Provisional counts of the number of deaths registered in England and Wales, by age, sex, region and Index of Multiple Deprivation (IMD), in the latest weeks for which data are available.

Updates

• Notice of data discontinuation: Since the start of the pandemic, AP has reported case and death counts from data provided by Johns Hopkins University. Johns Hopkins University has announced that they will stop their daily data collection efforts after March 10. As Johns Hopkins stops providing data, the AP will also stop collecting daily numbers for COVID cases and deaths. The HHS and CDC now collect and visualize key metrics for the pandemic. AP advises using those resources when reporting on the pandemic going forward.

  • CDC Weekly case and death counts (national and state level)
  • CDC County level cases and deaths
  • HHS New hospital admissions
  • CDC NowCast COVID variant proportions (national and regional level)

• April 9, 2020

  • The population estimate data for New York County, NY has been updated to include all five New York City counties (Kings County, Queens County, Bronx County, Richmond County and New York County). This has been done to match the Johns Hopkins COVID-19 data, which aggregates counts for the five New York City counties to New York County.

• April 20, 2020

  • Johns Hopkins death totals in the US now include confirmed and probable deaths in accordance with CDC guidelines as of April 14. One significant result of this change was an increase of more than 3,700 deaths in the New York City count. This change will likely result in increases for death counts elsewhere as well. The AP does not alter the Johns Hopkins source data, so probable deaths are included in this dataset as well.

• April 29, 2020

  • The AP is now providing timeseries data for counts of COVID-19 cases and deaths. The raw counts are provided here unaltered, along with a population column with Census ACS-5 estimates and calculated daily case and death rates per 100,000 people. Please read the updated caveats section for more information.

• September 1st, 2020

  • Johns Hopkins is now providing counts for the five New York City counties individually.

• February 12, 2021

  • The Ohio Department of Health recently announced that as many as 4,000 COVID-19 deaths may have been underreported through the state’s reporting system, and that the "daily reported death counts will be high for a two to three-day period."
  • Because deaths data will be anomalous for consecutive days, we have chosen to freeze Ohio's rolling average for daily deaths at the last valid measure until Johns Hopkins is able to back-distribute the data. The raw daily death counts, as reported by Johns Hopkins and including the backlogged death data, will still be present in the new_deaths column.

• February 16, 2021

  - Johns Hopkins has reconciled Ohio's historical deaths data with the state.

  Overview

The AP is using data collected by the Johns Hopkins University Center for Systems Science and Engineering as our source for outbreak caseloads and death counts for the United States and globally.

The Hopkins data is available at the county level in the United States. The AP has paired this data with population figures and county rural/urban designations, and has calculated caseload and death rates per 100,000 people. Be aware that caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.

This data is from the Hopkins dashboard that is updated regularly throughout the day. Like all organizations dealing with data, Hopkins is constantly refining and cleaning up their feed, so there may be brief moments where data does not appear correctly. At this link, you’ll find the Hopkins daily data reports, and a clean version of their feed.

The AP is updating this dataset hourly at 45 minutes past the hour.

To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

Queries

Use AP's queries to filter the data or to join to other datasets we've made available to help cover the coronavirus pandemic

• Filter cases by state here

• Rank states by their status as current hotspots. Calculates the 7-day rolling average of new cases per capita in each state: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=481e82a4-1b2f-41c2-9ea1-d91aa4b3b1ac

• Find recent hotspots within your state by running a query to calculate the 7-day rolling average of new cases by capita in each county: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=b566f1db-3231-40fe-8099-311909b7b687&showTemplatePreview=true

• Join county-level case data to an earlier dataset released by AP on local hospital capacity here. To find out more about the hospital capacity dataset, see the full details.

• Pull the 100 counties with the highest per-capita confirmed cases here

• Rank all the counties by the highest per-capita rate of new cases in the past 7 days here. Be aware that because this ranks per-capita caseloads, very small counties may rise to the very top, so take into account raw caseload figures as well.

Interactive

The AP has designed an interactive map to track COVID-19 cases reported by Johns Hopkins.

@(https://datawrapper.dwcdn.net/nRyaf/15/)

Interactive Embed Code

<iframe title="USA counties (2018) choropleth map Mapping COVID-19 cases by county" aria-describedby="" id="datawrapper-chart-nRyaf" src="https://datawrapper.dwcdn.net/nRyaf/10/" scrolling="no" frameborder="0" style="width: 0; min-width: 100% !important;" height="400"></iframe><script type="text/javascript">(function() {'use strict';window.addEventListener('message', function(event) {if (typeof event.data['datawrapper-height'] !== 'undefined') {for (var chartId in event.data['datawrapper-height']) {var iframe = document.getElementById('datawrapper-chart-' + chartId) || document.querySelector("iframe[src*='" + chartId + "']");if (!iframe) {continue;}iframe.style.height = event.data['datawrapper-height'][chartId] + 'px';}}});})();</script>

Caveats

• This data represents the number of cases and deaths reported by each state and has been collected by Johns Hopkins from a number of sources cited on their website.
• In some cases, deaths or cases of people who've crossed state lines -- either to receive treatment or because they became sick and couldn't return home while traveling -- are reported in a state they aren't currently in, because of state reporting rules.
• In some states, there are a number of cases not assigned to a specific county -- for those cases, the county name is "unassigned to a single county"
• This data should be credited to Johns Hopkins University's COVID-19 tracking project. The AP is simply making it available here for ease of use for reporters and members.
• Caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.
• Population estimates at the county level are drawn from 2014-18 5-year estimates from the American Community Survey.
• The Urban/Rural classification scheme is from the Center for Disease Control and Preventions's National Center for Health Statistics. It puts each county into one of six categories -- from Large Central Metro to Non-Core -- according to population and other characteristics. More details about the classifications can be found here.

Johns Hopkins timeseries data - Johns Hopkins pulls data regularly to update their dashboard. Once a day, around 8pm EDT, Johns Hopkins adds the counts for all areas they cover to the timeseries file. These counts are snapshots of the latest cumulative counts provided by the source on that day. This can lead to inconsistencies if a source updates their historical data for accuracy, either increasing or decreasing the latest cumulative count. - Johns Hopkins periodically edits their historical timeseries data for accuracy. They provide a file documenting all errors in their timeseries files that they have identified and fixed here

Attribution

This data should be credited to Johns Hopkins University COVID-19 tracking project

Number and percentage of deaths, by month and place of residence, 1991 to most recent year.

Effect of suicide rates on life expectancy dataset

Abstract
In 2015, approximately 55 million people died worldwide, of which 8 million committed suicide. In the USA, one of the main causes of death is the aforementioned suicide, therefore, this experiment is dealing with the question of how much suicide rates affects the statistics of average life expectancy.
The experiment takes two datasets, one with the number of suicides and life expectancy in the second one and combine data into one dataset. Subsequently, I try to find any patterns and correlations among the variables and perform statistical test using simple regression to confirm my assumptions.

Data

The experiment uses two datasets - WHO Suicide Statistics[1] and WHO Life Expectancy[2], which were firstly appropriately preprocessed. The final merged dataset to the experiment has 13 variables, where country and year are used as index: Country, Year, Suicides number, Life expectancy, Adult Mortality, which is probability of dying between 15 and 60 years per 1000 population, Infant deaths, which is number of Infant Deaths per 1000 population, Alcohol, which is alcohol, recorded per capita (15+) consumption, Under-five deaths, which is number of under-five deaths per 1000 population, HIV/AIDS, which is deaths per 1 000 live births HIV/AIDS, GDP, which is Gross Domestic Product per capita, Population, Income composition of resources, which is Human Development Index in terms of income composition of resources, and Schooling, which is number of years of schooling.

LICENSE

THE EXPERIMENT USES TWO DATASET - WHO SUICIDE STATISTICS AND WHO LIFE EXPECTANCY, WHICH WERE COLLEECTED FROM WHO AND UNITED NATIONS WEBSITE. THEREFORE, ALL DATASETS ARE UNDER THE LICENSE ATTRIBUTION-NONCOMMERCIAL-SHAREALIKE 3.0 IGO (https://creativecommons.org/licenses/by-nc-sa/3.0/igo/).

[1] https://www.kaggle.com/szamil/who-suicide-statistics

[2] https://www.kaggle.com/kumarajarshi/life-expectancy-who

The dataset shows death claims accepted by the CNESST from January 1 to December 31. The CNESST administers the occupational health and safety regime. The Law on Industrial Accidents and Occupational Diseases (LATMP) aims to compensate for occupational injuries and the consequences they cause for beneficiaries. The death claims presented in the data set meet the following criteria: * They are the consequence of a work accident or an occupational disease within the meaning of the LATMP. * These claims represent people who were covered by the occupational health and safety insurance plan administered by the CNESST. * The date of registration of the acceptance of the death claim is between January 1 and December 31 of the reference year. Note that the death may have occurred during a year prior to the reference year.

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.

La CNESST listens to your data needs! Answer our questionnaire.## Do you use or have specific needs in connection with [CNESST] data (https://www.cnesst.gouv.qc.ca/fr)? Take a few minutes to answer the following questionnaire: Consultation on your CNESST data needs To communicate with us about: - the questionnaire, write to us about: - the questionnaire, write to us at donnees.ouvertes@cnesst.gouv.qc.ca. - of a technical problem, write to us at consultation@cnesst.gouv.qc.ca or call us on 1 donnees.ouvertes@cnesst.gouv.qc.ca 866 216-7918, specifying the title of the consultation. Thanks for your participation! # # The dataset shows death claims accepted by the CNESST from January 1 to December 31. The CNESST administers the occupational health and safety regime. The Law on Industrial Accidents and Occupational Diseases (LATMP) aims to compensate for occupational injuries and the consequences they cause for beneficiaries. The death claims presented in the data set meet the following criteria: * They are the consequence of a work accident or an occupational disease within the meaning of the LATMP. * These claims represent people who were covered by the occupational health and safety insurance plan administered by the CNESST. * The date of registration of the acceptance of the death claim is between January 1 and December 31 of the reference year. Note that the death may have occurred during a year prior to the reference year.

I have created this dataset for people interested in League of Legends who want to approach the game from a more analytical side.

Most of the data was acquired from Games of Legends (https://gol.gg/tournament/tournament-stats/LEC%20Winter%202025/) and also from official account of the League of Legends EMEA Championship (https://www.youtube.com/c/LEC)

Dataset Contents:

• Player: Name of the player.
• Role: Role of the player (e.g., TOP, JUNGLE, MID, ADC, SUPPORT)
• Team: Name of the player's team
• Opponent_Team: Name of the opposing team
• Opponent_Player: Name of the opposing player
• Date: Date of the match
• Round: Round of the tournament
• Day: Specific day of the tournament
• Patch: Version of the game patch during the match
• Stage: Stage of the tournament
• No_Game: Game number in the series
• all_Games: Total number of games in the series
• Format: Format of the match (e.g., Best of 1, Best of 3)
• Game_of_day: Number of the game that day
• Side: Side of the map the team started on (Blue/Red)
• Time: Duration of the match

Team Performance Metrics:

• Kills_Team: Total kills by the team
• Turrets_Team: Total turrets destroyed by the team
• Dragon_Team: Total dragons killed by the team
• Baron_Team: Total barons killed by the team

Player Performance Metrics:

• Level: Final level of the player
• Kills: Number of kills by the player
• Deaths: Number of deaths of the player
• Assists: Number of assists by the player
• KDA: Kill/Death/Assist ratio
• CS: Creep Score (minions killed)
• CS in Team's Jungle: Creep Score in the team's jungle
• CS_in_Enemy_Jungle: Creep Score in the enemy's jungle
• CSM: Creep Score per Minute
• Golds: Total gold earned
• GPM: Gold Per Minute
• GOLD%: Percentage of team's total gold earned by the player

Vision and Warding:

• Vision_Score: Total vision score
• Wards_placed: Number of wards placed
• Wards_destroyed: Number of wards destroyed
• Control_Wards_Purchased: Number of control wards purchased
• Detector_Wards_Placed: Number of detector wards placed
• VSPM: Vision Score Per Minute
• WPM: Wards Placed per Minute
• VWPM: Vision Wards Placed per Minute
• WCPM: Wards Cleared per Minute
• VS%: Vision Score percentage

Damage Metrics:

• Total_damage_to_Champion: Total damage dealt to champions
• Physical_Damage: Total physical damage dealt
• Magic_Damage: Total magic damage dealt
• True_Damage: Total true damage dealt
• DPM: Damage Per Minute
• DMG%: Percentage of team’s total damage dealt by the player

Combat Metrics

• K+A_Per_Minute: Kills and Assists per Minute
• KP%: Kill Participation percentage
• Solo_kills: Number of solo kills
• Double_kills: Number of double kills
• Triple_kills: Number of triple kills
• Quadra_kills: Number of quadra kills
• Penta_kills: Number of pentakills

Early Game Metrics

• GD@15: Gold Difference at 15 minutes
• CSD@15: Creep Score Difference at 15 minutes
• XPD@15: Experience Difference at 15 minutes
• LVLD@15: Level Difference at 15 minutes

Objective Control:

• Objectives_Stolen: Number of objectives stolen
• Damage_dealt_to_turrets: Total damage dealt to turrets
• Damage_dealt_to_buildings: Total damage dealt to buildings

Healing and Mitigation:

• Total_heal: Total healing done
• Total_Heals_On_Teammates: Total healing done on teammates
• Damage_self_mitigated: Total damage self-mitigated
• Total_Damage_Shielded_On_Teammates: Total damage shielded on teammates

Crowd Control Metrics:

• Time_ccing_others: Time spent crowd controlling others
• Total_Time_CC_Dealt: Total crowd control time dealt

Survival and Economy:

• Total_damage_taken: Total damage taken
• Total_Time_Spent_Dead: Total time spent dead
• Consumables_purchased: Number of consumables purchased
• Items_Purchased: Number of items purchased
• Shutdown_bounty_collected: Total shutdown bounty collected
• Shutdown_bounty_lost: Total shutdown bounty lost

Currently, there are many datasets describing landslides caused by individual earthquakes, and global inventories of earthquake-induced landslides (EQIL). However, until recently, there were no datasets that provide a comprehensive description of the impacts of earthquake-induced landslide events. In this data release, we present an up-to-date, comprehensive global database containing all literature-documented earthquake-induced landslide events for the 249-year period from 1772 through August 2021. The database represents an update of the catalog developed by Seal et al. (2020), which summarized events through March 2020 and was based on the catalog developed by Nowicki Jessee et al. (2020). The revised catalog contains 281 historical earthquakes, 162 of which include documented landslide fatality counts. This represents an addition of 17 earthquakes since the previous version, 9 with documented landslide fatalities, and a removal of 2 duplicate entries. The database includes (where available) information on earthquake size (moment magnitude (Mw), surface-wave magnitude (Ms), and body-wave magnitude (mb)), depth, earthquake fault type, date and time, location, the availability of a ShakeMap, which estimates the spatial distribution of ground shaking from the USGS ShakeMap system (Worden and Wald, 2016), the availability of a geospatial landslide inventory, information about landslide occurrence (number of landslides, area or volume of landsliding, area affected by landsliding, landslide magnitude), earthquake/landslide impact (total fatalities, landslide fatalities, and number of injuries due to the effects of the earthquake), and USGS Ground Failure Tool estimates (estimated area and population exposed to landsliding). The full dataset of all known landslide-triggering events is provided as “EQIL Database 2022.csv,” including information on the data source(s) for each data component. A subset of the dataset, showing only those events for which landslide fatality counts are available, is provided as “EQIL Database LSFatality 2022.csv.” This subset only includes those columns from "EQIL Database 2022.csv" which are necessary for landslide fatality data analysis and omits columns such as source columns and secondary values.

Finding a good data source is the first step toward creating a database. Cardiovascular illnesses (CVDs) are the major cause of death worldwide. CVDs include coronary heart disease, cerebrovascular disease, rheumatic heart disease, and other heart and blood vessel problems. According to the World Health Organization, 17.9 million people die each year. Heart attacks and strokes account for more than four out of every five CVD deaths, with one-third of these deaths occurring before the age of 70 A comprehensive database for factors that contribute to a heart attack has been constructed , The main purpose here is to collect characteristics of Heart Attack or factors that contribute to it. As a result, a form is created to accomplish this. Microsoft Excel was used to create this form. Figure 1 depicts the form which It has nine fields, where eight fields for input fields and one field for output field. Age, gender, heart rate, systolic BP, diastolic BP, blood sugar, CK-MB, and Test-Troponin are representing the input fields, while the output field pertains to the presence of heart attack, which is divided into two categories (negative and positive).negative refers to the absence of a heart attack, while positive refers to the presence of a heart attack.Table 1 show the detailed information and max and min of values attributes for 1319 cases in the whole database.To confirm the validity of this data, we looked at the patient files in the hospital archive and compared them with the data stored in the laboratories system. On the other hand, we interviewed the patients and specialized doctors. Table 2 is a sample for 1320 cases, which shows 44 cases and the factors that lead to a heart attack in the whole database,After collecting this data, we checked the data if it has null values (invalid values) or if there was an error during data collection. The value is null if it is unknown. Null values necessitate special treatment. This value is used to indicate that the target isn’t a valid data element. When trying to retrieve data that isn't present, you can come across the keyword null in Processing. If you try to do arithmetic operations on a numeric column with one or more null values, the outcome will be null. An example of a null values processing is shown in Figure 2.The data used in this investigation were scaled between 0 and 1 to guarantee that all inputs and outputs received equal attention and to eliminate their dimensionality. Prior to the use of AI models, data normalization has two major advantages. The first is to avoid overshadowing qualities in smaller numeric ranges by employing attributes in larger numeric ranges. The second goal is to avoid any numerical problems throughout the process.After completion of the normalization process, we split the data set into two parts - training and test sets. In the test, we have utilized1060 for train 259 for testing Using the input and output variables, modeling was implemented.

Number, rate and percentage changes in rates of homicide victims, Canada, provinces and territories, 1961 to 2024.

Number and percentage of homicide victims, by type of firearm used to commit the homicide (total firearms; handgun; rifle or shotgun; fully automatic firearm; sawed-off rifle or shotgun; firearm-like weapons; other firearms, type unknown), Canada, 1974 to 2018.

A dataset of 12-lead ECGs with annotations. The dataset contains 345 779 exams from 233 770 patients. It was obtained through stratified sampling from the CODE dataset ( 15% of the patients). The data was collected by the Telehealth Network of Minas Gerais in the period between 2010 and 2016.

This repository contains the files `exams.csv` and the files `exams_part{i}.zip` for i = 0, 1, 2, ... 17.

• "exams.csv": is a comma-separated values (csv) file containing the columns
  • "exam_id": id used for identifying the exam;
  • "age": patient age in years at the moment of the exam;
  • "is_male": true if the patient is male;
  • "nn_predicted_age": age predicted by a neural network to the patient. As described in the paper "Deep neural network estimated electrocardiographic-age as a mortality predictor" bellow.
  • "1dAVb": Whether or not the patient has 1st degree AV block;
  • "RBBB": Whether or not the patient has right bundle branch block;
  • "LBBB": Whether or not the patient has left bundle branch block;
  • "SB": Whether or not the patient has sinus bradycardia;
  • "AF": Whether or not the patient has atrial fibrillation;
  • "ST": Whether or not the patient has sinus tachycardia;
  • "patient_id": id used for identifying the patient;
  • "normal_ecg": True if automatic annotation system say it is a normal ECG;
  • "death": true if the patient dies in the follow-up time. This data is available only in the first exam of the patient. Other exams will have this as an empty field;
  • "timey": if the patient dies it is the time to the death of the patient. If not, it is the follow-up time. This data is available only in the first exam of the patient. Other exams will have this as an empty field;
  • "trace_file": identify in which hdf5 file the file corresponding to this patient is located.
• "exams_part{i}.hdf5": The HDF5 file containing two datasets named `tracings` and other named `exam_id`. The `exam_id` is a tensor of dimension `(N,)` containing the exam id (the same as in the csv file) and the dataset `tracings` is a `(N, 4096, 12)` tensor containing the ECG tracings in the same order. The first dimension corresponds to the different exams; the second dimension corresponds to the 4096 signal samples; the third dimension to the 12 different leads of the ECG exams in the following order: `{DI, DII, DIII, AVR, AVL, AVF, V1, V2, V3, V4, V5, V6}`. The signals are sampled at 400 Hz. Some signals originally have a duration of 10 seconds (10 * 400 = 4000 samples) and others of 7 seconds (7 * 400 = 2800 samples). In order to make them all have the same size (4096 samples), we fill them with zeros on both sizes. For instance, for a 7 seconds ECG signal with 2800 samples we include 648 samples at the beginning and 648 samples at the end, yielding 4096 samples that are then saved in the hdf5 dataset.

  In python, one can read this file using h5py.
  ```python
  import h5py

  f = h5py.File(path_to_file, 'r')
  # Get ids
  traces_ids = np.array(self.f['id_exam'])
  x = f['signal']
  ```
  The `signal` dataset is too large to fit in memory, so don't convert it to a numpy array all at once.
  It is possible to access a chunk of it using: ``x[start:end, :, :]``.

The CODE dataset was collected by the Telehealth Network of Minas Gerais (TNMG) in the period between 2010 and 2016. TNMG is a public telehealth system assisting 811 out of the 853 municipalities in the state of Minas Gerais, Brazil. The dataset is described

Ribeiro, Antônio H., Manoel Horta Ribeiro, Gabriela M. M. Paixão, Derick M. Oliveira, Paulo R. Gomes, Jéssica A. Canazart, Milton P. S. Ferreira, et al. “Automatic Diagnosis of the 12-Lead ECG Using a Deep Neural Network.” Nature Communications 11, no. 1 (2020): 1760. https://doi.org/10.1038/s41467-020-15432-4

The CODE 15% dataset is obtained from stratified sampling from the CODE dataset. This subset of the code dataset is described in and used for assessing model performance:
"Deep neural network estimated electrocardiographic-age as a mortality predictor"
Emilly M Lima, Antônio H Ribeiro, Gabriela MM Paixão, Manoel Horta Ribeiro, Marcelo M Pinto Filho, Paulo R Gomes, Derick M Oliveira, Ester C Sabino, Bruce B Duncan, Luana Giatti, Sandhi M Barreto, Wagner Meira Jr, Thomas B Schön, Antonio Luiz P Ribeiro. MedRXiv (2021) https://www.doi.org/10.1101/2021.02.19.21251232

The companion code for reproducing the experiments in the two papers described above can be found, respectively, in:
- https://github.com/antonior92/automatic-ecg-diagnosis; and in,
- https://github.com/antonior92/ecg-age-prediction.

Note about authorship: Antônio H. Ribeiro, Emilly M. Lima and Gabriela M.M. Paixão contributed equally to this work.

Coronavirus (COVID-19) Data in the United States

[ U.S. State-Level Data (Raw CSV) | U.S. County-Level Data (Raw CSV) ]

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.

United States Data

Data on cumulative coronavirus cases and deaths can be found in two files for states and counties.

Each row of data reports cumulative counts based on our best reporting up to the moment we publish an update. We do our best to revise earlier entries in the data when we receive new information.

Both files contain FIPS codes, a standard geographic identifier, to make it easier for an analyst to combine this data with other data sets like a map file or population data.

Download all the data or clone this repository by clicking the green "Clone or download" button above.

State-Level Data

State-level data can be found in the states.csv file. (Raw CSV file here.)

date,state,fips,cases,deaths
2020-01-21,Washington,53,1,0
...

County-Level Data

County-level data can be found in the counties.csv file. (Raw CSV file here.)

date,county,state,fips,cases,deaths
2020-01-21,Snohomish,Washington,53061,1,0
...

In some cases, the geographies where cases are reported do not map to standard county boundaries. See the list of geographic exceptions for more detail on these.

Methodology and Definitions

The data is the product of dozens of journalists working across several time zones to monitor news conferences, analyze data releases and seek clarification from public officials on how they categorize cases.

It is also a response to a fragmented American public health system in which overwhelmed public servants at the state, county and territorial levels have sometimes struggled to report information accurately, consistently and speedily. On several occasions, officials have corrected information hours or days after first reporting it. At times, cases have disappeared from a local government database, or officials have moved a patient first identified in one state or county to another, often with no explanation. In those instances, which have become more common as the number of cases has grown, our team has made every effort to update the data to reflect the most current, accurate information while ensuring that every known case is counted.

When the information is available, we count patients where they are being treated, not necessarily where they live.

In most instances, the process of recording cases has been straightforward. But because of the patchwork of reporting methods for this data across more than 50 state and territorial governments and hundreds of local health departments, our journalists sometimes had to make difficult interpretations about how to count and record cases.

For those reasons, our data will in some cases not exactly match the information reported by states and counties. Those differences include these cases: When the federal government arranged flights to the United States for Americans exposed to the coronavirus in China and Japan, our team recorded those cases in the states where the patients subsequently were treated, even though local health departments generally did not. When a resident of Florida died in Los Angeles, we recorded her death as having occurred in California rather than Florida, though officials in Florida counted her case in their records. And when officials in some states reported new cases without immediately identifying where the patients were being treated, we attempted to add information about their locations later, once it became available.

• Confirmed Cases

Confirmed cases are patients who test positive for the coronavirus. We consider a case confirmed when it is reported by a federal, state, territorial or local government agency.

• Dates

For each date, we show the cumulative number of confirmed cases and deaths as reported that day in that county or state. All cases and deaths are counted on the date they are first announced.

• Counties

In some instances, we report data from multiple counties or other non-county geographies as a single county. For instance, we report a single value for New York City, comprising the cases for New York, Kings, Queens, Bronx and Richmond Counties. In these instances, the FIPS code field will be empty. (We may assign FIPS codes to these geographies in the future.) See the list of geographic exceptions.

Cities like St. Louis and Baltimore that are administered separately from an adjacent county of the same name are counted separately.

• “Unknown” Counties

Many state health departments choose to report cases separately when the patient’s county of residence is unknown or pending determination. In these instances, we record the county name as “Unknown.” As more information about these cases becomes available, the cumulative number of cases in “Unknown” counties may fluctuate.

Sometimes, cases are first reported in one county and then moved to another county. As a result, the cumulative number of cases may change for a given county.

Geographic Exceptions

• New York City

All cases for the five boroughs of New York City (New York, Kings, Queens, Bronx and Richmond counties) are assigned to a single area called New York City.

• Kansas City, Mo.

Four counties (Cass, Clay, Jackson, and Platte) overlap the municipality of Kansas City, Mo. The cases and deaths that we show for these four counties are only for the portions exclusive of Kansas City. Cases and deaths for Kansas City are reported as their line.

• Alameda, Calif.

Counts for Alameda County include cases and deaths from Berkeley and the Grand Princess cruise ship.

• Chicago

All cases and deaths for Chicago are reported as part of Cook County.

License and Attribution

In general, we are making this data publicly available for broad, noncommercial public use including by medical and public health researchers, policymakers, analysts and local news media.

If you use this data, you must attribute it to “The New York Times” in any publication. If you would like a more expanded description of the data, you could say “Data from The New York Times, based on reports from state and local health agencies.”

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Contributors

Mitch Smith, Karen Yourish, Sarah Almukhtar, Keith Collins, Danielle Ivory, and Amy Harmon have been leading our U.S. data collection efforts.

Data has also been compiled by Jordan Allen, Jeff Arnold, Aliza Aufrichtig, Mike Baker, Robin Berjon, Matthew Bloch, Nicholas Bogel-Burroughs, Maddie Burakoff, Christopher Calabrese, Andrew Chavez, Robert Chiarito, Carmen Cincotti, Alastair Coote, Matt Craig, John Eligon, Tiff Fehr, Andrew Fischer, Matt Furber, Rich Harris, Lauryn Higgins, Jake Holland, Will Houp, Jon Huang, Danya Issawi, Jacob LaGesse, Hugh Mandeville, Patricia Mazzei, Allison McCann, Jesse McKinley, Miles McKinley, Sarah Mervosh, Andrea Michelson, Blacki Migliozzi, Steven Moity, Richard A. Oppel Jr., Jugal K. Patel, Nina Pavlich, Azi Paybarah, Sean Plambeck, Carrie Price, Scott Reinhard, Thomas Rivas, Michael Robles, Alison Saldanha, Alex Schwartz, Libby Seline, Shelly Seroussi, Rachel Shorey, Anjali Singhvi, Charlie Smart, Ben Smithgall, Steven Speicher, Michael Strickland, Albert Sun, Thu Trinh, Tracey Tully, Maura Turcotte, Miles Watkins, Jeremy White, Josh Williams, and Jin Wu.

Context

There's a story behind every dataset and here's your opportunity to share yours.# Coronavirus (Covid-19) Data in the United States

[ U.S. State-Level Data ([Raw

PurposeThe risk of cardiovascular disease (CVD) mortality in patients with localized prostate cancer (PCa) by risk stratification remains unclear. The aim of this study was to determine the risk of CVD death in patients with localized PCa by risk stratification.Patients and methodsPopulation-based study of 340,806 cases in the Surveillance, Epidemiology, and End Results (SEER) database diagnosed with localized PCa between 2004 and 2016. The proportion of deaths identifies the primary cause of death, the competing risk model identifies the interaction between CVD and PCa, and the standardized mortality rate (SMR) quantifies the risk of CVD death in patients with PCa.ResultsCVD-related death was the leading cause of death in patients with localized PCa, and cumulative CVD-related death also surpassed PCa almost as soon as PCa was diagnosed in the low- and intermediate-risk groups. However, in the high-risk group, CVD surpassed PCa approximately 90 months later. Patients with localized PCa have a higher risk of CVD-related death compared to the general population and the risk increases steadily with survival (SMR = 4.8, 95% CI 4.6–5.1 to SMR = 13.6, 95% CI 12.8–14.5).ConclusionsCVD-related death is a major competing risk in patients with localized PCa, and cumulative CVD mortality increases steadily with survival time and exceeds PCa in all three stratifications (low, intermediate, and high risk). Patients with localized PCa have a higher CVD-related death than the general population. Management of patients with localized PCa requires attention to both the primary cancer and CVD.

This datasets displays the locations of all recorded earthquakes of a magnitude of 1 or greater around the world from the period of 6.30.08 to 7.7.08. The findings are from the US Geological Survey (USGS). Earthquake information is extracted from a merged catalog of earthquakes located by the USGS and contributing networks. Earthquakes will be broadcast within a few minutes for California events and within 30-minutes for world-wide events.

Über die Entstehung von Medicanes: Medicanes, ein Kunstwort, das aus den Begriffen »mediterranean« (engl. für mittelmeerisch) und »Hurricane« zusammengesetzt ist, sind gelegentlich beobachtete mesoskalige Zyklonen (Durchmesser bis zu 300 km) mit einer tropenähnlichen Struktur, die sich sporadisch über dem Mittelmeer bilden und einige dynamische und strukturelle Ähnlichkeiten mit tropischen Wirbelstürmen aufweisen. Sie wurden im westlichen und zentralen Mittelmeerraum, aber kaum im Osten beobachtet, und die Analyse der Zyklon-Spuren identifiziert zwei bevorzugte Gebiete: die Balearen und das Ionische Meer. Diese Stürme entwickeln sich eingebettet in Zyklonen größeren Ausmaßes, die Kaltlufteinbrüche über dem Mittelmeer verursachen. In der Regel charakterisiert ein kalter Kern mit bemerkenswertem Potential zur Wirbelbildung in höheren Stockwerken. Allerdings erreichen diese Systeme nicht die Hurrikanstärke (33 m/s an Windgeschwindigkeit bei 10 m, gemittelt über 10 Minuten). Medicanes werden von einer Kombination aus starken Winden und starken Niederschlägen begleitet, die gelegentlich schwere Schäden in der Infrastruktur, der Landwirtschaft und den Kommunikations- und Verkehrsnetzen verursachen oder zu Überschwemmungen in den Küstengebieten des Mittelmeers führen, die ein Risiko für Menschenleben darstellen. Eine bessere Kenntnis dieser Stürme ist notwendig, um Verluste und starke Auswirkungen auf die sozialen Systeme im Mittelmeerraum zu verhindern und zu verringern. Klimaszenarien ergeben ein wärmeres Mittelmeer und nach der angenommenen Dynamik der Medicanes könnte eine höhere Meeresoberflächentemperatur zu stärkeren Medicanes oder tropischen Wirbelstürmen führen. About the emergence of medicanes; Medicanes, an artificial word composed of the terms »mediterranean« and »hurricane«, are occasionally observed mesoscale cyclones (diameter up to 300 km) with a tropical-like structure that form sporadically over the Mediterranean Sea and present some dynamical and structural similarities with the tropical storms. They have been observed in the western and central Mediterranean but scarcely in the east, and the analysis of the cyclone tracks identifies two preferred areas of occurrence: the Balearic Islands and the Ionian sea. These depressions develop embedded in mature larger scale cyclones that cause cold air intrusions over the Mediterranean. Usually, a cold core cut off low with remarkable potential vorticity characterizes the higher levels. Although these systems do not reached the hurricane strength (33 m/s of wind speed at 10 m averaged over 10 minutes). Medicanes are accompanied by a combination of intense winds and heavy precipitation, causing occasional severe damages in infrastructure, agriculture and communication and transport networks, or resulting in flooding of populated areas in the coastal lines of the Mediterranean Sea, posing a risk to human life. A better knowledge of these storms is needed in order to prevent and diminish casualties and strong impact of the systems on the Mediterranean societies. Climate change scenarios give a warmer Mediterranean Sea and according to assumed dynamics of Medicanes, a higher Sea surface temperature might derive to stronger medicanes or tropical cyclones.

Context

It is been 20 years and still we remember how we have lost our love one's, our friend's, our people's . Here we're remembering all those Indian origin people's those who died on September 9 , 2001 at Twin Towers.

Content

Dataset contains 1 ) Names of victim's 2) Location (At the time of Impact) 3) Possible Floor where they work 4) Gender 5) Age 6) Resident 7) Offices where they use to work 8) Job Role or Occupation

Inspiration

On September 11, 2001, at 8:45 a.m. on a clear Tuesday morning, an American Airlines Boeing 767 loaded with 20,000 gallons of jet fuel crashed into the north tower of the World Trade Center in New York .The impact left a gaping, burning hole near the 80th floor of the 110-story skyscraper, instantly killing hundreds of people and trapping hundreds more in higher floors. As the evacuation of the tower and its twin got underway, television cameras broadcasted live images of what initially appeared to be a freak accident. Then, 18 minutes after the first plane hit, a second Boeing 767—United Airlines Flight 175—appeared out of the sky, turned sharply toward the World Trade Center and sliced into the south tower near the 60th floor. A total of 2,996 people were killed in the 9/11 attacks, including the 19 terrorist hijackers aboard the four airplanes. Citizens of 78 countries died in New York, Washington, D.C., and Pennsylvania.
At the World Trade Center, 2,763 died after the two planes slammed into the twin towers. That figure includes 343 firefighters and paramedics, 23 New York City police officers and 37 Port Authority police officers who were struggling to complete an evacuation of the buildings and save the office workers trapped on higher floors. ""More than 60 Indian origin and few Pakistanis and Bangladeshi's died and many of them are found missing after sorting the debris and dust"

Abstract copyright UK Data Service and data collection copyright owner.The National Child Development Study (NCDS) is a continuing longitudinal study that seeks to follow the lives of all those living in Great Britain who were born in one particular week in 1958. The aim of the study is to improve understanding of the factors affecting human development over the whole lifespan. The NCDS has its origins in the Perinatal Mortality Survey (PMS) (the original PMS study is held at the UK Data Archive under SN 2137). This study was sponsored by the National Birthday Trust Fund and designed to examine the social and obstetric factors associated with stillbirth and death in early infancy among the 17,000 children born in England, Scotland and Wales in that one week. Selected data from the PMS form NCDS sweep 0, held alongside NCDS sweeps 1-3, under SN 5565. Survey and Biomeasures Data (GN 33004):To date there have been nine attempts to trace all members of the birth cohort in order to monitor their physical, educational and social development. The first three sweeps were carried out by the National Children's Bureau, in 1965, when respondents were aged 7, in 1969, aged 11, and in 1974, aged 16 (these sweeps form NCDS1-3, held together with NCDS0 under SN 5565). The fourth sweep, also carried out by the National Children's Bureau, was conducted in 1981, when respondents were aged 23 (held under SN 5566). In 1985 the NCDS moved to the Social Statistics Research Unit (SSRU) - now known as the Centre for Longitudinal Studies (CLS). The fifth sweep was carried out in 1991, when respondents were aged 33 (held under SN 5567). For the sixth sweep, conducted in 1999-2000, when respondents were aged 42 (NCDS6, held under SN 5578), fieldwork was combined with the 1999-2000 wave of the 1970 Birth Cohort Study (BCS70), which was also conducted by CLS (and held under GN 33229). The seventh sweep was conducted in 2004-2005 when the respondents were aged 46 (held under SN 5579), the eighth sweep was conducted in 2008-2009 when respondents were aged 50 (held under SN 6137) and the ninth sweep was conducted in 2013 when respondents were aged 55 (held under SN 7669). Four separate datasets covering responses to NCDS over all sweeps are available. National Child Development Deaths Dataset: Special Licence Access (SN 7717) covers deaths; National Child Development Study Response and Outcomes Dataset (SN 5560) covers all other responses and outcomes; National Child Development Study: Partnership Histories (SN 6940) includes data on live-in relationships; and National Child Development Study: Activity Histories (SN 6942) covers work and non-work activities. Users are advised to order these studies alongside the other waves of NCDS.From 2002-2004, a Biomedical Survey was completed and is available under End User Licence (EUL) (SN 8731) and Special Licence (SL) (SN 5594). Proteomics analyses of blood samples are available under SL SN 9254.Linked Geographical Data (GN 33497): A number of geographical variables are available, under more restrictive access conditions, which can be linked to the NCDS EUL and SL access studies. Linked Administrative Data (GN 33396):A number of linked administrative datasets are available, under more restrictive access conditions, which can be linked to the NCDS EUL and SL access studies. These include a Deaths dataset (SN 7717) available under SL and the Linked Health Administrative Datasets (SN 8697) available under Secure Access.Additional Sub-Studies (GN 33562):In addition to the main NCDS sweeps, further studies have also been conducted on a range of subjects such as parent migration, unemployment, behavioural studies and respondent essays. The full list of NCDS studies available from the UK Data Service can be found on the NCDS series access data webpage. How to access genetic and/or bio-medical sample data from a range of longitudinal surveys:For information on how to access biomedical data from NCDS that are not held at the UKDS, see the CLS Genetic data and biological samples webpage.Further information about the full NCDS series can be found on the Centre for Longitudinal Studies website. The National Child Development Study (NCDS) originated in the Perinatal Mortality Survey (see SN 5565), which examined social and obstetric factors associated with still birth and infant mortality among over 17,000 babies born in Britain in one week in March 1958. Surviving members of this birth cohort have been surveyed on eight further occasions in order to monitor their changing health, education, social and economic circumstances - in 1965 at age 7, 1969 at age 11, 1974 at age 16 (the first three sweeps are also held under SN 5565), 1981 (age 23 - SN 5566), 1991 (age 33 - SN 5567), 1999/2000 (age 41/2 - SN 5578), 2004-2005 (age 46/47 - SN 5579), 2008-2009 (age 50 - SN 6137) and 2013 (age 55 - SN 7669).There have also been surveys of sub-samples of the cohort, the most recent occurring in 1995 (age 37), when a 10% representative sub-sample was assessed for difficulties with basic skills (SN 4992). Finally, during 2002-2004, 9,340 NCDS cohort members participated in a bio-medical survey, carried out by qualified nurses (SN 5594, available under more restrictive Special Licence access conditions; see catalogue record for details). The bio-medical survey did not cover any of the topics included in the 2004/2005 survey. Further NCDS data separate to the main surveys include a response and deaths dataset, parent migration studies, employment, activity and partnership histories, behavioural studies and essays - see the NCDS series page for details.Further information about the NCDS can be found on the Centre for Longitudinal Studies website.How to access genetic and/or bio-medical sample data from a range of longitudinal surveys:A useful overview of the governance routes for applying for genetic and bio-medical sample data, which are not available through the UK Data Service, can be found at Governance of data and sample access on the METADAC (Managing Ethico-social, Technical and Administrative issues in Data Access) website.Sample of Essays (Sweep 2, Age 11), 1969 When the children of the National Child Development Study (NCDS) were 11 years old, at the time of the NCDS 2 sweep, they were given a short questionnaire to complete at school about their interests outside school, the school subjects they enjoyed most, and what they thought they were most likely to do when they left secondary school. In addition, they were asked to write an essay about what they thought their life would be like at age 25. The instructions given were as follows: 'Imagine you are now 25 years old. Write about the life you are leading, your interests, your home life and your work at the age of 25. (You have 30 minutes to do this).' Of the 14,757 children who participated in the age 11 sweep of the NCDS (representing 90.8% of the target sample of 16,253 (Plewis et al. 2004), a total of 13,669 (92.6%) completed an essay about their imagined life at age 25. From this a sub-sample of essays was extracted for deposit based on three key variables: gender of the cohort member, social class and family background, and the ability of the cohort member using a general ability test. The original spelling and grammar of the essays was preserved. Users should note that a subset of 179 of these essays are also held with SN 6691, Social Participation and Identity, 2007-2010, a subproject conducted by CLS with a sample of NCDS participants at age 50.February 2021 releaseThis study was withdrawn in 2020, and then reinstated in February 2021 at the depositor's request. The title of the study has changed, but data and documentation materials remain the same. Main Topics: