The yield on US 7 Year Note Bond Yield eased to 3.97% on August 1, 2025, marking a 0.19 percentage point decrease from the previous session. Over the past month, the yield has fallen by 0.09 points, though it remains 0.30 points higher than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. United States 7 Year Note Yield - values, historical data, forecasts and news - updated on August of 2025.

The yield on US 10 Year Note Bond Yield eased to 4.22% on August 1, 2025, marking a 0.15 percentage point decrease from the previous session. Over the past month, the yield has fallen by 0.06 points, though it remains 0.43 points higher than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. US 10 Year Treasury Bond Note Yield - values, historical data, forecasts and news - updated on August of 2025.

The yield on US 30 Year Bond Yield eased to 4.84% on August 1, 2025, marking a 0.06 percentage point decrease from the previous session. Over the past month, the yield has edged up by 0.02 points and is 0.73 points higher than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. United States 30 Year Bond Yield - values, historical data, forecasts and news - updated on August of 2025.

The yield on Australia 7 Year Bond Yield eased to 3.92% on July 31, 2025, marking a 0.01 percentage point decrease from the previous session. Over the past month, the yield has edged up by 0.15 points, though it remains 0 points lower than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. This dataset includes a chart with historical data for Australia 7Y.

Six reports are posted to this page each month. 7(a) Lender Activity Report The 7(a) Lender Activity Report summarizes current fiscal year approvals by 7(a) Lenders. 504 CDC Activity Report The 504 CDC Activity Report summarizes current fiscal year approvals by 504 CDCs. 504 Third Party Lender Activity Report The 504 Third Party Lender Activity Report summarizes current fiscal year approvals by 504 TPLs. Congressional District Approvals Report The Congressional District Approvals Report summarizes loan activity by congressional district for the 7(a) and 504 loan programs and by state for the microloan program. Monthly & Yearly Activity Report The Monthly & Yearly Activity Report summarizes loan activity by month and year for the 7(a) and 504 loan programs from the current fiscal year to 1991. 7(a) and 504 Activity Report The 7(a) and 504 Activity Report summarizes fiscal year-to-date loan activity by segments for 7(a) and 504 loan programs from the current fiscal year and for the previous 5 fiscal years. This data is also available as an interactive dashboard All data is updated monthly.

The yield on US 2 Year Note Bond Yield eased to 3.70% on August 1, 2025, marking a 0.27 percentage point decrease from the previous session. Over the past month, the yield has fallen by 0.10 points and is 0.19 points lower than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. US 2 Year Treasury Bond Note Yield - values, historical data, forecasts and news - updated on August of 2025.

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 (total case counts) as the present dataset; however, NCHS Death Counts are based on death certificates that use information reported by physicians, medical examiners, or coroners in the cause-of-death section of each certificate. Data from each of these pages are considered provisional (not complete and pending verification) and are therefore subject to change. Counts from previous weeks are continually revised as more records are received and processed.

Number of Jurisdictions Reporting There are currently 60 public health jurisdictions reporting cases of COVID-19. This includes the 50 states, the District of Columbia, New York City, the U.S. territories of American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, Puerto Rico, and the U.S Virgin Islands as well as three independent countries in compacts of free association with the United States, Federated States of Micronesia, Republic of the Marshall Islands, and Republic of Palau. New York State’s reported case and death counts do not include New York City’s counts as they separately report nationally notifiable conditions to CDC.

CDC COVID-19 data are available to the public as summary or aggregate count files, including total counts of cases and deaths, available by state and by county. These and other data on COVID-19 are available from multiple public locations, such as:

https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/cases-in-us.html

https://www.cdc.gov/covid-data-tracker/index.html

https://www.cdc.gov/coronavirus/2019-ncov/covid-data/covidview/index.html

https://www.cdc.gov/coronavirus/2019-ncov/php/open-america/surveillance-data-analytics.html

Additional COVID-19 public use datasets, include line-level (patient-level) data, are available at: https://data.cdc.gov/browse?tags=covid-19.

Archived Data Notes:

November 3, 2022: Due to a reporting cadence issue, case rates for Missouri counties are calculated based on 11 days’ worth of case count data in the Weekly United States COVID-19 Cases and Deaths by State data released on November 3, 2022, instead of the customary 7 days’ worth of data.

November 10, 2022: Due to a reporting cadence change, case rates for Alabama counties are calculated based on 13 days’ worth of case count data in the Weekly United States COVID-19 Cases and Deaths by State data released on November 10, 2022, instead of the customary 7 days’ worth of data.

November 10, 2022: Per the request of the jurisdiction, cases and deaths among non-residents have been removed from all Hawaii county totals throughout the entire time series. Cumulative case and death counts reported by CDC will no longer match Hawaii’s COVID-19 Dashboard, which still includes non-resident cases and deaths. 

November 17, 2022: Two new columns, weekly historic cases and weekly historic deaths, were added to this dataset on November 17, 2022. These columns reflect case and death counts that were reported that week but were historical in nature and not reflective of the current burden within the jurisdiction. These historical cases and deaths are not included in the new weekly case and new weekly death columns; however, they are reflected in the cumulative totals provided for each jurisdiction. These data are used to account for artificial increases in case and death totals due to batched reporting of historical data.

December 1, 2022: Due to cadence changes over the Thanksgiving holiday, case rates for all Ohio counties are reported as 0 in the data released on December 1, 2022.

January 5, 2023: Due to North Carolina’s holiday reporting cadence, aggregate case and death data will contain 14 days’ worth of data instead of the customary 7 days. As a result, case and death metrics will appear higher than expected in the January 5, 2023, weekly release.

January 12, 2023: Due to data processing delays, Mississippi’s aggregate case and death data will be reported as 0. As a result, case and death metrics will appear lower than expected in the January 12, 2023, weekly release.

January 19, 2023: Due to a reporting cadence issue, Mississippi’s aggregate case and death data will be calculated based on 14 days’ worth of data instead of the customary 7 days in the January 19, 2023, weekly release.

January 26, 2023: Due to a reporting backlog of historic COVID-19 cases, case rates for two Michigan counties (Livingston and Washtenaw) were higher than expected in the January 19, 2023 weekly release.

January 26, 2023: Due to a backlog of historic COVID-19 cases being reported this week, aggregate case and death counts in Charlotte County and Sarasota County, Florida, will appear higher than expected in the January 26, 2023 weekly release.

January 26, 2023: Due to data processing delays, Mississippi’s aggregate case and death data will be reported as 0 in the weekly release posted on January 26, 2023.

February 2, 2023: As of the data collection deadline, CDC observed an abnormally large increase in aggregate COVID-19 cases and deaths reported for Washington State. In response, totals for new cases and new deaths released on February 2, 2023, have been displayed as zero at the state level until the issue is addressed with state officials. CDC is working with state officials to address the issue.

February 2, 2023: Due to a decrease reported in cumulative case counts by Wyoming, case rates will be reported as 0 in the February 2, 2023, weekly release. CDC is working with state officials to verify the data submitted.

February 16, 2023: Due to data processing delays, Utah’s aggregate case and death data will be reported as 0 in the weekly release posted on February 16, 2023. As a result, case and death metrics will appear lower than expected and should be interpreted with caution.

February 16, 2023: Due to a reporting cadence change, Maine’s

Mohegan Sun Footnotes: (1) Monthly contributions are due to the State by the 15th of the following month. (2) Mohegan Sun did not include the value of eBonus credits redeemed by patrons at slot machines in its video facsimile devices Win amounts; however, the value of eBonus credits wagered was included in the reported Handle. In addition, please be advised that the Casino Hold % column amounts may be understated and the Payout % column amounts may be overstated as a result of this. (3) From July 1, 2009 to June 30, 2012, if the aggregate amount of eBonus coupons or credits actually played on the Mohegan Tribe's Video Facsimiles during a particular month exceeded 5.5% of “gross operating revenues” for that month, the Mohegan Tribe paid to the State an amount equal to twenty-five percent (25%) of such excess face amount of eBonus coupons or credits used in such calendar month (the "eBonus Contribution"). Beginning on July 1, 2012, and for all months thereafter, the aggregate amount threshold for determining the eBonus Contribution increased from 5.5% to 11% of "gross operating revenues." (4) The value of eBonus free slot play credits redeemed during February 2009 totaled $1,910,268; however, it was determined that eBonus credits redeemed were overstated by $1,460,390 for January 2008 though January 2009. February 2009 is adjusted by this amount. March 2009 was was adjusted by an additional $8,139. (5) During fiscal year 2010 the Mohegan Tribe and the State of Connecticut settled a dispute regarding the proper treatment of eBonus for the period November 2007 through June 2009. As a result of this settlement, the State of Connecticut received $5,727,731, including interest. (6) For fiscal years 2007/2008 and 2008/2009, Poker Pro Electronic Table Rake Amounts of $401,309 and $42,188, respectively, were included in the calculation to determine the amount of Slot Machine Contributions to the State of Connecticut. (7) The Mohegan Sun Casino officially opened on Saturday, October 12, 1996. On October 8-10, video facsimile/slot machines were available for actual play during pre-opening charitable gaming nights. (8) Beginning with the month of May 2001, Mohegan Sun Casino reports video facsimile/slot machine win on an accrual basis, reflecting data captured and reported by an on-line slot accounting system. Reports were previously prepared on a cash basis, based on the coin and currency removed from the machines on each gaming day. (9) Cumulative Win amount total should be reduced by $1,452,341.21 to correct for an over reporting of slot revenues for prior periods related to errors in the accrual carry forward of estimated cash on floor. (10) In June 2019, Mohegan Sun amended their April 2019 Contribution Return to correct for an error related to a Wide Area Progressive Jackpot. This resulted in an increase to the Win of $48,035, and an increase in State Contributions of $12,009. The corrected amounts are shown above. (11) Cumulative Win amount was increased by $15,557 to correct for an error in calculating Tickets Issued from September 2022, this resulted in additional contributions of $3,887. Foxwoods Footnotes: (1) Monthly contributions are due to the State by the 15th of the following month. (2) The operation of the video facsimile/slot machines began at Foxwoods on January 16, 1993. (3) Foxwoods did not include the value of Free Play coupons redeemed by patrons at slot machines in its video facsimile devices Win amounts; however, the value of Free Play coupons wagered was included in the reported Handle. In addition, please be advised that the Casino Hold % column amounts may be understated and the Payout % column amounts may be overstated as a result of this. (4) From July 1, 2009 to June 30, 2012, if the aggregate amount of Free Play coupons or credits actually played on the Mashantucket Pequot Tribe's Video Facsimiles during a particular month exceeded 5.5% of “gross operating revenues” for that month, the Mashantucket Pequot T

THIS DATASET WAS LAST UPDATED AT 8:11 PM EASTERN ON JULY 30

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 related to the operation of the second KIOS CoE sandboxing use case (SUC2) which inclused 3 scenarios (S1-S3) which examins the behavious a WAP scheme of power grids in case of a short circuit fault and in case of two types of cyber attacks. The description of the architecture of the University of Cyprus/ KIOS CoE sandboxing environmnet used for extracting these datasets along with the full list of scenarios and their detailed implementation are described in the supporting documents.

Brief description of each of the 3 scenarios of this SUC2 are provided below.

The datasets for the first scenario (S1) of SUC2 examines the operation of a wide area protection scheme in a transmission line which receives data sent from PMUs at the two ends of the lines, when a short-circuit fault occurred in the range of the transmission line between buses 7 and 8 of the system. More details about the scenario SUC2/S1 related to this scenario's dataset can be found in Section 1.3.1 of the SUC2 supporting document. The dataset includes electrical measurements of the current flow in line 7-8 (of the IEEE 9-bus system), in both magnitude and sinusoidal form. The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV files, which were recorded with a 30-second and 40-second time resolution, respectively. The measurements of RMS values were recorded by the Typhoon controller as they were sent by the two PMUs, while the sine wave measurements were recorder through the OPAL-RT

The datasets for second scenario (S2) of SUC2 investigates the operation of a wide area protection scheme which receives data sent from PMUs when a MITM FDI cyber-attack is conducted on the measurements of bus 7, virtually implemented within the sandboxing, and introduces a multiplicative change to the current measurements before they are received by the Typhoon controller via IEEE C37.118 protocol. Section 1.3.2 of the SUC2 supporting document provides more details about the scenario related to this dataset. This dataset includes electrical measurements of the current flow, in magnitude and sinusoidal format, of the transmission line between buses 7 and 8 of the digital twin of the IEEE 9-bus system. The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV files which were recorded with a 30-second and 40-second time resolution, respectively. The measurements of magnitude values were recorded by the Typhoon controller, while the data from the sinusoidal waveform were recorder by OPAL-RT.

Thie dataset of the SUC2/S3 examines the operation of a wide area protection scheme which receives data sent from PMUs when a combined MITM with DoS cyber-attack is conducted, as actual attack, in the isolated communication network of the sandboxing environment, disrupting the C37.118 UDP communication exchanged between OPAL-RT 5707, where the digital twin of IEEE 9-bus system was implemented, and Typhoon controller. More details about this scenario associated to this dataset can be found in Section 1.3.3 of the supporting document of SUC2.

This dataset includes electrical measurements of current’s flow magnitude of the transmission line between buses 7 and 8 of the digital twin of the IEEE 9-bus system. The dataset was recorded by the Typhoon controller, and it is provided in the form of time-series measurements available as MATLAB (.mat) and CSV files which were recorded with a 30-second and 40-second time resolution, respectively. In addition, the dataset includes network traffic packets captured as .pcapng and .csv files.

The USGS’s FORE-SCE model was used to produce a long-term landscape dataset for the Delaware River Basin (DRB). Using historical landscape reconstruction and scenario-based future projections, the data provided land-use and land-cover (LULC) data for the DRB from year 1680 through 2100, with future projections from 2020-2100 modeled for 7 different socioeconomic-based scenarios, and 3 climate realizations for each socioeconomic scenario (21 scenario combinations in total). The projections are characterized by 1) high spatial resolution (30-meter cells), 2) high thematic resolution (20 land use and land cover classes), 3) broad spatial extent (covering the entirety of the Delaware River basin, corresponding to USGS HUC codes 020401 and 020402), 4) use of real land ownership boundaries to ensure realistic representation of landscape patterns, and 5) representation of both anthropogenic land use and natural vegetation change that respond to projected climate change. Data are provide ...

This dataset is comprised of data submitted to HCAI by prescription drug manufacturers for wholesale acquisition cost (WAC) increases that exceed the statutorily-mandated WAC increase threshold of an increase of more than 16% above the WAC of the drug product on December 31 of the calendar year three years prior to the current calendar year. This threshold applies to prescription drug products with a WAC greater than $40 for a course of therapy. Required WAC increase reports are to be submitted to HCAI within a month after the end of the quarter in which the WAC increase went into effect. Please see the statute and regulations for additional information regarding reporting thresholds and report due dates.

Key data elements in this dataset include the National Drug Code (NDC) maintained by the FDA, narrative descriptions of the reasons for the increase in WAC, and the five-year history of WAC increases for the NDC. A WAC Increase Report consists of 27 data elements that have been divided into two separate Excel data sets: Prescription Drug WAC Increase and Prescription Drug WAC Increase – 5 Year History. The datasets include manufacturer WAC Increase Reports received since January 1, 2019. The Prescription Drugs WAC Increase dataset consists of the information submitted by prescription drug manufacturers that pertains to the current WAC increase of a given report, including the amount of the current increase, the WAC after increase, and the effective date of the increase. The Prescription Drugs WAC Increase – 5 Year History dataset consists of the information submitted by prescription drug manufacturers for the data elements that comprise the 5-year history of WAC increases of a given report, including the amount of each increase and their effective dates.

There are 2 types of WAC Increase datasets below: Monthly and Annual. The Monthly datasets include the data in completed reports submitted by manufacturers for calendar year 2025, as of April 7, 2025. The Annual datasets include data in completed reports submitted by manufacturers for the specified year. The datasets may include reports that do not meet the specified minimum thresholds for reporting.

The Quick Guide explaining how to link the information in each data set to form complete reports is here: https://hcai.ca.gov/wp-content/uploads/2024/03/QuickGuide_LinkingTheDatasets.pdf

The program regulations are available here: https://hcai.ca.gov/wp-content/uploads/2024/03/CTRx-Regulations-Text.pdf

The data format and file specifications are available here: https://hcai.ca.gov/wp-content/uploads/2024/03/Format-and-File-Specifications-version-2.0-ada.pdf

DATA NOTES: Due to recent changes in Excel, it is not recommended that you save these files to .csv format. If you do, when importing back into Excel the leading zeros in the NDC number column will be dropped. If you need to save it into a different format other than .xlsx it must be .txt

DATA UPDATES: Annual datasets of reports from the preceding year are reviewed in the second half of the current year to identify if any revisions or additions have been made since the original release of the datasets. If revisions or additions have been found, an update of the datasets will be released. Datasets will be clearly marked with 'Updated' in their titles for convenient identification. Not all datasets may require an updated release. The review of previously released datasets will only be conducted once to determine if an updated release is necessary. Datasets with revisions or additions that may have been made after the one-time review can be requested. These requests should be sent via email to ctrx@hcai.ca.gov. Due to regulatory changes that went into effect April 1, 2024, reports submitted prior to April 1, 2024, will include the data field "Unit Sales Volume in US" and reports submitted on or after April 1, 2024, will instead include "Total Volume of Gross Sales in US Dollars".

Note: Reporting of new COVID-19 Case Surveillance data will be discontinued July 1, 2024, to align with the process of removing SARS-CoV-2 infections (COVID-19 cases) from the list of nationally notifiable diseases. Although these data will continue to be publicly available, the dataset will no longer be updated.

Authorizations to collect certain public health data expired at the end of the U.S. public health emergency declaration on May 11, 2023. The following jurisdictions discontinued COVID-19 case notifications to CDC: Iowa (11/8/21), Kansas (5/12/23), Kentucky (1/1/24), Louisiana (10/31/23), New Hampshire (5/23/23), and Oklahoma (5/2/23). Please note that these jurisdictions will not routinely send new case data after the dates indicated. As of 7/13/23, case notifications from Oregon will only include pediatric cases resulting in death.

This case surveillance public use dataset has 19 elements for all COVID-19 cases shared with CDC and includes demographics, geography (county and state of residence), any exposure history, disease severity indicators and outcomes, and presence of any underlying medical conditions and risk behaviors.

Currently, CDC provides the public with three versions of COVID-19 case surveillance line-listed data: this 19 data element dataset with geography, a 12 data element public use dataset, and a 33 data element restricted access dataset.

The following apply to the public use datasets and the restricted access dataset:

• Data elements can be found on the COVID-19 case report form located at www.cdc.gov/coronavirus/2019-ncov/downloads/pui-form.pdf.
• Data are considered provisional by CDC and are subject to change until the data are reconciled and verified with the state and territorial data providers.
• Some data are suppressed to protect individual privacy.
• Datasets will include all cases with the earliest date available in each record (date received by CDC or date related to illness/specimen collection) at least 14 days prior to the creation of the current datasets. This 14-day lag allows case reporting to be stabilized and ensure that time-dependent outcome data are accurately captured.
• Datasets are updated monthly.
• Datasets are created using CDC’s Policy on Public Health Research and Nonresearch Data Management and Access and include protections designed to protect individual privacy.
• For more information about data collection and reporting, please see https://www.cdc.gov/coronavirus/2019-ncov/covid-data/about-us-cases-deaths.html.
• For more information about the COVID-19 case surveillance data, please see https://www.cdc.gov/coronavirus/2019-ncov/covid-data/faq-surveillance.html
  

Overview

The COVID-19 case surveillance database includes individual-level data reported to U.S. states and autonomous reporting entities, including New York City and the District of Columbia (D.C.), as well as U.S. territories and affiliates. On April 5, 2020, COVID-19 was added to the Nationally Notifiable Condition List and classified as “immediately notifiable, urgent (within 24 hours)” by a Council of State and Territorial Epidemiologists (CSTE) Interim Position Statement (Interim-20-ID-01). CSTE updated the position statement on August 5, 2020, to clarify the interpretation of antigen detection tests and serologic test results within the case classification (Interim-20-ID-02). The statement also recommended that all states and territories enact laws to make COVID-19 reportable in their jurisdiction, and that jurisdictions conducting surveillance should submit case notifications to CDC. COVID-19 case surveillance data are collected by jurisdictions and reported voluntarily to CDC.

For more information: NNDSS Supports the COVID-19 Response | CDC.

COVID-19 Case Reports COVID-19 case reports are routinely submitted to CDC by public health jurisdictions using nationally standardized case reporting forms. On April 5, 2020, CSTE released an Interim Position Statement with national surveillance case definitions for COVID-19. Current versions of these case definitions are available at: https://ndc.services.cdc.gov/case-definitions/coronavirus-disease-2019-2021/. All cases reported on or after were requested to be shared by public health departments to CDC using the standardized case definitions for lab-confirmed or probable cases. On May 5, 2020, the standardized case reporting form was revised. States and territories continue to use this form.

Data are Considered Provisional

• The COVID-19 case surveillance data are dynamic; case reports can be modified at any time by the jurisdictions sharing COVID-19 data with CDC. CDC may update prior cases shared with CDC based on any updated information from jurisdictions. For instance, as new information is gathered about previously reported cases, health departments provide updated data to CDC. As more information and data become available, analyses might find changes in surveillance data and trends during a previously reported time window. Data may also be shared late with CDC due to the volume of COVID-19 cases.
• Annual finalized data: To create the final NNDSS data used in the annual tables, CDC works carefully with the reporting jurisdictions to reconcile the data received during the year until each state or territorial epidemiologist confirms that the data from their area are correct.

Access Addressing Gaps in Public Health Reporting of Race and Ethnicity for COVID-19, a report from the Council of State and Territorial Epidemiologists, to better understand the challenges in completing race and ethnicity data for COVID-19 and recommendations for improvement.

Data Limitations

To learn more about the limitations in using case surveillance data, visit FAQ: COVID-19 Data and Surveillance.

Data Quality Assurance Procedures

CDC’s Case Surveillance Section routinely performs data quality assurance procedures (i.e., ongoing corrections and logic checks to address data errors). To date, the following data cleaning steps have been implemented:

• Questions that have been left unanswered (blank) on the case report form are reclassified to a Missing value, if applicable to the question. For example, in the question "Was the individual hospitalized?" where the possible answer choices include "Yes," "No," or "Unknown," the blank value is recoded to "Missing" because the case report form did not include a response to the question.
• Logic checks are performed for date data. If an illogical date has been provided, CDC reviews the data with the reporting jurisdiction. For example, if a symptom onset date in the future is reported to CDC, this value is set to null until the reporting jurisdiction updates the date appropriately.
• Additional data quality processing to recode free text data is ongoing. Data on symptoms, race, ethnicity, and healthcare worker status have been prioritized.

Data Suppression

To prevent release of data that could be used to identify people, data cells are suppressed for low frequency (<11 COVID-19 case records with a given values). Suppression includes low frequency combinations of case month, geographic characteristics (county and state of residence), and demographic characteristics (sex, age group, race, and ethnicity). Suppressed values are re-coded to the NA answer option; records with data suppression are never removed.

Additional COVID-19 Data

COVID-19 data are available to the public as summary or aggregate count files, including total counts of cases and deaths by state and by county. These and other COVID-19 data are available from multiple public locations: COVID Data Tracker; United States COVID-19 Cases and Deaths by State; COVID-19 Vaccination Reporting Data Systems; and COVID-19 Death Data and Resources.

Notes:

March 1, 2022: The "COVID-19 Case Surveillance Public Use Data with Geography" will be updated on a monthly basis.

April 7, 2022: An adjustment was made to CDC’s cleaning algorithm for COVID-19 line level case notification data. An assumption in CDC's algorithm led to misclassifying deaths that were not COVID-19 related. The algorithm has since been revised, and this dataset update reflects corrected individual level information about death status for all cases collected to date.

June 25, 2024: An adjustment

Our dataset provides detailed and precise insights into the business, commercial, and industrial aspects of any given area in the USA (Including Point of Interest (POI) Data and Foot Traffic. The dataset is divided into 150x150 sqm areas (geohash 7) and has over 50 variables. - Use it for different applications: Our combined dataset, which includes POI and foot traffic data, can be employed for various purposes. Different data teams use it to guide retailers and FMCG brands in site selection, fuel marketing intelligence, analyze trade areas, and assess company risk. Our dataset has also proven to be useful for real estate investment.- Get reliable data: Our datasets have been processed, enriched, and tested so your data team can use them more quickly and accurately.- Ideal for trainning ML models. The high quality of our geographic information layers results from more than seven years of work dedicated to the deep understanding and modeling of geospatial Big Data. Among the features that distinguished this dataset is the use of anonymized and user-compliant mobile device GPS location, enriched with other alternative and public data.- Easy to use: Our dataset is user-friendly and can be easily integrated to your current models. Also, we can deliver your data in different formats, like .csv, according to your analysis requirements. - Get personalized guidance: In addition to providing reliable datasets, we advise your analysts on their correct implementation.Our data scientists can guide your internal team on the optimal algorithms and models to get the most out of the information we provide (without compromising the security of your internal data).Answer questions like: - What places does my target user visit in a particular area? Which are the best areas to place a new POS?- What is the average yearly income of users in a particular area?- What is the influx of visits that my competition receives?- What is the volume of traffic surrounding my current POS?This dataset is useful for getting insights from industries like:- Retail & FMCG- Banking, Finance, and Investment- Car Dealerships- Real Estate- Convenience Stores- Pharma and medical laboratories- Restaurant chains and franchises- Clothing chains and franchisesOur dataset includes more than 50 variables, such as:- Number of pedestrians seen in the area.- Number of vehicles seen in the area.- Average speed of movement of the vehicles seen in the area.- Point of Interest (POIs) (in number and type) seen in the area (supermarkets, pharmacies, recreational locations, restaurants, offices, hotels, parking lots, wholesalers, financial services, pet services, shopping malls, among others). - Average yearly income range (anonymized and aggregated) of the devices seen in the area.Notes to better understand this dataset:- POI confidence means the average confidence of POIs in the area. In this case, POIs are any kind of location, such as a restaurant, a hotel, or a library. - Category confidences, for example"food_drinks_tobacco_retail_confidence" indicates how confident we are in the existence of food/drink/tobacco retail locations in the area. - We added predictions for The Home Depot and Lowe's Home Improvement stores in the dataset sample. These predictions were the result of a machine-learning model that was trained with the data. Knowing where the current stores are, we can find the most similar areas for new stores to open.How efficient is a Geohash?Geohash is a faster, cost-effective geofencing option that reduces input data load and provides actionable information. Its benefits include faster querying, reduced cost, minimal configuration, and ease of use.Geohash ranges from 1 to 12 characters. The dataset can be split into variable-size geohashes, with the default being geohash7 (150m x 150m).

The PATH Study was launched in 2011 to inform the Food and Drug Administration's regulatory activities under the Family Smoking Prevention and Tobacco Control Act (TCA). The PATH Study is a collaboration between the National Institute on Drug Abuse (NIDA), National Institutes of Health (NIH), and the Center for Tobacco Products (CTP), Food and Drug Administration (FDA). The study sampled over 150,000 mailing addresses across the United States to create a national sample of people who use or do not use tobacco. 45,971 adults and youth constitute the first (baseline) wave, Wave 1, of data collected by this longitudinal cohort study. These 45,971 adults and youth along with 7,207 "shadow youth" (youth ages 9 to 11 sampled at Wave 1) make up the 53,178 participants that constitute the Wave 1 Cohort. Respondents are asked to complete an interview at each follow-up wave. Youth who turn 18 by the current wave of data collection are considered "aged-up adults" and are invited to complete the Adult Interview. Additionally, "shadow youth" are considered "aged-up youth" upon turning 12 years old, when they are asked to complete an interview after parental consent. At Wave 4, a probability sample of 14,098 adults, youth, and shadow youth ages 10 to 11 was selected from the civilian, noninstitutionalized population (CNP) at the time of Wave 4. This sample was recruited from residential addresses not selected for Wave 1 in the same sampled Primary Sampling Unit (PSU)s and segments using similar within-household sampling procedures. This "replenishment sample" was combined for estimation and analysis purposes with Wave 4 adult and youth respondents from the Wave 1 Cohort who were in the CNP at the time of Wave 4. This combined set of Wave 4 participants, 52,731 participants in total, forms the Wave 4 Cohort. At Wave 7, a probability sample of 14,863 adults, youth, and shadow youth ages 9 to 11 was selected from the CNP at the time of Wave 7. This sample was recruited from residential addresses not selected for Wave 1 or Wave 4 in the same sampled PSUs and segments using similar within-household sampling procedures. This "second replenishment sample" was combined for estimation and analysis purposes with the Wave 7 adult and youth respondents from the Wave 4 Cohorts who were at least age 15 and in the CNP at the time of Wave 7. This combined set of Wave 7 participants, 46,169 participants in total, forms the Wave 7 Cohort. Please refer to the Restricted-Use Files User Guide that provides further details about children designated as "shadow youth" and the formation of the Wave 1, Wave 4, and Wave 7 Cohorts. Dataset 0002 (DS0002) contains the data from the State Design Data. This file contains 7 variables and 82,139 cases. The state identifier in the State Design file reflects the participant's state of residence at the time of selection and recruitment for the PATH Study. Dataset 1011 (DS1011) contains the data from the Wave 1 Adult Questionnaire. This data file contains 2,021 variables and 32,320 cases. Each of the cases represents a single, completed interview. Dataset 1012 (DS1012) contains the data from the Wave 1 Youth and Parent Questionnaire. This file contains 1,431 variables and 13,651 cases. Dataset 1411 (DS1411) contains the Wave 1 State Identifier data for Adults and has 5 variables and 32,320 cases. Dataset 1412 (DS1412) contains the Wave 1 State Identifier data for Youth (and Parents) and has 5 variables and 13,651 cases. The same 5 variables are in each State Identifier dataset, including PERSONID for linking the State Identifier to the questionnaire and biomarker data and 3 variables designating the state (state Federal Information Processing System (FIPS), state abbreviation, and full name of the state). The State Identifier values in these datasets represent participants' state of residence at the time of Wave 1, which is also their state of residence at the time of recruitment. Dataset 1611 (DS1611) contains the Tobacco Universal Product Code (UPC) data from Wave 1. This data file contains 32 variables and 8,601 cases. This file contains UPC values on the packages of tobacco products used or in the possession of adult respondents at the time of Wave 1. The UPC values can be used to identify and validate the specific products used by respondents and augment the analyses of the characteristics of tobacco products used

About this dataset

Context

The dataset tabulates the population of Seven Points by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Seven Points. The dataset can be utilized to understand the population distribution of Seven Points by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Seven Points. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Seven Points.

Key observations

Largest age group (population): Male # 10-14 years (74) | Female # 15-19 years (81). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Seven Points population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Seven Points is shown in the following column.
• Population (Female): The female population in the Seven Points is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Seven Points for each age group.

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 Seven Points Population by Gender. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Seven Devils by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Seven Devils. The dataset can be utilized to understand the population distribution of Seven Devils by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Seven Devils. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Seven Devils.

Key observations

Largest age group (population): Male # 5-9 years (32) | Female # 65-69 years (34). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Seven Devils population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Seven Devils is shown in the following column.
• Population (Female): The female population in the Seven Devils is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Seven Devils for each age group.

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 Seven Devils Population by Gender. You can refer the same here

This dataset is a listing of all active City of Chicago employees, complete with full names, departments, positions, employment status (part-time or full-time), frequency of hourly employee –where applicable—and annual salaries or hourly rate. Please note that "active" has a specific meaning for Human Resources purposes and will sometimes exclude employees on certain types of temporary leave. For hourly employees, the City is providing the hourly rate and frequency of hourly employees (40, 35, 20 and 10) to allow dataset users to estimate annual wages for hourly employees. Please note that annual wages will vary by employee, depending on number of hours worked and seasonal status. For information on the positions and related salaries detailed in the annual budgets, see https://www.cityofchicago.org/city/en/depts/obm.html

Data Disclosure Exemptions: Information disclosed in this dataset is subject to FOIA Exemption Act, 5 ILCS 140/7 (Link:https://www.ilga.gov/legislation/ilcs/documents/000501400K7.htm)

About this dataset

Context

The dataset tabulates the population of Seven Springs by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Seven Springs. The dataset can be utilized to understand the population distribution of Seven Springs by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Seven Springs. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Seven Springs.

Key observations

Largest age group (population): Male # 30-34 years (1) | Female # 60-64 years (2). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Seven Springs population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Seven Springs is shown in the following column.
• Population (Female): The female population in the Seven Springs is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Seven Springs for each age group.

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 Seven Springs Population by Gender. You can refer the same here

The yield on UK 7 Year Bond Yield eased to 4.17% on July 31, 2025, marking a 0.02 percentage point decrease from the previous session. Over the past month, the yield has edged up by 0.10 points and is 0.48 points higher than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. This dataset includes a chart with historical data for UK 7Y.