As of May 2, 2023, the outbreak of the coronavirus disease (COVID-19) had spread to almost every country in the world, and more than 6.86 million people had died after contracting the respiratory virus. Over 1.16 million of these deaths occurred in the United States.

Waves of infections Almost every country and territory worldwide have been affected by the COVID-19 disease. At the end of 2021 the virus was once again circulating at very high rates, even in countries with relatively high vaccination rates such as the United States and Germany. As rates of new infections increased, some countries in Europe, like Germany and Austria, tightened restrictions once again, specifically targeting those who were not yet vaccinated. However, by spring 2022, rates of new infections had decreased in many countries and restrictions were once again lifted.

What are the symptoms of the virus? It can take up to 14 days for symptoms of the illness to start being noticed. The most commonly reported symptoms are a fever and a dry cough, leading to shortness of breath. The early symptoms are similar to other common viruses such as the common cold and flu. These illnesses spread more during cold months, but there is no conclusive evidence to suggest that temperature impacts the spread of the SARS-CoV-2 virus. Medical advice should be sought if you are experiencing any of these symptoms.

The World Health Organization reported 6932591 Coronavirus Deaths since the epidemic began. In addition, countries reported 766440796 Coronavirus Cases. This dataset provides - World Coronavirus Deaths- actual values, historical data, forecast, chart, statistics, economic calendar and news.

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

The difficulties of death figures

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

Where are these numbers coming from?

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

COVID-19 rate of death, or the known deaths divided by confirmed cases, was over ten percent in Yemen, the only country that has 1,000 or more cases. This according to a calculation that combines coronavirus stats on both deaths and registered cases for 221 different countries. Note that death rates are not the same as the chance of dying from an infection or the number of deaths based on an at-risk population. By April 26, 2022, the virus had infected over 510.2 million people worldwide, and led to a loss of 6.2 million. The source seemingly does not differentiate between "the Wuhan strain" (2019-nCOV) of COVID-19, "the Kent mutation" (B.1.1.7) that appeared in the UK in late 2020, the 2021 Delta variant (B.1.617.2) from India or the Omicron variant (B.1.1.529) from South Africa.

Where are these numbers coming from?

The numbers shown here were collected by Johns Hopkins University, a source that manually checks the data with domestic health authorities. For the majority of countries, this is from national authorities. In some cases, like China, the United States, Canada or Australia, city reports or other various state authorities were consulted. In this statistic, these separately reported numbers were put together. Note that Statista aims to also provide domestic source material for a more complete picture, and not to just look at one particular source. Examples are these statistics on the confirmed coronavirus cases in Russia or the COVID-19 cases in Italy, both of which are from domestic sources. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

A word on the flaws of numbers like this

People are right to ask whether these numbers are at all representative or not for several reasons. First, countries worldwide decide differently on who gets tested for the virus, meaning that comparing case numbers or death rates could to some extent be misleading. Germany, for example, started testing relatively early once the country’s first case was confirmed in Bavaria in January 2020, whereas Italy tests for the coronavirus postmortem. Second, not all people go to see (or can see, due to testing capacity) a doctor when they have mild symptoms. Countries like Norway and the Netherlands, for example, recommend people with non-severe symptoms to just stay at home. This means not all cases are known all the time, which could significantly alter the death rate as it is presented here. Third and finally, numbers like this change very frequently depending on how the pandemic spreads or the national healthcare capacity. It is therefore recommended to look at other (freely accessible) content that dives more into specifics, such as the coronavirus testing capacity in India or the number of hospital beds in the UK. Only with additional pieces of information can you get the full picture, something that this statistic in its current state simply cannot provide.

Daily updates of Covid-19 Global Excess Deaths from the Economist's GitHub repository: https://github.com/TheEconomist/covid-19-the-economist-global-excess-deaths-model

Interpreting estimates

Estimating excess deaths for every country every day since the pandemic began is a complex and difficult task. Rather than being overly confident in a single number, limited data means that we can often only give a very very wide range of plausible values. Focusing on central estimates in such cases would be misleading: unless ranges are very narrow, the 95% range should be reported when possible. The ranges assume that the conditions for bootstrap confidence intervals are met. Please see our tracker page and methodology for more information.

New variants

The Omicron variant, first detected in southern Africa in November 2021, appears to have characteristics that are different to earlier versions of sars-cov-2. Where this variant is now dominant, this change makes estimates uncertain beyond the ranges indicated. Other new variants may do the same. As more data is incorporated from places where new variants are dominant, predictions improve.

Non-reporting countries

Turkmenistan and the Democratic People's Republic of Korea have not reported any covid-19 figures since the start of the pandemic. They also have not published all-cause mortality data. Exports of estimates for the Democratic People's Republic of Korea have been temporarily disabled as it now issues contradictory data: reporting a significant outbreak through its state media, but zero confirmed covid-19 cases/deaths to the WHO.

Acknowledgements

A special thanks to all our sources and to those who have made the data to create these estimates available. We list all our sources in our methodology. Within script 1, the source for each variable is also given as the data is loaded, with the exception of our sources for excess deaths data, which we detail in on our free-to-read excess deaths tracker as well as on GitHub. The gradient booster implementation used to fit the models is aGTBoost, detailed here.

Calculating excess deaths for the entire world over multiple years is both complex and imprecise. We welcome any suggestions on how to improve the model, be it data, algorithm, or logic. If you have one, please open an issue.

The Economist would also like to acknowledge the many people who have helped us refine the model so far, be it through discussions, facilitating data access, or offering coding assistance. A special thanks to Ariel Karlinsky, Philip Schellekens, Oliver Watson, Lukas Appelhans, Berent Å. S. Lunde, Gideon Wakefield, Johannes Hunger, Carol D'Souza, Yun Wei, Mehran Hosseini, Samantha Dolan, Mollie Van Gordon, Rahul Arora, Austin Teda Atmaja, Dirk Eddelbuettel and Tom Wenseleers.

All coding and data collection to construct these models (and make them update dynamically) was done by Sondre Ulvund Solstad. Should you have any questions about them after reading the methodology, please open an issue or contact him at sondresolstad@economist.com.

Suggested citation The Economist and Solstad, S. (corresponding author), 2021. The pandemic’s true death toll. [online] The Economist. Available at: https://www.economist.com/graphic-detail/coronavirus-excess-deaths-estimates [Accessed ---]. First published in the article "Counting the dead", The Economist, issue 20, 2021.

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.

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

As of April 13, 2024, India had the highest number of confirmed deaths due to the outbreak of the novel coronavirus in the Asia-Pacific region, with over 533 thousand deaths. Comparatively, Indonesia, which had the second highest number of coronavirus deaths in the Asia-Pacific region, recorded approximately 162 thousand COVID-19 related deaths as of April 13, 2024. Contrastingly, Bhutan had reported 21 deaths due to COVID-19 as of April 13, 2024.

Understanding Global Excess Deaths During the COVID-19 Pandemic: Insights from The Economist Data

The COVID-19 pandemic has left an indelible mark on societies worldwide, not only through its direct impact on health but also through its ripple effects on various aspects of life. As we strive to comprehend the full extent of its toll, one crucial metric that emerges is excess deaths – a measure encompassing not only confirmed COVID-19 fatalities but also those indirectly caused by the pandemic. In this discourse, we delve into the comprehensive dataset provided by The Economist and processed by Our World in Data, shedding light on the central estimates and uncertainty intervals of global excess deaths.

The dataset, meticulously compiled and analyzed by The Economist, serves as a cornerstone for understanding the broader implications of the pandemic beyond official death counts. This invaluable resource, available for public scrutiny and further research, offers insights into the nuanced dynamics of excess mortality across different regions and timeframes.

Central to our exploration are the central estimates provided by The Economist, representing the best approximation of excess deaths attributable to the pandemic. These figures, derived through rigorous statistical methodologies, provide a foundational understanding of the pandemic's impact on mortality rates globally. By accounting for excess deaths beyond what would typically be expected, these estimates paint a clearer picture of the true toll of COVID-19.

Accompanying these central estimates are uncertainty intervals, reflecting the range within which the true value of excess deaths is likely to fall. As with any statistical analysis, uncertainties abound, stemming from various factors such as data collection methods, reporting inconsistencies, and the inherent complexity of modeling excess mortality. Acknowledging these uncertainties is paramount in interpreting the data accurately and avoiding overgeneralizations or misinterpretations.

Delving deeper into the dataset, it becomes evident that the magnitude of excess deaths varies significantly across different regions and time periods. Factors such as healthcare infrastructure, socio-economic disparities, and the stringency of public health measures exert profound influences on mortality outcomes. By dissecting these variations, policymakers and public health experts can glean invaluable insights to inform targeted interventions and mitigate future crises.

Moreover, the dataset underscores the interconnectedness of global health, highlighting how the impact of the pandemic transcends geographical boundaries. As nations grapple with containing the spread of the virus within their borders, the ripple effects of excess mortality reverberate across the international community. This interconnectedness underscores the importance of collective action and solidarity in addressing not only the immediate challenges posed by the pandemic but also the long-term ramifications on global health security.

It is essential to note that behind every data point lies a human story – a life lost, a family shattered, a community grieving. Amidst the statistical analyses and epidemiological models, it is imperative not to lose sight of the human dimension of the pandemic. Each excess death represents more than just a number; it embodies a profound loss and underscores the urgency of concerted efforts to prevent further tragedies.

In conclusion, the dataset provided by The Economist and processed by Our World in Data offers a comprehensive lens through which to understand the complexities of excess mortality during the COVID-19 pandemic. By interrogating the central estimates and uncertainty intervals, we gain critical insights into the multifaceted dimensions of the pandemic's impact on global mortality rates. Moving forward, leveraging these insights to inform evidence-based policies and interventions is paramount in mitigating the ongoing crisis and building resilient health systems for the future.

Trends in Covid total deaths per million. The latest data for over 100 countries around the world.

Provisional deaths registration data for single year of age and average age of death (median and mean) of persons whose death involved coronavirus (COVID-19), England and Wales. Includes deaths due to COVID-19 and breakdowns by sex.

This dataset reports the daily reported number of the 7-day moving average rates of Deaths involving COVID-19 by vaccination status and by age group. Learn how the Government of Ontario is helping to keep Ontarians safe during the 2019 Novel Coronavirus outbreak. Effective November 14, 2024 this page will no longer be updated. Information about COVID-19 and other respiratory viruses is available on Public Health Ontario’s interactive respiratory virus tool: https://www.publichealthontario.ca/en/Data-and-Analysis/Infectious-Disease/Respiratory-Virus-Tool Data includes: * Date on which the death occurred * Age group * 7-day moving average of the last seven days of the death rate per 100,000 for those not fully vaccinated * 7-day moving average of the last seven days of the death rate per 100,000 for those fully vaccinated * 7-day moving average of the last seven days of the death rate per 100,000 for those vaccinated with at least one booster ##Additional notes As of June 16, all COVID-19 datasets will be updated weekly on Thursdays by 2pm. As of January 12, 2024, data from the date of January 1, 2024 onwards reflect updated population estimates. This update specifically impacts data for the 'not fully vaccinated' category. On November 30, 2023 the count of COVID-19 deaths was updated to include missing historical deaths from January 15, 2020 to March 31, 2023. CCM is a dynamic disease reporting system which allows ongoing update to data previously entered. As a result, data extracted from CCM represents a snapshot at the time of extraction and may differ from previous or subsequent results. Public Health Units continually clean up COVID-19 data, correcting for missing or overcounted cases and deaths. These corrections can result in data spikes and current totals being different from previously reported cases and deaths. Observed trends over time should be interpreted with caution for the most recent period due to reporting and/or data entry lags. The data does not include vaccination data for people who did not provide consent for vaccination records to be entered into the provincial COVaxON system. This includes individual records as well as records from some Indigenous communities where those communities have not consented to including vaccination information in COVaxON. “Not fully vaccinated” category includes people with no vaccine and one dose of double-dose vaccine. “People with one dose of double-dose vaccine” category has a small and constantly changing number. The combination will stabilize the results. Spikes, negative numbers and other data anomalies: Due to ongoing data entry and data quality assurance activities in Case and Contact Management system (CCM) file, Public Health Units continually clean up COVID-19, correcting for missing or overcounted cases and deaths. These corrections can result in data spikes, negative numbers and current totals being different from previously reported case and death counts. Public Health Units report cause of death in the CCM based on information available to them at the time of reporting and in accordance with definitions provided by Public Health Ontario. The medical certificate of death is the official record and the cause of death could be different. Deaths are defined per the outcome field in CCM marked as “Fatal”. Deaths in COVID-19 cases identified as unrelated to COVID-19 are not included in the Deaths involving COVID-19 reported. Rates for the most recent days are subject to reporting lags All data reflects totals from 8 p.m. the previous day. This dataset is subject to change.

The seven-day average number of COVID-19 deaths in the U.S. decreased significantly from April to July 2020, but it remained higher than in other countries. Seven-day rolling averages are used to adjust for administrative delays in the reporting of deaths by authorities, commonly over weekends.

The challenges of tracking and reporting the disease The U.S. confirmed its first coronavirus case in mid-January 2020 – the virus was detected in a passenger who arrived in Seattle from China. Since that first case, around 945 people have died every day from COVID-19 in the United States as of August 23, 2020. In total, the U.S. has recorded more coronavirus deaths than any other country worldwide. Accurately tracking the number of COVID-19 deaths has proved complicated, with countries having different rules for what deaths to include in their official figures. Some nations have even changed which deaths they can attribute to the disease during the pandemic.

Young people urged to act responsibly Between January and May 2020, case fatality rates among COVID-19 patients in the United States increased with age, highlighting the particular risks faced by the elderly. However, COVID-19 is not only a disease that affects older adults. Surges in the number of new cases throughout July 2020 were blamed on young people. The World Health Organization has urged young people not to become complacent, reminding them to maintain social distancing guidelines and take precautions to protect themselves and others.

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...

Contain informative data related to COVID-19 pandemic. Specially, figure out about the First Case and First Death information for every single country. The datasets mainly focus on two major fields first one is First Case which consists of information of Date of First Case(s), Number of confirm Case(s) at First Day, Age of the patient(s) of First Case, Last Visited Country and the other one First Death information consist of Date of First Death and Age of the Patient who died first for every Country mentioning corresponding Continent. The datasets also contain the Binary Matrix of spread chain among different country and region.

*This is not a country. This is a ship. The name of the Cruise Ship was not given from the government.
"N+": the age is not specified but greater than N
“No Trace”: some data was not found
“Unspecified”: not available from the authority
“N/A”: for “Last Visited Country(s) of Confirmed Case(s)” column, “N/A” indicates that the confirmed case(s) of those countries do not have any travel history in recent past; in “Age of First Death(s)” column “N/A” indicates that those countries do not have may death case till May 16, 2020.

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

Abstract Mortality statistics due to COVID-19 worldwide are compared, by adjusting for the size of the population and the stage of the pandemic. Data from the European Centre for Disease Control and Prevention, and Our World in Data websites were used. Analyses are based on number of deaths per one million inhabitants. In order to account for the stage of the pandemic, the baseline date was defined as the day in which the 10th death was reported. The analyses included 78 countries and territories which reported 10 or more deaths by April 9. On day 10, India had 0.06 deaths per million, Belgium had 30.46 and San Marino 618.78. On day 20, India had 0.27 deaths per million, China had 0.71 and Spain 139.62. On day 30, four Asian countries had the lowest mortality figures, whereas eight European countries had the highest ones. In Italy and Spain, mortality on day 40 was greater than 250 per million, whereas in China and South Korea, mortality was below 4 per million. Mortality on day 10 was moderately correlated with life expectancy, but not with population density. Asian countries presented much lower mortality figures as compared to European ones. Life expectancy was found to be correlated with mortality.

Updates

• Notice of data discontinuation: Since the start of the pandemic, AP has reported case and death counts from data provided by Johns Hopkins University. Johns Hopkins University has announced that they will stop their daily data collection efforts after March 10. As Johns Hopkins stops providing data, the AP will also stop collecting daily numbers for COVID cases and deaths. The HHS and CDC now collect and visualize key metrics for the pandemic. AP advises using those resources when reporting on the pandemic going forward.

  • CDC Weekly case and death counts (national and state level)
  • CDC County level cases and deaths
  • HHS New hospital admissions
  • CDC NowCast COVID variant proportions (national and regional level)

• April 9, 2020

  • The population estimate data for New York County, NY has been updated to include all five New York City counties (Kings County, Queens County, Bronx County, Richmond County and New York County). This has been done to match the Johns Hopkins COVID-19 data, which aggregates counts for the five New York City counties to New York County.

• April 20, 2020

  • Johns Hopkins death totals in the US now include confirmed and probable deaths in accordance with CDC guidelines as of April 14. One significant result of this change was an increase of more than 3,700 deaths in the New York City count. This change will likely result in increases for death counts elsewhere as well. The AP does not alter the Johns Hopkins source data, so probable deaths are included in this dataset as well.

• April 29, 2020

  • The AP is now providing timeseries data for counts of COVID-19 cases and deaths. The raw counts are provided here unaltered, along with a population column with Census ACS-5 estimates and calculated daily case and death rates per 100,000 people. Please read the updated caveats section for more information.

• September 1st, 2020

  • Johns Hopkins is now providing counts for the five New York City counties individually.

• February 12, 2021

  • The Ohio Department of Health recently announced that as many as 4,000 COVID-19 deaths may have been underreported through the state’s reporting system, and that the "daily reported death counts will be high for a two to three-day period."
  • Because deaths data will be anomalous for consecutive days, we have chosen to freeze Ohio's rolling average for daily deaths at the last valid measure until Johns Hopkins is able to back-distribute the data. The raw daily death counts, as reported by Johns Hopkins and including the backlogged death data, will still be present in the new_deaths column.

• February 16, 2021

  - Johns Hopkins has reconciled Ohio's historical deaths data with the state.

  Overview

The AP is using data collected by the Johns Hopkins University Center for Systems Science and Engineering as our source for outbreak caseloads and death counts for the United States and globally.

The Hopkins data is available at the county level in the United States. The AP has paired this data with population figures and county rural/urban designations, and has calculated caseload and death rates per 100,000 people. Be aware that caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.

This data is from the Hopkins dashboard that is updated regularly throughout the day. Like all organizations dealing with data, Hopkins is constantly refining and cleaning up their feed, so there may be brief moments where data does not appear correctly. At this link, you’ll find the Hopkins daily data reports, and a clean version of their feed.

The AP is updating this dataset hourly at 45 minutes past the hour.

To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

Queries

Use AP's queries to filter the data or to join to other datasets we've made available to help cover the coronavirus pandemic

• Filter cases by state here

• Rank states by their status as current hotspots. Calculates the 7-day rolling average of new cases per capita in each state: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=481e82a4-1b2f-41c2-9ea1-d91aa4b3b1ac

• Find recent hotspots within your state by running a query to calculate the 7-day rolling average of new cases by capita in each county: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=b566f1db-3231-40fe-8099-311909b7b687&showTemplatePreview=true

• Join county-level case data to an earlier dataset released by AP on local hospital capacity here. To find out more about the hospital capacity dataset, see the full details.

• Pull the 100 counties with the highest per-capita confirmed cases here

• Rank all the counties by the highest per-capita rate of new cases in the past 7 days here. Be aware that because this ranks per-capita caseloads, very small counties may rise to the very top, so take into account raw caseload figures as well.

Interactive

The AP has designed an interactive map to track COVID-19 cases reported by Johns Hopkins.

@(https://datawrapper.dwcdn.net/nRyaf/15/)

Interactive Embed Code

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Caveats

• This data represents the number of cases and deaths reported by each state and has been collected by Johns Hopkins from a number of sources cited on their website.
• In some cases, deaths or cases of people who've crossed state lines -- either to receive treatment or because they became sick and couldn't return home while traveling -- are reported in a state they aren't currently in, because of state reporting rules.
• In some states, there are a number of cases not assigned to a specific county -- for those cases, the county name is "unassigned to a single county"
• This data should be credited to Johns Hopkins University's COVID-19 tracking project. The AP is simply making it available here for ease of use for reporters and members.
• Caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.
• Population estimates at the county level are drawn from 2014-18 5-year estimates from the American Community Survey.
• The Urban/Rural classification scheme is from the Center for Disease Control and Preventions's National Center for Health Statistics. It puts each county into one of six categories -- from Large Central Metro to Non-Core -- according to population and other characteristics. More details about the classifications can be found here.

Johns Hopkins timeseries data - Johns Hopkins pulls data regularly to update their dashboard. Once a day, around 8pm EDT, Johns Hopkins adds the counts for all areas they cover to the timeseries file. These counts are snapshots of the latest cumulative counts provided by the source on that day. This can lead to inconsistencies if a source updates their historical data for accuracy, either increasing or decreasing the latest cumulative count. - Johns Hopkins periodically edits their historical timeseries data for accuracy. They provide a file documenting all errors in their timeseries files that they have identified and fixed here

Attribution

This data should be credited to Johns Hopkins University COVID-19 tracking project

Note: DPH is updating and streamlining the COVID-19 cases, deaths, and testing data. As of 6/27/2022, the data will be published in four tables instead of twelve.

The COVID-19 Cases, Deaths, and Tests by Day dataset contains cases and test data by date of sample submission. The death data are by date of death. This dataset is updated daily and contains information back to the beginning of the pandemic. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Cases-Deaths-and-Tests-by-Day/g9vi-2ahj.

The COVID-19 State Metrics dataset contains over 93 columns of data. This dataset is updated daily and currently contains information starting June 21, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-State-Level-Data/qmgw-5kp6 .

The COVID-19 County Metrics dataset contains 25 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-County-Level-Data/ujiq-dy22 .

The COVID-19 Town Metrics dataset contains 16 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Town-Level-Data/icxw-cada . To protect confidentiality, if a town has fewer than 5 cases or positive NAAT tests over the past 7 days, those data will be suppressed.

COVID-19 cases and associated deaths that have been reported among Connecticut residents, broken down by race and ethnicity. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. Deaths reported to the either the Office of the Chief Medical Examiner (OCME) or Department of Public Health (DPH) are included in the COVID-19 update.

The following data show the number of COVID-19 cases and associated deaths per 100,000 population by race and ethnicity. Crude rates represent the total cases or deaths per 100,000 people. Age-adjusted rates consider the age of the person at diagnosis or death when estimating the rate and use a standardized population to provide a fair comparison between population groups with different age distributions. Age-adjustment is important in Connecticut as the median age of among the non-Hispanic white population is 47 years, whereas it is 34 years among non-Hispanic blacks, and 29 years among Hispanics. Because most non-Hispanic white residents who died were over 75 years of age, the age-adjusted rates are lower than the unadjusted rates. In contrast, Hispanic residents who died tend to be younger than 75 years of age which results in higher age-adjusted rates.

The population data used to calculate rates is based on the CT DPH population statistics for 2019, which is available online here: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Population/Population-Statistics. Prior to 5/10/2021, the population estimates from 2018 were used.

Rates are standardized to the 2000 US Millions Standard population (data available here: https://seer.cancer.gov/stdpopulations/). Standardization was done using 19 age groups (0, 1-4, 5-9, 10-14, ..., 80-84, 85 years and older). More information about direct standardization for age adjustment is available here: https://www.cdc.gov/nchs/data/statnt/statnt06rv.pdf

Categories are mutually exclusive. The category “multiracial” includes people who answered ‘yes’ to more than one race category. Counts may not add up to total case counts as data on race and ethnicity may be missing. Age adjusted rates calculated only for groups with more than 20 deaths. Abbreviation: NH=Non-Hispanic.

Data on Connecticut deaths were obtained from the Connecticut Deaths Registry maintained by the DPH Office of Vital Records. Cause of death was determined by a death certifier (e.g., physician, APRN, medical examiner) using their best clinical judgment. Additionally, all COVID-19 deaths, including suspected or related, are required to be reported to OCME. On April 4, 2020, CT DPH and OCME released a joint memo to providers and facilities within Connecticut providing guidelines for certifying deaths due to COVID-19 that were consistent with the CDC’s guidelines and a reminder of the required reporting to OCME.25,26 As of July 1, 2021, OCME had reviewed every case reported and performed additional investigation on about one-third of reported deaths to better ascertain if COVID-19 did or did not cause or contribute to the death. Some of these investigations resulted in the OCME performing postmortem swabs for PCR testing on individuals whose deaths were suspected to be due to COVID-19, but antemortem diagnosis was unable to be made.31 The OCME issued or re-issued about 10% of COVID-19 death certificates and, when appropriate, removed COVID-19 from the death certificate. For standardization and tabulation of mortality statistics, written cause of death statements made by the certifiers on death certificates are sent to the National Center for Health Statistics (NCHS) at the CDC which assigns cause of death codes according to the International Causes of Disease 10th Revision (ICD-10) classification system.25,26 COVID-19 deaths in this report are defined as those for which the death certificate has an ICD-10 code of U07.1 as either a primary (underlying) or a contributing cause of death. More information on COVID-19 mortality can be found at the following link: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Mortality/Mortality-Statistics

Data are subject to future revision as reporting changes.

Starting in July 2020, this dataset will be updated every weekday.

Additional notes: A delay in the data pull schedule occurred on 06/23/2020. Data from 06/22/2020 was processed on 06/23/2020 at 3:30 PM. The normal data cycle resumed with the data for 06/23/2020.

A network outage on 05/19/2020 resulted in a change in the data pull schedule. Data from 5/19/2020 was processed on 05/20/2020 at 12:00 PM. Data from 5/20/2020 was processed on 5/20/2020 8:30 PM. The normal data cycle resumed on 05/20/2020 with the 8:30 PM data pull. As a result of the network outage, the timestamp on the datasets on the Open Data Portal differ from the timestamp in DPH's daily PDF reports.

Starting 5/10/2021, the date field will represent the date this data was updated on data.ct.gov. Previously the date the data was pulled by DPH was listed, which typically coincided with the date before the data was published on data.ct.gov. This change was made to standardize the COVID-19 data sets on data.ct.gov.

Age-standardised mortality rates for deaths involving coronavirus (COVID-19), non-COVID-19 deaths and all deaths by vaccination status, broken down by age group.