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

Since the first reported coronavirus case in Washington State on Jan. 21, 2020, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.

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

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

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.

Effective September 27, 2023, this dataset will no longer be updated. Similar data are accessible from wonder.cdc.gov.

Deaths involving COVID-19, pneumonia, and influenza reported to NCHS by sex, age group, and jurisdiction of occurrence.

This file contains COVID-19 death counts, death rates, and percent of total deaths by jurisdiction of residence. The data is grouped by different time periods including 3-month period, weekly, and total (cumulative since January 1, 2020). United States death counts and rates include the 50 states, plus the District of Columbia and New York City. New York state estimates exclude New York City. Puerto Rico is included in HHS Region 2 estimates. Deaths with confirmed or presumed COVID-19, coded to ICD–10 code U07.1. Number of deaths reported in this file are the total number of COVID-19 deaths received and coded as of the date of analysis and may not represent all deaths that occurred in that period. Counts of deaths occurring before or after the reporting period are not included in the file. Data during recent periods are incomplete because of the lag in time between when the death occurred and when the death certificate is completed, submitted to NCHS and processed for reporting purposes. This delay can range from 1 week to 8 weeks or more, depending on the jurisdiction and cause of death. Death counts should not be compared across states. Data timeliness varies by state. Some states report deaths on a daily basis, while other states report deaths weekly or monthly. The ten (10) United States Department of Health and Human Services (HHS) regions include the following jurisdictions. Region 1: Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, Vermont; Region 2: New Jersey, New York, New York City, Puerto Rico; Region 3: Delaware, District of Columbia, Maryland, Pennsylvania, Virginia, West Virginia; Region 4: Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South Carolina, Tennessee; Region 5: Illinois, Indiana, Michigan, Minnesota, Ohio, Wisconsin; Region 6: Arkansas, Louisiana, New Mexico, Oklahoma, Texas; Region 7: Iowa, Kansas, Missouri, Nebraska; Region 8: Colorado, Montana, North Dakota, South Dakota, Utah, Wyoming; Region 9: Arizona, California, Hawaii, Nevada; Region 10: Alaska, Idaho, Oregon, Washington. Rates were calculated using the population estimates for 2021, which are estimated as of July 1, 2021 based on the Blended Base produced by the US Census Bureau in lieu of the April 1, 2020 decennial population count. The Blended Base consists of the blend of Vintage 2020 postcensal population estimates, 2020 Demographic Analysis Estimates, and 2020 Census PL 94-171 Redistricting File (see https://www2.census.gov/programs-surveys/popest/technical-documentation/methodology/2020-2021/methods-statement-v2021.pdf). Rates are based on deaths occurring in the specified week/month and are age-adjusted to the 2000 standard population using the direct method (see https://www.cdc.gov/nchs/data/nvsr/nvsr70/nvsr70-08-508.pdf). These rates differ from annual age-adjusted rates, typically presented in NCHS publications based on a full year of data and annualized weekly/monthly age-adjusted rates which have been adjusted to allow comparison with annual rates. Annualization rates presents deaths per year per 100,000 population that would be expected in a year if the observed period specific (weekly/monthly) rate prevailed for a full year. Sub-national death counts between 1-9 are suppressed in accordance with NCHS data confidentiality standards. Rates based on death counts less than 20 are suppressed in accordance with NCHS standards of reliability as specified in NCHS Data Presentation Standards for Proportions (available from: https://www.cdc.gov/nchs/data/series/sr_02/sr02_175.pdf.).

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.

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.

United States recorded 1127152 Coronavirus Deaths since the epidemic began, according to the World Health Organization (WHO). In addition, United States reported 103436829 Coronavirus Cases. This dataset includes a chart with historical data for the United States Coronavirus Deaths.

Rank, number of deaths, percentage of deaths, and age-specific mortality rates for the leading causes of death, by age group and sex, 2000 to most recent year.

Afghanistan recorded 218720 Coronavirus Cases since the epidemic began, according to the World Health Organization (WHO). In addition, Afghanistan reported 7908 Coronavirus Deaths. This dataset includes a chart with historical data for Afghanistan Coronavirus Cases.

BackgroundThe deluge of COVID-19 misinformation makes people confused, and acting on such misinformation can kill, leading to the tragic outcome of death. This makes it necessary to identify significant factors associated with college students’ susceptibility.ObjectiveThis descriptive study sought to ascertain factors significantly associated with college students’ susceptibility to online COVID-19 misinformation.MethodsTo assess college students’ susceptibility to COVID-19 misinformation, we first chose as independent variables some demographic information, some well-developed, validated literacy tools, and the Patient Health Questionnaire-9 Items. Second, we selected as the dependent variable COVID-19 myths from some authoritative, official websites. Third, we integrated the independent and dependent variables into an online questionnaire. Fourth, we recruited students from Nantong University in China to participate in an online questionnaire survey. Finally, based on the data collected, we conducted quantitative and qualitative analyses to relate the independent variables to the dependent variable.ResultsFive hundred forty-six students participated in the survey voluntarily, and all questionnaires they answered were valid. The participants had an average of 2.32 (SD = 0.99) years of higher education. They have a mean age of 20.44 (SD = 1.52) years. 434 (79.5%) of the 546 participants were females. The frequency of their Internet use averaged 3.91 (SD = 0.41), indicating that they logged onto the Internet almost every day. Their self-reported Internet skill was rated 3.79 (SD = 1.07), indicating that the participants rated their Internet skills as basically “good.” The mean scores of the sub-constructs in the AAHLS were 6.14 (SD = 1.37) for functional health literacy, 5.10 (SD = 1.65) for communicative health literacy, and 11.13 (SD = 2.65) for critical health literacy. These mean scores indicated that the participants needed help to read health-related materials “sometimes,” the frequency that they knew how to communicate effectively with professional health providers was between “often” and “sometimes,” and the frequency that they were critical about health information was between “often” and “sometimes,” respectively. The sum of their scores for eHealth literacy averaged 28.29 (SD = 5.31), showing that they had a relatively high eHealth literacy level. The mean score for each question in the GHNT was determined at 1.31 (SD = 0.46), 1.36 (SD = 0.48), 1.41 (SD = 0.49), 1.77 (SD = 0.42), 1.51 (SD = 0.50), and 1.54 (SD = 0.50), respectively. These mean scores showed that a high percentage of the participants answered the 6 questions wrongly, especially Questions 4–6. Similarly, participants performed unsatisfactorily in answering the 3 questions in the CRT, with a mean score of 1.75 (SD = 0.43), 1.55 (SD = 0.50), and 1.59 (SD = 0.49) for each question, respectively. In the PHQ-9, the participants reported that they never felt depressed or felt depressed only for 1–3 days in the past week. The mean score for myths 1–6 and 9–10 ranged from 1.15 (SD = 0.36) to 1.29 (SD = 0.46). This meant that the participants rated these myths false. However, most of the participants rated myths 7–8 true (1.54, SD = 0.50; 1.49, SD = 0.50), showing that they were highly susceptible to these 2 pieces of misinformation. Through data analysis via Logistic Regression (forward stepwise), we found that (1) at an average threshold of 0.5, Internet use frequency, functional health literacy, general health numeracy, reflective thinking tendency, and depression severity were significant predictors of susceptibility to misinformation for both male and female students, (2) at a higher threshold of 0.8, aggregated general health numeracy scores and functional health literacy scores, as well as depression severity were predictors of susceptibility to misinformation for both male and female students, (3) functional health literacy, general health literacy, and depression predicted resistance to misinformation for female students, and (4) internet use frequency and self-reported digital health literacy predicted resistance to misinformation for male students.ConclusionWe revealed the complexity, dynamics, and differences in age, gender, education, Internet exposure, communicative health literacy, and cognitive skills concerning college students’ susceptibility to online COVID-19 misinformation. Hopefully, this study can provide valuable implications for counteracting COVID-19 misinformation among Chinese college students.

Uganda recorded 3626 Coronavirus Deaths since the epidemic began, according to the World Health Organization (WHO). In addition, Uganda reported 170775 Coronavirus Cases. This dataset includes a chart with historical data for Uganda Coronavirus Deaths.

Nigeria recorded 3155 Coronavirus Deaths since the epidemic began, according to the World Health Organization (WHO). In addition, Nigeria reported 266675 Coronavirus Cases. This dataset includes a chart with historical data for Nigeria Coronavirus Deaths.

Ghana recorded 1456 Coronavirus Deaths since the epidemic began, according to the World Health Organization (WHO). In addition, Ghana reported 171653 Coronavirus Cases. This dataset includes a chart with historical data for Ghana Coronavirus Deaths.

South Africa recorded 102595 Coronavirus Deaths since the epidemic began, according to the World Health Organization (WHO). In addition, South Africa reported 4072533 Coronavirus Cases. This dataset includes a chart with historical data for South Africa Coronavirus Deaths.

India recorded 531794 Coronavirus Deaths since the epidemic began, according to the World Health Organization (WHO). In addition, India reported 44983152 Coronavirus Cases. This dataset includes a chart with historical data for India Coronavirus Deaths.