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

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

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

As of May 2, 2023, the coronavirus disease (COVID-19) had been confirmed in almost every country and territory around the world. There had been roughly 687 million cases and 6.86 million deaths.

Vaccine approval in the United States The United States has recorded more coronavirus infections and deaths than any other country in the world. The regulatory agency in the country authorized three COVID-19 vaccines for emergency use. Both the Pfizer-BioNTech and Moderna vaccines were approved in December 2020, while the Johnson & Johnson vaccine was approved in February 2021. As of April 26, 2023, the number of COVID-19 vaccine doses administered in the U.S. had reached 675 million.

The difference between vaccines and antivirals Medications can help with the symptoms of viruses, but it is the role of the immune system to take care of them over time. However, the use of vaccines and antivirals can help the immune system in doing its job. The most tried and tested vaccine method is to inject an inactive or weakened form of a virus, encouraging the immune system to produce protective antibodies. The immune system keeps the virus in its memory, and if the real one appears, the body will recognize it and attack it more efficiently. Antivirals are designed to help target viruses, limiting their ability to reproduce and spread to other cells. They are used by patients who are already infected by a virus and can make the infection less severe.

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

On March 10, 2023, the Johns Hopkins Coronavirus Resource Center ceased its collecting and reporting of global COVID-19 data. For updated cases, deaths, and vaccine data please visit: World Health Organization (WHO)For more information, visit the Johns Hopkins Coronavirus Resource Center.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.DOI: https://doi.org/10.6084/m9.figshare.125529863/7/2022 - Adjusted the rate of active cases calculation in the U.S. to reflect the rates of serious and severe cases due nearly completely dominant Omicron variant.6/24/2020 - Expanded Case Rates discussion to include fix on 6/23 for calculating active cases.6/22/2020 - Added Executive Summary and Subsequent Outbreaks sectionsRevisions on 6/10/2020 based on updated CDC reporting. This affects the estimate of active cases by revising the average duration of cases with hospital stays downward from 30 days to 25 days. The result shifted 76 U.S. counties out of Epidemic to Spreading trend and no change for national level trends.Methodology 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.Correction on 6/1/2020Discussion of our assertion of an abundance of caution in assigning trends in rural counties added 5/7/2020. Revisions added on 4/30/2020 are highlighted.Revisions added on 4/23/2020 are highlighted.Executive SummaryCOVID-19 Trends is a methodology for characterizing the current trend for places during the COVID-19 global pandemic. Each day we assign one of five trends: Emergent, Spreading, Epidemic, Controlled, or End Stage to geographic areas to geographic areas based on the number of new cases, the number of active cases, the total population, and an algorithm (described below) that contextualize the most recent fourteen days with the overall COVID-19 case history. Currently we analyze the countries of the world and the U.S. Counties. The purpose is to give policymakers, citizens, and analysts a fact-based data driven sense for the direction each place is currently going. When a place has the initial cases, they are assigned Emergent, and if that place controls the rate of new cases, they can move directly to Controlled, and even to End Stage in a short time. However, if the reporting or measures to curtail spread are not adequate and significant numbers of new cases continue, they are assigned to Spreading, and in cases where the spread is clearly uncontrolled, Epidemic trend.We analyze the data reported by Johns Hopkins University to produce the trends, and we report the rates of cases, spikes of new cases, the number of days since the last reported case, and number of deaths. We also make adjustments to the assignments based on population so rural areas are not assigned trends based solely on case rates, which can be quite high relative to local populations.Two key factors are not consistently known or available and should be taken into consideration with the assigned trend. First is the amount of resources, e.g., hospital beds, physicians, etc.that are currently available in each area. Second is the number of recoveries, which are often not tested or reported. On the latter, we provide a probable number of active cases based on CDC guidance for the typical duration of mild to severe cases.Reasons for undertaking this work in March of 2020: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 15-25 days + 5% from past 26-49 days - total deaths. On 3/17/2022, the U.S. calculation was adjusted to: Active Cases = 100% of new cases in past 14 days + 6% from past 15-25 days + 3% from past 26-49 days - total deaths. Sources: https://www.cdc.gov/mmwr/volumes/71/wr/mm7104e4.htm https://covid.cdc.gov/covid-data-tracker/#variant-proportions If a new variant arrives and appears to cause higher rates of serious cases, we will roll back this adjustment. 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

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.

Context

This dataset is created as a part of covid-19 global forecasting challenge. It contains parameters for the SIR model for different locations worldwide. But the main value of the dataset is estimated transmission period (average period between single infected individual infects next susceptible in pure susceptible population) per week per location.

The model is defined as ODE system as follows: https://wikimedia.org/api/rest_v1/media/math/render/svg/29728a7d4bebe8197dca7d873d81b9dce954522e" alt="SIR ODE equations">

In order to reflect the transmission rate changes caused by spread constraining measures (social distancing, etc.) the Beta parameter is modelled separately as spline model (spline node estimate for every week). See paramsWeekly.csv which holds the Beta parameter values for every week as well as estimated R0 values (derived from Beta and Gamma paramters) for every week.

The models are fitted on John Hopkins University data (time series) using several runs of Nelder-Mead simplex optimization method (best run is taken) starting at different initial locations and RMSE as a loss.

What parameters are fitted (estimated) per country/province: * the day when the infection emerged in the country * the initial infected count on the first day of the infection * beta (separate value for every week) - an average number of contacts (sufficient to spread the disease) per day each infected individual has * gamma - fixed fraction of the infected group that will recover during any given day * R0 - Equals beta/gamma

How to read the figures. * points are real observed data provided by Johns Hopkins University * curves are model prediction

• blue is susceptible population - people that are not yet infected but can get the infection
• red is infected population
• green is removed population (recovered or dead). people that are not susceptible any more as they came through the infection.

Content

The dataset contains 3 data portions:

1. Fitted SIR model parameters for different locations worldwide. a. Params.csv - parameters (and derived values) constant over time b. ParamsWeekly.csv - parameters (and derived values) that are estimated for every week separatly
2. Figures directory that visually show how the fitted parameters match the data points.
3. Predictions directory with CSV files with prediction for one year in the future for each individual location.

Warning

Always do visual check of the model fit (Figures directory) for quality control before start to use the corresponding parameter values in your analysis, as the dataset is obtained by automatic fitting procedure without manual quality control.

Acknowledgements

Thanks a lot Kaggle for organizing data sharing and challenges that make the world better.

Also many thanks to John Hopkins University for their hard work of gathering COVID-19 statistics worldwide.

Inspiration

You can try to find correlation between model parameters (e.g. gamma - patient recovery rate) and other properties of the modelled locations worldwide (e.g. weather, population density, level of medical care, etc.)

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 the first reported coronavirus case in Washington State on Jan. 21, 2020, 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.

Since the spread of the coronavirus (COVID-19) in Italy started in February 2020, the number of cases has increased daily. However, the vast majority of people who contracted the virus have recovered. As of January 8, 2025, the number of individuals who recovered from coronavirus in Italy reached over 26.5 million. Conversely, the number of deaths also kept increasing, reaching over 198.6 thousand. When looking at the regional level, the region with the highest number of recoveries was Lombardy. The region, however, registered the highest number of coronavirus cases in the country. 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....

Overview

The COVID-19 Patient Recovery Dataset is a synthetic collection of anonymized records for around 70,000 COVID-19 patients. It aims to assist with classification tasks in machine learning and epidemiological research. The dataset includes detailed clinical and demographic information, such as symptoms, existing health issues, vaccination status, COVID-19 variants, treatment details, and outcomes related to recovery or mortality. This dataset is great for predicting patient recovery (recovered), mortality (death), disease severity (severity), or the need for intensive care (icu_admission) using algorithms like Logistic Regression, Random Forest, XGBoost, or Neural Networks. It also allows for exploratory data analysis (EDA), statistical modeling, and time-series studies to find patterns in COVID-19 outcomes.
The data is synthetic and reflects realistic trends found in public health data, based on sources like WHO reports. It ensures privacy and follows ethical guidelines. Dates are provided in Excel serial format, meaning 44447 corresponds to September 8, 2021, and can be converted to standard dates using Python’s datetime or Excel. With 70,000 records and 28 columns, this dataset serves as a valuable resource for data scientists, researchers, and students interested in health-related machine learning or pandemic trends.

Data Source and Collection

Source: Synthetic data based on public health patterns from sources like the World Health Organization (WHO). It includes placeholder URLs.
Collection Period: Simulated from early 2020 to mid-2022, covering the Alpha, Delta, and Omicron waves.
Number of Records: 70,000.
File Format: CSV, which works with Pandas, R, Excel, and more.
Data Quality Notes:

About 5% of the values are missing in fields like symptoms_2, symptoms_3, treatment_given_2, and date.
There are rare inconsistencies, such as between recovery/death flags and dates, which may need some preprocessing.
Unique, anonymized patient IDs.

Key Column Details

patient_id: Unique identifier (e.g., P000001).
country: Reporting country (e.g., India, USA, Brazil, Germany, China, Pakistan, South Africa, UK).
region/state: Sub-national region (e.g., Sindh, California, São Paulo, Beijing).
date_reported, date_of_recovery, date_of_death: Excel serial dates (convert using datetime(1899,12,30) + timedelta(days=value)).
age: Patient age (1–100 years).
gender: Male or Female.
comorbidities: Pre-existing conditions (e.g., Diabetes, Hypertension, Cancer, Heart Disease, Asthma, None).
symptoms_1, symptoms_2, symptoms_3: Reported symptoms (e.g., Cough, Fever, Fatigue, Loss of Smell, Sore Throat, or empty).
severity: Case severity (Mild, Moderate, Severe, Critical).
hospitalized, icu_admission, ventilator_support: Binary (1 = Yes, 0 = No).
vaccination_status: None, Partial, Full, or Booster.
variant: COVID-19 variant (Omicron, Delta, Alpha).
treatment_given_1, treatment_given_2: Treatments administered (e.g., Antibiotics, Remdesivir, Oxygen, Steroids, Paracetamol, or empty).
days_to_recovery: Days from report to recovery (5–30, or empty if not recovered).
recovered, death: Binary outcomes (1 = Yes, 0 = No; generally mutually exclusive).
tests_conducted: Number of tests (1–5).
test_type: PCR or Antigen.
hospital_name: Fictional hospital (e.g., Aga Khan, Mayo Clinic, NHS Trust).
doctor_assigned: Fictional doctor name (e.g., Dr. Smith, Dr. Müller).
source_url: Placeholder.

Summary Statistics

Total Patients: 70,000.
Age: Mean ~50 years, Min 1, Max 100, evenly distributed.
Gender: ~50% Male, ~50% Female.
Top Countries: USA (20%), India (18%), Brazil (15%), China (12%), Germany (10%).
Comorbidities: Diabetes (25%), Hypertension (20%), Cancer (15%), Heart Disease (15%), Asthma (10%), None (15%).
Severity: Mild (60%), Moderate (25%), Severe (10%), Critical (5%).
Recovery Rate: ~60% recovered (recovered=1), ~30% deceased (death=1), ~10% unresolved (both 0).
Vaccination: None (40%), Full (30%), Partial (15%), Booster (15%).
Variants: Omicron (50%), Delt...

Throughout history, the human race has often faced pandemics with substantial numbers of fatalities. As the COVID-19 pandemic has now affected the whole planet, even countries with moderate to strong healthcare support and expenditure have struggled to contain disease transmission and casualties. Countries affected by COVID-19 have different demographics, socioeconomic, and lifestyle health indicators. In this context, it is important to find out to what extent these parametric variations are modulating disease outcomes. To answer this, this study selected demographic, socioeconomic, and health indicators e.g., population density, percentage of the urban population, median age, health expenditure per capita, obesity, diabetes prevalence, alcohol intake, tobacco use, case fatality of non-communicable diseases (NCDs) as independent variables. Countries were grouped according to these variables and influence on dependent variables e.g., COVID-19 positive tests, case fatality, and case recovery rates were statistically analyzed. The results suggested that countries with variable median age had a significantly different outcome on positive test rate (P < 0.01). Both the median age (P = 0.0397) and health expenditure per capita (P = 0.0041) showed a positive relation with case recovery. An increasing number of tests per 100 K of the population showed a positive and negative relationship with the number of positives per 100 K population (P = 0.0001) and the percentage of positive tests (P < 0.0001), respectively. Alcohol intake per capita in liter (P = 0.0046), diabetes prevalence (P = 0.0389), and NCDs mortalities (P = 0.0477) also showed a statistical relation to the case fatality rate. Further analysis revealed that countries with high healthcare expenditure along with high median age and increased urban population showed more case fatality but also had a better recovery rate. Investment in the health sector alone is insufficient in controlling the severity of the pandemic. Intelligent and sustainable healthcare both in urban and rural settings and healthy lifestyle acquired immunity may reduce disease transmission and comorbidity induced fatalities, respectively.

As of June 9, 2020, the coronavirus outbreak posed a level ***** threat to businesses, meaning that severe and widespread economic impacts were likely. The composite index, which has level *** as its highest warning, was raised to level *** on March 12 and to level ***** on April 13.

Strong plans needed in response to coronavirus Countries are taking small steps on the road to economic recovery by gradually lifting lockdown measures. Manufacturing firms were among the first to return to work, and governments are now permitting shops, bars, and restaurants to reopen. However, there is no guarantee that consumers will return to their normal habits. In order to reduce the risks, businesses are being encouraged to activate contingency plans that include separating all essential operations from non-essential and focusing on high-priority areas and clients.

A focus on the U.S. economy COVID-19 has left the United States facing an economic crisis, and the country’s GDP fell by *** percent in the first quarter of 2020. Record numbers of Americans have lost their jobs during the pandemic, and the unemployment rate jumped to **** percent in April 2020. The Dow Jones, which monitors the stock prices of the ** largest companies in the United States, has rallied since the U.S. economy restarted but continues to feel the effects of a destructive period that wiped out years of gains in a matter of weeks.

According to data from McKinsey, ** percent of respondents from the United States were optimistic about their country's economic recovery following COVID-19. ** percent of American respondents were pessimistic.

As of April 13, 2024, South Korea had the highest number of coronavirus recoveries among the selected economies in the Asia-Pacific region, with about 34.5 million recoveries. Australia had the second highest number of coronavirus recoveries among the economies with available data in the Asia-Pacific region, with over 11.8 million recoveries as of April 13, 2024.

According to data from McKinsey, ** percent of respondents from Italy said that their personal income reduced either slightly or reduced a lot as a result of the COVID-19 pandemic.

Among COVID-19 patients in the United States from February 12 to March 16, 2020, estimated case-fatality rates were highest for adults aged 85 years and older. Younger people appeared to have milder symptoms, and there were no deaths reported among persons aged 19 years and under.

Tracking the virus in the United States The outbreak of a previously unknown viral pneumonia was first reported in China toward the end of December 2019. The first U.S. case of COVID-19 was recorded in mid-January 2020, confirmed in a patient who had returned to the United States from China. The virus quickly started to spread, and the first community-acquired case was confirmed one month later in California. Overall, there had been approximately 4.5 million coronavirus cases in the country by the start of August 2020.

U.S. health care system stretched California, Florida, and Texas are among the states with the most coronavirus cases. Even the best-resourced hospitals in the United States have struggled to cope with the crisis, and certain areas of the country were dealt further blows by new waves of infections in July 2020. Attention is rightly focused on fighting the pandemic, but as health workers are redirected to care for COVID-19 patients, the United States must not lose sight of other important health care issues.

Context

This is a Covid 19 data set for India. The data set is updated frequently and is analysed using tableau. Click on the link to visit the tableau story. Click each of the caption in the story to unveil its content.

https://public.tableau.com/profile/ambili.nair#!/vizhome/COVID19Indiastory/Indiastory?publish=yes

The first Covid 19 case in India was reported on 30th January 2020 in South Indian state of Kerala on a medical student who was pursuing the studies at Wuhan University, China. Two more students were found to be infected in Kerala in the consecutive days. The Kerala government was successful in containing the disease with its proactive measures back then. The second outbreak of Covid 19 in India started in the first week of March from various parts of India in various people who visited the foreign countries and in some of the tourists from different countries.

The tableau story consists of the following data analysis : 1. State-wise number of infected and number of death count in India map. Hover the mouse on each state in the India map to know the count. 2. Click on the next caption to know the state-wise number of confirmed, active, recovered and deceased cases in the form of bar chart. 3. The next caption takes you to the bar chart which shows the number of cases getting confirmed in India each day starting from January 30, 2020. 4. Next caption takes us to an analysis of the Mortality rate and the Recovery rate (in percentage) of each of the Indian state. We get an idea how hard each of the state is hit by the pandemic. 5. Next caption gives a detailed analysis of the state Kerala which has the mortality rate of 0.806% and the recovery rate of 74.4% as of now. Hover the mouse to know the count in each district. Don't forget to have a look at the line graph of 'number of active cases' in Kerala. It looks almost flattened ! As everyday we hear the increasing number of cases and deaths across the country, this graph may make you feel better...! 6. Finally the caption takes you to the statistics from the topmost district of Kerala - Kasaragod. The total number of cases reported is 179 at Kasaragod. The active number of cases is just 12 as of now... !!! Have a look at the statistics from Kasaragod and the story of 'Kasaragod model' as some of the national media in India call it !!!

Content

This data set consists of the following data: 1. state-wise statistics - Confirmed, Active, Recovered, Deceased cases 2. day-wise count of infected and deceased from various states 3. Statistics from Kerala - day-wise count of confirmed, Active, Recovered, Deceased cases 4. Statistics from Kasaragod district, Kerala - day-wise count of confirmed, Active, Recovered, Deceased cases 5. Count of confirmed cases from various districts of India

Acknowledgements

Ministry of Health and Family Welfare - India covid19india.org Wikipedia page - Covid 19 Pandemic India Govt. of Kerala dashboard - official Kerala Covid 19 statistics

Inspiration

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

This 6MB download is a zip file containing 5 pdf documents and 2 xlsx spreadsheets. Presentation on COVID-19 and the potential impacts on employment

May 2020Waka Kotahi wants to better understand the potential implications of the COVID-19 downturn on the land transport system, particularly the potential impacts on regional economies and communities.

To do this, in May 2020 Waka Kotahi commissioned Martin Jenkins and Infometrics to consider the potential impacts of COVID-19 on New Zealand’s economy and demographics, as these are two key drivers of transport demand. In addition to providing a scan of national and international COVID-19 trends, the research involved modelling the economic impacts of three of the Treasury’s COVID-19 scenarios, to a regional scale, to help us understand where the impacts might be greatest.

Waka Kotahi studied this modelling by comparing the percentage difference in employment forecasts from the Treasury’s three COVID-19 scenarios compared to the business as usual scenario.

The source tables from the modelling (Tables 1-40), and the percentage difference in employment forecasts (Tables 41-43), are available as spreadsheets.

Arataki - potential impacts of COVID-19 Final Report

Employment modelling - interactive dashboard

The modelling produced employment forecasts for each region and district over three time periods – 2021, 2025 and 2031. In May 2020, the forecasts for 2021 carried greater certainty as they reflected the impacts of current events, such as border restrictions, reduction in international visitors and students etc. The 2025 and 2031 forecasts were less certain because of the potential for significant shifts in the socio-economic situation over the intervening years. While these later forecasts were useful in helping to understand the relative scale and duration of potential COVID-19 related impacts around the country, they needed to be treated with care recognising the higher levels of uncertainty.

The May 2020 research suggested that the ‘slow recovery scenario’ (Treasury’s scenario 5) was the most likely due to continuing high levels of uncertainty regarding global efforts to manage the pandemic (and the duration and scale of the resulting economic downturn).

The updates to Arataki V2 were framed around the ‘Slower Recovery Scenario’, as that scenario remained the most closely aligned with the unfolding impacts of COVID-19 in New Zealand and globally at that time.

Find out more about Arataki, our 10-year plan for the land transport system

May 2021The May 2021 update to employment modelling used to inform Arataki Version 2 is now available. Employment modelling dashboard - updated 2021Arataki used the May 2020 information to compare how various regions and industries might be impacted by COVID-19. Almost a year later, it is clear that New Zealand fared better than forecast in May 2020.Waka Kotahi therefore commissioned an update to the projections through a high-level review of:the original projections for 2020/21 against performancethe implications of the most recent global (eg International monetary fund world economic Outlook) and national economic forecasts (eg Treasury half year economic and fiscal update)The treasury updated its scenarios in its December half year fiscal and economic update (HYEFU) and these new scenarios have been used for the revised projections.Considerable uncertainty remains about the potential scale and duration of the COVID-19 downturn, for example with regards to the duration of border restrictions, update of immunisation programmes. The updated analysis provides us with additional information regarding which sectors and parts of the country are likely to be most impacted. We continue to monitor the situation and keep up to date with other cross-Government scenario development and COVID-19 related work. The updated modelling has produced employment forecasts for each region and district over three time periods - 2022, 2025, 2031.The 2022 forecasts carry greater certainty as they reflect the impacts of current events. The 2025 and 2031 forecasts are less certain because of the potential for significant shifts over that time.

Data reuse caveats: as per license.

Additionally, please read / use this data in conjunction with the Infometrics and Martin Jenkins reports, to understand the uncertainties and assumptions involved in modelling the potential impacts of COVID-19.

COVID-19’s effect on industry and regional economic outcomes for NZ Transport Agency [PDF 620 KB]

Data quality statement: while the modelling undertaken is high quality, it represents two point-in-time analyses undertaken during a period of considerable uncertainty. This uncertainty comes from several factors relating to the COVID-19 pandemic, including:

a lack of clarity about the size of the global downturn and how quickly the international economy might recover differing views about the ability of the New Zealand economy to bounce back from the significant job losses that are occurring and how much of a structural change in the economy is required the possibility of a further wave of COVID-19 cases within New Zealand that might require a return to Alert Levels 3 or 4.

While high levels of uncertainty remain around the scale of impacts from the pandemic, particularly in coming years, the modelling is useful in indicating the direction of travel and the relative scale of impacts in different parts of the country.

Data quality caveats: as noted above, there is considerable uncertainty about the potential scale and duration of the COVID-19 downturn. Please treat the specific results of the modelling carefully, particularly in the forecasts to later years (2025, 2031), given the potential for significant shifts in New Zealand's socio-economic situation before then.

As such, please use the modelling results as a guide to the potential scale of the impacts of the downturn in different locations, rather than as a precise assessment of impacts over the coming decade.

The monthly excess mortality indicator is based on the exceptional data collection on weekly deaths that Eurostat and the National Statistical Institutes set up, in April 2020, in order to support the policy and research efforts related to the COVID-19 pandemic. With that data collection, Eurostat's target was to provide quickly statistics assessing the changing situation of the total number of deaths on a weekly basis, from early 2020 onwards.

The National Statistical Institutes transmit available data on total weekly deaths, classified by sex, 5-year age groups and NUTS3 regions (NUTS2021) over the last 20 years, on a voluntary basis. The resulting online tables, and complementary metadata, are available in the folder Weekly deaths - special data collection (demomwk).

Starting in 2025, the weekly deaths data collected on a quarterly basis. The database updated on the 16th of June 2025 (1st quarter), on the 16 th of September 2025 (2nd quarter), and next update will be in mid-December 2025 (3rd quarter), and mid-February 2026 (4th quarter).

In December 2020, Eurostat released the European Recovery Statistical Dashboard containing also indicators tracking economic and social developments, including health. In this context, “excess mortality” offers elements for monitoring and further analysing direct and indirect effects of the COVID-19 pandemic.

The monthly excess mortality indicator draws attention to the magnitude of the crisis by providing a comprehensive comparison of additional deaths amongst the European countries and allowing for further analysis of its causes. The number of deaths from all causes is compared with the expected number of deaths during a certain period in the past (baseline period, 2016-2019).

The reasons that excess mortality may vary according to different phenomena are that the indicator is comparing the total number of deaths from all causes with the expected number of deaths during a certain period in the past (baseline). While a substantial increase largely coincides with a COVID-19 outbreak in each country, the indicator does not make a distinction between causes of death. Similarly, it does not take into account changes over time and differences between countries in terms of the size and age/sex structure of the population Statistics on excess deaths provide information about the burden of mortality potentially related to the COVID-19 pandemic, thereby covering not only deaths that are directly attributed to the virus but also those indirectly related to or even due to another reason. For example, In July 2022, several countries recorded unusually high numbers of excess deaths compared to the same month of 2020 and 2021, a situation probably connected not only to COVID-19 but also to the heatwaves that affected parts of Europe during the reference period.

In addition to confirmed deaths, excess mortality captures COVID-19 deaths that were not correctly diagnosed and reported, as well as deaths from other causes that may be attributed to the overall crisis. It also accounts for the partial absence of deaths from other causes like accidents that did not occur due, for example, to the limitations in commuting or travel during the lockdown periods.

Explore global COVID-19 data with interactive charts and visualisations. Compare countries, analyse daily trends, and understand mortality and recovery rates. Beginner-friendly, visually appealing, and perfect for learning data analysis with Python and Plot.