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

Description

Dive into the dynamic narrative of the 'Global Covid Trend' dataset, capturing the ebb and flow of COVID-19 cases worldwide. Uncover trends, patterns, and vital statistics meticulously compiled to paint a comprehensive picture of the pandemic's journey across the globe.

Key Features

• Province/State: Province or state of the location
• Country/Region: Country or region of the location
• Lat: Latitude coordinate
• Long: Longitude coordinate
• 1/22/2020 - 1/2/2022: Daily COVID-19 cases from January 22, 2020, to February 2, 2022

Sample Data

Dataset Information

• Data Format: CSV (Comma-Separated Values)

It's important to note that the data does not contain any personally identifiable information (PII) to ensure privacy and comply with ethical guidelines.

If this was helpful, a vote is appreciated 🙂❤️

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 Stats & Trends

Context

This dataset seeks to provide insights into what has changed due to policies aimed at combating COVID-19 and evaluate the changes in community activities and its relation to reduced confirmed cases of COVID-19. The reports chart movement trends, compared to an expected baseline, over time (from 2020/02/15 to 2020/02/05) by geography (across 133 countries), as well as some other stats about the country that might help explain the evolution of the disease.

Content

1. Grocery & Pharmacy: Mobility trends for places like grocery markets, food warehouses, farmers' markets, specialty food shops, drug stores, and pharmacies.
2. Parks: Mobility trends for places like national parks, public beaches, marinas, dog parks, plazas, and public gardens.
3. Residential: Mobility trends for places of residence.
4. Retail & Recreation: Mobility trends for places like restaurants, cafes, shopping centers, theme parks, museums, libraries, and movie theaters.
5. Transit stations: Mobility trends for places like public transport hubs such as subway, bus, and train stations.
6. Workplaces: Mobility trends for places of work.
7. Total Cases: Total number of people infected with the SARS-CoV-2.
8. Fatalities: Total number of deaths caused by CoV-19.
9. Government Response Stringency Index: Additive score of nine indicators of government response to CoV-19: School closures, workplace closures, cancellation of public events, public information campaigns, stay at home policies, restrictions on internal movement, international travel controls, testing policy, and contact tracing.
10. COVID-19 Testing: Total number of tests performed.
11. Total Vaccinations: Total number of shots given.
12. Total People Vaccinated: Total number of people given a shot.
13. Total People Fully Vaccinated: Total number of people fully vaccinated (might require two shots of some vaccines).
14. Population: Total number of inhabitants.
15. Population Density per km2: Number of human inhabitants per square kilometer.
16. Health System Index: Overall performance of the health system.
17. Human Development Index (HDI): Summary index based on life expectancy at birth, expected years of schooling for children and mean years of schooling for adults, and GNI per capita.
18. GDP (PPP) per capita: Gross Domestic Product (GDP) per capita based on Purchasing Power Parity (PPP), taking into account the relative cost of local goods, services and inflation rates of the country, rather than using international market exchange rates, which may distort the real differences in per capita income.
19. Elderly Population (percentage): Percentage of the population above the age of 65 years old.

References & Acknowledgements

Bing COVID-19 data. Available at: https://github.com/microsoft/Bing-COVID-19-Data COVID-19 Community Mobility Report. Available at: https://www.google.com/covid19/mobility/ COVID-19: Government Response Stringency Index. Available at: https://ourworldindata.org/grapher/covid-stringency-index Coronavirus (COVID-19) Testing. Available at: https://github.com/owid/covid-19-data/blob/master/public/data/testing/covid-testing-all-observations.csv Coronavirus (COVID-19) Vaccination. Available at: https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/vaccinations/vaccinations.csv List of countries and dependencies by population. Available at: https://www.kaggle.com/tanuprabhu/population-by-country-2020 List of countries and dependencies by population density. Available at: https://www.kaggle.com/tanuprabhu/population-by-country-2020 List of countries by Human Development Index. Available at: http://hdr.undp.org/en/data Measuring Overall Health System Performance. Available at: https://www.who.int/healthinfo/paper30.pdf?ua=1 List of countries by GDP (PPP) per capita. Available at: https://data.worldbank.org/indicator/NY.GDP.PCAP.PP.CD List of countries by age structure (65+). Available at: https://data.worldbank.org/indicator/SP.POP.65UP.TO.ZS

Authors

• Diogo Silva, up201706892@fe.up.pt

Reporting of Aggregate Case and Death Count data was discontinued on May 11, 2023, with the expiration of the COVID-19 public health emergency declaration. Although these data will continue to be publicly available, this dataset will no longer be updated.

The surveillance case definition for COVID-19, a nationally notifiable disease, was first described in a position statement from the Council for State and Territorial Epidemiologists, which was later revised. However, there is some variation in how jurisdictions implemented these case definitions. More information on how CDC collects COVID-19 case surveillance data can be found at FAQ: COVID-19 Data and Surveillance.

Aggregate Data Collection Process Since the beginning of the COVID-19 pandemic, data were reported from state and local health departments through a robust process with the following steps:

• Aggregate county-level counts were obtained indirectly, via automated overnight web collection, or directly, via a data submission process.
• If more than one official county data source existed, CDC used a comprehensive data selection process comparing each official county data source to retrieve the highest case and death counts, unless otherwise specified by the state.
• A CDC data team reviewed counts for congruency prior to integration and set up alerts to monitor for discrepancies in the data.
• CDC routinely compiled these data and post the finalized information on COVID Data Tracker.
• County level data were aggregated to obtain state- and territory- specific totals.
• Counting of cases and deaths is based on date of report and not on the date of symptom onset. CDC calculates rates in these data by using population estimates provided by the US Census Bureau Population Estimates Program (2019 Vintage).
• COVID-19 aggregate case and death data are organized in a time series that includes cumulative number of cases and deaths as reported by a jurisdiction on a given date. New case and death counts are calculated as the week-to-week change in cumulative counts of cases and deaths reported (i.e., newly reported cases and deaths = cumulative number of cases/deaths reported this week minus the cumulative total reported the prior week.

This process was collaborative, with CDC and jurisdictions working together to ensure the accuracy of COVID-19 case and death numbers. County counts provided the most up-to-date numbers on cases and deaths by report date. Throughout data collection, CDC retrospectively updated counts to correct known data quality issues.

Description This archived public use dataset focuses on the cumulative and weekly case and death rates per 100,000 persons within various sociodemographic factors across all states and their counties. All resulting data are expressed as rates calculated as the number of cases or deaths per 100,000 persons in counties meeting various classification criteria using the US Census Bureau Population Estimates Program (2019 Vintage).

Each county within jurisdictions is classified into multiple categories for each factor. All rates in this dataset are based on classification of counties by the characteristics of their population, not individual-level factors. This applies to each of the available factors observed in this dataset. Specific factors and their corresponding categories are detailed below.

Population-level factors Each unique population factor is detailed below. Please note that the “Classification” column describes each of the 12 factors in the dataset, including a data dict

The COVID-19 dashboard includes data on city/town COVID-19 activity, confirmed and probable cases of COVID-19, confirmed and probable deaths related to COVID-19, and the demographic characteristics of cases and deaths.

I scraped data from Google Trends relating to serach queries of the virus 'Coronavirus' and 'COVID-19'. This is based on several research articles showing that google trends has the potential to help with the prediction and detection of disease outbreaks, such as the 2015 Zika outbreak, Influenza and Dengue fever. Other search queries such as the symptoms of the virus can in turn be used to make predictions as they should be correlated with people feeling unwell and presenting with symptoms as well as physician visits. I have not yet scraped the data for search queries for symptoms keywords, but would highly encourage someone else to do it!

Values are calculated on a scale from 0 to 100, where 100 is the location with the most popularity as a fraction of total searches in that location, a value of 50 indicates a location which is half as popular. A value of 0 indicates a location where there was not enough data for this term. Note: A higher value means a higher proportion of all queries, not a higher absolute query count. So a tiny country where 80% of the queries are for "covid19" will get twice the score of a giant country where only 40% of the queries are for "covid19".

UPDATED: Keywords 'lockdown', 'symptoms of coronavirus', 'social distancing'

This is an archived dataset & will no longer be updated. Case and Death data related to COVID-19.

As of April 1, 2022 MODHSS is no longer providing negative test data. As a result we will no longer publish total tests per day

Cases are based on the date an individual was tested for COVID-19. Using date tested means counts for most recent dates are likely to change as tests are reported to the the Health Department. Cases include those without an address assigned to KCMO by MODHSS to investigate. Antigen tests are not included at this time. Deaths are based on the date the death was reported to the Health Department.

Additional data available in the link below. Data definitions are also available in the link below.

COVID-19 Global Case and Death Data

Global COVID-19 Cases and Deaths Over Time

By Coronavirus (COVID-19) Data Hub [source]

About this dataset

The COVID-19 Global Time Series Case and Death Data is a comprehensive collection of global COVID-19 case and death information recorded over time. This dataset includes data from various sources such as JHU CSSE COVID-19 Data and The New York Times.

The dataset consists of several columns providing detailed information on different aspects of the COVID-19 situation. The COUNTRY_SHORT_NAME column represents the short name of the country where the data is recorded, while the Data_Source column indicates the source from which the data was obtained.

Other important columns include Cases, which denotes the number of COVID-19 cases reported, and Difference, which indicates the difference in case numbers compared to the previous day. Additionally, there are columns such as CONTINENT_NAME, DATA_SOURCE_NAME, COUNTRY_ALPHA_3_CODE, COUNTRY_ALPHA_2_CODE that provide additional details about countries and continents.

Furthermore, this dataset also includes information on deaths related to COVID-19. The column PEOPLE_DEATH_NEW_COUNT shows the number of new deaths reported on a specific date.

To provide more context to the data, certain columns offer demographic details about locations. For instance, Population_Count provides population counts for different areas. Moreover,**FIPS** code is available for provincial/state regions for identification purposes.

It is important to note that this dataset covers both confirmed cases (Case_Type: confirmed) as well as probable cases (Case_Type: probable). These classifications help differentiate between various types of COVID-19 infections.

Overall, this dataset offers a comprehensive picture of global COVID-19 situations by providing accurate and up-to-date information on cases, deaths, demographic details like population count or FIPS code), source references (such as JHU CSSE or NY Times), geographical information (country names coded with ALPHA codes) , etcetera making it useful for researchers studying patterns and trends associated with this pandemic

How to use the dataset

• Understanding the Dataset Structure:

  • The dataset is available in two files: COVID-19 Activity.csv and COVID-19 Cases.csv.
  • Both files contain different columns that provide information about the COVID-19 cases and deaths.
  • Some important columns to look out for are: a. PEOPLE_POSITIVE_CASES_COUNT: The total number of confirmed positive COVID-19 cases. b. COUNTY_NAME: The name of the county where the data is recorded. c. PROVINCE_STATE_NAME: The name of the province or state where the data is recorded. d. REPORT_DATE: The date when the data was reported. e. CONTINENT_NAME: The name of the continent where the data is recorded. f. DATA_SOURCE_NAME: The name of the data source. g. PEOPLE_DEATH_NEW_COUNT: The number of new deaths reported on a specific date. h.COUNTRY_ALPHA_3_CODE :The three-letter alpha code represents country f.Lat,Long :latitude and longitude coordinates represent location i.Country_Region or COUNTRY_SHORT_NAME:The country or region where cases were reported.

• Choosing Relevant Columns: It's important to determine which columns are relevant to your analysis or research question before proceeding with further analysis.

• Exploring Data Patterns: Use various statistical techniques like summarizing statistics, creating visualizations (e.g., bar charts, line graphs), etc., to explore patterns in different variables over time or across regions/countries.

• Filtering Data: You can filter your dataset based on specific criteria using column(s) such as COUNTRY_SHORT_NAME, CONTINENT_NAME, or PROVINCE_STATE_NAME to focus on specific countries, continents, or regions of interest.

• Combining Data: You can combine data from different sources (e.g., COVID-19 cases and deaths) to perform advanced analysis or create insightful visualizations.

• Analyzing Trends: Use the dataset to analyze and identify trends in COVID-19 cases and deaths over time. You can examine factors such as population count, testing count, hospitalization count, etc., to gain deeper insights into the impact of the virus.

• Comparing Countries/Regions: Compare COVID-19

Research Ideas

• Trend Analysis: This dataset can be used to analyze and track the trends of COVID-19 cases and deaths over time. It provides comprehensive global data, allowing researchers and po...

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.

As global communities responded to COVID-19, we heard from public health officials that the same type of aggregated, anonymized insights we use in products such as Google Maps would be helpful as they made critical decisions to combat COVID-19. These Community Mobility Reports aimed to provide insights into what changed in response to policies aimed at combating COVID-19. The reports charted movement trends over time by geography, across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential.

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: U.S. Centers for Disease Control and Prevention (CDC)For more information, visit the Johns Hopkins Coronavirus Resource Center.This map is updated weekly. It shows COVID-19 Trend for the most recent Monday with a colored dot for each county. The larger the dot, the longer the county has had this trend. Includes Puerto Rico, Guam, Northern Marianas, U.S. Virgin Islands.Note: Nebraska Stopped reporting county level-results on 5/25/2021 and re-started on 9/26/21 with a lump-sum representing the previous four months - this impacted the weekly sum of cases fields.The intent of this map is to give more context than just the current day of new data because daily data for COVID-19 cases is volatile and can be unreliable on the day it is first reported. Weekly summaries in the counts of new cases smooth out this volatility.Click or tap on a county to see a history of trend changes and a weekly graph of new cases going back to February 1, 2020.For more information about COVID-19 trends, see the full methodology.Data Source: Johns Hopkins University CSSE US Cases by County dashboard.

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: U.S. Centers for Disease Control and Prevention (CDC)For more information, visit the Johns Hopkins Coronavirus Resource Center.This map is updated weekly and currently shows data through March 5, 2023, which will be the final update of this map.Note: Nebraska stopped reporting county level-results on 5/25/2021 and re-started on 9/26/21 with a lump-sum representing the previous four months - this impacted the weekly sum of cases fields.It shows COVID-19 Trend for the most recent Monday with a colored dot for each county. The larger the dot, the longer the county has had this trend. Includes Puerto Rico, Guam, Northern Marianas, U.S. Virgin Islands.The intent of this map is to give more context than just the current day of new data because daily data for COVID-19 cases is volatile and can be unreliable on the day it is first reported. Weekly summaries in the counts of new cases smooth out this volatility. Click or tap on a county to see a history of trend changes and a weekly graph of new cases going back to February 8, 2020. This map is updated every Monday* based on data through the previous Sunday. See also this version of the map for another perspective.COVID-19 Trends show how each county is doing and are updated daily. We base the trend assignment on the number of new cases in the past two weeks and the number of active cases per 100,000 people. To learn the details for how trends are assigned, see the full methodology. There are five trends:Emergent - New cases for the first time or in counties that have had zero new cases for 60 or more days.Spreading - Low to moderate rates of new cases each day. Likely controlled by local policies and individuals taking measures such as wearing masks and curtailing unnecessary activities.Epidemic - Accelerating and uncontrolled rates of new cases.Controlled - Very low rates of new cases.End Stage - One or fewer new cases every 5 days in larger populations and fewer in rural areas.*Starting 8/22/2021 we began updating on Mondays instead of Tuesdays as a result of optimizing the scripts that produce the weekly analysis. For more information about COVID-19 trends, see the full methodology. Data Source: Johns Hopkins University CSSE US Cases by County dashboard and USAFacts for Utah County level Data.

Background: Alongside the COVID-19 pandemic, government authorities around the world have had to face a growing infodemic capable of causing serious damages to public health and economy. In this context, the use of infoveillance tools has become a primary necessity.Objective: The aim of this study is to test the reliability of a widely used infoveillance tool which is Google Trends. In particular, the paper focuses on the analysis of relative search volumes (RSVs) quantifying their dependence on the day they are collected.Methods: RSVs of the query coronavirus + covid during February 1—December 4, 2020 (period 1), and February 20—May 18, 2020 (period 2), were collected daily by Google Trends from December 8 to 27, 2020. The survey covered Italian regions and cities, and countries and cities worldwide. The search category was set to all categories. Each dataset was analyzed to observe any dependencies of RSVs from the day they were gathered. To do this, by calling i the country, region, or city under investigation and j the day its RSV was collected, a Gaussian distribution Xi=X(σi,x¯i) was used to represent the trend of daily variations of xij=RSVsij. When a missing value was revealed (anomaly), the affected country, region or city was excluded from the analysis. When the anomalies exceeded 20% of the sample size, the whole sample was excluded from the statistical analysis. Pearson and Spearman correlations between RSVs and the number of COVID-19 cases were calculated day by day thus to highlight any variations related to the day RSVs were collected. Welch’s t-test was used to assess the statistical significance of the differences between the average RSVs of the various countries, regions, or cities of a given dataset. Two RSVs were considered statistical confident when t

This dataset gives a cursory glimpse at the overall sentiment trend of the public discourse regarding the COVID-19 pandemic on Twitter. The live scatter plot of this dataset is available as The Overall Trend block at https://live.rlamsal.com.np. The trend graph reveals multiple peaks and drops that need further analysis. The n-grams during those peaks and drops can prove beneficial for better understanding the discourse.

Content

This dataset contains data of weekly trend of Covid-19 in Europe (September 13- September 20,2023)

Attribute Information

1. Country/Other
2. Cases in the last 7 days
3. Cases in the preceding 7 days
4. Weekly Case % Change
5. Cases in the last 7 days/1M pop
6. Deaths in the last 7 days
7. Deaths in the preceding 7 days
8. Weekly Death % Change
9. Deaths in the last 7 days/1M pop
10. Population

Source

Link : https://www.worldometers.info/coronavirus/weekly-trends/#weekly_table

Other Updated Covid Datasets

Link : https://www.kaggle.com/anandhuh/datasets

Thank You

Indicators from the Opinions and Lifestyle Survey (OPN) related to the impact of the coronavirus (COVID-19) pandemic and other illnesses on people, households and communities in Great Britain.

As of June 7, 2022, there were 2,523,915 active coronavirus COVID-19 cases and a total of 3,090 deaths registered in Taiwan. Despite the island's proximity to the mainland China, Taiwan had managed to contain the virus before the outbreak of the Delta variant of COVID-19. The success was due to an effective disease control system developed from the experience in the SARS epidemic. The highly contagious Omicron variant had brought a spike in new infections in Taiwan since March 2022.

With the onset of COVID-19, hospitals statewide saw a sharp drop in inpatient discharges, emergency department utilization, and ambulatory surgeries. These datasets contain monthly counts of encounters and in-hospital mortalities in those three settings and are also broken down by the following common health conditions/categories: anxiety, asthma, behavioral syndromes, cancer, cardiac arrest, chronic obstructive pulmonary disease (COPD), COVID-19, depression, diabetes, homeless, hypertension, mood disorders (excluding depression), non-mood psychotic disorders, nonpsychotic disorders (excluding anxiety), obesity, pneumonia, respiratory arrest/failure, sepsis, stroke, substance use disorders, and unspecified mental disorders.

This dataset includes CSV files that contain IDs and sentiment scores of the tweets related to the COVID-19 pandemic. The tweets have been collected by an on-going project deployed at https://live.rlamsal.com.np. The model monitors the real-time Twitter feed for coronavirus-related tweets using 90+ different keywords and hashtags that are commonly used while referencing the pandemic. This dataset has been wholly re-designed on March 20, 2020, to comply with the content redistribution policy set by Twitter.The paper associated with this dataset is available here: Design and analysis of a large-scale COVID-19 tweets dataset-------------------------------------Related datasets:(a) Tweets Originating from India During COVID-19 Lockdowns(b) Coronavirus (COVID-19) Tweets Sentiment Trend (Global)-------------------------------------Below is the quick overview of this dataset.— Dataset name: COV19Tweets Dataset— Number of tweets : 857,809,018 tweets— Coverage : Global— Language : English (EN)— Dataset usage terms : By using this dataset, you agree to (i) use the content of this dataset and the data generated from the content of this dataset for non-commercial research only, (ii) remain in compliance with Twitter's Developer Policy and (iii) cite the following paper:Lamsal, R. Design and analysis of a large-scale COVID-19 tweets dataset. Applied Intelligence (2020). https://doi.org/10.1007/s10489-020-02029-z— Geo-tagged Version: Coronavirus (COVID-19) Geo-tagged Tweets Dataset (GeoCOV19Tweets Dataset)— Dataset updates : Everyday— Active keywords and hashtags (archive: keywords.tsv) : "corona", "#corona", "coronavirus", "#coronavirus", "covid", "#covid", "covid19", "#covid19", "covid-19", "#covid-19", "sarscov2", "#sarscov2", "sars cov2", "sars cov 2", "covid_19", "#covid_19", "#ncov", "ncov", "#ncov2019", "ncov2019", "2019-ncov", "#2019-ncov", "pandemic", "#pandemic" "#2019ncov", "2019ncov", "quarantine", "#quarantine", "flatten the curve", "flattening the curve", "#flatteningthecurve", "#flattenthecurve", "hand sanitizer", "#handsanitizer", "#lockdown", "lockdown", "social distancing", "#socialdistancing", "work from home", "#workfromhome", "working from home", "#workingfromhome", "ppe", "n95", "#ppe", "#n95", "#covidiots", "covidiots", "herd immunity", "#herdimmunity", "pneumonia", "#pneumonia", "chinese virus", "#chinesevirus", "wuhan virus", "#wuhanvirus", "kung flu", "#kungflu", "wearamask", "#wearamask", "wear a mask", "vaccine", "vaccines", "#vaccine", "#vaccines", "corona vaccine", "corona vaccines", "#coronavaccine", "#coronavaccines", "face shield", "#faceshield", "face shields", "#faceshields", "health worker", "#healthworker", "health workers", "#healthworkers", "#stayhomestaysafe", "#coronaupdate", "#frontlineheroes", "#coronawarriors", "#homeschool", "#homeschooling", "#hometasking", "#masks4all", "#wfh", "wash ur hands", "wash your hands", "#washurhands", "#washyourhands", "#stayathome", "#stayhome", "#selfisolating", "self isolating"Dataset Files (the local time mentioned below is GMT+5:45)corona_tweets_01.csv + corona_tweets_02.csv + corona_tweets_03.csv: 2,475,980 tweets (March 20, 2020 01:37 AM - March 21, 2020 09:25 AM)corona_tweets_04.csv: 1,233,340 tweets (March 21, 2020 09:27 AM - March 22, 2020 07:46 AM)corona_tweets_05.csv: 1,782,157 tweets (March 22, 2020 07:50 AM - March 23, 2020 09:08 AM)corona_tweets_06.csv: 1,771,295 tweets (March 23, 2020 09:11 AM - March 24, 2020 11:35 AM)corona_tweets_07.csv: 1,479,651 tweets (March 24, 2020 11:42 AM - March 25, 2020 11:43 AM)corona_tweets_08.csv: 1,272,592 tweets (March 25, 2020 11:47 AM - March 26, 2020 12:46 PM)corona_tweets_09.csv: 1,091,429 tweets (March 26, 2020 12:51 PM - March 27, 2020 11:53 AM)corona_tweets_10.csv: 1,172,013 tweets (March 27, 2020 11:56 AM - March 28, 2020 01:59 PM)corona_tweets_11.csv: 1,141,210 tweets (March 28, 2020 02:03 PM - March 29, 2020 04:01 PM)corona_tweets_12.csv: 793,417 tweets (March 30, 2020 02:01 PM - March 31, 2020 10:16 AM)corona_tweets_13.csv: 1,029,294 tweets (March 31, 2020 10:20 AM - April 01, 2020 10:59 AM)corona_tweets_14.csv: 920,076 tweets (April 01, 2020 11:02 AM - April 02, 2020 12:19 PM)corona_tweets_15.csv: 826,271 tweets (April 02, 2020 12:21 PM - April 03, 2020 02:38 PM)corona_tweets_16.csv: 612,512 tweets (April 03, 2020 02:40 PM - April 04, 2020 11:54 AM)corona_tweets_17.csv: 685,560 tweets (April 04, 2020 11:56 AM - April 05, 2020 12:54 PM)corona_tweets_18.csv: 717,301 tweets (April 05, 2020 12:56 PM - April 06, 2020 10:57 AM)corona_tweets_19.csv: 722,921 tweets (April 06, 2020 10:58 AM - April 07, 2020 12:28 PM)corona_tweets_20.csv: 554,012 tweets (April 07, 2020 12:29 PM - April 08, 2020 12:34 PM)corona_tweets_21.csv: 589,679 tweets (April 08, 2020 12:37 PM - April 09, 2020 12:18 PM)corona_tweets_22.csv: 517,718 tweets (April 09, 2020 12:20 PM - April 10, 2020 09:20 AM)corona_tweets_23.csv: 601,199 tweets (April 10, 2020 09:22 AM - April 11, 2020 10:22 AM)corona_tweets_24.csv: 497,655 tweets (April 11, 2020 10:24 AM - April 12, 2020 10:53 AM)corona_tweets_25.csv: 477,182 tweets (April 12, 2020 10:57 AM - April 13, 2020 11:43 AM)corona_tweets_26.csv: 288,277 tweets (April 13, 2020 11:46 AM - April 14, 2020 12:49 AM)corona_tweets_27.csv: 515,739 tweets (April 14, 2020 11:09 AM - April 15, 2020 12:38 PM)corona_tweets_28.csv: 427,088 tweets (April 15, 2020 12:40 PM - April 16, 2020 10:03 AM)corona_tweets_29.csv: 433,368 tweets (April 16, 2020 10:04 AM - April 17, 2020 10:38 AM)corona_tweets_30.csv: 392,847 tweets (April 17, 2020 10:40 AM - April 18, 2020 10:17 AM)> With the addition of some more coronavirus specific keywords, the number of tweets captured day has increased significantly, therefore, the CSV files hereafter will be zipped. Lets save some bandwidth.corona_tweets_31.csv: 2,671,818 tweets (April 18, 2020 10:19 AM - April 19, 2020 09:34 AM)corona_tweets_32.csv: 2,393,006 tweets (April 19, 2020 09:43 AM - April 20, 2020 10:45 AM)corona_tweets_33.csv: 2,227,579 tweets (April 20, 2020 10:56 AM - April 21, 2020 10:47 AM)corona_tweets_34.csv: 2,211,689 tweets (April 21, 2020 10:54 AM - April 22, 2020 10:33 AM)corona_tweets_35.csv: 2,265,189 tweets (April 22, 2020 10:45 AM - April 23, 2020 10:49 AM)corona_tweets_36.csv: 2,201,138 tweets (April 23, 2020 11:08 AM - April 24, 2020 10:39 AM)corona_tweets_37.csv: 2,338,713 tweets (April 24, 2020 10:51 AM - April 25, 2020 11:50 AM)corona_tweets_38.csv: 1,981,835 tweets (April 25, 2020 12:20 PM - April 26, 2020 09:13 AM)corona_tweets_39.csv: 2,348,827 tweets (April 26, 2020 09:16 AM - April 27, 2020 10:21 AM)corona_tweets_40.csv: 2,212,216 tweets (April 27, 2020 10:33 AM - April 28, 2020 10:09 AM)corona_tweets_41.csv: 2,118,853 tweets (April 28, 2020 10:20 AM - April 29, 2020 08:48 AM)corona_tweets_42.csv: 2,390,703 tweets (April 29, 2020 09:09 AM - April 30, 2020 10:33 AM)corona_tweets_43.csv: 2,184,439 tweets (April 30, 2020 10:53 AM - May 01, 2020 10:18 AM)corona_tweets_44.csv: 2,223,013 tweets (May 01, 2020 10:23 AM - May 02, 2020 09:54 AM)corona_tweets_45.csv: 2,216,553 tweets (May 02, 2020 10:18 AM - May 03, 2020 09:57 AM)corona_tweets_46.csv: 2,266,373 tweets (May 03, 2020 10:09 AM - May 04, 2020 10:17 AM)corona_tweets_47.csv: 2,227,489 tweets (May 04, 2020 10:32 AM - May 05, 2020 10:17 AM)corona_tweets_48.csv: 2,218,774 tweets (May 05, 2020 10:38 AM - May 06, 2020 10:26 AM)corona_tweets_49.csv: 2,164,251 tweets (May 06, 2020 10:35 AM - May 07, 2020 09:33 AM)corona_tweets_50.csv: 2,203,686 tweets (May 07, 2020 09:55 AM - May 08, 2020 09:35 AM)corona_tweets_51.csv: 2,250,019 tweets (May 08, 2020 09:39 AM - May 09, 2020 09:49 AM)corona_tweets_52.csv: 2,273,705 tweets (May 09, 2020 09:55 AM - May 10, 2020 10:11 AM)corona_tweets_53.csv: 2,208,264 tweets (May 10, 2020 10:23 AM - May 11, 2020 09:57 AM)corona_tweets_54.csv: 2,216,845 tweets (May 11, 2020 10:08 AM - May 12, 2020 09:52 AM)corona_tweets_55.csv: 2,264,472 tweets (May 12, 2020 09:59 AM - May 13, 2020 10:14 AM)corona_tweets_56.csv: 2,339,709 tweets (May 13, 2020 10:24 AM - May 14, 2020 11:21 AM)corona_tweets_57.csv: 2,096,878 tweets (May 14, 2020 11:38 AM - May 15, 2020 09:58 AM)corona_tweets_58.csv: 2,214,205 tweets (May 15, 2020 10:13 AM - May 16, 2020 09:43 AM)> The server and the databases have been optimized; therefore, there is a significant rise in the number of tweets captured per day.corona_tweets_59.csv: 3,389,090 tweets (May 16, 2020 09:58 AM - May 17, 2020 10:34 AM)corona_tweets_60.csv: 3,530,933 tweets (May 17, 2020 10:36 AM - May 18, 2020 10:07 AM)corona_tweets_61.csv: 3,899,631 tweets (May 18, 2020 10:08 AM - May 19, 2020 10:07 AM)corona_tweets_62.csv: 3,767,009 tweets (May 19, 2020 10:08 AM - May 20, 2020 10:06 AM)corona_tweets_63.csv: 3,790,455 tweets (May 20, 2020 10:06 AM - May 21, 2020 10:15 AM)corona_tweets_64.csv: 3,582,020 tweets (May 21, 2020 10:16 AM - May 22, 2020 10:13 AM)corona_tweets_65.csv: 3,461,470 tweets (May 22, 2020 10:14 AM - May 23, 2020 10:08 AM)corona_tweets_66.csv: 3,477,564 tweets (May 23, 2020 10:08 AM - May 24, 2020 10:02 AM)corona_tweets_67.csv: 3,656,446 tweets (May 24, 2020 10:02 AM - May 25, 2020 10:10 AM)corona_tweets_68.csv: 3,474,952 tweets (May 25, 2020 10:11 AM - May 26, 2020 10:22 AM)corona_tweets_69.csv: 3,422,960 tweets (May 26, 2020 10:22 AM - May 27, 2020 10:16 AM)corona_tweets_70.csv: 3,480,999 tweets (May 27, 2020 10:17 AM - May 28, 2020 10:35 AM)corona_tweets_71.csv: 3,446,008 tweets (May 28, 2020 10:36 AM - May 29, 2020 10:07 AM)corona_tweets_72.csv: 3,492,841 tweets (May 29, 2020 10:07 AM - May 30, 2020 10:14 AM)corona_tweets_73.csv: 3,098,817 tweets (May 30, 2020 10:15 AM - May 31, 2020 10:13 AM)corona_tweets_74.csv: 3,234,848 tweets (May 31, 2020 10:13 AM - June 01, 2020 10:14 AM)corona_tweets_75.csv: 3,206,132 tweets (June 01, 2020 10:15 AM - June 02, 2020 10:07 AM)corona_tweets_76.csv: 3,206,417 tweets (June 02, 2020 10:08 AM - June 03, 2020 10:26 AM)corona_tweets_77.csv: 3,256,225 tweets (June 03, 2020