Note: Note: Starting October 10th, 2025 this dataset is deprecated and is no longer being updated.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.

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.

Read the associated blogpost for a detailed description of how this dataset was prepared; plus extra code for producing animated maps.

Context

The 2019 Novel Coronavirus (COVID-19) continues to spread in countries around the world. This dataset provides daily updated number of reported cases & deaths in Germany on the federal state (Bundesland) and county (Landkreis/Stadtkreis) level. In April 2021 I added a dataset on vaccination progress. In addition, I provide geospatial shape files and general state-level population demographics to aid the analysis.

Content

The dataset consists of thre main csv files: covid_de.csv, demgraphics_de.csv, and covid_de_vaccines.csv. The geospatial shapes are included in the de_state.* files. See the column descriptions below for more detailed information.

• covid_de.csv: COVID-19 cases and deaths which will be updated daily. The original data are being collected by Germany's Robert Koch Institute and can be download through the National Platform for Geographic Data (the latter site also hosts an interactive dashboard). I reshaped and translated the data (using R tidyverse tools) to make it better accessible. This blogpost explains how I prepared the data, and describes how to produces animated maps.

• demographics_de.csv: General Demographic Data about Germany on the federal state level. Those have been downloaded from Germany's Federal Office for Statistics (Statistisches Bundesamt) through their Open Data platform GENESIS. The data reflect the (most recent available) estimates on 2018-12-31. You can find the corresponding table here.

• covid_de_vaccines.csv: In April 2021 I added this file that contains the Covid-19 vaccination progress for Germany as a whole. It details daily doses, broken down cumulatively by manufacturer, as well as the cumulative number of people having received their first and full vaccination. The earliest data are from 2020-12-27.

• de_state.*: Geospatial shape files for Germany's 16 federal states. Downloaded via Germany's Federal Agency for Cartography and Geodesy . Specifically, the shape file was obtained from this link.

Column Description

COVID-19 dataset covid_de.csv:

• state: Name of the German federal state. Germany has 16 federal states. I removed converted special characters from the original data.

• county: The name of the German Landkreis (LK) or Stadtkreis (SK), which correspond roughly to US counties.

• age_group: The COVID-19 data is being reported for 6 age groups: 0-4, 5-14, 15-34, 35-59, 60-79, and above 80 years old. As a shortcut the last category I'm using "80-99", but there might well be persons above 99 years old in this dataset. This column has a few NA entries.

• gender: Reported as male (M) or female (F). This column has a few NA entries.

• date: The calendar date of when a case or death were reported. There might be delays that will be corrected by retroactively assigning cases to earlier dates.

• cases: COVID-19 cases that have been confirmed through laboratory work. This and the following 2 columns are counts per day, not cumulative counts.

• deaths: COVID-19 related deaths.

• recovered: Recovered cases.

Demographic dataset demographics_de.csv:

• state, gender, age_group: same as above. The demographic data is available in higher age resolution, but I have binned it here to match the corresponding age groups in the covid_de.csv file.

• population: Population counts for the respective categories. These numbers reflect the (most recent available) estimates on 2018-12-31.

Vaccination progress dataset covid_de_vaccines.csv:

• date: calendar date of vaccination

• doses, doses_first, doses_second: Daily count of administered doses: total, 1st shot, 2nd shot.

• pfizer_cumul, moderna_cumul, astrazeneca_cumul: Daily cumulative number of administered vaccinations by manufacturer.

• persons_first_cumul, persons_full_cumul: Daily cumulative number of people having received their 1st shot and full vaccination, respectively.

Acknowledgements

All the data have been extracted from open data sources which are being gratefully acknowledged:

• The [Robert ...

This dataset is a per-state amalgamation of demographic, public health and other relevant predictors for COVID-19.

Deaths, Infections and Tests by State

The COVID Tracking Project: https://covidtracking.com/data/api

Used positive, death and totalTestResults from the API for, respectively, Infected, Deaths and Tested in this dataset. Please read the documentation of the API for more context on those columns

Predictor Data and Sources

Population (2020)

Density is people per meter squared https://worldpopulationreview.com/states/

ICU Beds and Age 60+

https://khn.org/news/as-coronavirus-spreads-widely-millions-of-older-americans-live-in-counties-with-no-icu-beds/

GDP

https://worldpopulationreview.com/states/gdp-by-state/

Income per capita (2018)

https://worldpopulationreview.com/states/per-capita-income-by-state/

Gini

https://en.wikipedia.org/wiki/List_of_U.S._states_by_Gini_coefficient

Unemployment (2020)

Rates from Feb 2020 and are percentage of labor force
https://www.bls.gov/web/laus/laumstrk.htm

Sex (2017)

Ratio is Male / Female
https://www.kff.org/other/state-indicator/distribution-by-gender/

Smoking Percentage (2020)

https://worldpopulationreview.com/states/smoking-rates-by-state/

Influenza and Pneumonia Death Rate (2018)

Death rate per 100,000 people
https://www.cdc.gov/nchs/pressroom/sosmap/flu_pneumonia_mortality/flu_pneumonia.htm

Chronic Lower Respiratory Disease Death Rate (2018)

Death rate per 100,000 people
https://www.cdc.gov/nchs/pressroom/sosmap/lung_disease_mortality/lung_disease.htm

Active Physicians (2019)

https://www.kff.org/other/state-indicator/total-active-physicians/

Hospitals (2018)

https://www.kff.org/other/state-indicator/total-hospitals

Health spending per capita

Includes spending for all health care services and products by state of residence. Hospital spending is included and reflects the total net revenue. Costs such as insurance, administration, research, and construction expenses are not included.
https://www.kff.org/other/state-indicator/avg-annual-growth-per-capita/

Pollution (2019)

Pollution: Average exposure of the general public to particulate matter of 2.5 microns or less (PM2.5) measured in micrograms per cubic meter (3-year estimate)
https://www.americashealthrankings.org/explore/annual/measure/air/state/ALL

Medium and Large Airports

For each state, number of medium and large airports https://en.wikipedia.org/wiki/List_of_the_busiest_airports_in_the_United_States

Temperature (2019)

Note that FL was incorrect in the table, but is corrected in the Hottest States paragraph
https://worldpopulationreview.com/states/average-temperatures-by-state/
District of Columbia temperature computed as the average of Maryland and Virginia

Urbanization (2010)

Urbanization as a percentage of the population https://www.icip.iastate.edu/tables/population/urban-pct-states

Age Groups (2018)

https://www.kff.org/other/state-indicator/distribution-by-age/

School Closure Dates

Schools that haven't closed are marked NaN https://www.edweek.org/ew/section/multimedia/map-coronavirus-and-school-closures.html

Note that some datasets above did not contain data for District of Columbia, this missing data was found via Google searches manually entered.

Age-standardised mortality rates for deaths involving coronavirus (COVID-19), non-COVID-19 deaths and all deaths by vaccination status, broken down by age group.

Background: To develop an effective countermeasure and determine our susceptibilities to the outbreak of COVID-19 is challenging for a densely populated developing country like Bangladesh and a systematic review of the disease on a continuous basis is necessary.Methods: Publicly available and globally acclaimed datasets (4 March 2020–30 September 2020) from IEDCR, Bangladesh, JHU, and ECDC database are used for this study. Visual exploratory data analysis is used and we fitted a polynomial model for the number of deaths. A comparison of Bangladesh scenario over different time points as well as with global perspectives is made.Results: In Bangladesh, the number of active cases had decreased, after reaching a peak, with a constant pattern of death rate at from July to the end of September, 2020. Seventy-one percent of the cases and 77% of the deceased were males. People aged between 21 and 40 years were most vulnerable to the coronavirus and most of the fatalities (51.49%) were in the 60+ population. A strong positive correlation (0.93) between the number of tests and confirmed cases and a constant incidence rate (around 21%) from June 1 to August 31, 2020 was observed. The case fatality ratio was between 1 and 2. The number of cases and the number of deaths in Bangladesh were much lower compared to other countries.Conclusions: This study will help to understand the patterns of spread and transition in Bangladesh, possible measures, effectiveness of the preparedness, implementation gaps, and their consequences to gather vital information and prevent future pandemics.

INFORMATION 25/01/2023

Following a seizure error, the number of deaths in week 03 in ESMS has been reduced. The necessary corrections have been made and explain the artificial decline in the number of total deaths that have occurred since the beginning of the epidemic.

⚠** 15/11/2022 Following the suspension of activity by some of the Private Medical Biology Laboratories since 14 November, the number of “New cases confirmed since the previous day” is underestimated as of Tuesday 15/11. Similarly, the incidence rate and the screening rate will be underestimated as of Thursday 17/11. The teams of Public Health France remain mobilised to monitor the epidemic, which is based on multi-source surveillance.

08/06/2022 Given the current favourable trend and the decline of the main indicators, as of 11 June 2022, COVID-19 indicators produced by Santé publique France will be updated on Géodes and data.gouv.fr every day with the exception of weekends and public holidays.

Description of the dataset

This dataset includes most of the summary indicators allowing the monitoring of the COVID-19 outbreak in France. An inventory of COVID-19 data on data.gouv.fr is available here.

These data are shown in particular on the tab overview of the epidemic monitoring dashboard available on government.fr. The latter presents data on the COVID-19 outbreak in France since 28 March 2020.

This tool whose source code is free was developed under the leadership of Etalab and with the collaboration of civil society. It provides a consolidated view of the available official data.

Data description

The data contained in the dataset are published daily.

Contextual data:

— ‘‘‘date’’’ = Date

— ‘‘‘DEP’’= Department

— ‘‘‘Reg’’= Region

— ‘‘‘lib_dep’’’= department wording

— ‘‘‘lib_reg’’’= denominated region

Data on hospital situation

— ‘‘‘Hosp’’= Number of patients currently hospitalised for COVID-19. — ‘‘‘incid_hosp’’= Number of new patients hospitalised in the last 24 hours.

— ‘‘‘REA’’= Number of patients currently undergoing resuscitation or intensive care. — ‘‘‘incid_rea’’= Number of new patients admitted to resuscitation in the last 24 hours.

— ‘‘‘RAD’’= Cumulative number of patients who have been hospitalised for COVID-19 and return home due to improved health status. — ‘‘‘incid_rad’’= New home returns in the last 24 hours.

Data on death from COVID-19

— ‘‘‘dchosp’’= Death in hospital — ‘‘‘incid_dchosp’’= New patients who died in the hospital in the last 24 hours.

— ‘‘‘esms_dc’’’= Death in ESMS

— ‘‘‘dc_tot’’’= Cumulus of deaths (cumulative of deaths recorded in hospital and EMS)

Test data

— ‘‘‘CONF’’= Number of confirmed cases — ‘‘‘conf_j1’’’= Number of new confirmed cases (J-1 results date) — ‘‘‘POS’’= Number of persons declared positive (J-3 withdrawal date) — ‘‘pos_7j’'’ = Number of persons declared positive over one week (D-3 sampling date)

— ‘‘‘esms_cas’’’ = Cases confirmed in ESMS

Data on COVID-19 monitoring indicators

— ‘‘‘tx_pos’’= Positiveness rate of virological tests (The positivity rate corresponds to the number of people tested positive (RT-PCR and antigenic test) for the first time in more than 60 days compared to the total number of people tested positive or negative over a given period; and that have never been tested positive in the previous 60 days.)

— ‘‘‘tx_incid’’= ** Incidence rate** (epidemic activity: The incidence rate is the number of people tested positive (RT-PCR and antigenic test) for the first time in more than 60 days compared to population size. It is expressed per 100000 inhabitants)

— ‘‘‘to’’= Occupancy rate: hospital stress on resuscitation capacity (proportion of COVID-19 patients currently in resuscitation, intensive care, or continuous surveillance unit reported to total beds in initial capacity, i.e. before increasing the capacity of resuscitation beds in a hospital).

— ‘‘‘R’’= ** Virus reproductive factor** (R0 evolution: The number of reproduction of the virus: this is the average number of people an infected person can contaminate. If the actual R is greater than 1, the epidemic develops; if it is less than 1, the epidemic decreases)

— ** Attention points**: — Data collection methods have evolved over time; — During the summer of 2020, the data were not published during weekends and holidays.

resources

— view dashboard — see COVID-19 data inventory on data.gouv.fr — view data from Santé publique France — **[consult data from the Ministry of Solidarity and Health](https://www.data.gouv.fr/fr/organizations/ministere-des-solidarites-et-de-

Context

Dataset aims to further a county by county analysis of potential risk factors that could heighten Covid 19 transmission rates or deaths. The data has now been split between general population and over 60 estimates and converted to counts for ease of use.

Content

It includes a subset of county by county ACS estimates of:

The data includes information on:

1) County level indicators for over 60 populations including population density, race, poverty level, housing size, sources of income, employment status, whether living alone, language barriers, immigration status, and disability status.

2) County level indicators for the general population including race, poverty level, housing size, sources of income, employment status, whether living alone, language barriers, immigration status, and disability status, modes of transportation stats, and industry stats.

Acknowledgements

For traceability and recreation purposes, I published a kernel with the R code outlining the process used to produce the data set. https://www.kaggle.com/jtourkis/kernel3f7cd0a961

The information comes from 2018 5 Year estimates from the American Community Survey (ACS).

ACSST5Y2018.S0102 ACSST5Y2018.S0804 ACSST5Y2018.S2403

Note: ACS five year estimates are selections limited to counties with populations over 20,000. https://www.census.gov/programs-surveys/acs/guidance/estimates.html

Link to ACS/Census Tables:

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

It also includes 4/16 and 4/22 Daily Spread Estimates from John Hopkins and Population Density from the CDC Social Vulnerability Index.

Centers for Disease Control and Prevention/ Agency for Toxic Substances and Disease Registry/ Geospatial Research, Analysis, and Services Program. Social Vulnerability Index 2018 Database US. data-and-tools-download.html. Accessed on 4/16.

Inspiration

I hope this data will help bring people closer to understanding what economic factors correlate to or influence disease spread.

Context - Covid data falsification discussion:

An active discussion about the mortality data in Moscow has erupted in the days. The Moscow Times newspaper drew attention to a significant increase in official mortality rates in April 2020: "Moscow recorded 20% more fatalities in April 2020 compared to its average April mortality total over the past decade, according to newly published preliminary data from Moscow’s civil registry office. The data comes as Russia sees the fastest growth in coronavirus infections in Europe, while its mortality rate remains much lower than in many countries. Moscow, the epicenter of Russia’s coronavirus outbreak, has continued to see daily spikes in new cases despite being under lockdown since March 30. According to the official data, 11,846 people died in Russia’s capital in April of this year, roughly a 20% increase from the 10-year average for April deaths, which is 9,866. The numbers suggest that the city’s statistics of coronavirus deaths may be higher in reality than official numbers indicate. Russia boasts a relatively low coronavirus mortality rate of 0.9%, which experts believe is linked to the way coronavirus-related deaths are counted."

After this publication have been realesed The Moscow Department of Health has denied the statement of the inaccuracy of counting.:

First, Moscow is a region that openly publishes mortality data on its websites. Moscow on an initiative basis published data for April before the federal structures did it. Secondly, the comparison of mortality rates in the monthly dynamics is incorrect and is not a clear evidence of any trends. In April 2020, indeed, according to the Civil Registry Office in Moscow, 11,846 death certificates were issued. So, the increase compared to April 2019 amounted to 1841 people, and compared to the same month of 2018 - 985 people, i.e. 2 times less. Thirdly, the diagnosis of coronavirus-infected deaths in Moscow is established after a mandatory autopsy is performed in strict accordance with the Provisional Guidelines of the Russian Ministry of Health.Of the total number of deaths in April 2020, 639 are people whose cause of death is coronavirus infection and its complications, most often pneumonia.It should be emphasized that the pathological autopsy of the dead with suspected CoV-19 in Russia and Moscow is carried out in 100% of cases, unlike most other countries.It is impossible to name the cause of death of COVID-19 in other cases. For example, over 60% of deaths occurred from obvious alternative causes, such as vascular accidents (myocardial infarction and stroke), stage 4 malignant diseases (essentially palliative patients), leukemia, systemic diseases with the development of organ failure (e.g. amyloidosis and terminal renal insufficiency) and other non-curable deadly diseases. Fourth, any seasonal increase in the incidence of SARS, not to mention the pandemic caused by the spread of the new coronavirus, is always accompanied by an increase in mortality. This is due to the appearance of the dead directly from an infectious disease, but to an even greater extent from other diseases, the exacerbation of which and the decompensation of the condition of patients suffering from these diseases also leads to death. In these cases, the infectious onset is a catalyst for the rapid progression of chronic diseases and the manifestation of new diseases. Fifthly, a similar situation with statistics is observed in other countries - mortality from COVID-19 is lower than the overall increase in mortality. According to the official sites of cities:In New York, mortality from coronavirus in April amounted to 11,861 people. At the same time, the total increase in mortality compared to the same period in 2019 is 15709.In London, in April, 3,589 people died with a diagnosis of coronavirus, while the total increase was 5531 Sixth, even if all the additional mortality for April in Moscow is attributed to coronavirus, the mortality from COVID will be slightly more than 3%, which is lower than the official mortality in New York and London (10% and 23%, respectively). Moreover, if you make such a recount in these cities, the mortality rate in them will be 13% and 32%, respectively. Seventh, Moscow is open for discussion and is ready to share experience with both Russian and foreign experts.

Content

I think community members would be interested in studying the data on mortality in the Russian capital themselves and conducting a competent statistical check.

This may be of particular interest in connection with that he [US announced a grant of $ 250 thousand to "expose the disinformation of health care" in Russia](https://www....

This record contains raw data related to article "High mortality in COVID-19 patients with mild respiratory disease"

Introduction: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected 189 000 people in Italy, with more than 25 000 deaths. Several predictive factors of mortality have been identified; however, none has been validated in patients presenting with mild disease.

Methods: Patients with a diagnosis of interstitial pneumonia caused by SARS-CoV-2, presenting with mild symptoms, and requiring hospitalization in a non-intensive care unit with known discharge status were prospectively collected and retrospectively analysed. Demographical, clinical and biochemical parameters were recorded, as need for non-invasive mechanical ventilation and admission in intensive care unit. Univariate and multivariate logistic regression analyses were used to identify independent predictors of death.

Results: Between 28 February and 10 April 2020, 229 consecutive patients were included in the study cohort; the majority were males with a mean age of 60 years. 54% of patients had at least one comorbidity, with hypertension being the most commonly represented, followed by diabetes mellitus. 196 patients were discharged after a mean of 9 days, while 14.4% died during hospitalization because of respiratory failure. Age higher than 75 years, low platelet count (<150 × 10³ /mm³ ) and higher ferritin levels (>750 ng/mL) were independent predictors of death. Comorbidities were not independently associated with in-hospital mortality.

Conclusions: In-hospital mortality of patients with COVID-19 presenting with mild symptoms is high and is associated with older age, platelet count and ferritin levels. Identifying early predictors of outcome can be useful in the clinical practice to better stratify and manage patients with COVID-19.

This file contains the following characteristics per case tested positive in the Netherlands: Date for statistics, Age group, Sex, Hospital admission, Death, Week of death, Province, Notifying GGD

The file is structured as follows: A record for every laboratory confirmed COVID-19 patient in the Netherlands, since the first COVID-19 report in the Netherlands on 27/02/2020 (Date for statistics may be earlier). The file is refreshed daily at 4 pm, based on the data as registered at 10 am that day in the national system for notifiable infectious diseases (Osiris AIZ).

Description of the variables

Date_file: Date and time when the data was published by RIVM

Date_statistics: Date for statistics; first day of illness, if unknown, date lab positive, if unknown, report date to GGD (format: yyyy-mm-dd)

Date_statistics_type: Type of date that was available for date for the variable "Date for statistics", where: DOO = Date of disease onset : First day of illness as reported by GGD. Please note: it is not always known whether this first day of illness was really Covid-19. DPL = Date of first Positive Labresult : Date of the (first) positive lab result. DON = Date of Notification : Date on which the notification was received by the GGD.

Agegroup: Age group at life; 0-9, 10-19, ..., 90+; at death <50, 50-59, 60-69, 70-79, 80-89, 90+, Unknown = Unknown

Sex: Sex; Unknown = Unknown, Male = Male, Female = Female

Province: Name of the province (based on the patient's whereabouts)

Hospital_admission: Hospital admission reported by the GGD. Unknown = Unknown, Yes = Yes, No = No From May 1, 2020, the indication of hospitalization will be related to Covid-19. If not, the value of this column is "No". Until June 1, only seriously ill people were tested, a large part of these people had already been or were admitted shortly afterwards. As a result, the hospital admissions registered by the GGD were more complete during the first wave. As of June 1, everyone can be tested and more people will be tested at an early stage. As a result, the GGD is not always informed, or with a delay, of a hospital admission. That is why RIVM has been actively naming the registered hospital admissions of the NICE Foundation (https://data.rivm.nl/geonetwork/srv/dut/catalog.search#/metadata/4f4ad069-8f24-4fe8-b2a7-533ef27a899f) since 6 October. RIVM uses these figures as a guideline because they provide a more complete picture than the hospital admissions reported by the GGD. Click here (https://www.rivm.nl/nieuws/nummer-nieuw-melde-covid-19-verzekeraars-stable) for more information about this.

Deceased: Death. Unknown = Unknown, Yes = Yes, No = No

Week of Death: Week of death. YYYYMM according to ISO week notation (start from Monday to Sunday)

Municipal_health_service: GGD that made the report.

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

BackgroundProlonged confinement can lead to personal deterioration at various levels. We studied this phenomenon during the nationwide COVID-19 lockdown in a functionally dependent population of the Orcasitas neighborhood of Madrid, Spain, by measuring their ability to perform basic activities of daily living and their mortality rate.MethodsA total of 127 patients were included in the Orcasitas cohort. Of this cohort, 78.7% were female, 21.3% were male, and their mean age was 86 years. All participants had a Barthel index of ≤ 60. Changes from pre- to post-confinement and 3 years afterward were analyzed, and the effect of these changes on survival was assessed (2020–2023).ResultsThe post-confinement functional assessment showed significant improvement in independence over pre-confinement for both the Barthel score (t = −5.823; p < 0.001) and the classification level (z = −2.988; p < 0.003). This improvement progressively disappeared in the following 3 years, and 40.9% of the patients in this cohort died during this period. These outcomes were associated with the Barthel index (z = −3.646; p < 0.001) and the level of dependence (hazard ratio 2.227; CI 1.514–3.276). Higher mortality was observed among men (HR 1.745; CI 1.045–2.915) and those with severe dependence (HR 2.169; CI 1.469–3.201). Setting the cutoff point of the Barthel index at 40 provided the best detection of the risk of death associated with dependence.ConclusionsHome confinement and the risk of death due to the COVID-19 pandemic awakened a form of resilience in the face of adversity among the population of functionally dependent adults. The Barthel index is a good predictor of medium- and long-term mortality and is a useful method for detecting populations at risk in health planning. A cutoff score of 40 is useful for this purpose. To a certain extent, the non-institutionalized dependent population is an invisible population. Future studies should analyze the causes of the high mortality observed.

The following dataset is the World Drug Report 2021 produced by the United Nations Office on Drugs and Crime. https://www.unodc.org/unodc/en/data-and-analysis/wdr2021_annex.html

The Executive Summary: https://www.unodc.org/res/wdr2021/field/WDR21_Booklet_1.pdf

Special points of interest from the report: - Cannabis has come to be seen as less risky by adolescents from 1995 to 2019, but the herb potency has increased 4x in that time period. - Web-based sales have increased dramatically. - Number of drug users in Africa is projected to rise by 40 per cent by 2030, based on expected population growth in the 15-64 demographic. - Drug markets quickly recovered after the onset of the pandemic, but some trafficking dynamics have been accelerated during Covid-19 - Non-medical use of cannabis and sedatives has increased globally during the pandemic

On Opioids specifically: - The two pharmaceutical opioids most commonly used to treat people with opioid use disorders, methadone and buprenorphine, have become increasingly accessible over the past two decades. The amount available for medical use has increased sixfold since 1999, from 557 million daily doses in that year to 3,317 million by 2019. - The amounts of fentanyl and its analogues seized globally have risen rapidly in recent years, and by more than 60 per cent in 2019 compared with a year earlier. Overall, these amounts have risen more than twenty-fold since 2015. The largest quantities were seized in North America. - Elsewhere in the world, other pharmaceutical opioids (codeine and tramadol) predominate. Over the period 2015–2019, the largest quantities of tramadol seized were reported in West and Central Africa; they accounted for 86 per cent of the global total. Codeine was intercepted in large quantities in Asia, often in the form of diverted cough syrups. - Almost 50,000 people died from overdose deaths attributed to opioids in the United States in 2019, more than double the 2010 figure. By comparison, in the European Union, the figure for all drug-related overdoses (mostly relating to opioid use) stood at 8,300 in 2018, despite the larger population. - However, the opioid crisis in North America is evolving. The number of deaths attributed to heroin and the non-medical use of pharmaceutical opioids such as oxycodone or hydrocodone has been declining over the past five years. - The crisis is now driven mainly by overdose deaths attributed to synthetic opioids such as fentanyl and its analogues. Among the reasons for the large number of overdose deaths attributed to fentanyls is that the lethal doses of them are often small when compared with other opioids. Fentanyl is up to 100 times more potent than morphine. - The impact of fentanyl is illustrated even further by the fact that more than half of the deaths attributed to heroin also involve fentanyls. Synthetic opioids also contribute significantly to the increased number of overdose deaths attributed to cocaine and other psychostimulants, such as methamphetamine.

This work presents simulation results for different mitigation and confinement scenarios for the propagation of COVID-19 in the metropolitan area of Madrid. These scenarios were implemented and tested using EpiGraph, an epidemic simulator which has been extended to simulate COVID-19 propagation. EpiGraph implements a social interaction model, which realistically captures a large number of characteristics of individuals and groups, as well as their individual interconnections, which are extracted from connection patterns in social networks. Besides the epidemiological and social interaction components, it also models people's short and long-distance movements as part of a transportation model. These features, together with the capacity to simulate scenarios with millions of individuals and apply different contention and mitigation measures, gives EpiGraph the potential to reproduce the COVID-19 evolution and study medium-term effects of the virus when applying mitigation methods. EpiGraph, obtains closely aligned infected and death curves related to the first wave in the Madrid metropolitan area, achieving similar seroprevalence values. We also show that selective lockdown for people over 60 would reduce the number of deaths. In addition, evaluate the effect of the use of face masks after the first wave, which shows that the percentage of people that comply with mask use is a crucial factor for mitigating the infection's spread.