As of November 24, 2024 there were over 274 million confirmed cases of coronavirus (COVID-19) across the whole of Europe since the first confirmed cases in France in January 2020. France has been the worst affected country in Europe with 39,028,437 confirmed cases, followed by Germany with 38,437,756 cases. Italy and the UK have approximately 26.8 million and 25 million cases respectively. For further information about the coronavirus pandemic, please visit our dedicated Facts and Figures page.

COVID-19 Trends MethodologyOur goal is to analyze and present daily updates in the form of recent trends within countries, states, or counties during the COVID-19 global pandemic. The data we are analyzing is taken directly from the Johns Hopkins University Coronavirus COVID-19 Global Cases Dashboard, though we expect to be one day behind the dashboard’s live feeds to allow for quality assurance of the data.Revisions added on 4/23/2020 are highlighted.Revisions added on 4/30/2020 are highlighted.Discussion of our assertion of an abundance of caution in assigning trends in rural counties added 5/7/2020. Correction on 6/1/2020Methodology update on 6/2/2020: This sets the length of the tail of new cases to 6 to a maximum of 14 days, rather than 21 days as determined by the last 1/3 of cases. This was done to align trends and criteria for them with U.S. CDC guidance. The impact is areas transition into Controlled trend sooner for not bearing the burden of new case 15-21 days earlier.Reasons for undertaking this work:The popular online maps and dashboards show counts of confirmed cases, deaths, and recoveries by country or administrative sub-region. Comparing the counts of one country to another can only provide a basis for comparison during the initial stages of the outbreak when counts were low and the number of local outbreaks in each country was low. By late March 2020, countries with small populations were being left out of the mainstream news because it was not easy to recognize they had high per capita rates of cases (Switzerland, Luxembourg, Iceland, etc.). Additionally, comparing countries that have had confirmed COVID-19 cases for high numbers of days to countries where the outbreak occurred recently is also a poor basis for comparison.The graphs of confirmed cases and daily increases in cases were fit into a standard size rectangle, though the Y-axis for one country had a maximum value of 50, and for another country 100,000, which potentially misled people interpreting the slope of the curve. Such misleading circumstances affected comparing large population countries to small population counties or countries with low numbers of cases to China which had a large count of cases in the early part of the outbreak. These challenges for interpreting and comparing these graphs represent work each reader must do based on their experience and ability. Thus, we felt it would be a service to attempt to automate the thought process experts would use when visually analyzing these graphs, particularly the most recent tail of the graph, and provide readers with an a resulting synthesis to characterize the state of the pandemic in that country, state, or county.The lack of reliable data for confirmed recoveries and therefore active cases. Merely subtracting deaths from total cases to arrive at this figure progressively loses accuracy after two weeks. The reason is 81% of cases recover after experiencing mild symptoms in 10 to 14 days. Severe cases are 14% and last 15-30 days (based on average days with symptoms of 11 when admitted to hospital plus 12 days median stay, and plus of one week to include a full range of severely affected people who recover). Critical cases are 5% and last 31-56 days. Sources:U.S. CDC. April 3, 2020 Interim Clinical Guidance for Management of Patients with Confirmed Coronavirus Disease (COVID-19). Accessed online. Initial older guidance was also obtained online. Additionally, many people who recover may not be tested, and many who are, may not be tracked due to privacy laws. Thus, the formula used to compute an estimate of active cases is: Active Cases = 100% of new cases in past 14 days + 19% from past 15-30 days + 5% from past 31-56 days - total deaths.We’ve never been inside a pandemic with the ability to learn of new cases as they are confirmed anywhere in the world. After reviewing epidemiological and pandemic scientific literature, three needs arose. We need to specify which portions of the pandemic lifecycle this map cover. The World Health Organization (WHO) specifies six phases. The source data for this map begins just after the beginning of Phase 5: human to human spread and encompasses Phase 6: pandemic phase. Phase six is only characterized in terms of pre- and post-peak. However, these two phases are after-the-fact analyses and cannot ascertained during the event. Instead, we describe (below) a series of five trends for Phase 6 of the COVID-19 pandemic.Choosing terms to describe the five trends was informed by the scientific literature, particularly the use of epidemic, which signifies uncontrolled spread. The five trends are: Emergent, Spreading, Epidemic, Controlled, and End Stage. Not every locale will experience all five, but all will experience at least three: emergent, controlled, and end stage.This layer presents the current trends for the COVID-19 pandemic by country (or appropriate level). There are five trends:Emergent: Early stages of outbreak. Spreading: Early stages and depending on an administrative area’s capacity, this may represent a manageable rate of spread. Epidemic: Uncontrolled spread. Controlled: Very low levels of new casesEnd Stage: No New cases These trends can be applied at several levels of administration: Local: Ex., City, District or County – a.k.a. Admin level 2State: Ex., State or Province – a.k.a. Admin level 1National: Country – a.k.a. Admin level 0Recommend that at least 100,000 persons be represented by a unit; granted this may not be possible, and then the case rate per 100,000 will become more important.Key Concepts and Basis for Methodology: 10 Total Cases minimum threshold: Empirically, there must be enough cases to constitute an outbreak. Ideally, this would be 5.0 per 100,000, but not every area has a population of 100,000 or more. Ten, or fewer, cases are also relatively less difficult to track and trace to sources. 21 Days of Cases minimum threshold: Empirically based on COVID-19 and would need to be adjusted for any other event. 21 days is also the minimum threshold for analyzing the “tail” of the new cases curve, providing seven cases as the basis for a likely trend (note that 21 days in the tail is preferred). This is the minimum needed to encompass the onset and duration of a normal case (5-7 days plus 10-14 days). Specifically, a median of 5.1 days incubation time, and 11.2 days for 97.5% of cases to incubate. This is also driven by pressure to understand trends and could easily be adjusted to 28 days. Source used as basis:Stephen A. Lauer, MS, PhD *; Kyra H. Grantz, BA *; Qifang Bi, MHS; Forrest K. Jones, MPH; Qulu Zheng, MHS; Hannah R. Meredith, PhD; Andrew S. Azman, PhD; Nicholas G. Reich, PhD; Justin Lessler, PhD. 2020. The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application. Annals of Internal Medicine DOI: 10.7326/M20-0504.New Cases per Day (NCD) = Measures the daily spread of COVID-19. This is the basis for all rates. Back-casting revisions: In the Johns Hopkins’ data, the structure is to provide the cumulative number of cases per day, which presumes an ever-increasing sequence of numbers, e.g., 0,0,1,1,2,5,7,7,7, etc. However, revisions do occur and would look like, 0,0,1,1,2,5,7,7,6. To accommodate this, we revised the lists to eliminate decreases, which make this list look like, 0,0,1,1,2,5,6,6,6.Reporting Interval: In the early weeks, Johns Hopkins' data provided reporting every day regardless of change. In late April, this changed allowing for days to be skipped if no new data was available. The day was still included, but the value of total cases was set to Null. The processing therefore was updated to include tracking of the spacing between intervals with valid values.100 News Cases in a day as a spike threshold: Empirically, this is based on COVID-19’s rate of spread, or r0 of ~2.5, which indicates each case will infect between two and three other people. There is a point at which each administrative area’s capacity will not have the resources to trace and account for all contacts of each patient. Thus, this is an indicator of uncontrolled or epidemic trend. Spiking activity in combination with the rate of new cases is the basis for determining whether an area has a spreading or epidemic trend (see below). Source used as basis:World Health Organization (WHO). 16-24 Feb 2020. Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19). Obtained online.Mean of Recent Tail of NCD = Empirical, and a COVID-19-specific basis for establishing a recent trend. The recent mean of NCD is taken from the most recent fourteen days. A minimum of 21 days of cases is required for analysis but cannot be considered reliable. Thus, a preference of 42 days of cases ensures much higher reliability. This analysis is not explanatory and thus, merely represents a likely trend. The tail is analyzed for the following:Most recent 2 days: In terms of likelihood, this does not mean much, but can indicate a reason for hope and a basis to share positive change that is not yet a trend. There are two worthwhile indicators:Last 2 days count of new cases is less than any in either the past five or 14 days. Past 2 days has only one or fewer new cases – this is an extremely positive outcome if the rate of testing has continued at the same rate as the previous 5 days or 14 days. Most recent 5 days: In terms of likelihood, this is more meaningful, as it does represent at short-term trend. There are five worthwhile indicators:Past five days is greater than past 2 days and past 14 days indicates the potential of the past 2 days being an aberration. Past five days is greater than past 14 days and less than past 2 days indicates slight positive trend, but likely still within peak trend time frame.Past five days is less than the past 14 days. This means a downward trend. This would be an

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.

This dataset provides values for CORONAVIRUS CASES reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Brazil is the Latin American country affected the most by the COVID-19 pandemic. As of May 2025, the country had reported around 38 million cases. It was followed by Argentina, with approximately ten million confirmed cases of COVID-19. In total, the region had registered more than 83 million diagnosed patients, as well as a growing number of fatal COVID-19 cases. The research marathon Normally, the development of vaccines takes years of research and testing until options are available to the general public. However, with an alarming and threatening situation as that of the COVID-19 pandemic, scientists quickly got on board in a vaccine marathon to develop a safe and effective way to prevent and control the spread of the virus in record time. Over two years after the first cases were reported, the world had around 1,521 drugs and vaccines targeting the COVID-19 disease. As of June 2022, a total of 39 candidates were already launched and countries all over the world had started negotiations and acquisition of the vaccine, along with immunization campaigns. COVID vaccination rates in Latin America As immunization against the spread of the disease continues to progress, regional disparities in vaccination coverage persist. While Brazil, Argentina, and Mexico were among the Latin American nations with the most COVID-19 cases, those that administered the highest number of COVID-19 doses per 100 population are Cuba, Chile, and Peru. Leading the vaccination coverage in the region is the Caribbean nation, with more than 406 COVID-19 vaccines administered per every 100 inhabitants as of January 5, 2024.For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

Context:

From World Health Organization - On 31 December 2019, WHO was alerted to several cases of pneumonia in Wuhan City, Hubei Province of China. The virus did not match any other known virus. This raised concern because when a virus is new, we do not know how it affects people.

So daily level information on the affected people can give some interesting insights when it is made available to the broader data science community.

Johns Hopkins University has made an excellent dashboard using the affected cases data. Data is extracted from the google sheets associated and made available here.

Edited:

Now data is available as csv files in the Johns Hopkins Github repository. Please refer to the github repository for the Terms of Use details. Uploading it here for using it in Kaggle kernels and getting insights from the broader DS community.

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 daily level information on the number of affected cases, deaths and recovery from 2019 novel coronavirus. Please note that this is a time series data and so the number of cases on any given day is the cumulative number.

The data is available from 22 Jan, 2020.

Here’s a polished version suitable for a professional Kaggle dataset description:

Dataset Description

This dataset contains time-series and case-level records of the COVID-19 pandemic. The primary file is covid_19_data.csv, with supporting files for earlier records and individual-level line list data.

Files and Columns

1. covid_19_data.csv (Main File)

This is the primary dataset and contains aggregated COVID-19 statistics by location and date.

• Sno – Serial number of the record
• ObservationDate – Date of the observation (MM/DD/YYYY)
• Province/State – Province or state of the observation (may be missing for some entries)
• Country/Region – Country of the observation
• Last Update – Timestamp (UTC) when the record was last updated (not standardized, requires cleaning before use)
• Confirmed – Cumulative number of confirmed cases on that date
• Deaths – Cumulative number of deaths on that date
• Recovered – Cumulative number of recoveries on that date

2. 2019_ncov_data.csv (Legacy File)

This file contains earlier COVID-19 records. It is no longer updated and is provided only for historical reference. For current analysis, please use covid_19_data.csv.

3. COVID_open_line_list_data.csv

This file provides individual-level case information, obtained from an open data source. It includes patient demographics, travel history, and case outcomes.

4. COVID19_line_list_data.csv

Another individual-level case dataset, also obtained from public sources, with detailed patient-level information useful for micro-level epidemiological analysis.

✅ Use covid_19_data.csv for up-to-date aggregated global trends.

✅ Use the line list datasets for detailed, individual-level case analysis.

Country level datasets:

If you are interested in knowing country level data, please refer to the following Kaggle datasets:

India - https://www.kaggle.com/sudalairajkumar/covid19-in-india

South Korea - https://www.kaggle.com/kimjihoo/coronavirusdataset

Italy - https://www.kaggle.com/sudalairajkumar/covid19-in-italy

Brazil - https://www.kaggle.com/unanimad/corona-virus-brazil

USA - https://www.kaggle.com/sudalairajkumar/covid19-in-usa

Switzerland - https://www.kaggle.com/daenuprobst/covid19-cases-switzerland

Indonesia - https://www.kaggle.com/ardisragen/indonesia-coronavirus-cases

Acknowledgements :

Johns Hopkins University for making the data available for educational and academic research purposes

MoBS lab - https://www.mobs-lab.org/2019ncov.html

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....

2019 Novel Coronavirus COVID-19 (2019-nCoV) Visual Dashboard and Map:
https://www.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6

• Confirmed Cases by Country/Region/Sovereignty
• Confirmed Cases by Province/State/Dependency
• Deaths
• Recovered

Downloadable data:
https://github.com/CSSEGISandData/COVID-19

Additional Information about the Visual Dashboard:
https://systems.jhu.edu/research/public-health/ncov

The outbreak of the novel coronavirus in Wuhan, China, saw infection cases spread throughout the Asia-Pacific region. By April 13, 2024, India had faced over 45 million coronavirus cases. South Korea followed behind India as having had the second highest number of coronavirus cases in the Asia-Pacific region, with about 34.6 million cases. At the same time, Japan had almost 34 million cases. At the beginning of the outbreak, people in South Korea had been optimistic and predicted that the number of cases would start to stabilize. What is SARS CoV 2?Novel coronavirus, officially known as SARS CoV 2, is a disease which causes respiratory problems which can lead to difficulty breathing and pneumonia. The illness is similar to that of SARS which spread throughout China in 2003. After the outbreak of the coronavirus, various businesses and shops closed to prevent further spread of the disease. Impacts from flight cancellations and travel plans were felt across the Asia-Pacific region. Many people expressed feelings of anxiety as to how the virus would progress. Impact throughout Asia-PacificThe Coronavirus and its variants have affected the Asia-Pacific region in various ways. Out of all Asia-Pacific countries, India was highly affected by the pandemic and experienced more than 50 thousand deaths. However, the country also saw the highest number of recoveries within the APAC region, followed by South Korea and Japan.

The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.

Since late January, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.

We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak.

The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository.

First regression: Rate of rise of confirmed cases of COVID-19 in 50 countries, and the associated local meteorological and demographic variables.

As of March 13, Austria had the highest rate of coronavirus (COVID-19) cases reported in the previous seven days in Europe at 224 cases per 100,000. Luxembourg and Slovenia have recorded 122 and 108 cases per 100,000 people respectively in the past week. Furthermore, San Marino had a rate of 97 cases in the last seven days.
Since the pandemic outbreak, France has been the worst affected country in Europe with over 38.3 million cases as of January 13. The overall incidence of cases in every European country can be found here.

For further information about the coronavirus pandemic, please visit our dedicated Facts and Figures page.

World Coronavirus : Analysis Based on Country/State Population¶

There is a huge discussion about coronavirus cases and increasing numbers of confirmed cases. Although the numbers may cause terror, a more careful analysis must be done, based on the percentage of Population. You will see that the population of China parts consists of dozens of millions, many of them much more that a European country.

This dataframe includes the population of Province/State of each Country affected by coronavirus. All values are tracked from wikipedia.

There is a quick analysis here

Context

From World Health Organization - On 31 December 2019, WHO was alerted to several cases of pneumonia in Wuhan City, Hubei Province of China. The virus did not match any other known virus. This raised concern because when a virus is new, we do not know how it affects people.

So daily level information on the affected people can give some interesting insights when it is made available to the broader data science community.

Johns Hopkins University has made an excellent dashboard using the affected cases data. Data is extracted from the google sheets associated and made available here.

Edited: Now data is available as csv files in the Johns Hopkins Github repository. Please refer to the github repository for the Terms of Use details. Uploading it here for using it in Kaggle kernels and getting insights from the broader DS community.

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 daily level information on the number of affected cases, deaths and recovery from 2019 novel coronavirus. Please note that this is a time series data and so the number of cases on any given day is the cumulative number.

The data is available from 22 Jan, 2020.

Column Description

Main file in this dataset is covid_19_data.csv and the detailed descriptions are below.

covid_19_data.csv

• Sno - Serial number
• ObservationDate - Date of the observation in MM/DD/YYYY
• Province/State - Province or state of the observation (Could be empty when missing)
• Country/Region - Country of observation
• Last Update - Time in UTC at which the row is updated for the given province or country. (Not standardised and so please clean before using it)
• Confirmed - Cumulative number of confirmed cases till that date
• Deaths - Cumulative number of of deaths till that date
• Recovered - Cumulative number of recovered cases till that date

Apart from that these two files have individual level information

COVID_open_line_list_data.csv This file is originally obtained from this link

COVID19_line_list_data.csv This files is originally obtained from this link

Country level datasets If you are interested in knowing country level data, please refer to the following Kaggle datasets: South Korea - https://www.kaggle.com/kimjihoo/coronavirusdataset Italy -
https://www.kaggle.com/sudalairajkumar/covid19-in-italy

Acknowledgements

• Johns Hopkins University for making the data available for educational and academic research purposes
• MoBS lab - https://www.mobs-lab.org/2019ncov.html 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

Inspiration

Some useful insi...

Please, If you enjoyed this dataset, don't forget to upvote it.

Content

From Novel Corona Virus 2019 Dataset:

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 cases in Brazil. Please note that this is a time series data and so the number of cases on any given day is a cumulative number.

The data is available from Jan/30/2020, when the first suspect case appeared in Brazil.

Acknowledgements

1. Avisos/Advertising - Please, before start working with this data, take a break to read this discussion.
2. Plataforma COVID Brazil - Public platform to share informations about the COVID-19 cases in Brazil.

Country level datasets

If you are interested in know about another country, please follow these Kaggle datasets:

• Novel Corona Virus 2019 Dataset - https://www.kaggle.com/sudalairajkumar/novel-corona-virus-2019-dataset
• India - https://www.kaggle.com/sudalairajkumar/covid19-in-india
• South Korea - https://www.kaggle.com/kimjihoo/coronavirusdataset
• Italy - https://www.kaggle.com/sudalairajkumar/covid19-in-italy
• USA - https://www.kaggle.com/sudalairajkumar/covid19-in-usa
• Switzerland - https://www.kaggle.com/daenuprobst/covid19-cases-switzerland
• Indonesia - https://www.kaggle.com/ardisragen/indonesia-coronavirus-cases
• Turkey - https://www.kaggle.com/gkhan496/covid19-in-turkey
• France - https://www.kaggle.com/lperez/coronavirus-france-dataset
• Tunisia - https://www.kaggle.com/ghassen1302/coronavirus-tunisia
• Japan - https://www.kaggle.com/tsubasatwi/close-contact-status-of-corona-in-japan

Inspiration

1. Changes in number of cases over time
2. Change in cases over time at state level

Italy experienced a sharp rise in the number of positive infections shortly after confirming its 100th coronavirus case. In the space of just 17 days, the number of cases in Italy had soared to more than 12,000. In comparison, the spread of the virus was much slower in Japan.

The COVID-19 outbreak in Italy Italy was the first European nation to be severely impacted by COVID-19. There had been approximately 35,400 coronavirus-related deaths recorded in the country as of August 17, 2020. Following a two-month lockdown period, restrictions in Italy were eased in early May, and citizens are now permitted to travel between regions and abroad. However, the risk of a resurgence remains, and the country’s state of emergency has been extended until October 15, 2020. It is looking increasingly likely that restrictions will not be completely lifted until a vaccine for the disease is discovered.

Pfizer confident of vaccine success Pfizer and BioNTech are jointly developing one candidate vaccine that is under clinical evaluation. In July 2020, the two companies announced an agreement with the U.S. government that will bring millions of doses to the American people. The BNT162 mRNA-based vaccine is currently being produced even though it has not received regulatory approval from the FDA. This is a risky approach and is one that could cost the companies millions of dollars should the vaccine be rejected. However, if regulatory approval is received, the safe and effective vaccine can be shipped quickly.

The data set provides numbers of COVID-19 infections in Vietnam and some neighboring countries in Asia. In the data set in Vietnam, there is a classification of the total number of new cases, new cases and new infections in the community. The number of infections in the community is the number of unknown infections. The case data is published on a daily basis and is aggregated to the time of current statistics.

First reported in Wuhan, China, in December 2019, now more than 846,200 confirmed cases of COVID-19 are spread across 187 countries worldwide. The US and several countries in Europe such as Italy, Spain, and Belgium have continued to see a decrease in daily cases. Russia, Brazil, and Latin American countries are seeing increasing trends. India has also seen an increase in the number of new cases reported despite strict distancing measures taken early on.
Special populations analysis covered in the report include the following:
COVID-19 in children may result in systemic multisystem syndrome with severe outcomes.
Childhood routine vaccination rates drop during pandemic.
COVID-19’s impact in pregnant women unclear, though most cases are asymptomatic.
The COVID-19 pandemic could cause an increase in the prevalence of post-traumatic stress disorder (PTSD).
Complications of opioid addiction will be challenging for the management of disease during the COVID-19 pandemic. Read More

Context

• A new coronavirus designated 2019-nCoV was first identified in Wuhan, the capital of China's Hubei province
• People developed pneumonia without a clear cause and for which existing vaccines or treatments were not effective
• The virus has shown evidence of human-to-human transmission
• Korea has defended well against coronavirus until summer, but it increased many confirmed cases from fall
• As of 24th Dec. approximately 53K cases have been confirmed, and daily around 1K cases are getting confirmed
• This datasets are prepared to cheer Korea up fighting against coronavirus

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4837224%2Ff829b8bd45aacf4c63b17e0116cb52c9%2Fcover_photo.PNG?generation=1608792447857317&alt=media" alt="">

Content

• 3 files attached which are 1) COVID Korea Status 2) COVID Korea Demo 3) COVID Korea Geo

• 1) COVID Korea Status : General daily update . STATE_DT : standard date . STATE_TIME : standard time . DECIDE_CNT : confirmed cases . CLEAR_CNT : clear cases after hospitalization . EXAM_CNT : examination cases . DEATH_CNT : death counts . CARE_CNT : counts on care . RESUTL_NEG_CNT : negative results after examination . ACC_EXAM_CNT : accumulative examination counts . ACC_EXAM_COMP_CNT: accumulative examination completes count . ACC_DEF_RATE : accumulative confirmed rate . CREATE_DT : posted date and time . UPDATE_DT : updated date and time

• 2) COVID Korea Demo : Updates with demographic information . GUBUN : classified by gender and age . CONF_CASE : confirmed cases . CONF_CASE_RATE : confirmed case rate . DEATH : death counts . DEATH_RATE : death rate . CRITICAL_RATE : critical rate . CREATE_DT : created date and time . UPDATE_DT : updated date and time

• 3) COVID Korea Geo : Updates with geographic information
  . CREATE_DT : created date and time
  . DEATH_CNT : death counts
  . GUBUN : city name
  . GUBUN_CN : city name in Chinese
  . GUBUN_EN : city name in English
  . INC_DEC : increase/decrease vs. past day
  . ISOL_CLEAR_CNT : clear counts from isolation
  . QUR_RATE : confirmed rate per 100K people
  . STD_DAY : standard day
  . UPDATE_DT : updated date and time
  . DEF_CNT : confirmed cases
  . ISOL_ING_CNT : isolated cases
  . OVER_FLOW_CNT : confirmed cases from foreign countries
  . LOCAL_OCC_CNT : domestic confirmed cases

Acknowledgements

• http://www.kdca.go.kr/cdc/
• https://data.go.kr/index.do

If these are useful, I will frequently update. Thanks.

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.

Second regression: The association of temperature and dewpoint with the rate of rise of COVID-19 cases.