Among the ten major virus outbreaks in the last 50 years, Marburg ranked first in terms of the fatality rate with 80 percent. In comparison, the recent novel coronavirus, originating from the Chinese city of Wuhan, had an estimated fatality rate of 2.2 percent as of January 31, 2020.

Alarming COVID-19 fatality rate in Mexico More than 812,000 people worldwide had died from COVID-19 as of August 24, 2020. Three of the most populous countries in the world have reported particularly large numbers of coronavirus-related deaths: Mexico, Brazil, and the United States. Out of those three nations, Mexico has the highest COVID-19 death rate, with around one in ten confirmed cases resulting in death. The high fatality rate in Mexico indicates that cases may be much higher than reported because testing capacity has been severely stretched.

Post-lockdown complacency a real danger In March 2020, each infected person was estimated to transmit the COVID-19 virus to between 1.5 and 3.5 other people, which was a higher infection rate than the seasonal flu. The coronavirus is primarily spread through respiratory droplets, and transmission commonly occurs when people are in close contact. As lockdowns ease around the world, people are being urged not to become complacent; continue to wear face coverings and practice social distancing, which can help to prevent further infections.

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.

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 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.

Note: This COVID-19 data set is no longer being updated as of December 1, 2023. Access current COVID-19 data on the CDPH respiratory virus dashboard (https://www.cdph.ca.gov/Programs/CID/DCDC/Pages/Respiratory-Viruses/RespiratoryDashboard.aspx) or in open data format (https://data.chhs.ca.gov/dataset/respiratory-virus-dashboard-metrics).

As of August 17, 2023, data is being updated each Friday.

For death data after December 31, 2022, California uses Provisional Deaths from the Center for Disease Control and Prevention’s National Center for Health Statistics (NCHS) National Vital Statistics System (NVSS). Prior to January 1, 2023, death data was sourced from the COVID-19 registry. The change in data source occurred in July 2023 and was applied retroactively to all 2023 data to provide a consistent source of death data for the year of 2023.

As of May 11, 2023, data on cases, deaths, and testing is being updated each Thursday. Metrics by report date have been removed, but previous versions of files with report date metrics are archived below.

All metrics include people in state and federal prisons, US Immigration and Customs Enforcement facilities, US Marshal detention facilities, and Department of State Hospitals facilities. Members of California's tribal communities are also included.

The "Total Tests" and "Positive Tests" columns show totals based on the collection date. There is a lag between when a specimen is collected and when it is reported in this dataset. As a result, the most recent dates on the table will temporarily show NONE in the "Total Tests" and "Positive Tests" columns. This should not be interpreted as no tests being conducted on these dates. Instead, these values will be updated with the number of tests conducted as data is received.

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.

Monthly COVID-19 death rates per 100,000 population stratified by age group, race/ethnicity, sex, and region, with race/ethnicity by age group and age group by race/ethnicity double stratification

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.

BackgroundIn a given population the age pattern of mortality is an important determinant of total number of deaths, age structure, and through effects on age structure, the number of births and thereby growth. Good mortality models exist for most populations except those experiencing generalized HIV epidemics and some developing country populations. The large number of deaths concentrated at very young and adult ages in HIV-affected populations produce a unique ‘humped’ age pattern of mortality that is not reproduced by any existing mortality models. Both burden of disease reporting and population projection methods require age-specific mortality rates to estimate numbers of deaths and produce plausible age structures. For countries with generalized HIV epidemics these estimates should take into account the future trajectory of HIV prevalence and its effects on age-specific mortality. In this paper we present a parsimonious model of age-specific mortality for countries with generalized HIV/AIDS epidemics.Methods and FindingsThe model represents a vector of age-specific mortality rates as the weighted sum of three independent age-varying components. We derive the age-varying components from a Singular Value Decomposition of the matrix of age-specific mortality rate schedules. The weights are modeled as a function of HIV prevalence and one of three possible sets of inputs: life expectancy at birth, a measure of child mortality, or child mortality with a measure of adult mortality. We calibrate the model with 320 five-year life tables for each sex from the World Population Prospects 2010 revision that come from the 40 countries of the world that have and are experiencing a generalized HIV epidemic. Cross validation shows that the model is able to outperform several existing model life table systems.ConclusionsWe present a flexible, parsimonious model of age-specific mortality for countries with generalized HIV epidemics. Combined with the outputs of existing epidemiological and demographic models, this model makes it possible to project future age-specific mortality profiles and number of deaths for countries with generalized HIV epidemics.

This study explored the change in mortality rates of respiratory disease during the corona virus disease 2019 (COVID-19) pandemic. Death data of registered residents of Suzhou from 2014 to 2020 were collected and the weekly mortality rates due to respiratory disease and all deaths were analyzed. The differences in mortality rates during the pandemic and the same period in previous years were compared. Before the pandemic, the crude mortality rate (CMR) and standardized mortality rate (SMR) of Suzhou residents including respiratory disease, were not much different from those in previous years. During the emergency period, the CMR of Suzhou residents was 180.2/100,000 and the SMR was 85.5/100,000, decreasing by 9.1% and 14.6%, respectively; the CMR of respiratory disease was 16.4/100,000 and the SMR was 6.8/100,000, down 41.4% and 44.9%, respectively. Regardless of the mortality rates of all deaths or respiratory disease, the rates were higher in males than in females, although males had aslightly greater decrease in all deaths during the emergency period compared with females, and the opposite was true for respiratory disease. During the pandemic, the death rate of residents decreased, especially that due to respiratory disease.

NNDSS - Table II. Varicella to West Nile virus disease - 2014.In this Table, all conditions with a 5-year average annual national total of more than or equals 1,000 cases but less than or equals 10,000 cases will be displayed (��� 1,000 and ��_ 10,000). The Table includes total number of cases reported in the United States, by region and by states, in accordance with the current method of displaying MMWR data. Data on United States exclude counts from US territories. Note:These are provisional cases of selected national notifiable diseases, from the National Notifiable Diseases Surveillance System (NNDSS). NNDSS data reported by the 50 states, New York City, the District of Columbia, and the U.S. territories are collated and published weekly as numbered tables printed in the back of the Morbidity and Mortality Weekly Report (MMWR). Cases reported by state health departments to CDC for weekly publication are provisional because of ongoing revision of information and delayed reporting. Case counts in this table are presented as they were published in the MMWR issues. Therefore, numbers listed in later MMWR weeks may reflect changes made to these counts as additional information becomes available. Footnotes:C.N.M.I.: Commonwealth of Northern Mariana Islands. U: Unavailable. -: No reported cases. N: Not reportable. NN: Not Nationally Notifiable Cum: Cumulative year-to-date counts. Med: Median. Max: Maximum. * Case counts for reporting years 2013 and 2014 are provisional and subject to change. For further information on interpretation of these data, see http://wwwn.cdc.gov/nndss/document/ProvisionalNationaNotifiableDiseasesSurveillanceData20100927.pdf. Data for TB are displayed in Table IV, which appears quarterly. ��� Updated weekly from reports to the Division of Vector-Borne Infectious Diseases, National Center for Zoonotic, Vector-Borne, and Enteric Diseases (ArboNet Surveillance). Data for California serogroup, eastern equine, Powassan, St. Louis, and western equine diseases are available in Table I. �� Not reportable in all states. Data from states where the condition is not reportable are excluded from this table, except starting in 2007 for the Arboviral diseases and influenza-associated pediatric mortality, and in 2003 for SARS-CoV. Reporting exceptions are available at http://wwwn.cdc.gov/nndss/document/SRCA_FINAL_REPORT_2006-2012_final.xlsx.More information on NNDSS is available at http://wwwn.cdc.gov/nndss/.

As of March 10, 2023, the death rate from COVID-19 in the state of New York was 397 per 100,000 people. New York is one of the states with the highest number of COVID-19 cases.

Highly pathogenic avian influenza virus (HPAIV) has caused extensive mortalities in wild birds, with a disproportionate impact on raptors since 2021. The population-level impact of HPAIV can be informed by telemetry studies that track large samples of initially healthy, wild birds. We leveraged movement data from 71 rough-legged hawks (Buteo lagopus) across all major North American migratory bird flyways concurrent with the 2022–2023 HPAIV outbreak and identified a total of 29 mortalities, of which 11 were confirmed, and an additional ~9 were estimated to have been caused by HPAIV. We estimated a 28% HPAIV cause-specific mortality rate among rough-legged hawks during a single year concurrent with the HPAIV outbreak in North America. Additionally, the overall annual mortality rate during the HPAIV outbreak (47%) was significantly higher than baseline annual mortality rates (3–17%), suggesting that HPAIV-caused deaths were additive above baseline mortality levels. HPAIV mortalities were concentrated within the Central and Atlantic flyways during pre-breeding migration and peaked in April 2022 when large-scale HPAIV mortalities were reported in other wild birds throughout North America. HPAIV exposure was most likely caused by scavenging or preying on infected waterfowl, as rough-legged hawks are known to opportunistically scavenge during the nonbreeding season. We utilized movement data to identify a continental-scale HPAIV cause-specific mortality event in rough-legged hawks that has the potential to exacerbate ongoing population declines. Our study highlights the usefulness of monitoring movement data to pinpoint sources of mortality that can help better understand the drivers of population change, even if studies are focused on other research questions.

*Sp  =  Species (CK  =  Chicken, DK  =  Duck, SPR  =  Sparrow); FR  =  Fatality Rate (%); MDT  =  Mean Death Time (days); MVL  =  Mean Viral Load (no. RNA copies) per mL of swab's supernatant (Swabs), per gram of feathers (Feather), per gram of organ (Organ); NA  =  Not Applicable.†MVL reported here include data recorded from lethally infected birds only.‡All values appearing in the # column correspond to the 7 different experimental settings described in Table 1: A  =  Sparrows' ability to transmit H5N1 virus to each other when freed in the isolator (A.1) or caged (A.2.); B.1 & B.2  =  Sparrows' ability to transmit H5N1 virus to chickens when freed in the isolator (B.1) or caged (B.2); B.3 & B.4  =  Chickens' susceptibility to H5N1 when inoculated with 105.23 EID50, dose determined as representing the total amount of virus shed by 24 infected sparrows during one hour through their feces, through direct inoculation (B.3.) or through exposure to contaminated water (B.4); C  =  Sparrows' ability to be contaminated through contact with infected ducks.

Note: This COVID-19 data set is no longer being updated as of December 1, 2023. Access current COVID-19 data on the CDPH respiratory virus dashboard (https://www.cdph.ca.gov/Programs/CID/DCDC/Pages/Respiratory-Viruses/RespiratoryDashboard.aspx) or in open data format (https://data.chhs.ca.gov/dataset/respiratory-virus-dashboard-metrics). As of August 17, 2023, data is being updated each Friday. For death data after December 31, 2022, California uses Provisional Deaths from the Center for Disease Control and Prevention’s National Center for Health Statistics (NCHS) National Vital Statistics System (NVSS). Prior to January 1, 2023, death data was sourced from the COVID-19 registry. The change in data source occurred in July 2023 and was applied retroactively to all 2023 data to provide a consistent source of death data for the year of 2023. As of May 11, 2023, data on cases, deaths, and testing is being updated each Thursday. Metrics by report date have been removed, but previous versions of files with report date metrics are archived below. All metrics include people in state and federal prisons, US Immigration and Customs Enforcement facilities, US Marshal detention facilities, and Department of State Hospitals facilities. Members of California's tribal communities are also included. The "Total Tests" and "Positive Tests" columns show totals based on the collection date. There is a lag between when a specimen is collected and when it is reported in this dataset. As a result, the most recent dates on the table will temporarily show NONE in the "Total Tests" and "Positive Tests" columns. This should not be interpreted as no tests being conducted on these dates. Instead, these values will be updated with the number of tests conducted as data is received.

The Colorado Death Tables dataset contains data and statistics as published to the Viral Respiratory Diseases website for deaths due to COVID-19 per county. The data in this file updates each Wednesday and includes the following fields for Colorado resident demographic data and total deaths due to COVID-19 in the State of Colorado reported from Colorado Vital Statistics by month from January 2020 through the most current date:section: (Death)subsection: (Age, Historical Trends, Month, Pediatric Deaths, Race/Ethnicity, Sex, Summary)level: (Statewide)metric: (definitions corresponding to published demographic values)pathogen: (COVID-19)date: (By Month)mmwr_week: (By Month)countratepublish_date (date that all of the published values in this dataset were calculated/assembled and published)For more information, data definitions, and context, please visit Colorado’s Viral Respiratory Diseases data website (https://cdphe.colorado.gov/viral-respiratory-diseases-report).

This dataset reports the daily reported number of deaths involving COVID-19 by fatality type.

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
• Total number of deaths involving COVID-19
• Number of deaths with “COVID-19 as the underlying cause of death”
• Number of deaths with “COVID-19 contributed but not underlying cause”
• Number of deaths where the “Cause of death unknown” or “Cause of death missing”

Additional Notes

The method used to count COVID-19 deaths has changed, effective December 1, 2022. Prior to December 1 2022, deaths were counted based on the date the death was updated in the public health unit’s system. Going forward, deaths are counted on the date they occurred.

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.

As of December 1, 2022, data are based on the date on which the death occurred. This reporting method differs from the prior method which is based on net change in COVID-19 deaths reported day over day.

Data are based on net change in COVID-19 deaths for which COVID-19 caused the death reported day over day. Deaths are not reported by the date on which death happened as reporting may include deaths that happened on previous dates.

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 number of deaths involving COVID-19 reported.

"_Cause of death unknown_" is the category of death for COVID-19 positive individuals with cause of death still under investigation, or for which the public health unit was unable to determine cause of death. The category may change later when the cause of death is confirmed either as “COVID-19 as the underlying cause of death”, “COVID-19 contributed but not underlying cause,” or “COVID-19 unrelated”.

"_Cause of death missing_" is the category of death for COVID-19 positive individuals with the cause of death missing in CCM.

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 Colorado COVID-19 Deaths by County dataset contains statistics for deaths due to COVID-19 per county for counties with a population greater than 15,000 people and is reported on Colorado’s Viral Respiratory Diseases data website. The data in this file updates each Wednesday and includes the following fields for Colorado county's total deaths due to COVID-19 in the State of Colorado reported from Colorado Vital Statistics for the year of 2023:County: (All 64 Colorado counties, Unknown)Year: (2023)Deaths_Due_to_COVID_19: (death count)publish_dateFor more information, data definitions, and context, please visit Colorado’s Viral Respiratory Diseases data website (https://cdphe.colorado.gov/viral-respiratory-diseases-report).

VSD48 - COVID-19 (virus identified) and COVID-19 (virus not identified) Deaths Registered. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).COVID-19 (virus identified) and COVID-19 (virus not identified) Deaths Registered...

This file contains the provisional percent of total deaths by week for COVID-19, Influenza, and Respiratory Syncytial Virus for deaths occurring among residents in the United States. Provisional data are based on non-final counts of deaths based on the flow of mortality data in National Vital Statistics System.