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.

Due to changes in the collection and availability of data on COVID-19 this page will no longer be updated. The webpage will no longer be available as of 11 May 2023. On-going, reliable sources of data for COVID-19 are available via the COVID-19 dashboard, Office for National Statistics, and the UKHSA This page provides a weekly summary of data on deaths related to COVID-19 published by NHS England and the Office for National Statistics. More frequent reporting on COVID-19 deaths is now available here, alongside data on cases, hospitalisations, and vaccinations. This update contains data on deaths related to COVID-19 from: NHS England COVID-19 Daily Deaths - last updated on 28 June 2022 with data up to and including 27 June 2022. ONS weekly deaths by Local Authority - last updated on 16 August 2022 with data up to and including 05 August 2022. Summary notes about each these sources are provided at the end of this document. Note on interpreting deaths data: statistics from the available sources differ in definition, timing and completeness. It is important to understand these differences when interpreting the data or comparing between sources. Weekly Key Points An additional 24 deaths in London hospitals of patients who had tested positive for COVID-19 and an additional 5 where COVID-19 was mentioned on the death certificate were announced in the week ending 27 June 2022. This compares with 40 and 3 for the previous week. A total of 306 deaths in hospitals of patients who had tested positive for COVID-19 and 27 where COVID-19 was mentioned on the death certificate were announced for England as whole. This compares with 301 and 26 for the previous week. The total number of COVID-19 deaths reported in London hospitals of patients who had tested positive for COVID-19 is now 19,102. The total number of deaths in London hospitals where COVID-19 was mentioned on the death certificate is now 1,590. This compares to figures of 119,237 and 8,197 for English hospitals as a whole. Due to the delay between death occurrence and reporting, the estimated number of deaths to this point will be revised upwards over coming days These figures do not include deaths that occurred outside of hospitals. Data from ONS has indicated that the majority (79%) of COVID-19 deaths in London have taken place in hospitals. Recently announced deaths in Hospitals 21 June 22 June 23 June 24 June 25 June 26 June 27 June London No positive test 0 0 1 4 0 0 0 London Positive test 3 7 2 10 0 0 2 Rest of England No positive test 2 6 4 4 0 0 6 Rest of England Positive test 47 49 41 58 6 0 81 16 May 23 May 30 May 06 June 13 June 20 June 27 June London No positive test 14 3 4 0 4 3 5 London Positive test 45 34 55 20 62 40 24 Rest of England No positive test 41 58 33 23 47 23 22 Rest of England Positive test 456 375 266 218 254 261 282 Deaths by date of occurrence 21 June 22 June 23 June 24 June 25 June 26 June 27 June London 20,683 20,686 20,690 20,691 20,692 20,692 20,692 Rest of England 106,604 106,635 106,679 106,697 106,713 106,733 106,742 Interpreting the data The data published by NHS England are incomplete due to: delays in the occurrence and subsequent reporting of deaths deaths occurring outside of hospitals not being included The total deaths reported up to a given point are therefore less than the actual number that have occurred by the same point. Delays in reporting NHS provide the following guidance regarding the delay between occurrence and reporting of deaths: Confirmation of COVID-19 diagnosis, death notification and reporting in central figures can take up to several days and the hospitals providing the data are under significant operational pressure. This means that the totals reported at 5pm on each day may not include all deaths that occurred on that day or on recent prior days. The data published by NHS England for reporting periods from April 1st onward includes both date of occurrence and date of reporting and so it is possible to illustrate the distribution of these reporting delays. This data shows that approximately 10% of COVID-19 deaths occurring in London hospitals are included in the reporting period ending on the same day, and that approximately two-thirds of deaths were reported by two days after the date of occurrence. Deaths outside of hospitals The data published by NHS England does not include deaths that occur outside of hospitals, i.e. those in homes, hospices, and care homes. ONS have published data for deaths by place of occurrence. This shows that, up to 05 August, 79% of deaths in London recorded as involving COVID-19 occurred in hospitals (this compares with 44% for all causes of death). This would suggest that the NHS England data may underestimate overall deaths from COVID-19 by around 20%. Number of deaths Proportion of deaths Week ending Hospital Care home Home Other Hospital Care home Home Other 06 Mar 2020 1 1 0 0 50% 50% 0% 0% 13 Mar 2020 13 0 4 0 76% 0% 24% 0% 20 Mar 2020 148 9 11 0 88% 5% 7% 0% 27 Mar 2020 610 45 53 14 84% 6% 7% 2% 03 Apr 2020 1,215 132 143 27 80% 9% 9% 2% 10 Apr 2020 1,495 282 162 32 76% 14% 8% 2% 17 Apr 2020 1,076 295 101 29 72% 20% 7% 2% 24 Apr 2020 669 210 72 35 68% 21% 7% 4% 01 May 2020 348 125 43 15 66% 24% 8% 3% 08 May 2020 261 93 29 16 65% 23% 7% 4% 15 May 2020 152 51 22 5 66% 22% 10% 2% 22 May 2020 93 51 10 3 59% 32% 6% 2% 29 May 2020 62 25 7 6 62% 25% 7% 6% 05 Jun 2020 53 23 4 1 65% 28% 5% 1% 12 Jun 2020 27 11 9 3 54% 22% 18% 6% 19 Jun 2020 22 7 6 2 59% 19% 16% 5% 26 Jun 2020 14 14 5 1 41% 41% 15% 3% 03 Jul 2020 10 5 2 5 45% 23% 9% 23% 10 Jul 2020 15 3 0 1 79% 16% 0% 5% 17 Jul 2020 8 7 2 0 47% 41% 12% 0% 24 Jul 2020 15 1 0 2 83% 6% 0% 11% 31 Jul 2020 6 2 1 0 67% 22% 11% 0% 07 Aug 2020 6 2 0 1 67% 22% 0% 11% 14 Aug 2020 7 4 2 1 50% 29% 14% 7% 21 Aug 2020 4 0 0 0 100% 0% 0% 0% 28 Aug 2020 1 2 0 0 33% 67% 0% 0% 04 Sep 2020 3 0 1 0 75% 0% 25% 0% 11 Sep 2020 7 2 0 1 70% 20% 0% 10% 18 Sep 2020 9 2 1 0 75% 17% 8% 0% 25 Sep 2020 23 3 3 0 79% 10% 10% 0% 02 Oct 2020 27 3 2 0 84% 9% 6% 0% 09 Oct 2020 36 3 3 0 86% 7% 7% 0% 16 Oct 2020 41 0 2 0 95% 0% 5% 0% 23 Oct 2020 47 4 4 0 85% 7% 7% 0% 30 Oct 2020 91 3 5 1 91% 3% 5% 1% 06 Nov 2020 93 7 5 2 87% 7% 5% 2% 13 Nov 2020 109 11 10 2 83% 8% 8% 2% 20 Nov 2020 162 5 8 4 91% 3% 4% 2% 27 Nov 2020 175 8 14 5 87% 4% 7% 2% 04 Dec 2020 190 10 13 10 85% 4% 6% 4% 11 Dec 2020 199 9 13 6 88% 4% 6% 3% 18 Dec 2020 267 15 25 4 86% 5% 8% 1% 25 Dec 2020 403 30 43 7 83% 6% 9% 1% 01 Jan 2021 677 35 109 28 80% 4% 13% 3% 08 Jan 2021 959 73 167 36 78% 6% 14% 3% 15 Jan 2021 1,125 84 165 39 80% 6% 12% 3% 22 Jan 2021 1,163 96 142 43 81% 7% 10% 3% 29 Jan 2021 863 82 101 28 80% 8% 9% 3% 05 Feb 2021 605 70 59 38 78% 9% 8% 5% 12 Feb 2021 439 29 49 14 83% 5% 9% 3% 19 Feb 2021 338 29 33 12 82% 7% 8% 3% 26 Feb 2021 214 19 19 11 81% 7% 7% 4% 05 Mar 2021 141 11 19 5 80% 6% 11% 3% 12 Mar 2021 99 9 7 1 85% 8% 6% 1% 19 Mar 2021 65 10 1 1 84% 13% 1% 1% 26 Mar 2021 41 9 4 2 73% 16% 7% 4% 02 Apr 2021 35 5 4 0 80% 11% 9% 0% 09 Apr 2021 29 2 3 0 85% 6% 9% 0% 16 Apr 2021 24 6 2 0 75% 19% 6% 0% 23 Apr 2021 14 1 0 0 93% 7% 0% 0% 30 Apr 2021 13 1 1 0 87% 7% 7% 0% 07 May 2021 14 3 0 0 82% 18% 0% 0% 14 May 2021 6 2 0 0 75% 25% 0% 0% 21 May 2021 8 1 1 0 80% 10% 10% 0% 28 May 2021 11 1 2 1 73% 7% 13% 7% 04 Jun 2021 9 0 0 0 100% 0% 0% 0% 11 Jun 2021 11 3 0 0 79% 21% 0% 0% 18 Jun 2021 11 4 2 1 61% 22% 11% 6% 25 Jun 2021 10 0 0 1 91% 0% 0% 9% 02 Jul 2021 14 1 2 0 82% 6% 12% 0% 09 Jul 2021 12 1 4 1 67% 6% 22% 6% 16 Jul 2021 18 3 2 0 78% 13% 9% 0% 23 Jul 2021 48 0 7 1 86% 0% 12% 2% 30 Jul 2021 49 2 4 4 83% 3% 7% 7% 06 Aug 2021 66 1 9 1 86% 1% 12% 1% 13 Aug 2021 60 1 12 1 81% 1% 16% 1% 20 Aug 2021 84 1 5 1 92% 1% 5% 1% 27 Aug 2021 78 3 10 3 83% 3% 11% 3% 03 Sep 2021 85 3 7 1 89% 3% 7% 1% 10 Sep 2021 83 2 10 2 86% 2% 10% 2% 17 Sep 2021 65 2 9 1 84% 3% 12% 1% 24 Sep 2021 76 5 5 0 88% 6% 6% 0% 01 Oct 2021 88 2 15 1 83% 2% 14% 1% 08 Oct 2021 65 2 7 1 87% 3% 9% 1% 15 Oct 2021 62 1 9 4 82% 1% 12% 5% 22 Oct 2021 64 2 11 2 81% 3% 14% 3% 29 Oct 2021 66 3 11 1 81% 4% 14% 1% 05 Nov 2021 67 3 10 5 79% 4% 12% 6% 12 Nov 2021 84 2 12 1 85% 2% 12% 1% 19 Nov 2021 63 2 2 0 94% 3% 3% 0% 26 Nov 2021 68 2 8 0 87% 3% 10% 0% 03 Dec 2021 72 2 10 1 85% 2% 12% 1% 10 Dec 2021 81 3 12 4 81% 3% 12% 4% 17 Dec 2021 91 1 12 3 85% 1% 11% 3% 24 Dec 2021 101 8 15 3 80% 6% 12% 2% 31 Dec 2021 129 11 19 6 78% 7% 12% 4% 07 Jan 2022 178 18 19 4 81% 8% 9% 2% 14 Jan 2022 194 23 16 14 79% 9% 6% 6% 21 Jan 2022 165 25 11 4 80% 12% 5% 2% 28 Jan 2022 119 20 13 5 76% 13% 8% 3% 04 Feb 2022 97 13 8 2 81% 11% 7% 2% 11 Feb 2022 51 10 6 6 70% 14% 8% 8% 18 Feb 2022 62 6 9 3 78% 8% 11% 4% 25 Feb 2022 55 2 2 1 92% 3% 3% 2% 04 Mar 2022 47 2 2 2 89% 4% 4% 4% 11 Mar 2022 48 3 4 0 87% 5% 7% 0% 18 Mar 2022 60 7 8 4 76% 9% 10% 5% 25 Mar 2022 51 11 5 2 74% 16% 7% 3% 01 Apr 2022 60 8 5 2 80% 11% 7% 3% 08 Apr 2022 78 4 7 3 85% 4% 8% 3% 15 Apr 2022 74 6 6 3 83% 7% 7% 3% 22 Apr 2022 58 10 7 6 72% 12% 9% 7% 29 Apr 2022 39 8 3 4 72% 15% 6% 7% 06 May 2022 44 3 4 0 86% 6% 8% 0% 13 May 2022 29 2 4 2 78% 5% 11% 5% 20 May 2022 16 4 0 2 73% 18% 0% 9% 27 May 2022 34 3 3 1 83% 7% 7% 2% 03 Jun 2022 18 1 1 0 90% 5% 5% 0% 10 Jun 2022 18 1 3 0 82% 5% 14% 0% 17 Jun 2022 22 1 2 0 88% 4% 8% 0% 24 Jun 2022 33 2 3 1 85% 5% 8% 3% 01 Jul 2022 33 2 2 0 89% 5% 5% 0% 08 Jul 2022 51 4 4 4 81% 6% 6% 6% 15 Jul 2022 60 5 4 2 85% 7% 6% 3% 22 Jul 2022 71 9 10 3 76% 10% 11% 3% 29 Jul 2022 48 7 9 0 75% 11% 14% 0% 05 Aug 2022 35 1 3 4 81% 2% 7% 9% Total 18,924 2,390 2,152 634 79% 10% 9% 3% Comparison with all cause mortality Comparison of data sources Note on data sources NHS England provides numbers of patients who have died in hospitals in England and had tested positive for COVID-19, and from 25 April, the number of patients where COVID-19 is mentioned on the death certificate and no positive COVID-19 test result was received. Figures are updated each day at 2pm with deaths reported up to 5pm the previous day. There is a delay between the occurrence of a death to it being captured in the

Note: Starting April 27, 2023 updates change 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.

Background There is an urgent need to develop a simplified risk tool that enables rapid triaging of SARS CoV-2 positive patients during hospital admission, which complements current practice. Many predictive tools developed to date are complex, rely on multiple blood results and past medical history, do not include chest X ray results and rely on Artificial Intelligence rather than simplified algorithms. Our aim was to develop a simplified risk-tool based on five parameters and CXR image data that predicts the 60-day survival of adult SARS CoV-2 positive patients at hospital admission. Methods We analysed the NCCID database of patient blood variables and CXR images from 19 hospitals across the UK contributed clinical data on SARS CoV-2 positive patients using multivariable logistic regression. The initial dataset was non-randomly split between development and internal validation dataset with 1434 and 310 SARS CoV-2 positive patients, respectively. External validation of final model conducted on 741 Accident and Emergency admissions with suspected SARS CoV-2 infection from a separate NHS Trust which was not part of the initial NCCID data set. Findings The LUCAS mortality score included five strongest predictors (lymphocyte count, urea, CRP, age, sex), which are available at any point of care with rapid turnaround of results. Our simple multivariable logistic model showed high discrimination for fatal outcome with the AUC-ROC in development cohort 0.765 (95% confidence interval (CI): 0.738 - 0.790), in internal validation cohort 0.744 (CI: 0.673 - 0.808), and in external validation cohort 0.752 (CI: 0.713 - 0.787). The discriminatory power of LUCAS mortality score was increased slightly when including the CXR image data (for normal versus abnormal): internal validation AUC-ROC 0.770 (CI: 0.695 - 0.836) and external validation AUC-ROC 0.791 (CI: 0.746 - 0.833). The discriminatory power of LUCAS and LUCAS + CXR performed in the upper quartile of pre-existing risk stratification scores with the added advantage of using only 5 predictors. Interpretation This simplified prognostic tool derived from objective parameters can be used to obtain valid predictions of mortality in patients within 60 days SARS CoV-2 RT-PCR results. This free-to-use simplified tool can be used to assist the triage of patients into low, moderate, high or very high risk of fatality and is available at https://mdscore.net/. What is already known on this topic? Clinical prediction models such as NEWS2 is currently used in practice as mortality risk assessment. In a rapid response to support COVID-19 patient assessment and resource management, published risk tools and models have been found to have a high risk of bias and therefore cannot be translated into clinical practice. What this study adds? A newly developed and validated risk tool (LUCAS) based on rapid and routine blood tests predicts the mortality of patients infected with SARS-CoV-2 virus. This prediction model has both high and robust predictive power and has been tested on an external set of patients and therefore can be used to effectively triage patients when resources are limited. In addition, LUCAS can be used with chest imaging information and NEWS2 score.

60-day COVID-19 mortality among solid organ transplant recipients, in propensity score matched cohort (N = 282).

The pandemic caused by the new coronavirus Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is currently affecting more than 200 countries. The most lethal clinical presentation is respiratory insufficiency, requiring attention in intensive care units (ICU). The most susceptible people are over 60 years old with comorbidities. The health systems organization may represent a transcendental role in survival.Objective: To analyze the correlation of sociodemographic factors, comorbidities and health system organization variables with survival in cases infected by SARS-CoV-2 during the first 7 months of the pandemic in Mexico.Methods: The cohort study was performed in a health system public basis from March 1st to September 30th, 2020. The included subjects were positive for the SARS-CoV-2 test, and the target variable was mortality in 60 days. The risk variables studied were: age, sex, geographic distribution, comorbidities, health system, hospitalization, and access to ICU. Bivariate statistics (X2-test), calculation of fatality rates, survival analyses and adjustment of confusing variables with Cox proportional-hazards were performed.Results: A total of 753,090 subjects were analyzed, of which the 52% were men. There were 78,492 deaths (10.3% of general fatality and 43% inpatient). The variables associated with a higher risk of hospital mortality were age (from 60 years onwards), care in public sectors, geographic areas with higher numbers of infection and endotracheal intubation without management in the ICU.Conclusions: The variables associated with a lower survival in cases affected by SARS-CoV-2 were age, comorbidities, and respiratory insufficiency (with endotracheal intubation without care in the ICU). Additionally, an interaction was observed between the geographic location and health sector where they were treated.