Note: Note: Starting October 10th, 2025 this dataset is deprecated and is no longer being updated. As of April 27, 2023 updates changed from daily to weekly. Summary The cumulative number of confirmed COVID-19 deaths among Maryland residents by age: 0-9; 10-19; 20-29; 30-39; 40-49; 50-59; 60-69; 70-79; 80+; Unknown. Description The MD COVID-19 - Confirmed Deaths by Age Distribution data layer is a collection of the statewide confirmed COVID-19 related deaths that have been reported each day by the Vital Statistics Administration by designated age ranges. A death is classified as confirmed if the person had a laboratory-confirmed positive COVID-19 test result. Some data on deaths may be unavailable due to the time lag between the death, typically reported by a hospital or other facility, and the submission of the complete death certificate. Probable deaths are available from the MD COVID-19 - Probable Deaths by Age Distribution data layer. Terms of Use The Spatial Data, and the information therein, (collectively the "Data") is provided "as is" without warranty of any kind, either expressed, implied, or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted, nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct, indirect, incidental, consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data, nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

It was estimated that around 20 percent of those with underlying health conditions who had COVID-19 in the United States from January 22 to May 30, 2020 died from the disease, compared to just 2 percent of COVID-patients without underlying health conditions. Underlying health conditions such as cardiovascular disease, chronic lung disease, or diabetes greatly increase the chance of death due to COVID-19. This statistic shows the percentage of people in the U.S. who had COVID-19 from January 22 to May 30, 2020 with and without underlying health conditions who died, by age.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

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.

Black men and women in the United Kingdom were four times more likely to die from Coronavirus than white people of the same gender as of April 2020. Several other ethnic groups were also at an increased risk from Coronavirus than the white population, with men of Bangladeshi or Pakistani origin 3.6 times more likely, and women 3.4 more likely to die from Coronavirus.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

Note: Starting April 27, 2023 updates change from daily to weekly.

Summary The cumulative number of confirmed COVID-19 deaths among Maryland residents.

Description The MD COVID-19 - Total Confirmed Deaths Statewide data layer is a collection of the statewide confirmed COVID-19 related deaths that have been reported each day by the Vital Statistics Administration. A death is classified as confirmed if the person had a laboratory-confirmed positive COVID-19 test result. Some data on deaths may be unavailable due to the time lag between the death, typically reported by a hospital or other facility, and the submission of the complete death certificate. Probable deaths are available from the MD COVID-19 - Total Probable Deaths Statewide data layer. Update 5/27/21: The Maryland Department of Health (MDH) Vital Statistics Administration (VSA) revised the state’s COVID-19 data to include deaths that were not properly classified by medical certifiers over the past year. VSA identified these deaths as COVID-19 deaths through an information reconciliation process utilizing other sources of data. Learn more: https://health.maryland.gov/newsroom/Pages/Maryland-Department-of-Health-Vital-Statistics-Administration-issues-revision-of-COVID-19-death-data.aspx

Terms of Use The Spatial Data, and the information therein, (collectively the "Data") is provided "as is" without warranty of any kind, either expressed, implied, or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted, nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct, indirect, incidental, consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data, nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

Provisional counts of the number of deaths and age-standardised mortality rates involving the coronavirus (COVID-19), by occupational groups, for deaths registered between 9 March and 28 December 2020 in England and Wales. Figures are provided for males and females.

Data for CDC’s COVID Data Tracker site on Rates of COVID-19 Cases and Deaths by Vaccination Status. Click 'More' for important dataset description and footnotes

Dataset and data visualization details: These data were posted on October 21, 2022, archived on November 18, 2022, and revised on February 22, 2023. These data reflect cases among persons with a positive specimen collection date through September 24, 2022, and deaths among persons with a positive specimen collection date through September 3, 2022.

Vaccination status: A person vaccinated with a primary series had SARS-CoV-2 RNA or antigen detected on a respiratory specimen collected ≥14 days after verifiably completing the primary series of an FDA-authorized or approved COVID-19 vaccine. An unvaccinated person had SARS-CoV-2 RNA or antigen detected on a respiratory specimen and has not been verified to have received COVID-19 vaccine. Excluded were partially vaccinated people who received at least one FDA-authorized vaccine dose but did not complete a primary series ≥14 days before collection of a specimen where SARS-CoV-2 RNA or antigen was detected. Additional or booster dose: A person vaccinated with a primary series and an additional or booster dose had SARS-CoV-2 RNA or antigen detected on a respiratory specimen collected ≥14 days after receipt of an additional or booster dose of any COVID-19 vaccine on or after August 13, 2021. For people ages 18 years and older, data are graphed starting the week including September 24, 2021, when a COVID-19 booster dose was first recommended by CDC for adults 65+ years old and people in certain populations and high risk occupational and institutional settings. For people ages 12-17 years, data are graphed starting the week of December 26, 2021, 2 weeks after the first recommendation for a booster dose for adolescents ages 16-17 years. For people ages 5-11 years, data are included starting the week of June 5, 2022, 2 weeks after the first recommendation for a booster dose for children aged 5-11 years. For people ages 50 years and older, data on second booster doses are graphed starting the week including March 29, 2022, when the recommendation was made for second boosters. Vertical lines represent dates when changes occurred in U.S. policy for COVID-19 vaccination (details provided above). Reporting is by primary series vaccine type rather than additional or booster dose vaccine type. The booster dose vaccine type may be different than the primary series vaccine type. ** Because data on the immune status of cases and associated deaths are unavailable, an additional dose in an immunocompromised person cannot be distinguished from a booster dose. This is a relevant consideration because vaccines can be less effective in this group. Deaths: A COVID-19–associated death occurred in a person with a documented COVID-19 diagnosis who died; health department staff reviewed to make a determination using vital records, public health investigation, or other data sources. Rates of COVID-19 deaths by vaccination status are reported based on when the patient was tested for COVID-19, not the date they died. Deaths usually occur up to 30 days after COVID-19 diagnosis. Participating jurisdictions: Currently, these 31 health departments that regularly link their case surveillance to immunization information system data are included in these incidence rate estimates: Alabama, Arizona, Arkansas, California, Colorado, Connecticut, District of Columbia, Florida, Georgia, Idaho, Indiana, Kansas, Kentucky, Louisiana, Massachusetts, Michigan, Minnesota, Nebraska, New Jersey, New Mexico, New York, New York City (New York), North Carolina, Philadelphia (Pennsylvania), Rhode Island, South Dakota, Tennessee, Texas, Utah, Washington, and West Virginia; 30 jurisdictions also report deaths among vaccinated and unvaccinated people. These jurisdictions represent 72% of the total U.S. population and all ten of the Health and Human Services Regions. Data on cases

Relative risk (RR) of COVID-19 related hospitalization/death.

Understanding the social determinants of Covid-19 infection and death is vital for effective Covid-19 early detection and mitigation strategies. This study aims to examine social determinants of Covid-19 infection and death in the context of rural Indonesia. We used Malang district government Covid-19 contact tracing data from 14,264 individuals, spanning the period from March 1, 2020 to July 29, 2020. The contact tracing data was merged with administrative data from 390 villages to determine whether village characteristics (i.e., the number of health workers, number of community-based healthcare interventions, access to Covid-19 referred hospitals, number of indigenous socio-cultural activities, poverty level and distance to a Covid-19 epicentre city) are associated with Covid-19 infection and death. We used multilevel logistic regression to take advantage of the nested structure of data at the village level. We found among the 14,264 samples, 551 individuals were confirmed infected with Covid-19, and 62 died of Covid-19. Individuals aged 18 and older, civil servants (non-health workers), and those having close contact with people with confirmed cases had a higher likelihood of infection with Covid-19. Greater numbers of community-based healthcare interventions and a lesser distance to a pandemic epicentre reduced the likelihood of infection with the virus. Males, older people, individuals with hypertension, individuals diagnosed with pneumonia, and those diagnosed with respiratory failure had a higher likelihood of death due to Covid-19. A greater number of community-based healthcare interventions seems to reduce the likelihood of Covid-19 infection, while better access to a Covid-19 referred hospital seems to reduce the risk of death among Covid-19 patients. The findings suggest the government to prioritise strategies to control the pandemic in rural area through empowering rural community in health education to prevent Covid-19 and in monitoring people mobility, while providing Covid-19 emergency services for rural areas for reducing mortality.

Abstract We investigated the predictors of delay in the diagnosis and mortality of patients with COVID-19 in Rio de Janeiro, Brazil. A cohort of 3,656 patients were evaluated (Feb-Apr 2020) and patients’ sociodemographic characteristics, and social development index (SDI) were used as determinant factors of diagnosis delays and mortality. Kaplan-Meier survival analyses, time-dependent Cox regression models, and multivariate logistic regression analyses were conducted. The median time from symptoms onset to diagnosis was eight days (interquartile range [IQR] 7.23-8.99 days). Half of the patients recovered during the evaluated period, and 8.3% died. Mortality rates were higher in men. Delays in diagnosis were associated with male gender (p = 0.015) and patients living in low SDI areas (p < 0.001). The age groups statistically associated with death were: 70-79 years, 80-89 years, and 90-99 years. Delays to diagnosis greater than eight days were also risk factors for death. Delays in diagnosis and risk factors for death from COVID-19 were associated with male gender, age under 60 years, and patients living in regions with lower SDI. Delays superior to eight days to diagnosis increased mortality rates.

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.

COVID-19Plots and analysis relating to the coronavirus pandemic. Includes five sets of plots and associated R code to generate them.1) HeatmapsUpdated every few days - heatmaps of COVID-19 case and death trajectories for Local Authorities (or equivalent) in England, Wales, Scotland, Ireland and Germany.2) All cause mortalityUpdated on Tuesday (for England & Wales), Wednesday (for Scotland) and Friday (for Northern Ireland) - analysis and plots of weekly all-cause deaths in 2020 compared to previous years by country, age, sex and region. Also a set of international comparisons using data from mortality.org3) ExposuresNo longer updated - mapping of potential COVID-19 mortality exposure at local levels (LSOAs) in England based on the age-sex structure of the population and levels of poor health.There is also a Shiny app which creates slightly lower resolution versions of the same plots online, which you can find here: https://victimofmaths.shinyapps.io/covidmapper/, on GitHub https://github.com/VictimOfMaths/COVIDmapper and uploaded to this record4) Index of Multiple Deprivation No longer updated - preliminary analysis of the inequality impacts of COVID-19 based on Local Authority level cases and levels of deprivation. 5) Socioeconomic inequalities. No longer updated (unless ONS release more data) - Analysis of published ONS figures of COVID-19 and other cause mortality in 2020 compared to previous years by deprivation decile.Latest versions of plots and associated analysis can be found on Twitter: https://twitter.com/victimofmathsThis work is described in more detail on the UK Data Service Impact and Innovation Lab blog: https://blog.ukdataservice.ac.uk/visualising-high-risk-areas-for-covid-19-mortality/Adapted from data from the Office for National Statistics licensed under the Open Government Licence v.1.0.http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/

Predicted lethal duration, (in days), in different countries due to COVID-19 for the given probability of death (π).

Estimates of the risk of all-cause and cardiac death in the 12 weeks after vaccination or positive SARS-CoV-2 test compared with subsequent weeks for people aged 12 to 29 years in England using two sources of mortality data: ONS death registrations and deaths recorded in Hospital Episode Statistics. 8 December 2020 to 25 May 2022. Experimental Statistics.

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.

As of the end of February 2020 and based on data available from China, the probability of dying as a result of a coronavirus infection was 4.7 percent among confirmed cases in males and 2.8 percent among confirmed cases in females. This statistic depicts the death rate of confirmed and all cases of COVID-19 worldwide as of February 28, 2020, by gender.

Context

Dataset aims to facilitate a state by state comparison of potential risk factors that may heighten Covid 19 transmission rates or deaths. It includes state by state estimates of: covid 19 positives/deaths, flu/pneumonia deaths, major city population densities, available hospital resources, high risk health condition prevalance, population over 60, means of work transportation rates, housing characteristics (ie number of large apartment complexes/seniors living alone), and industry information.

Content

The Data Includes:

1) Covid 19 Outcome Stats:

Covid_Death : Covid Deaths by State

Covid_Positive : Covid Positive Tests by State

2) US Major City Population Density by State: CBSA_Major_City_max_weighted_density

3) KFF Estimates of Total Hospital Beds by State:

Kaiser_Total_Hospital_Beds

4) 2018 Season Flu and Pneumonia Death Stats:

FLUVIEW_TOTAL_PNEUMONIA_DEATHS_Season_2018

FLUVIEW_TOTAL_INFLUENZA_DEATHS_Season_2018

5)US Total Rates of Flu Hospitalization by Underlying Condition:

Fluview_US_FLU_Hospitalization_Rate_....

6) State by State BRFSS Prevalance Rates of Conditions Associated with Higher Flu Hospitalization Rates

BRFSS_Diabetes_Prevalance BRFSS_Asthma_Prevalance BRFSS_COPD_Prevalance
BRFSS_Obesity BMI Prevalance BRFSS_Other_Cancer_Prevalance BRFSS_Kidney_Disease_Prevalance BRFSS_Obesity BMI Prevalance BRFSS_2017_High_Cholestoral_Prevalance BRFSS_2017_High_Blood_Pressure_Prevalance Census_Population_Over_60

7)State by state breakdown of Means of Work Transpotation:

COMMUTE_Census_Worker_Public_Transportation_Rate

8) State by state breakdown of Housing Characteristics

9) State by State breakdown of Industry Information

Acknowledgements

Links to data sources:

https://worldpopulationreview.com/states/

https://covidtracking.com/data/

https://gis.cdc.gov/GRASP/Fluview/FluHospRates.html https://www.kff.org/health-costs/issue-brief/state-data-and-policy-actions-to-address-coronavirus/#stateleveldata

https://data.census.gov/cedsci/table?q=United%20States&tid=ACSDP1Y2018.DP05&hidePreview=true&vintage=2018&layer=VT_2018_040_00_PY_D1&cid=S0103_C01_001E

Census Tables: ACSST1Y2018.S1811 ACSST1Y2018.S0102 ACSST1Y2018.S2403 ACSST1Y2018.S2501 ACSST1Y2018.S2504

https://www.census.gov/library/visualizations/2012/dec/c2010sr-01-density.html

https://gis.cdc.gov/grasp/fluview/mortality.html

Inspiration

I hope to show the existence of correlations that warrant a deeper county by county analysis to identify areas of increased risk requiring increased resource allocation or increased attention to preventative measures.