Reporting of new Aggregate Case and Death Count data was discontinued May 11, 2023, with the expiration of the COVID-19 public health emergency declaration. This dataset will receive a final update on June 1, 2023, to reconcile historical data through May 10, 2023, and will remain publicly available.

Aggregate Data Collection Process Since the start of the COVID-19 pandemic, data have been gathered through a robust process with the following steps:

• A CDC data team reviews and validates the information obtained from jurisdictions’ state and local websites via an overnight data review process.
• If more than one official county data source exists, CDC uses a comprehensive data selection process comparing each official county data source, and takes the highest case and death counts respectively, unless otherwise specified by the state.
• CDC compiles these data and posts the finalized information on COVID Data Tracker.
• County level data is aggregated to obtain state and territory specific totals.

This process is collaborative, with CDC and jurisdictions working together to ensure the accuracy of COVID-19 case and death numbers. County counts provide the most up-to-date numbers on cases and deaths by report date. CDC may retrospectively update counts to correct data quality issues.

Methodology Changes Several differences exist between the current, weekly-updated dataset and the archived version:

• Source: The current Weekly-Updated Version is based on county-level aggregate count data, while the Archived Version is based on State-level aggregate count data.
• Confirmed/Probable Cases/Death breakdown:  While the probable cases and deaths are included in the total case and total death counts in both versions (if applicable), they were reported separately from the confirmed cases and deaths by jurisdiction in the Archived Version.  In the current Weekly-Updated Version, the counts by jurisdiction are not reported by confirmed or probable status (See Confirmed and Probable Counts section for more detail).
• Time Series Frequency: The current Weekly-Updated Version contains weekly time series data (i.e., one record per week per jurisdiction), while the Archived Version contains daily time series data (i.e., one record per day per jurisdiction).
• Update Frequency: The current Weekly-Updated Version is updated weekly, while the Archived Version was updated twice daily up to October 20, 2022.

Important note: The counts reflected during a given time period in this dataset may not match the counts reflected for the same time period in the archived dataset noted above. Discrepancies may exist due to differences between county and state COVID-19 case surveillance and reconciliation efforts.

Confirmed and Probable Counts In this dataset, counts by jurisdiction are not displayed by confirmed or probable status. Instead, confirmed and probable cases and deaths are included in the Total Cases and Total Deaths columns, when available. Not all jurisdictions report probable cases and deaths to CDC.* Confirmed and probable case definition criteria are described here:

Council of State and Territorial Epidemiologists (ymaws.com).

Deaths CDC reports death data on other sections of the website: CDC COVID Data Tracker: Home, CDC COVID Data Tracker: Cases, Deaths, and Testing, and NCHS Provisional Death Counts. Information presented on the COVID Data Tracker pages is based on the same source (to

Annual data on death registrations by single year of age for the UK (1974 onwards) and England and Wales (1963 onwards).

THIS DATASET WAS LAST UPDATED AT 2:10 AM EASTERN ON JUNE 29

OVERVIEW

2019 had the most mass killings since at least the 1970s, according to the Associated Press/USA TODAY/Northeastern University Mass Killings Database.

In all, there were 45 mass killings, defined as when four or more people are killed excluding the perpetrator. Of those, 33 were mass shootings . This summer was especially violent, with three high-profile public mass shootings occurring in the span of just four weeks, leaving 38 killed and 66 injured.

A total of 229 people died in mass killings in 2019.

The AP's analysis found that more than 50% of the incidents were family annihilations, which is similar to prior years. Although they are far less common, the 9 public mass shootings during the year were the most deadly type of mass murder, resulting in 73 people's deaths, not including the assailants.

One-third of the offenders died at the scene of the killing or soon after, half from suicides.

About this Dataset

The Associated Press/USA TODAY/Northeastern University Mass Killings database tracks all U.S. homicides since 2006 involving four or more people killed (not including the offender) over a short period of time (24 hours) regardless of weapon, location, victim-offender relationship or motive. The database includes information on these and other characteristics concerning the incidents, offenders, and victims.

The AP/USA TODAY/Northeastern database represents the most complete tracking of mass murders by the above definition currently available. Other efforts, such as the Gun Violence Archive or Everytown for Gun Safety may include events that do not meet our criteria, but a review of these sites and others indicates that this database contains every event that matches the definition, including some not tracked by other organizations.

This data will be updated periodically and can be used as an ongoing resource to help cover these events.

Using this Dataset

To get basic counts of incidents of mass killings and mass shootings by year nationwide, use these queries:

Mass killings by year

Mass shootings by year

To get these counts just for your state:

Filter killings by state

Definition of "mass murder"

Mass murder is defined as the intentional killing of four or more victims by any means within a 24-hour period, excluding the deaths of unborn children and the offender(s). The standard of four or more dead was initially set by the FBI.

This definition does not exclude cases based on method (e.g., shootings only), type or motivation (e.g., public only), victim-offender relationship (e.g., strangers only), or number of locations (e.g., one). The time frame of 24 hours was chosen to eliminate conflation with spree killers, who kill multiple victims in quick succession in different locations or incidents, and to satisfy the traditional requirement of occurring in a “single incident.”

Offenders who commit mass murder during a spree (before or after committing additional homicides) are included in the database, and all victims within seven days of the mass murder are included in the victim count. Negligent homicides related to driving under the influence or accidental fires are excluded due to the lack of offender intent. Only incidents occurring within the 50 states and Washington D.C. are considered.

Methodology

Project researchers first identified potential incidents using the Federal Bureau of Investigation’s Supplementary Homicide Reports (SHR). Homicide incidents in the SHR were flagged as potential mass murder cases if four or more victims were reported on the same record, and the type of death was murder or non-negligent manslaughter.

Cases were subsequently verified utilizing media accounts, court documents, academic journal articles, books, and local law enforcement records obtained through Freedom of Information Act (FOIA) requests. Each data point was corroborated by multiple sources, which were compiled into a single document to assess the quality of information.

In case(s) of contradiction among sources, official law enforcement or court records were used, when available, followed by the most recent media or academic source.

Case information was subsequently compared with every other known mass murder database to ensure reliability and validity. Incidents listed in the SHR that could not be independently verified were excluded from the database.

Project researchers also conducted extensive searches for incidents not reported in the SHR during the time period, utilizing internet search engines, Lexis-Nexis, and Newspapers.com. Search terms include: [number] dead, [number] killed, [number] slain, [number] murdered, [number] homicide, mass murder, mass shooting, massacre, rampage, family killing, familicide, and arson murder. Offender, victim, and location names were also directly searched when available.

This project started at USA TODAY in 2012.

Contacts

Contact AP Data Editor Justin Myers with questions, suggestions or comments about this dataset at jmyers@ap.org. The Northeastern University researcher working with AP and USA TODAY is Professor James Alan Fox, who can be reached at j.fox@northeastern.edu or 617-416-4400.

This dataset is about books. It has 2 rows and is filtered where the book is The many deaths of the Firefly Brothers. It features 7 columns including author, publication date, language, and book publisher.

A dataset to advance the study of life-cycle interactions of biomedical and socioeconomic factors in the aging process. The EI project has assembled a variety of large datasets covering the life histories of approximately 39,616 white male volunteers (drawn from a random sample of 331 companies) who served in the Union Army (UA), and of about 6,000 African-American veterans from 51 randomly selected United States Colored Troops companies (USCT). Their military records were linked to pension and medical records that detailed the soldiers������?? health status and socioeconomic and family characteristics. Each soldier was searched for in the US decennial census for the years in which they were most likely to be found alive (1850, 1860, 1880, 1900, 1910). In addition, a sample consisting of 70,000 men examined for service in the Union Army between September 1864 and April 1865 has been assembled and linked only to census records. These records will be useful for life-cycle comparisons of those accepted and rejected for service. Military Data: The military service and wartime medical histories of the UA and USCT men were collected from the Union Army and United States Colored Troops military service records, carded medical records, and other wartime documents. Pension Data: Wherever possible, the UA and USCT samples have been linked to pension records, including surgeon''''s certificates. About 70% of men in the Union Army sample have a pension. These records provide the bulk of the socioeconomic and demographic information on these men from the late 1800s through the early 1900s, including family structure and employment information. In addition, the surgeon''''s certificates provide rich medical histories, with an average of 5 examinations per linked recruit for the UA, and about 2.5 exams per USCT recruit. Census Data: Both early and late-age familial and socioeconomic information is collected from the manuscript schedules of the federal censuses of 1850, 1860, 1870 (incomplete), 1880, 1900, and 1910. Data Availability: All of the datasets (Military Union Army; linked Census; Surgeon''''s Certificates; Examination Records, and supporting ecological and environmental variables) are publicly available from ICPSR. In addition, copies on CD-ROM may be obtained from the CPE, which also maintains an interactive Internet Data Archive and Documentation Library, which can be accessed on the Project Website. * Dates of Study: 1850-1910 * Study Features: Longitudinal, Minority Oversamples * Sample Size: ** Union Army: 35,747 ** Colored Troops: 6,187 ** Examination Sample: 70,800 ICPSR Link: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06836

The numbers reflect incidents that were reported to and tracked by the Ministry of Labour. They exclude death from natural causes, death of non- workers at a workplace, suicides, death as a result of a criminal act or traffic accident (unless the OHSA is also implicated) and death from occupational exposures that occurred in the past.

Data from the Ministry of Labour reflects Occupational Health and Safety (OHS) and Employment Standards (ES) information at a point in time and/or for specific reporting purposes. As a result, the information above may not align with other data sources.

Notes on critical injuries :

For the purposes of the data provided, a critical injury of a serious nature includes injuries that:

1. "Place life in jeopardy"
2. "Produce unconsciousness"
3. "Result in substantial loss of blood"
4. "Involve the fracture of a leg or arm but not a finger or toe"
5. "Involve the amputation of a leg, arm, hand or foot but not a finger or toe"
6. "Consist of burns to a major portion of the body"
7. "Cause the loss of sight

Only critical injury events reported to the ministry are included here. This represents data that was reported to the ministry and may not represent what actually occurred at the workplace. The critical injury numbers represent critical injuries reported to the ministry and not necessarily critical injuries as defined by the Occupational Health and Safety Act (OHSA). Non- workers who are critically injured may also be included in the ministry's data. Critical injuries data is presented by calendar year to be consistent with Workplace Safety and Insurance Board harmonized data;

Data is reported based on calendar year

Individual data for the Health Care program is available for Jan. 1 to Mar. 31, 2011 only. From April 2011 onwards Health Care data is included in the Industrial Health and Safety numbers.

Notes on Fatalities :

Only events reported to the ministry are included here. The ministry tracks and reports fatalities at workplaces covered by the OHSA. This excludes death from natural causes, death of non-workers at a workplace, suicides, death as a result of a criminal act or traffic accident (unless the OHSA is also implicated) and death from occupational exposures that occurred many years ago. Fatalities data is presented by calendar year to be consistent with Workplace Safety and Insurance Board harmonized data. Fatality data is reported by year of event.

*[OHSA]: Occupational Health and Safety Act *[Mar.]: March *[Jan.]: January

The National Death Index (NDI) is a centralized database of death record information on file in state vital statistics offices. Working with these state offices, the National Center for Health Statistics (NCHS) established the NDI as a resource to aid epidemiologists and other health and medical investigators with their mortality ascertainment activities. Assists investigators in determining whether persons in their studies have died and, if so, provide the names of the states in which those deaths occurred, the dates of death, and the corresponding death certificate numbers. Investigators can then make arrangements with the appropriate state offices to obtain copies of death certificates or specific statistical information such as manner of death or educational level. Cause of death codes may also be obtained using the NDI Plus service. Records from 1979 through 2011 are currently available and contain a standard set of identifying information on each death. Death records are added to the NDI file annually, approximately 12 months after the end of a particular calendar year. 2012 should be available summer 2014. Early Release Program for 2013 is now available. The NDI service is available to investigators solely for statistical purposes in medical and health research. The service is not accessible to organizations or the general public for legal, administrative, or genealogy purposes.

COVID-19 Cases information is reported through the Pennsylvania State Department’s National Electronic Disease Surveillance System (PA-NEDSS). As new cases are passed to the Allegheny County Health Department they are investigated by case investigators. During investigation some cases which are initially determined by the State to be in the Allegheny County jurisdiction may change, which can account for differences between publication of the files on the number of cases, deaths and tests. Additionally, information is not always reported to the State in a timely manner, delays can range from days to weeks, which can also account for discrepancies between previous and current files. Test and Case information will be updated daily. This resource contains individuals who received a COVID-19 test and individuals whom are probable cases. Every day, these records are overwritten with updates. Each row in the data reflects a person that is tested, not tests that are conducted. People that are tested more than once will have their testing and case data updated using the following rules:

1. Positive tests overwrite negative tests.
2. Polymerase chain reaction (PCR) tests overwrite antibody or antigen (AG) tests.
3. The first positive PCR test is never overwritten. Data collected from additional tests do not replace the first positive PCR test.

Note: On April 4th 2022 the Pennsylvania Department of Health no longer required labs to report negative AG tests. Therefore aggregated counts that included AG tests have been removed from the Municipality/Neighborhood files going forward. Versions of this data up to this cut-off have been retained as archived files.

Individual Test information is also updated daily. This resource contains the details and results of individual tests along with demographic information of the individual tested. Only PCR and AG tests are included. Every day, these records are overwritten with updates. This resource should be used to determine positivity rates.

The remaining datasets provide statistics on death demographics. Demographic, municipality and neighborhood information for deaths are reported on a weekly schedule and are not included with individual cases or tests. This has been done to protect the privacy and security of individuals and their families in accordance with the Health Insurance Portability and Accountability Act (HIPAA). Municipality or City of Pittsburgh Neighborhood is based off the geocoded home address of the individual tested.

Individuals whose home address is incomplete may not be in Allegheny County but whose temporary residency, work or other mitigating circumstance are determined to be in Allegheny County by the Pennsylvania Department of Health are counted as "Undefined".

Since the start of the pandemic, the ACHD has mapped every day’s COVID tests, cases, and deaths to their Allegheny County municipality and neighborhood. Tests were mapped to patient address, and if this was not available, to the provider location. This has recently resulted in apparent testing rates that exceeded the populations of various municipalities -- mostly those with healthcare providers. As this was brought to our attention, the health department and our data partners began researching and comparing methods to most accurately display the data. This has led us to leave those with missing home addresses off the map. Although these data will still appear in test, case and death counts, there will be over 20,000 fewer tests and almost 1000 fewer cases on the map. In addition to these map changes, we have identified specific health systems and laboratories that had data uploading errors that resulted in missing locations, and are working with them to correct these errors.

Due to minor discrepancies in the Municipal boundary and the City of Pittsburgh Neighborhood files individuals whose City Neighborhood cannot be identified are be counted as “Undefined (Pittsburgh)”.

On May 19, 2023, with the rescinding of the COVID-19 public health emergency, changes in data and reporting mechanisms prompted a change to an annual data sharing schedule for tests, cases, hospitalizations, and deaths. Dates for annual release are TBD. The weekly municipal counts and individual data produced before this changed are maintained as archive files.

Support for Health Equity datasets and tools provided by Amazon Web Services (AWS) through their Health Equity Initiative.

A. SUMMARY Medical provider confirmed COVID-19 cases and confirmed COVID-19 related deaths in San Francisco, CA aggregated by Census ZIP Code Tabulation Areas and normalized by 2018 American Community Survey (ACS) 5-year estimates for population data to calculate rate per 10,000 residents.

Cases and deaths are both mapped to the residence of the individual, not to where they were infected or died. For example, if one was infected in San Francisco at work but lives in the East Bay, those are not counted as SF Cases or if one dies in Zuckerberg San Francisco General but is from another county, that is also not counted in this dataset.

Dataset is cumulative and covers cases going back to March 2nd, 2020 when testing began. It is updated daily.

B. HOW THE DATASET IS CREATED Addresses from medical data are geocoded by the San Francisco Department of Public Health (SFDPH). Those addresses are spatially joined to the geographic areas. Counts are generated based on the number of address points that match each geographic area. The 2018 ACS estimates for population provided by the Census are used to create a rate which is equal to ([count] / [acs_population]) * 10000) representing the number of cases per 10,000 residents.

C. UPDATE PROCESS Geographic analysis is scripted by SFDPH staff and synced to this dataset each day.

D. HOW TO USE THIS DATASET Privacy rules in effect To protect privacy, certain rules are in effect: 1. Case counts greater than 0 and less than 10 are dropped - these will be null (blank) values 2. Cases dropped altogether for areas where acs_population < 1000

Rate suppression in effect where counts lower than 20 Rates are not calculated unless the case count is greater than or equal to 20. Rates are generally unstable at small numbers, so we avoid calculating them directly. We advise you to apply the same approach as this is best practice in epidemiology.

A note on Census ZIP Code Tabulation Areas (ZCTAs) ZIP Code Tabulation Areas are special boundaries created by the U.S. Census based on ZIP Codes developed by the USPS. They are not, however, the same thing. ZCTAs are polygonal representations of USPS ZIP Code service area routes. Read how the Census develops ZCTAs on their website.

This dataset is a filtered view of another dataset You can find a full dataset of cases and deaths summarized by this and other geographic areas.

E. CHANGE LOG

• 9/11/2023 - data on COVID-19 cases and deaths summarized by ZIP code tabulation area are no longer being updated. This data is currently through 9/6/2023 and will not include any new data after this date.

This dataset is about book subjects. It has 1 row and is filtered where the books is Strange deaths. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

This dataset is about books. It has 1 row and is filtered where the book is The many deaths of Jew Süss : the notorious trial and execution of an eighteenth-century court Jew. It features 7 columns including author, publication date, language, and book publisher.

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Dead Lake township. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2021

Based on our analysis ACS 2017-2021 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Dead Lake township, the median income for all workers aged 15 years and older, regardless of work hours, was $52,154 for males and $34,319 for females.

These income figures highlight a substantial gender-based income gap in Dead Lake township. Women, regardless of work hours, earn 66 cents for each dollar earned by men. This significant gender pay gap, approximately 34%, underscores concerning gender-based income inequality in the township of Dead Lake township.

- Full-time workers, aged 15 years and older: In Dead Lake township, among full-time, year-round workers aged 15 years and older, males earned a median income of $66,881, while females earned $45,864, leading to a 31% gender pay gap among full-time workers. This illustrates that women earn 69 cents for each dollar earned by men in full-time roles. This level of income gap emphasizes the urgency to address and rectify this ongoing disparity, where women, despite working full-time, face a more significant wage discrepancy compared to men in the same employment roles.

Remarkably, across all roles, including non-full-time employment, women displayed a similar gender pay gap percentage. This indicates a consistent gender pay gap scenario across various employment types in Dead Lake township, showcasing a consistent income pattern irrespective of employment status.

https://i.neilsberg.com/ch/dead-lake-township-mn-income-by-gender.jpeg" alt="Dead Lake Township, Minnesota gender based income disparity">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2022-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2022
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Dead Lake township median household income by gender. You can refer the same here

Summary This layer has been DEPRECATED (last updated 12/1/2021). Was formerly a weekly update. The Outbreak-Associated Cases in Congregate Living data dashboard on coronavirus.maryland.gov was redesigned on 11/17/21 to align with other outbreak reporting. Visit https://opendata.maryland.gov/dataset/MD-COVID-19-Congregate-Outbreak/ey5n-qn5s to view Outbreak-Associated Cases in Congregate Living data as reported after 11/17/21. Confirmed COVID-19 deaths among Maryland residents who live and work in congregate living facilities in Maryland for the reporting period. Description The MD COVID-19 - Total Deaths in Congregate Facility Settings data layer is a total of deaths confirmed by a positive COVID-19 test result that have been reported to MDH in nursing homes, assisted living facilities, group homes of 10 or more and state and local facilities for the reporting period. Data are reported to MDH by local health departments, the Department of Public Safety and Correctional Services and the Department of Juvenile Services. To appear on the list, facilities report at least one confirmed case of COVID-19 over the prior 14 days. Facilities are removed from the list when health officials determine 14 days have passed with no new cases and no tests pending. The list provides a point-in-time picture of COVID-19 case activity among these facilities. Numbers reported for each facility listed reflect totals ever reported for deaths. Data are updated once weekly. 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.

## Attributes age (years) - age at death weight (lbs) sex (m/f) height (in.) sys_bp smoker y/n nic_other y/n num_meds occup_danger (0/1/2) - low, medium, high ls_danger - low, medium, high (lifesytle) canabis y/n opioids y/n other_drugs y/n drinks_aweek addiction y/n major_surgery_num diabetes y/n hds y/n (heart disease or stroke) cholesterol asthma y/n immune_defic y/n family_cancer y/n family_heart_disease y/n family_cholesterol y/n

This dataset is about books. It has 4 rows and is filtered where the book is Dead men. It features 7 columns including author, publication date, language, and book publisher.

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.

Note: The cumulative case count for some counties (with small population) is higher than expected due to the inclusion of non-permanent residents in COVID-19 case counts.

Reporting of Aggregate Case and Death Count data was discontinued on May 11, 2023, with the expiration of the COVID-19 public health emergency declaration. Although these data will continue to be publicly available, this dataset will no longer be updated.

Aggregate Data Collection Process Since the beginning of the COVID-19 pandemic, data were reported through a robust process with the following steps:

• Aggregate county-level counts were obtained indirectly, via automated overnight web collection, or directly, via a data submission process.
• If more than one official county data source existed, CDC used a comprehensive data selection process comparing each official county data source to retrieve the highest case and death counts, unless otherwise specified by the state.
• A CDC data team reviewed counts for congruency prior to integration. CDC routinely compiled these data and post the finalized information on COVID Data Tracker.
• Cases and deaths are based on date of report and not on the date of symptom onset. CDC calculates rates in this data by using population estimates provided by the US Census Bureau Population Estimates Program (2019 Vintage).
• COVID-19 aggregate case and death data were organized in a time series that includes cumulative number of cases and deaths as reported by a jurisdiction on a given date. New case and death counts were calculated as the week-to-week change in reported cumulative cases and deaths (i.e., newly reported cases and deaths = cumulative number of cases/deaths reported this week minus the cumulative total reported the week before.

This process was collaborative, with CDC and jurisdictions working together to ensure the accuracy of COVID-19 case and death numbers. County counts provided the most up-to-date numbers on cases and deaths by report date. Throughout data collection, CDC retrospectively updated counts to correct known data quality issues. CDC also worked with jurisdictions after the end of the public health emergency declaration to finalize county data.

• Source: The weekly archived dataset is based on county-level aggregate count data
• Confirmed/Probable Cases/Death breakdown: Cumulative cases and deaths for each county are included. Total reported cases include probable and confirmed cases.
• Time Series Frequency: The weekly archived dataset contains weekly time series data (i.e., one record per week per county)

Important note: The counts reflected during a given time period in this dataset may not match the counts reflected for the same time period in the daily archived dataset noted above. Discrepancies may exist due to differences between county and state COVID-19 case surveillance and reconciliation efforts.

The surveillance case definition for COVID-19, a nationally notifiable disease, was first described in a position statement from the Council for State and Territorial Epidemiologists, which was later revised. However, there is some variation in how jurisdictions implement these case classifications. More information on how CDC collects COVID-19 case surveillance data can be found at FAQ: COVID-19 Data and Surveillance.

Confirmed and Probable Counts In this dataset, counts by jurisdiction are not displayed by confirmed or probable status. Instead, counts of confirmed and probable cases and deaths are included in the Total Cases and Total Deaths columns, when available. Not all jurisdictions report

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

This dataset is about books. It has 1 row and is filtered where the book is The man who killed the deer. It features 7 columns including author, publication date, language, and book publisher.

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