Context

The 2019–20 coronavirus pandemic is an ongoing global pandemic of coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The virus first emerged in Wuhan, Hubei, China, in December 2019. On 11 March 2020, the World Health Organization declared the outbreak a pandemic. As of 11 March 2020, over 126,000 cases have been confirmed in more than 110 countries and territories, with major outbreaks in mainland China, Italy, South Korea, and Iran. More than 4,600 have died from the disease and 67,000 have recovered.

Content

2019 Novel Coronavirus (2019-nCoV) is a virus (more specifically, a coronavirus) identified as the cause of an outbreak of respiratory illness first detected in Wuhan, China. Early on, many of the patients in the outbreak in Wuhan, China reportedly had some link to a large seafood and animal market, suggesting animal-to-person spread. However, a growing number of patients reportedly have not had exposure to animal markets, indicating person-to-person spread is occurring. At this time, it’s unclear how easily or sustainably this virus is spreading between people - CDC

This dataset has information on the number of affected cases, deaths and recovery from 2019 novel coronavirus. Please note that this data was scrapped from https://www.worldometers.info/coronavirus/.This data is solely for education purposes only.

Acknowledgements

This data is solely belongs to https://www.worldometers.info/coronavirus/. for licensing visit https://www.worldometers.info/licensing/

Context

I would love to see notebooks! Keep bringin' em.

Content

Worldometer manually analyzes, validates, and aggregates data from thousands of sources in real time and provides global COVID-19 live statistics for a wide audience of caring people around the world.

Our data is also trusted and used by the UK Government, Johns Hopkins CSSE, the Government of Thailand, the Government of Vietnam, the Government of Pakistan, Financial Times, The New York Times, Business Insider, BBC, and many others.

Acknowledgements

Acknowledge Sujay S

Inspiration

Thanks to blogs out there on medium! That made me do this!

Context

This data was collected as part of a university research paper where COVID-19 cases were analysed using a cross-sectional regression model as at 17th May 2020. In order to better understand COVID-19 cases growth at a country level I decided to create a dataset containing key dates in the progression of the virus globally.

Content

210 rows, 6 columns.

This dataset contains data relating to COVID-19 cases for 210 countries globally. Data was collected using the most recent and reliable information as at 17th May 2020. The majority of data was collected from Worldometer. https://www.worldometers.info/coronavirus/#countries

This dataset contains dates for the 1st coronavirus case, 100th coronavirus case, and (50th coronavirus case per 1 million people) for 210 countries. Data is also provided for the number of days between the 1st case and the 100th as well as the 1st case and the 50th per 1 million people.

Data prior to 15th February 2020, was not easily accessible at the country level from Worldometer. Therefore any dates prior to 15th February 2020 were not sourced from Worldometer but reputable government and local media sources.

Blanks (null values) indicate that the country in question has not reached either 50 coronavirus cases per 1 million people or 100 coronavirus cases. These were left blank.

Acknowledgements

I would like to acknowledge Worldometer for providing the vast majority of the data in this file. Worldometer is a website that provides real time statistics on topics such as coronavirus cases. Its sources include government official reports as well as trusted local media sources all of which are referenced on their website.

Inspiration

Hopefully this data can be used to better understand the growth of COVID-19 cases globally.

Based on a comparison of coronavirus deaths in 210 countries relative to their population, Peru had the most losses to COVID-19 up until July 13, 2022. As of the same date, the virus had infected over 557.8 million people worldwide, and the number of deaths had totaled more than 6.3 million. Note, however, that COVID-19 test rates can vary per country. Additionally, big differences show up between countries when combining the number of deaths against confirmed COVID-19 cases. The source seemingly does not differentiate between "the Wuhan strain" (2019-nCOV) of COVID-19, "the Kent mutation" (B.1.1.7) that appeared in the UK in late 2020, the 2021 Delta variant (B.1.617.2) from India or the Omicron variant (B.1.1.529) from South Africa.

The difficulties of death figures

This table aims to provide a complete picture on the topic, but it very much relies on data that has become more difficult to compare. As the coronavirus pandemic developed across the world, countries already used different methods to count fatalities, and they sometimes changed them during the course of the pandemic. On April 16, for example, the Chinese city of Wuhan added a 50 percent increase in their death figures to account for community deaths. These deaths occurred outside of hospitals and went unaccounted for so far. The state of New York did something similar two days before, revising their figures with 3,700 new deaths as they started to include “assumed” coronavirus victims. The United Kingdom started counting deaths in care homes and private households on April 29, adjusting their number with about 5,000 new deaths (which were corrected lowered again by the same amount on August 18). This makes an already difficult comparison even more difficult. Belgium, for example, counts suspected coronavirus deaths in their figures, whereas other countries have not done that (yet). This means two things. First, it could have a big impact on both current as well as future figures. On April 16 already, UK health experts stated that if their numbers were corrected for community deaths like in Wuhan, the UK number would change from 205 to “above 300”. This is exactly what happened two weeks later. Second, it is difficult to pinpoint exactly which countries already have “revised” numbers (like Belgium, Wuhan or New York) and which ones do not. One work-around could be to look at (freely accessible) timelines that track the reported daily increase of deaths in certain countries. Several of these are available on our platform, such as for Belgium, Italy and Sweden. A sudden large increase might be an indicator that the domestic sources changed their methodology.

Where are these numbers coming from?

The numbers shown here were collected by Johns Hopkins University, a source that manually checks the data with domestic health authorities. For the majority of countries, this is from national authorities. In some cases, like China, the United States, Canada or Australia, city reports or other various state authorities were consulted. In this statistic, these separately reported numbers were put together. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

Based on data extracted from Worldometer: https://www.worldometers.info/coronavirus/

COVID-19 statistics from Worldometers. Covers 213 countries/ territories. Recorded as of 22nd May 2020, 14:56 PM IST. The purpose of this data is to understand and analyse the trends of COVID-19, and the extent of its spread.

Note: The new_cases column is full of strings that look like numbers. To convert them to numbers, see the following kernel: https://www.kaggle.com/danoozy44/coronavirus-predicting-new-cases

The new_cases and new_deaths columns pertain to 22/05/2020 only.

All credit goes to Worldometers, and its constituent data gatherers. The official link is here: https://www.worldometers.info/coronavirus/

Data for COVID-19 Coronavirus Pandemic from Worldometer (March 27, 2020)

The high sensitivity of COVID-19 and the need for high accuracy calculations necessitate collecting the required data sets from reliable sources. Thus, all information was collected and categorized from reputable sources such as WHO (World Health Organization) and worldometers site (www.worldometers.info). The worldometers site contains information such as daily mortality statistics, recovery, and newly confirmed cases. Research data including observation data is obtained from a collection of Iranian samples’ reports in three parts (i.e. death, confirmed and recovered). This countrywide daily information is confirmed by the WHO. It should be noted that the relevant data was collected between February 19 and May 16, 2020.

Associated with manuscript titled: Fifty Muslim-majority countries have fewer COVID-19 cases and deaths than the 50 richest non-Muslim countriesThe objective of this research was to determine the difference in the total number of COVID-19 cases and deaths between Muslim-majority and non-Muslim countries, and investigate reasons for the disparities. Methods: The 50 Muslim-majority countries had more than 50.0% Muslims with an average of 87.5%. The non-Muslim country sample consisted of 50 countries with the highest GDP while omitting any Muslim-majority countries listed. The non-Muslim countries’ average percentage of Muslims was 4.7%. Data pulled on September 18, 2020 included the percentage of Muslim population per country by World Population Review15 and GDP per country, population count, and total number of COVID-19 cases and deaths by Worldometers.16 The data set was transferred via an Excel spreadsheet on September 23, 2020 and analyzed. To measure COVID-19’s incidence in the countries, three different Average Treatment Methods (ATE) were used to validate the results. Results published as a preprint at https://doi.org/10.31235/osf.io/84zq5(15) Muslim Majority Countries 2020 [Internet]. Walnut (CA): World Population Review. 2020- [Cited 2020 Sept 28]. Available from: http://worldpopulationreview.com/country-rankings/muslim-majority-countries (16) Worldometers.info. Worldometer. Dover (DE): Worldometer; 2020 [cited 2020 Sept 28]. Available from: http://worldometers.info

As of May 2, 2023, the outbreak of the coronavirus disease (COVID-19) had been confirmed in almost every country in the world. The virus had infected over 687 million people worldwide, and the number of deaths had reached almost 6.87 million. The most severely affected countries include the U.S., India, and Brazil.

COVID-19: background information COVID-19 is a novel coronavirus that had not previously been identified in humans. The first case was detected in the Hubei province of China at the end of December 2019. The virus is highly transmissible and coughing and sneezing are the most common forms of transmission, which is similar to the outbreak of the SARS coronavirus that began in 2002 and was thought to have spread via cough and sneeze droplets expelled into the air by infected persons.

Naming the coronavirus disease Coronaviruses are a group of viruses that can be transmitted between animals and people, causing illnesses that may range from the common cold to more severe respiratory syndromes. In February 2020, the International Committee on Taxonomy of Viruses and the World Health Organization announced official names for both the virus and the disease it causes: SARS-CoV-2 and COVID-19, respectively. The name of the disease is derived from the words corona, virus, and disease, while the number 19 represents the year that it emerged.

Context

Validate discussions in the media about the effect of temperature on coronavirus.

Content

• Based on the data available in https://www.worldometers.info/coronavirus/#countries dated March 2, 2020
• Mean monthly temperature data for Feb 2020 taken from https://www.accuweather.com/

Acknowledgements

Data from www.worldometers.info & https://www.accuweather.com/ Banner Photo by CDC on Unsplash

Inspiration

Kindly provide feedback

Context

The 2019–20 coronavirus pandemic is an ongoing global pandemic of coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The virus first emerged in Wuhan, Hubei, China, in December 2019. On 11 March 2020, the World Health Organization declared the outbreak a pandemic. As of 11 March 2020, over 126,000 cases have been confirmed in more than 110 countries and territories, with major outbreaks in mainland China, Italy, South Korea, and Iran. More than 4,600 have died from the disease and 67,000 have recovered.

Content

2019 Novel Coronavirus (2019-nCoV) is a virus (more specifically, a coronavirus) identified as the cause of an outbreak of respiratory illness first detected in Wuhan, China. Early on, many of the patients in the outbreak in Wuhan, China reportedly had some link to a large seafood and animal market, suggesting animal-to-person spread. However, a growing number of patients reportedly have not had exposure to animal markets, indicating person-to-person spread is occurring. At this time, it’s unclear how easily or sustainably this virus is spreading between people - CDC

This dataset has information on the number of affected cases, deaths and recovery from 2019 novel coronavirus. Please note that this data was scrapped from https://www.worldometers.info/coronavirus/.This data is solely for education purposes only.

More - Find More Exciting🙀 Datasets Here - An Upvote👍 A Dayᕙ(`▿´)ᕗ , Keeps Aman Hurray Hurray..... ٩(˘◡˘)۶Hehe

Acknowledgements

This data is solely belongs to https://www.worldometers.info/coronavirus/. for licensing visit https://www.worldometers.info/licensing/

Snapshot img { margin: 10px !important; } This pipeline analysis report provides detailed insights into the clinical trials landscape of COVID-19 therapeutics, including molecules at pre-clinical and discovery stage. The report also offers comprehensive information about the therapeutic assessment of the pipeline molecules based on various segmentations such as therapy, route of administration (RoA), target, mechanism of action (MoA), and therapeutic modalities. Furthermore, the report provides an analysis of the companies currently involved in the development of pipeline molecules for COVID-19 including AbbVie Inc., AIM ImmunoTech Inc., Ansun BioPharma, APEIRON Biologics AG, Ascletis Pharma Inc., Blade Therapeutics Inc., Can-Fite BioPharma Ltd., CanSino Biologics Inc., Clover Biopharmaceuticals, F. Hoffmann-La Roche Ltd., FUJIFILM Corp., Gilead Sciences Inc., IMV Inc., InflaRx GmbH, Inovio Pharmaceuticals Inc., and Johnson & Johnson Services Inc.

Overview of Therapeutic Pipeline for COVID-19

The COVID-19 pandemic started in China in the last quarter of 2019 and spread globally by early 2020. Globally, the incidence and prevalence of COVID-19 are increasing aggressively. According to the Worldometers.info report, updated on April 13, 2020, the number of COVID-19 cases reported was 1,862,254, out of which 6.17% of the people have lost their lives, and about 23.18% people have been recovered globally.

Coronavirus disease (COVID-19) is infectious and is caused by a new coronavirus strain, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Most of the people affected with COVID-19 experience mild-to-moderate respiratory illness and recover without requiring special treatment. However, older people and people with certain medical conditions, including cardiovascular diseases, diabetes, chronic respiratory disease, and cancer, are more likely to develop serious illness. Currently, there are no specific vaccines or treatments for COVID-19. However, owing to the above-mentioned factors, many ongoing clinical trials are evaluating potential treatments, especially target molecules for the immune system. Over 26% of the molecules in the pipeline are currently in the pre-clinical stages.

Companies covered

Several companies are involved in the development of pipeline molecules for COVID-19. In addition to the companies, major institutes, universities, and hospitals are also conducting studies on COVID-19. Moreover, companies are collaborating with institutions such as the US Department of Health & Human Services and the University of British Columbia to use the available technologies properly for the further development and commercialization of molecules.

The report covers detailed information on several companies actively involved in the development of molecules for COVID-19 including -

AbbVie Inc.
AIM ImmunoTech Inc.
Ansun BioPharma
APEIRON Biologics AG
Ascletis Pharma Inc.
Blade Therapeutics Inc.
Can-Fite BioPharma Ltd.
CanSino Biologics Inc.
Clover Biopharmaceuticals
F. Hoffmann-La Roche Ltd.
FUJIFILM Corp.
Gilead Sciences Inc.
IMV Inc.
InflaRx GmbH
Inovio Pharmaceuticals Inc.
Johnson & Johnson Services Inc.
Mallinckrodt Plc
Moderna Inc.
NeuroRx Inc.
Novavax Inc.
OncoImmune Inc.
OyaGen Inc.
Pulmotect Inc.
RedHill Biopharma Ltd.
Regeneron Pharmaceuticals Inc.
Sanofi
SLA Pharma AG
Sorrento Therapeutics Inc.
Swedish Orphan Biovitrum AB
Symvivo Corp.
Takeda Pharmaceutical Co.
Tonix Pharmaceuticals Holding Corp.
Vanda Pharmaceuticals Inc.
Vaxart Inc.

COVID-19 - Pipeline Analysis: Therapeutic Assessment of the Molecules for COVID-19 by Route of Administration

Oral
Intravenous
Nasal
Intradermal
Intramuscular
Unknown

Most of the pipeline molecules for COVID-19 treatment are being developed for oral administration. Additionally, companies and institutions are also focusing on developing drugs that can be delivered through the subcutis (intravenous RoA).

COVID-19 - Pipeline Analysis: Therapeutic Assessment of the Molecules for COVID-19 by Therapy

Monotherapy
Combination therapy

Monotherapy uses a single drug to treat a disorder. Over 64% of the molecules that are currently in the pipeline to treat COVID-19 are being developed as monotherapy drugs.

COVID-19 - Pipeline Analysis: Key Highlights of the Report

What are the therapy molecules used in the various development stages of COVID-19?
What are the companies that are currently involved in the development of therapeutic molecules for COVID-19?
Insight into discontinued/inactive molecules with appropriate reasoning?
What are the major regulatory authorities approving drugs in various regions?
Detailed profiling of each active molecule

We can help! Our analysts can customize this report to meet your requirements. Get in touch

Coronavirus disease 2019 (COVID-19) has developed into a global pandemic, affecting every nation and territory in the world. Machine learning-based approaches are useful when trying to understand the complexity behind the spread of the disease and how to contain its spread effectively. The unsupervised learning method could be useful to evaluate the shortcomings of health facilities in areas of increased infection as well as what strategies are necessary to prevent disease spread within or outside of the country. To contribute toward the well-being of society, this paper focusses on the implementation of machine learning techniques for identifying common prevailing public health care facilities and concerns related to COVID-19 as well as attitudes to infection prevention strategies held by people from different countries concerning the current pandemic situation. Regression tree, random forest, cluster analysis and principal component machine learning techniques are used to analyze the global COVID-19 data of 133 countries obtained from the Worldometer website as of April 17, 2020. The analysis revealed that there are four major clusters among the countries. Eight countries having the highest cumulative infected cases and deaths, forming the first cluster. Seven countries, United States, Spain, Italy, France, Germany, United Kingdom, and Iran, play a vital role in explaining the 60% variation of the total variations by us of the first component characterized by all variables except for the rate variables. The remaining countries explain only 20% of the variation of the total variation by use of the second component characterized by only rate variables. Most strikingly, the analysis found that the variable number of tests by the country did not play a vital role in the prediction of the cumulative number of confirmed cases.

Context

Try to scrap data from official website of South Korea & France linked to COVID-19 confirmed cases and death in 2020

Content

Script to scrap data (France Publique Santé et South Korean KCDC) Results of scrapy : Data of COVID-19 confirmed cases & deaths Use direct link to differents sources : look at Acknowledgements

I use a very simple R0 model to try to evaluate what would happened without lock-down in Hubei, France, South-Korea, Italy in this https://www.kaggle.com/jeugregg/coronavirus-visualization-modeling

Acknowledgements

The world data is taken from https://github.com/CSSEGISandData/COVID-19 provided by JHU CSSE

South Korea areas data are retrieved with scrapy from online KCDC Press Release articles at https://www.cdc.go.kr/board/board.es?mid=a30402000000&bid=0030.

France areas data are taken with scrapy from online santepubliquefrance.fr Press articles at 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 and https://www.worldometers.info/coronavirus/country/france/ but until 25th March 2020.

For Global France, data are from https://www.data.gouv.fr/fr/datasets/donnees-relatives-aux-resultats-des-tests-virologiques-covid-19/

For Global Italy, Germany, Hubei data are from https://www.worldometers.info/coronavirus/

Inspiration

What is the result of how each countries try to struggle this virus ?

Context

https://github.com/Aditta-das/scrap-worlodometers.com

Columns

Tot Cases/ 1M pop = total confirmed cases per 1 million population Deaths/1M pop = total deaths per 1 million population Tests/1M pop =total tests per 1 million population

Context

WHO declared COVID-19 as the global pandemic. Data science and research communities all over the world came together to fight against it in this tough time. This dataset contains the datewise updates of the number of confirmed, deaths, recovered, quarantine and released from quarantine cases for Bangladesh. Hopefully it will help the local community to find meaningful insight and find the pattern of the pandemic which may save millions of life.

Content

All of data are taken from the Govt.site, WHO, DGHS and Worldometer open source data. The dataset contains all data from the date of March 1, 2020 to April 3, 2020.

Column Description

Date- Specific Date
Confirmed - The number of confirmed cases
Recovered - The number of recovered cases
Deaths- The number of death cases
Quarantine - The number of quarantined cases
Released From Quarantine - The number of released quarantine cases

Acknowledgements

• corona.gov.bd: http://www.corona.gov.bd/
• DGHS: https://corona.gov.bd/press-release
• WHO: https://www.who.int/countries/bgd/en/
• Worldometer :https://www.worldometers.info/coronavirus/

Inspiration

As the dataset contains datewise updates of the coronavirus cases in Bangladesh, feel free to prepare meaningful insights from the data. Share and collaborate to find the factors of pandemic for Bangladesh, make time series calculation and so on. Don't forget to suggest useful dataset to merge along with this dataset. Thanks.

Context

This is the data repository for the 2019 Novel Coronavirus Visual Dashboard operated by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). Also, Supported by ESRI Living Atlas Team and the Johns Hopkins University Applied Physics Lab (JHU APL).

Data Source

• 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/
• COVID Tracking Project: https://covidtracking.com/data. (US Testing and Hospitalization Data. We use the maximum reported value from "Currently" and - - - "Cumulative" Hospitalized for our hospitalization number reported for each state.)
• French Government: https://dashboard.covid19.data.gouv.fr/

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.

CONTEXT - As of January 30, 2020, a novel coronavirus named 2019-nCoV was identified in Wuhan, China. -It caused pneumonia-like symptoms in individuals with no clear cause, and existing vaccines and treatments were ineffective. -The virus exhibited evidence of human-to-human transmission, and the transmission rate appeared to increase significantly in mid-January 2020. -By that date, approximately 8,243 confirmed cases had been reported.

DATA SOURCE https://github.com/CSSEGISandData/COVID-19 https://www.worldometers.info/

COVID-19 Dataset for Epidemic Model Development

I combined several data sources to gain an integrated dataset involving country-level COVID-19 confirmed, recovered and fatalities cases which can be used to build some epidemic models such as SIR, SIR with mortality. Adding information regarding population which can be used for calculating incidence rate and prevalence rate. One of my applications based on this dataset is published at https://dylansp.shinyapps.io/COVID19_Visualization_Analysis_Tool/.

Content

My approach is to retrieve cumulative confirmed cases, fatalities and recovered cases since 2020-01-22 onwards from the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE) COVID-19 dataset, merged with country code as well as population of each country. For the purpose of building epidemic models, I calculated information regarding daily new confirmed cases, recovered cases, and fatalities, together with remaining confirmed cases which equal to cumulative confirmed cases - cumulative recovered cases - cumulative fatalities. I haven't yet to find creditable data sources regarding probable cases of various countries yet. I'll add them once I found them.

• Date: The date of the record.
• Country_Region: The name of the country/region. -alpha-3_code: country code for that can be used for map visualization.
• Population: The population of the given country/region.
• Total_Confirmed_Cases: Cumulative confirmed cases.
• Total_Fatalities: Cumulative fatalities.
• Total_Recovered_Cases: Cumulative recovered cases.
• New_Confirmed_Cases: Daily new confirmed cases.
• New_Fatalities: Daily new fatalities.
• New_Recovered_Cases: Daily new recovered cases.
• Remaining_Confirmed_Cases: Remaining infected cases which equal to (cumulative confirmed cases - cumulative recovered cases - cumulative fatalities).

Acknowledgements

1. The data source of confirmed cases, recovered cases and deaths is JHU CSSE https://github.com/CSSEGISandData/COVID-19;
2. The data source of the country-level population mainly comes from https://storage.guidotti.dev/covid19/data/ and Worldometer (https://www.worldometers.info/population/).

Inspiration

1. Building up the country-level COVID-19 case track dashboard.
2. Insights regarding the incidence rate, prevalence rate, mortality and recovery rate of various countries.
3. Building up epidemic models for forecasting.