Effective September 27, 2023, this dataset will no longer be updated. Similar data are accessible from wonder.cdc.gov. Estimates of excess deaths can provide information about the burden of mortality potentially related to COVID-19, beyond the number of deaths that are directly attributed to COVID-19. Excess deaths are typically defined as the difference between observed numbers of deaths and expected numbers. This visualization provides weekly data on excess deaths by jurisdiction of occurrence. Counts of deaths in more recent weeks are compared with historical trends to determine whether the number of deaths is significantly higher than expected. Estimates of excess deaths can be calculated in a variety of ways, and will vary depending on the methodology and assumptions about how many deaths are expected to occur. Estimates of excess deaths presented in this webpage were calculated using Farrington surveillance algorithms (1). For each jurisdiction, a model is used to generate a set of expected counts, and the upper bound of the 95% Confidence Intervals (95% CI) of these expected counts is used as a threshold to estimate excess deaths. Observed counts are compared to these upper bound estimates to determine whether a significant increase in deaths has occurred. Provisional counts are weighted to account for potential underreporting in the most recent weeks. However, data for the most recent week(s) are still likely to be incomplete. Only about 60% of deaths are reported within 10 days of the date of death, and there is considerable variation by jurisdiction. More detail about the methods, weighting, data, and limitations can be found in the Technical Notes.

Dataset description

Daily official UK Covid data. The data is available per country (England, Scotland, Wales and Northern Ireland) and for different regions in England. The different regions are split into two different files as part of the data is directly gathered by the NHS (National Health Service). The files that contain the word 'nhsregion' in their name, include data related to hospitals only, such as number of admissions or number of people in respirators. The files containing the word 'region' in their name, include the rest of the data, such as number of cases, number of vaccinated people or number of tests performed per day. The next paragraphs describe the columns for the different file types.

Region files

Files related to regions (word 'region' included in the file name) have the following columns: - "date": date in YYYY-MM-DD format - "area type": type of area covered in the file (region or nation) - "area name": name of area covered in the file (region or nation name) - "daily cases": new cases on a given date - "cum cases": cumulative cases - "new deaths 28days": new deaths within 28 days of a positive test - "cum deaths 28days": cumulative deaths within 28 days of a positive test - "new deaths_60days": new deaths within 60 days of a positive test - "cum deaths 60days": cumulative deaths within 60 days of a positive test - "new_first_episode": new first episodes by date - "cum_first_episode": cumulative first episodes by date - "new_reinfections": new reinfections by specimen data - "cum_reinfections": cumualtive reinfections by specimen data - "new_virus_test": new virus tests by date - "cum_virus_test": cumulative virus tests by date - "new_pcr_test": new PCR tests by date - "cum_pcr_test": cumulative PCR tests by date - "new_lfd_test": new LFD tests by date - "cum_lfd_test": cumulative LFD tests by date - "test_roll_pos_pct": percentage of unique case positivity by date rolling sum - "test_roll_people": unique people tested by date rolling sum - "new first dose": new people vaccinated with a first dose - "cum first dose": cumulative people vaccinated with a first dose - "new second dose": new people vaccinated with a first dose - "cum second dose": cumulative people vaccinated with a first dose - "new third dose": new people vaccinated with a booster or third dose - "cum third dose": cumulative people vaccinated with a booster or third dose

Country files

Files related to countries (England, Northern Ireland, Scotland and Wales) have the above columns and also: - "new admissions": new admissions, - "cum admissions": cumulative admissions, - "hospital cases": patients in hospitals, - "ventilator beds": COVID occupied mechanical ventilator beds - "trans_rate_min": minimum transmission rate (R) - "trans_rate_max": maximum transmission rate (R) - "trans_growth_min": transmission rate growth min - "trans_growth_max": transmission rate growth max

NHS Region files

Files related to nhsregion (word 'nhsregion' included in the file name) have the following columns: - "new admissions": new admissions, - "cum admissions": cumulative admissions, - "hospital cases": patients in hospitals, - "ventilator beds": COVID occupied mechanical ventilator beds - "trans_rate_min": minimum transmission rate (R) - "trans_rate_max": maximum transmission rate (R) - "trans_growth_min": transmission rate growth min - "trans_growth_max": transmission rate growth max

It's worth noting that the dataset hasn't been cleaned and it needs cleaning. Also, different files have different null columns. This isn't an error in the dataset but the way different countries and regions report the data.

60-day COVID-19 mortality among solid organ transplant recipients.

Association of patient characteristics with 60-day post-ICU admission mortality using ICU length of stay as time variable (univariate/multivariate cox regression models).

Context

Estimates of excess deaths can provide information about the burden of mortality potentially related to COVID-19, beyond the number of deaths that are directly attributed to COVID-19.

Content

Estimates of excess deaths can provide information about the burden of mortality potentially related to COVID-19, beyond the number of deaths that are directly attributed to COVID-19. Excess deaths are typically defined as the difference between observed numbers of deaths and expected numbers. This visualization provides weekly data on excess deaths by the jurisdiction of occurrence. Counts of deaths in more recent weeks are compared with historical trends to determine whether the number of deaths is significantly higher than expected.

Estimates of excess deaths can be calculated in a variety of ways and will vary depending on the methodology and assumptions about how many deaths are expected to occur. Estimates of excess deaths presented in this webpage were calculated using Farrington surveillance algorithms (1). For each jurisdiction, a model is used to generate a set of expected counts, and the upper bound of the 95% Confidence Intervals (95% CI) of these expected counts is used as a threshold to estimate excess deaths. Observed counts are compared to these upper bound estimates to determine whether a significant increase in deaths has occurred. Provisional counts are weighted to account for potential underreporting in the most recent weeks. However, data for the most recent week(s) are still likely to be incomplete. Only about 60% of deaths are reported within 10 days of the date of death, and there is considerable variation by jurisdiction. More detail about the methods, weighting, data, and limitations can be found in the Technical Notes.

• Source: data.cdc.gov
• Publisher: Centers for Disease Control and Prevention, cdc.gov
• Homepage URL: https://data.cdc.gov/d/xkkf-xrst
• License: https://www.usa.gov/government-works
• Contact Email: cdcinfo@cdc.gov
• Public Access Level: Public

Additional information

Dashboard: https://www.cdc.gov/nchs/nvss/vsrr/covid19/excess_deaths.htm

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Acknowledgements

Thanks to:
- data.cdc.gov - healthdata.gov

References

• Noufaily A, Enki DG, Farrington P, Garthwaite P, Andrews N, Charlett A. An Improved Algorithm for Outbreak Detection in Multiple Surveillance Systems. Statistics in Medicine 2012;32(7):1206-1222.
• Salmon M, Schumacher D, Hohle M. Monitoring Count Time Series in R: Aberration Detection in Public Health Surveillance. Journal of Statistical Software 2016;70(10):1-35.
• Rue H, Martino S, Chopin N. Approximate Bayesian inference for latent Gaussian models using integrated nested Laplace approximations (with discussion). Journal of the Royal Statistical Society Series B 2009;71(2):319-392.
• Spencer MR, Ahmad F. Timeliness of death certificate data for mortality surveillance and provisional estimates. National Center for Health Statistics. 2016. http://www.cdc.gov/nchs/data/vsrr/report001.pdf.pdf icon

Estimates of excess deaths can provide information about the burden of mortality potentially related to COVID-19, beyond the number of deaths that are directly attributed to COVID-19. Excess deaths are typically defined as the difference between observed numbers of deaths and expected numbers. This visualization provides weekly data on excess deaths by jurisdiction of occurrence. Counts of deaths in more recent weeks are compared with historical trends to determine whether the number of deaths is significantly higher than expected. Estimates of excess deaths can be calculated in a variety of ways, and will vary depending on the methodology and assumptions about how many deaths are expected to occur. Estimates of excess deaths presented in this webpage were calculated using Farrington surveillance algorithms (1). For each jurisdiction, a model is used to generate a set of expected counts, and the upper bound of the 95% Confidence Intervals (95% CI) of these expected counts is used as a threshold to estimate excess deaths. Observed counts are compared to these upper bound estimates to determine whether a significant increase in deaths has occurred. Provisional counts are weighted to account for potential underreporting in the most recent weeks. However, data for the most recent week(s) are still likely to be incomplete. Only about 60% of deaths are reported within 10 days of the date of death, and there is considerable variation by jurisdiction. More detail about the methods, weighting, data, and limitations can be found in the Technical Notes.

t-test results for number of reported deaths from first case and first death at 30, 60, and 90 days, and for Hofstede cultural dimensions.

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.

Regression analysis for USA states: Mortality per million with independent variable days from the 22nd of January with break point at day 59 (time interval 46–59 and 60–70 days).

Basic and laboratory characteristics of COVID-19 patients in the intensive care unit during admission.

Hospital infrastructure and facilities (n = 11).

Objective. We aimed to identify mortality trends associated with COVID-19 deaths in Peru during April through November 2020, when mass treatments with ivermectin (IVM), a drug of Nobel Prize-honored distinction, were autonomously deployed at different times and to different extents in Peru's 25 states under a national policy that authorized these treatments.

Design. Ecological study of publicly available data. Excess deaths were analyzed state by state. To identify potential confounding factors, Google mobility data, population densities, SARS-CoV-2 genetic variations, seropositivity rates and other auxiliary data were also examined.

Primary outcome. Reductions in excess deaths, state by state, as compared with extent and time period of IVM treatments.

Participants. The study population was restricted to ages ≥ 60 to eliminate confounding effects of changing age distributions of COVID-19 incidence.

Results. The 25 states of Peru were grouped by extent of IVM distributions: maximal (mass IVM distributions through operation MOT, a broadside effort led by the army); medium (locally managed IVM distributions); and minimal (restrictive policies in one state, Lima). The mean reduction in excess deaths 30 days after peak deaths was 74% for the maximal IVM distribution group, 53% for the medium group and 25% for Lima. Reduction of excess deaths correlated with extent of IVM distribution by state with p<0.002 using the Kendall τ_btest. Nationwide, excess deaths decreased 14-fold over four months through December 1, 2020, after which deaths then increased 13-fold when IVM use was restricted under a new president.

Conclusion. Mass treatments with IVM, a drug safely used in 3.7 billion doses worldwide since 1987, most likely caused these reductions in deaths during the time periods in which it was deployed. The indicated biological mechanism of IVM, competitive binding with SARS-CoV-2 spike protein, is likely non-epitope specific, possibly yielding full efficacy against emerging viral mutant strains.

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.

On March 10, 2023, the Johns Hopkins Coronavirus Resource Center ceased its collecting and reporting of global COVID-19 data. For updated cases, deaths, and vaccine data please visit: U.S. Centers for Disease Control and Prevention (CDC)For more information, visit the Johns Hopkins Coronavirus Resource Center.This map is updated weekly and currently shows data through March 5, 2023, which will be the final update of this map.Note: Nebraska stopped reporting county level-results on 5/25/2021 and re-started on 9/26/21 with a lump-sum representing the previous four months - this impacted the weekly sum of cases fields.It shows COVID-19 Trend for the most recent Monday with a colored dot for each county. The larger the dot, the longer the county has had this trend. Includes Puerto Rico, Guam, Northern Marianas, U.S. Virgin Islands.The intent of this map is to give more context than just the current day of new data because daily data for COVID-19 cases is volatile and can be unreliable on the day it is first reported. Weekly summaries in the counts of new cases smooth out this volatility. Click or tap on a county to see a history of trend changes and a weekly graph of new cases going back to February 8, 2020. This map is updated every Monday* based on data through the previous Sunday. See also this version of the map for another perspective.COVID-19 Trends show how each county is doing and are updated daily. We base the trend assignment on the number of new cases in the past two weeks and the number of active cases per 100,000 people. To learn the details for how trends are assigned, see the full methodology. There are five trends:Emergent - New cases for the first time or in counties that have had zero new cases for 60 or more days.Spreading - Low to moderate rates of new cases each day. Likely controlled by local policies and individuals taking measures such as wearing masks and curtailing unnecessary activities.Epidemic - Accelerating and uncontrolled rates of new cases.Controlled - Very low rates of new cases.End Stage - One or fewer new cases every 5 days in larger populations and fewer in rural areas.*Starting 8/22/2021 we began updating on Mondays instead of Tuesdays as a result of optimizing the scripts that produce the weekly analysis. For more information about COVID-19 trends, see the full methodology. Data Source: Johns Hopkins University CSSE US Cases by County dashboard and USAFacts for Utah County level Data.

Sequential Organ Failure Assessment (SOFA) and quick SOFA (qSOFA) score at admission.

The dataset comprises of 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+; and unknown. It is a collection of the 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. The dataset can be viewed and downloaded in a CSV file format.

Listing of Washoe County COVID-19 case data, by day posted to public dashboard. This table is based on best available information from the Washoe County Health District. Not all fields are populated for all dates.Name FieldName FieldType Comment

OBJECTID OBJECTID ObjectID System generated unique ID

Date Reported reportdt Date Effective date of this row of data

Confirmed confirmed Integer Total number of confirmed cases to date

Recovered recovered Integer Number of recoveries to date

Deaths deaths Integer Number of deaths to date

Active active Integer Current number of active cases

Male Male Small Integer Total confirmed cases to date: Male

Female Female Small Integer Total confirmed cases to date: Female

OtherGender GenderOther Small Integer Total confirmed cases to date: OtherGender

Total Cases 0-9 Age0to9 Small Integer Total confirmed cases to date: Total Cases 0-9

Total Cases 10-19 Age10to19 Small Integer Total confirmed cases to date: Total Cases 10-19

Total Cases 20-29 Age20to29 Small Integer Total confirmed cases to date: Total Cases 20-29

Total Cases 30-39 Age30to39 Small Integer Total confirmed cases to date: Total Cases 30-39

Total Cases 40-49 Age40to49 Small Integer Total confirmed cases to date: Total Cases 40-49

Total Cases 50-59 Age50to59 Small Integer Total confirmed cases to date: Total Cases 50-59

Total Cases 60-69 Age60to69 Small Integer Total confirmed cases to date: Total Cases 60-69

Total Cases 70-79 Age70to79 Small Integer Total confirmed cases to date: Total Cases 70-79

Total Cases 80-89 Age80to89 Small Integer Total confirmed cases to date: Total Cases 80-89

Total Cases 90-99 Age90to99 Small Integer Total confirmed cases to date: Total Cases 90-99

Total Cases 100+ Age100plus Small Integer Total confirmed cases to date: Total Cases 100+

UnknownAge AgeNA Small Integer Total confirmed cases to date: UnknownAge

Native American E_NativeAmerican Integer Total Cases to date: Native American

Asian E_Asian Integer Total Cases to date: Asian

African American E_Black Integer Total Cases to date: African American

Hispanic E_Hispanic Integer Total Cases to date: Hispanic

Hawaiian or Pacific Islander E_HawaiianPacific Integer Total Cases to date: Hawaiian or Pacific Islander

Caucasian E_White Integer Total Cases to date: Caucasian

Multiple E_Multiple Integer Total Cases to date: Multiple

OtherEthnicity E_Other Integer Total Cases to date: OtherEthnicity

EthnicityUnknown E_Unknown Integer Total Cases to date: EthnicityUnknown

New Cases 7 Day Moving Average NewCases7DMA Double Average New Cases over last 7 days

NewCases NewCases Integer New Cases in last day

ActiveCasesAge0to9per100K Age0to9_100K Double Active Cases per 100,000: Age0to9

ActiveCasesAge10to19per100K Age10to19_100K Double Active Cases per 100,000: Age10to19

ActiveCasesAge20to29per100K Age20to29_100K Double Active Cases per 100,000: Age20to29

ActiveCasesAge30to39per100K Age30to39_100K Double Active Cases per 100,000: Age30to39

ActiveCasesAge40to49per100K Age40to49_100K Double Active Cases per 100,000: Age40to49

ActiveCasesAge50to59per100K Age50to59_100K Double Active Cases per 100,000: Age50to59

ActiveCasesAge60to69per100K Age60to69_100K Double Active Cases per 100,000: Age60to69

ActiveCasesAge70to79per100K Age70to79_100K Double Active Cases per 100,000: Age70to79

ActiveCasesAge80to89per100K Age80to89_100K Double Active Cases per 100,000: Age80to89

ActiveCasesAge90to99per100K Age90to99_100K Double Active Cases per 100,000: Age90to99

ActiveCasesAge100plusper100K Age100plus_100K Double Active Cases per 100,000: Age100plus

BackgroundSevere acute respiratory syndrome caused by a coronavirus (SARS-CoV-2) is responsible for the COVID-19 disease pandemic that began in Wuhan, China, in December 2019. Since then, nearly seven million deaths have occurred worldwide due to COVID-19. Mexicans are especially vulnerable to the COVID-19 pandemic as Mexico has nearly the worst observed case-fatality ratio (4.5%). As Mexican Latinos represent a vulnerable population, this study aimed to determine significant predictors of mortality in Mexicans with COVID-19 who were admitted to a large acute care hospital.MethodsIn this observational, cross-sectional study, 247 adult patients were consecutively admitted to a third-level referral center in Yucatan, Mexico, from March 1st, 2020, to August 31st, 2020, with COVID-19-related symptoms, participated in this study. Lasso logistic and binary logistic regression were used to identify clinical predictors of death.ResultsAfter a hospital stay of about eight days, 146 (60%) patients were discharged; however, 40% died by the twelfth day (on average) after hospital admission. Out of 22 possible predictors, five crucial predictors of death were found, ranked by the most to least important: (1) needing to be placed on a mechanical ventilator, (2) reduced platelet concentration at admission, (3) increased derived neutrophil to lymphocyte ratio, (4) increased age, and (5) reduced pulse oximetry saturation at admission. The model revealed that these five variables shared ~83% variance in outcome.ConclusionOf the 247 Mexican Latinos patients admitted with COVID-19, 40% died 12  days after admission. The patients’ need for mechanical ventilation (due to severe illness) was the most important predictor of mortality, as it increased the odds of death by nearly 200-fold.

BackgroundThe new coronavirus SARS-CoV-2 pandemic has been relatively less lethal in children; however, poor prognosis and mortality has been associated with factors such as access to health services. Mexico remained on the list of the ten countries with the highest case fatality rate (CFR) in adults. It is of interest to know the behavior of COVID-19 in the pediatric population. The aim of this study was to identify clinical and sociodemographic variables associated with mortality due to COVID-19 in pediatric patients.ObjectiveUsing National open data and information from the Ministry of Health, Mexico, this cohort study aimed to identify clinical and sociodemographic variables associated with COVID-19 mortality in pediatric patients.MethodA cohort study was designed based on National open data from the Ministry of Health, Mexico, for the period April 2020 to January 2022, and included patients under 18 years of age with confirmed SARS-CoV-2 infection. Variables analyzed were age, health services used, and comorbidities (obesity, diabetes, asthma, cardiovascular disease, immunosuppression, high blood pressure, and chronic kidney disease). Follow-up duration was 60 days, and primary outcomes were death, hospitalization, and requirement of intensive care. Statistical analysis included survival analysis, prediction models created using the Cox proportional hazards model, and Kaplan-Meier estimation curves.ResultsThe cohort included 261,099 cases with a mean age of 11.2 ± 4 years, and of these, 11,569 (4.43%) were hospitalized and 1,028 (0.39%) died. Variables associated with risk of mortality were age under 12 months, the presence of comorbidities, health sector where they were treated, and first wave of infection.ConclusionBased on data in the National database, we show that the pediatric fatality rate due to SARS-CoV-2 is similar to that seen in other countries. Access to health services and distribution of mortality were heterogeneous. Vulnerable groups were patients younger than 12 months and those with comorbidities.