range: Covers over a year of data, capturing various waves of the pandemic. Granularity: Daily records, providing detailed insights into the daily progression of the pandemic. Size: The dataset is compact, with a file size of 7.91 KB, making it easy to handle and analyze. Cite this paper

@InProceedings{10.1007/978-981-19-2445-3_38, author="Rahman, Ashifur and Hossain, Md. Akbar and Moon, Mohasina Jannat", editor="Hossain, Sazzad and Hossain, Md. Shahadat and Kaiser, M. Shamim and Majumder, Satya Prasad and Ray, Kanad", title="An LSTM-Based Forecast Of COVID-19 For Bangladesh", booktitle="Proceedings of International Conference on Fourth Industrial Revolution and Beyond 2021 ", year="2022", publisher="Springer Nature Singapore", address="Singapore", pages="551--561", abstract="Preoperative events can be predicted using deep learning-based forecasting techniques. It can help to improve future decision-making. Deep learning has traditionally been used to identify and evaluate adverse risks in a variety of major applications. Numerous prediction approaches are commonly applied to deal with forecasting challenges. The number of infected people, as well as the mortality rate of COVID-19, is increasing every day. Many countries, including India, Brazil, and the United States, were severely affected; however, since the very first case was identified, the transmission rate has decreased dramatically after a set time period. Bangladesh, on the other hand, was unable to keep the rate of infection low. In this situation, several methods have been developed to forecast the number of affected, time to recover, and the number of deaths. This research illustrates the ability of DL models to forecast the number of affected and dead people as a result of COVID-19, which is now regarded as a possible threat to humanity. As part of this study, we developed an LSTM based method to predict the next 100 days of death and newly identified COVID-19 cases in Bangladesh. To do this experiment we collect data on death and newly detected COVID-19 cases through Bangladesh's national COVID-19 help desk website. After collecting data we processed it to make a dataset for training our LSTM model. After completing the training, we predict our model with the test dataset. The result of our model is very robust on the basis of the training and testing dataset. Finally, we forecast the subsequent 100 days of deaths and newly infected COVID-19 cases in Bangladesh.", isbn="978-981-19-2445-3" }

IntroductionGiven the lack of evidence on how the COVID-19 pandemic impacted antiseizure medication (ASM) use, we examined the trends of ASMs before and during COVID-19.MethodsWe conducted a population-based study using provincial-level health databases from Manitoba, Canada, between 1 June 2016 and 1 March 2021. We used interrupted time series autoregressive models to examine changes in the prevalence and incidence of ASM prescription rates associated with COVID-19 public health restrictions.ResultsAmong prevalent users, the COVID-19 pandemic led to a significant increase in new-generation ASMs with a percentage change of 0.09% (p = 0.03) and a significant decrease in incidence use of all ASMs with a percentage change of −4.35% (p = 0.04). Significant trend changes were observed in the prevalent use of new-generation ASMs (p = 0.04) and incidence use of all (p = 0.04) and new-generation ASMs (p = 0.02). Gabapentin and clonazepam prescriptions contributed 37% of prevalent and 54% of incident use.ConclusionWith the introduction of public health measures during COVID-19, small but significant changes in the incident and prevalent use of ASM prescriptions were observed. Further studies are needed to examine whether barriers to medication access were associated with potential deterioration in seizure control among patients.Conference presentationThe results from this study have been presented as an oral presentation at the 38th ICPE, International Society of Pharmacoepidemiology (ISPE) annual conference in Copenhagen.

Context

COVID-19 CORONAVIRUS PANDEMIC has over 1 million cases worldwide. This dataset is created in an attempt to uncover if there is a co-relation of the country wise weather parameters with growing number of cases day by day.

Many questions raised on the effects of Seasonality to SARS-CoV-2.

According to the officials of WHO, press conference transcript on 05-mar-2020 speaker Dr Maria van Kerkhove answered - "so we’ve had some questions previously about what this virus will do in different climates, in different temperatures ?"

We have no reason to believe that this virus would behave differently in different temperatures. We have no reason to believe that this virus would behave differently in different temperatures, which is why we want aggressive action in all countries to make sure that we prevent onward transmission, and that it’s taken seriously in every country. But this is something that will be of interest. We have the... In the northern hemisphere we have the flu season, which was ending fairly soon, and in the southern hemisphere we’ll have the flu season starting. And so it will be interesting to see what will happen in the northern hemisphere and the southern hemisphere. But to look at seasonality you need to look at patterns over time, and we do need some of that time to be able to see what happens. So it’s important that we aggressively look for cases, and so that we can understand the extent of infection and how the virus behaves in different populations.

Some believe temperature will play a role in the outbreak but that the subject was worth investigating. Few studies by Harward CSPH, BBC, Bloomberg, Centre for Evidence-Based Medicine develops

Content

Basic weather parameters like, min/max temperature and humidity captured since 1/22/2020. Each country has three rows defining the weather parameters over the time. The structure is kept to be inline with Data Repository by Johns Hopkins CSSE.

Acknowledgements

Country names are picked from: https://github.com/CSSEGISandData/COVID-19

Inspiration

https://github.com/kakumanu-sudhir/covid19/tree/master/weather_data_extraction The data begins with the first reported coronavirus case on Jan. 21, 2020. I plan to publish regular updates (weekly twice till WK23) to the data in this repository.

The number of daily active users of Microsoft Teams has stayed the same in the past year, around *** million. Due to the impact of the coronavirus (COVID-19) outbreak and the growing practices of social distancing and working from home, Microsoft has seen dramatic increases in the daily use of their communication and collaboration platform within a short period of time. Microsoft Teams is part of Microsoft 365, a set of collaboration apps and services launched in *********. Increased data consumption from “staying at home”    The average daily in-home data usage in the United States has increased significantly during the coronavirus (COVID-19) outbreak in **********. Compared to the same amount of days in **********, the daily average in-home data usage increased by a total of *** gigabytes in **********, a roughly ** percent increase. Data consumption from the usage of gaming consoles and smartphones increased the most, although the increases can be observed across nearly all device categories. Social media platforms and video and conference all platforms are the technology services that are used the most during the outbreak in the U.S.

Please cite the following paper when using this dataset:

N. Thakur, “Five Years of COVID-19 Discourse on Instagram: A Labeled Instagram Dataset of Over Half a Million Posts for Multilingual Sentiment Analysis”, Proceedings of the 7th International Conference on Machine Learning and Natural Language Processing (MLNLP 2024), Chengdu, China, October 18-20, 2024 (Paper accepted for publication, Preprint available at: https://arxiv.org/abs/2410.03293)

Abstract

The outbreak of COVID-19 served as a catalyst for content creation and dissemination on social media platforms, as such platforms serve as virtual communities where people can connect and communicate with one another seamlessly. While there have been several works related to the mining and analysis of COVID-19-related posts on social media platforms such as Twitter (or X), YouTube, Facebook, and TikTok, there is still limited research that focuses on the public discourse on Instagram in this context. Furthermore, the prior works in this field have only focused on the development and analysis of datasets of Instagram posts published during the first few months of the outbreak. The work presented in this paper aims to address this research gap and presents a novel multilingual dataset of 500,153 Instagram posts about COVID-19 published between January 2020 and September 2024. This dataset contains Instagram posts in 161 different languages. After the development of this dataset, multilingual sentiment analysis was performed using VADER and twitter-xlm-roberta-base-sentiment. This process involved classifying each post as positive, negative, or neutral. The results of sentiment analysis are presented as a separate attribute in this dataset.

For each of these posts, the Post ID, Post Description, Date of publication, language code, full version of the language, and sentiment label are presented as separate attributes in the dataset.

The Instagram posts in this dataset are present in 161 different languages out of which the top 10 languages in terms of frequency are English (343041 posts), Spanish (30220 posts), Hindi (15832 posts), Portuguese (15779 posts), Indonesian (11491 posts), Tamil (9592 posts), Arabic (9416 posts), German (7822 posts), Italian (5162 posts), Turkish (4632 posts)

There are 535,021 distinct hashtags in this dataset with the top 10 hashtags in terms of frequency being #covid19 (169865 posts), #covid (132485 posts), #coronavirus (117518 posts), #covid_19 (104069 posts), #covidtesting (95095 posts), #coronavirusupdates (75439 posts), #corona (39416 posts), #healthcare (38975 posts), #staysafe (36740 posts), #coronavirusoutbreak (34567 posts)

The following is a description of the attributes present in this dataset

Post ID: Unique ID of each Instagram post

Post Description: Complete description of each post in the language in which it was originally published

Date: Date of publication in MM/DD/YYYY format

Language code: Language code (for example: “en”) that represents the language of the post as detected using the Google Translate API

Full Language: Full form of the language (for example: “English”) that represents the language of the post as detected using the Google Translate API

Sentiment: Results of sentiment analysis (using the preprocessed version of each post) where each post was classified as positive, negative, or neutral

Open Research Questions

This dataset is expected to be helpful for the investigation of the following research questions and even beyond:

How does sentiment toward COVID-19 vary across different languages?

How has public sentiment toward COVID-19 evolved from 2020 to the present?

How do cultural differences affect social media discourse about COVID-19 across various languages?

How has COVID-19 impacted mental health, as reflected in social media posts across different languages?

How effective were public health campaigns in shifting public sentiment in different languages?

What patterns of vaccine hesitancy or support are present in different languages?

How did geopolitical events influence public sentiment about COVID-19 in multilingual social media discourse?

What role does social media discourse play in shaping public behavior toward COVID-19 in different linguistic communities?

How does the sentiment of minority or underrepresented languages compare to that of major world languages regarding COVID-19?

What insights can be gained by comparing the sentiment of COVID-19 posts in widely spoken languages (e.g., English, Spanish) to those in less common languages?

All the Instagram posts that were collected during this data mining process to develop this dataset were publicly available on Instagram and did not require a user to log in to Instagram to view the same (at the time of writing this paper).

By April 2020, Zoom Video Communications had 300 million daily meeting participants worldwide. Only six months before that, at the end of 2019, this number stood at ** million meeting participants. The outbreak of the COVID-19 pandemic led businesses around the world to adopt Zoom as a solution to stay connected to employees and customers when working from different locations. This increased usage of the platform in 2020. Additionally, individuals use the Zoom video platform to stay connected to friends and family.

Updated with cases as of April 6st, 1830 hrs

Check the completely interactive Uber-KeplerGL map of the cases as shown in the image below

Context

Coronavirus Emergency: Nation-wide Quarantine

10th Match 2020, Italian Prime Minister Giuseppe Conte announced the extension of Italy's emergency coronavirus measures, which include travel restrictions and a ban on public gatherings, from 15 provinces to the entire nation. Italy is by far the most affected country outside China with thousands of cases and hundreds of deaths.

The Department of Civil Protection of Italy has taken actions to keep citizens well informed on the spread of the virus while the country is in lockdown. The department has released an interactive geographical dashboard to monitor the crisis [Desktop] [Mobile] and is updated every day at 18:30 after the department's press conference.

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Inspiration

This Kaggle dataset is created only to make it easy for the community to draw further and useful insights from the data.

This inspiration to put this data on Kaggle is not only to draw raw statistics on cases and deaths but to mine more useful data that could be actively used right now. How?

Leveraging the longitude and latitude information of cases, visualizing them with the distinction between old and new cases along with the temporal information would give better insight into the spread of the virus in a much-magnified perspective. This could be very helpful for the locals to avoid going through those regions

Content

This dataset currently provides national, provincial, and regional data of the CoVID-19 cases in Italy. Check out the script to used to convert the original json files and the started notebook in the kernels.

The time-series data starts from 24th February 2020 till the epidemic ends.

The contents of this dataset include 20 cleaned, deidentified interview transcripts from the dissertation project titled: "Exploring the COVID-19 Infodemic in Alaska". The NSF grant # is 2309906. Interviews took place via Zoom between January and March 2024 and included participants from across Alaska. The COVID-19 pandemic has been accompanied by an unprecedented infodemic, characterized by the proliferation of both accurate and misleading information. Efforts to better describe the impacts of misinformation during the pandemic can facilitate the development of tools and policies aimed at managing future infodemics. We aimed to investigate the infodemic experiences of COVID-19 responders and identify themes that cut across sectors. This study explored how the circulation of false, incomplete, and excessive information affected individuals responding to the COVID-19 pandemic, including healthcare providers, public health professionals, leadership, members of the media, K-12 school staff, tribal organizations, and others. Using a One Health framework to guide recruitment, we conducted 20 semi-structured interviews over video conference and analyzed them using mixed inductive/deductive thematic analysis. Our findings coalesced around three principal themes: misinformation management, misinformation impacts and lessons learned. Building trust, promoting equity, and ensuring adequate resources (such as staffing and time) stood out as critical components to successfully combating misinformation. Conversely, a lack of communication/collaboration and intense politicization of COVID-19 made the response exceedingly difficult. The infodemic had direct impacts on the community, professional practice across fields and mental and physical health, many of which will have a continued effect moving forward. The lessons learned from this study can be applied towards efforts to better prepare us for the next public health emergency by enabling a more informed and agile response.

Each column is a different type of NLP measurement applied to the data. Each row is a day. Cells represent the returns of the values compared to the value y0 of the initial day, February 1st 2020 (i.e., (yt/y0)-1).Measurements belong to different categories, identified by prefixes:- liwc_ : LIWC measures (http://liwc.wpengine.com/)- emolex_: Emolex measures (https://saifmohammad.com/WebPages/NRC-Emotion-Lexicon.htm)- morality_: lexicon of moral foundations (Graham et al. "Liberals and conservatives rely on different sets of moral foundations", Journal of personality and social psychology, 2009)- prosocial_: prosocial behaviour lexicon (Frimer et al. "Moral actor, selfish agent" Journal of personality and social psychology, 2014)- socialdims_: social dimensions of conversations extracted through deep learning (Choi et al, "Ten Social Dimensions of Conversations and Relationships" The Web Conference 2020)healthconditions_: mentions of mental and physical medical conditions extracted through deep learning (Šćepanović et al. "Extracting Medical Entities from Social Media", ACM Conference on Health, Inference, and Learning)phase_: aggregated measures that identify different phases of the evolution of epidemic psychology facets over time

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 with over 2.8 Million confirmed cases and over 197,000 deaths as of April 25, 2020. The African continent started confirming its first cases of COVID-19 in late January and early February of 2020 in some of its countries. The disease has since spread across 52 of the 54 African countries with over 29,000 confirmed cases and over 1,300 deaths as of April 25, 2020.

Content

The COVID-19 Africa dataset contains daily level information about the COVID-19 cases in Africa since January 27th, 2020. It is a time-series data and the number of cases on any given day is cumulative. I extracted the data from the World COVID-19 dataset which was uploaded on Kaggle. The R script that I used to prepare this dataset is also available on my Github repository. The original datasets can also be found on the John Hopkins University Github repository. I will be updating the COVID-19 Africa dataset on a daily basis, with every update from John Hopkins University.

Field description

• ObservationDate: Date of observation in YY/MM/DD
• Country: name of an African country
• 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)

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. SRk for uploading a global COVID-19 dataset on Kaggle: https://www.kaggle.com/sudalairajkumar/novel-corona-virus-2019-dataset#covid_19_data.csv
4. United Nations Department of General Assembly and Conference Management: https://www.un.org/depts/DGACM/RegionalGroups.shtml
5. African Arguments: https://africanarguments.org/2020/04/07/coronavirus-in-africa-tracker-how-many-cases-and-where-latest/)
6. Wallpapercave.com: https://wallpapercave.com/covid-19-wallpapers

Inspiration

Possible Insights 1. The current number of COVID-19 cases in Africa 2. The current number of COVID-19 cases -19 cases by country 3. The number of COVID-19 cases in Africa / African country(s) by April 30, 2020 (Any future date)

Context:

Covid-19 Vaccination in RURAL AREA

Acknowledgements & References:

Users are allowed to use, download, copy, distribute and cite the dataset for their pet projects and training. Please cite it as follows: “Dr. Asif Khan, Covid-19 Vaccination in RURAL AREA, Kaggle Dataset Repository, Nov11, 2022.” Past Research Ghani, Akbar, Asif Khan, and Khushnood Abbas. "Covid-19 Vaccination Concerns and its way out in Rustic Zones." 2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence). IEEE, 2022.

Features:

Name
Age Gender
Marital status
Desease DESEASE NAME
Smoking/Tobacco Alcohol Worker group
Preacaution medicine
convence for vaccination
Mentally Prepare
Health effect
fever
pain
Taste effect
Reason prepare for second dose

Data Description: The data set is filled up with 5400 cells, including 300 rows and 18 columns. Each and every column have their respective importance in analysis. Every rows has the information with respect to the the column indication. The first column of the data set have the name of vaccinated person ,second column include the name of the vaccinated person for uniqueness of the data points. Up- Next column to the first column indicate toward the age of the person , data of age enter in the numeric form with range 18 to 85. The third column of the data set titled with GENDER contain the information about the gender type (Male and Female) of the vaccinated person. The column attached with the Gender column has the heading Marital Status gives the information about marital status about the vaccinated person in form of (M and UM ), M abbreviates to married and UM abbreviates to Unmarried .The fifth column of the data set is all about that the person is suffering from any disease or not containing the information with indication of YES or NO and the next column specified the disease name, the person is suffering with, before the time period of vaccination. The seventh column of information set define the smoking and tobacco consumption behavior of vaccinated person with the indication of (YES OR NO) and the subsequent cue with caption Alcoholdefine the alcoholic behavior of person in form of (YES OR NO) i.e object data type. The 9th and middle column of information workbook with the description of headline Worker Group define whether the vaccinated person belongs to labor working category or not . Overview of precautionary medicine taken after the vaccination by person is described in mainstay tenth of the data dictionary in the form of (YES or NO) object data type.13-16. The column thirteen to sixteen of the data set about the health effect after the vaccination among the people ,the Health effect column describe whether there is a health effect or not. Consequence columns explore the whether the population suffered in from fever, pain and taste effect after the vaccination in the form of (YES or NO) object data type. The next to column sixteen have the data of peoples behavior toward the vaccination of second dose in form of object data type whether they are ready or not ready for the second dose of vaccine ,and the last column describe the reason for the people are not ready for the second dose of vaccine.