In 2022, ** percent of surveyed Generation Z said that protecting access to clean water and fresh air was important to the kind of America in which they want to live, followed by ** percent who advocated for the importance of reducing gun violence and mass shootings in America. A Gen Z perspective These findings may reflect the fact that Americans have been experiencing a rise in gun-related violence, mass shootings, and climate change concerns in the last few years, at the same time that Gen Z has been aging into adulthood. In addition, Gen Z have had to grow up in midst of political tension following social conflicts such as the COVID-19 pandemic disproportionately affecting marginalized groups, race-related protests over police brutality, and a ban on abortion recently implemented in many U.S. states, making it unsurprising that the majority of the generation also promoted the importance of preserving individual rights and freedoms, addressing systemic racism, upholding women’s reproductive rights, and safeguarding the rights of vulnerable populations. Generational differences Differing from older generations in the U.S., Gen Z were least likely to identify as Republican, to trust in the government, and to say that they are proud to live in the United States, with only ** percent of Gen Z being proud to live in the U.S. in comparison to ** percent of Baby Boomers. Gen Z were also found less likely to have a religious upbringing or have participated in religious services when they were growing up. In addition, they were found more likely to identify as part of the LGBTQ community.

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

According to a May 2022 survey, 56 percent of alternative social media news consumers mentioned staying informed about current issues as a significant reason for using these platforms. Another 45 percent said they used the platforms to follow specific users or accounts. Discussing issues and events with others was the third-most-popular reason.

Reporting of Aggregate Case and Death Count data was discontinued 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.

This archived public use dataset has 11 data elements reflecting United States COVID-19 community levels for all available counties.

The COVID-19 community levels were developed using a combination of three metrics — new COVID-19 admissions per 100,000 population in the past 7 days, the percent of staffed inpatient beds occupied by COVID-19 patients, and total new COVID-19 cases per 100,000 population in the past 7 days. The COVID-19 community level was determined by the higher of the new admissions and inpatient beds metrics, based on the current level of new cases per 100,000 population in the past 7 days. New COVID-19 admissions and the percent of staffed inpatient beds occupied represent the current potential for strain on the health system. Data on new cases acts as an early warning indicator of potential increases in health system strain in the event of a COVID-19 surge.

Using these data, the COVID-19 community level was classified as low, medium, or high.

COVID-19 Community Levels were used to help communities and individuals make decisions based on their local context and their unique needs. Community vaccination coverage and other local information, like early alerts from surveillance, such as through wastewater or the number of emergency department visits for COVID-19, when available, can also inform decision making for health officials and individuals.

For the most accurate and up-to-date data for any county or state, visit the relevant health department website. COVID Data Tracker may display data that differ from state and local websites. This can be due to differences in how data were collected, how metrics were calculated, or the timing of web updates.

Archived Data Notes:

This dataset was renamed from "United States COVID-19 Community Levels by County as Originally Posted" to "United States COVID-19 Community Levels by County" on March 31, 2022.

March 31, 2022: Column name for county population was changed to “county_population”. No change was made to the data points previous released.

March 31, 2022: New column, “health_service_area_population”, was added to the dataset to denote the total population in the designated Health Service Area based on 2019 Census estimate.

March 31, 2022: FIPS codes for territories American Samoa, Guam, Commonwealth of the Northern Mariana Islands, and United States Virgin Islands were re-formatted to 5-digit numeric for records released on 3/3/2022 to be consistent with other records in the dataset.

March 31, 2022: Changes were made to the text fields in variables “county”, “state”, and “health_service_area” so the formats are consistent across releases.

March 31, 2022: The “%” sign was removed from the text field in column “covid_inpatient_bed_utilization”. No change was made to the data. As indicated in the column description, values in this column represent the percentage of staffed inpatient beds occupied by COVID-19 patients (7-day average).

March 31, 2022: Data values for columns, “county_population”, “health_service_area_number”, and “health_service_area” were backfilled for records released on 2/24/2022. These columns were added since the week of 3/3/2022, thus the values were previously missing for records released the week prior.

April 7, 2022: Updates made to data released on 3/24/2022 for Guam, Commonwealth of the Northern Mariana Islands, and United States Virgin Islands to correct a data mapping error.

April 21, 2022: COVID-19 Community Level (CCL) data released for counties in Nebraska for the week of April 21, 2022 have 3 counties identified in the high category and 37 in the medium category. CDC has been working with state officials t

According to a survey conducted in 2022, ** percent of Americans completely agreed that providing a faith perspective on pressing social concerns is an important part of a church's role in its community and that if enough people were brought to Christ, social issues would take care of themselves. In comparison, ** percent of Americans completely disagreed that their church risks losing members by talking too much about race.

Since 2007, the American Psychological Association (APA) has commissioned an annual nationwide survey as part of its Mind/Body Health campaign to examine the state of stress across the country and understand its impact. The Stress in America survey measures attitudes and perceptions of stress among the general public and identifies leading sources of stress, common behaviors used to manage stress and the impact of stress on our lives. The results of the survey draw attention to the serious physical and emotional implications of stress and the inextricable link between the mind and body. From 2007 to 2023, the research has documented this connection among the general public as well as various sub-segments of the public. Each year, the Stress in America surveys aims to uncover different aspects of the stress/health connection via focusing on a particular topic and/or subgroup of the population. Below is a list of the focus of each of the Stress in America surveys. 2007-2018 Cumulative Dataset 2007 General Population 2008 Gender and Stress 2009 Parent Perceptions of Children's Stress 2010 Health Impact of Stress on Children and Families 2011 Our Health Risk 2012 Missing the Health Care Connection 2013 Are Teens Adopting Adults' Stress Habits 2014 Paying With Our Health 2015 The Impact of Discrimination 2016 Coping with Change, Part 1 2016 Coping with Change, Part 2: Technology and Social Media 2017 The State of Our Nation 2018 Stress and Generation Z 2019-2023 Cumulative Dataset 2019 Stress and Current Events 2020 COVID Tracker Wave 1 2020 COVID Tracker Wave 2 2020 COVID Tracker Wave 3 2020 A National Mental Health Crisis 2021 Pandemic Anniversary Survey 2021 Stress and Decision-Making During the Pandemic 2022 Pandemic Anniversary Survey 2022 Concerned for the Future, Beset by Inflation 2023 A Nation Recovering From Collective Trauma

OverviewThis feature layer visualizes the 2022 overall SVI for U.S. counties and tractsSocial Vulnerability Index (SVI) indicates the relative vulnerability of every U.S. county and tract16 social factors grouped into four major themesIndex value calculated for each county for the 16 social factors, four major themes, and the overall rankWhat is CDC/ATSDR Social Vulnerability Index?ATSDR’s Geospatial Research, Analysis & Services Program (GRASP) has created the Social Vulnerability Index (SVI) to help emergency response planners and public health officials identify and map the communities that will most likely need support before, during, and after a hazardous event.SVI uses U.S. Census data to determine the social vulnerability of every county and tract. CDC SVI ranks each county and tract on 16 social factors, including poverty, lack of vehicle access, and crowded housing, and groups them into four related themes:Socioeconomic StatusHousehold CharacteristicsRacial & Ethnic Minority StatusHousing Type & TransportationVariablesFor a detailed description of variable uses, please refer to the full SVI 2022 documentation.RankingsWe ranked counties and tracts for the entire United States against one another. This feature layer can be used for mapping and analysis of relative vulnerability of counties in multiple states, or across the U.S. as a whole. Rankings are based on percentiles. Percentile ranking values range from 0 to 1, with higher values indicating greater vulnerability. For each county and tract, we generated its percentile rank among all counties and tracts for 1) the sixteen individual variables, 2) the four themes, and 3) its overall position.Overall Rankings:We totaled the sums for each theme, ordered the counties, and then calculated overall percentile rankings. Please note: taking the sum of the sums for each theme is the same as summing individual variable rankings.The overall tract summary ranking variable is RPL_THEMES.Theme rankings:For each of the four themes, we summed the percentiles for the variables comprising each theme. We ordered the summed percentiles for each theme to determine theme-specific percentile rankings. The four summary theme ranking variables are:Socioeconomic Status - RPL_THEME1Household Characteristics - RPL_THEME2Racial & Ethnic Minority Status - RPL_THEME3Housing Type & Transportation - RPL_THEME4FlagsCounties and tracts in the top 10%, i.e., at the 90th percentile of values, are given a value of 1 to indicate high vulnerability. Counties and tracts below the 90th percentile are given a value of 0. For a theme, the flag value is the number of flags for variables comprising the theme. We calculated the overall flag value for each county as the total number of all variable flags.SVI Informational VideosIntroduction to CDC Social Vulnerability Index (SVI)More Questions?CDC SVI 2022 Full DocumentationSVI Home PageContact the SVI Coordinator

The ANES 2020-2022 Social Media Study was a two-wave survey before and after the 2020 presidential election and a third survey following the 2022 midterm elections in the United States. Data from these surveys are available as a public use file from the American National Election Studies (ANES) website. The three questionnaires have largely the same content, affording repeated measures of the same constructs. The questionnaire covers voter turnout and candidate choice in the 2020 presidential primaries and general election, the coronavirus pandemic, the economy, feeling thermometers, feelings about how things are going in the country, trust in institutions, political knowledge and misinformation, political participation, political stereotyping, political diversity of social networks, and campaign/policy issues including health insurance, immigration, guns, and climate change.

Inflation Rate in the United States remained unchanged at 2.70 percent in July. This dataset provides - United States Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Deepening partisan polarization has caused extreme discontent with the existing two-party system. But despite bipartisan concerns about third party vote splitting, there has been little attempt to measure the policy preferences of current Republicans and Democrats who want a third party alternative to the two dominant parties. From April 19 to May 1, 2022, we surveyed 1,862 American adults to determine if these “disaffected partisans” are more or less polarized. First, we confirm previous findings that most Americans — Republicans and Democrats alike — now say a third party is needed. For 14 different issues, we then compare the policy preferences of partisans who think a third party is needed and those who do not. Contrary to the popular narrative that disaffected partisans might converge around a centrist or moderate third party, we find that disaffected partisans are just as polarized or more on most issues.

Unemployment Rate in the United States increased to 4.20 percent in July from 4.10 percent in June of 2025. This dataset provides the latest reported value for - United States Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This survey of 12th-grade students is part of a series that explores changes in important values, behaviors, and lifestyle orientations of contemporary American youth. Students are randomly assigned to complete one of six questionnaires, each with a different subset of topical questions, but all containing a set of "core" questions on demographics and drug use. There are about 1,400 variables across the questionnaires. Drugs covered by this survey include tobacco, smokeless tobacco, alcohol, marijuana, hashish, prescription medications, over-the-counter medications, LSD, hallucinogens, amphetamines (stimulants), Ritalin (methylphenidate), Quaaludes (methaqualone), barbiturates (tranquilizers), cocaine, crack cocaine, GHB (gamma hydroxy butyrate), ecstasy, methamphetamine, and heroin. Other topics include attitudes toward religion, changing roles for women, educational aspirations, self-esteem, exposure to drug education, and violence and crime (both in and out of school). Highlights for 2022: Continuation of randomized blocks of questions presented to students. Please see Appendix D of the codebook. Change to the question stem for some lifetime, 12 month, and 30 day heroin and marijuana use questions. Please see the Highlights for 2022 section in the codebook for more details. Change to the heroin use questions: Separate questions about heroin use with a needle and heroin use without a needle for lifetime, past 12 months, and past 30 day timeframes are no longer asked. The separate questions have been replaced by the single question, "On how many occasions (if any), have you taken heroin... ...in your lifetime? ...during the last 12 months? ...during the last 30 days? Please see the Highlights for 2022 section in the codebook for more details. Additional information is documented in the MTFQchanges2022byForm.pdf and MTFQchanges2022byType.pdf files available for download.

Fire history metrics enable rapidly increasing amounts of burned area data to be collapsed into a handful of data layers that can be used efficiently by diverse stakeholders. In this effort, the U.S. Geological Survey's Landsat Burned Area product was used to identify burned area across CONUS over a 41-year period (1984-2024). The Landsat BA product was consolidated into a suite of annual BA products, which in-turn were used to calculate a series of contemporary fire history metrics (30 m resolution). Fire history metrics included: (1) fire frequency (FRQ), (2) time since last burn (TSLB) and (3) year of last burn (YLB), (4) longest fire-free interval (LFFI), and (5) average fire interval length (FIL). All metrics were reported using years as the unit. The FRQ, TSLB and YLB metrics are useful across a wide range of fire regimes, and can be used to inform risk of wildfire, answer fire-management questions, or support fire model parameterization. The FIL and LFFI, alternatively, provide data on the distribution of fire events across the period of record and can help guide land management in regions with frequent fire, such as the Midwest and Southeast.

A. SUMMARY This archived dataset includes data for population characteristics that are no longer being reported publicly. The date on which each population characteristic type was archived can be found in the field “data_loaded_at”.

B. HOW THE DATASET IS CREATED Data on the population characteristics of COVID-19 cases are from:  * Case interviews  * Laboratories  * Medical providers    These multiple streams of data are merged, deduplicated, and undergo data verification processes.  

Race/ethnicity * We include all race/ethnicity categories that are collected for COVID-19 cases. * The population estimates for the "Other" or “Multi-racial” groups should be considered with caution. The Census definition is likely not exactly aligned with how the City collects this data. For that reason, we do not recommend calculating population rates for these groups.

Gender * The City collects information on gender identity using these guidelines.

Skilled Nursing Facility (SNF) occupancy * A Skilled Nursing Facility (SNF) is a type of long-term care facility that provides care to individuals, generally in their 60s and older, who need functional assistance in their daily lives.  * This dataset includes data for COVID-19 cases reported in Skilled Nursing Facilities (SNFs) through 12/31/2022, archived on 1/5/2023. These data were identified where “Characteristic_Type” = ‘Skilled Nursing Facility Occupancy’.

Sexual orientation * The City began asking adults 18 years old or older for their sexual orientation identification during case interviews as of April 28, 2020. Sexual orientation data prior to this date is unavailable. * The City doesn’t collect or report information about sexual orientation for persons under 12 years of age. * Case investigation interviews transitioned to the California Department of Public Health, Virtual Assistant information gathering beginning December 2021. The Virtual Assistant is only sent to adults who are 18+ years old. https://www.sfdph.org/dph/files/PoliciesProcedures/COM9_SexualOrientationGuidelines.pdf">Learn more about our data collection guidelines pertaining to sexual orientation.

Comorbidities * Underlying conditions are reported when a person has one or more underlying health conditions at the time of diagnosis or death.

Homelessness Persons are identified as homeless based on several data sources: * self-reported living situation * the location at the time of testing * Department of Public Health homelessness and health databases * Residents in Single-Room Occupancy hotels are not included in these figures. These methods serve as an estimate of persons experiencing homelessness. They may not meet other homelessness definitions.

Single Room Occupancy (SRO) tenancy * SRO buildings are defined by the San Francisco Housing Code as having six or more "residential guest rooms" which may be attached to shared bathrooms, kitchens, and living spaces. * The details of a person's living arrangements are verified during case interviews.

Transmission Type * Information on transmission of COVID-19 is based on case interviews with individuals who have a confirmed positive test. Individuals are asked if they have been in close contact with a known COVID-19 case. If they answer yes, transmission category is recorded as contact with a known case. If they report no contact with a known case, transmission category is recorded as community transmission. If the case is not interviewed or was not asked the question, they are counted as unknown.

C. UPDATE PROCESS This dataset has been archived and will no longer update as of 9/11/2023.

D. HOW TO USE THIS DATASET Population estimates are only available for age groups and race/ethnicity categories. San Francisco population estimates for race/ethnicity and age groups can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS).

This dataset includes many different types of characteristics. Filter the “Characteristic Type” column to explore a topic area. Then, the “Characteristic Group” column shows each group or category within that topic area and the number of cases on each date.

New cases are the count of cases within that characteristic group where the positive tests were collected on that specific specimen collection date. Cumulative cases are the running total of all San Francisco cases in that characteristic group up to the specimen collection date listed.

This data may not be immediately available for recently reported cases. Data updates as more information becomes available.

To explore data on the total number of cases, use the ARCHIVED: COVID-19 Cases Over Time dataset.

E. CHANGE LOG

• 9/11/2023 - data on COVID-19 cases by population characteristics over time are no longer being updated. The date on which each population characteristic type was archived can be found in the field “data_loaded_at”.
• 6/6/2023 - data on cases by transmission type have been removed. See section ARCHIVED DATA for more detail.
• 5/16/2023 - data on cases by sexual orientation, comorbidities, homelessness, and single room occupancy have been removed. See section ARCHIVED DATA for more detail.
• 4/6/2023 - the State implemented system updates to improve the integrity of historical data.
• 2/21/2023 - system updates to improve reliability and accuracy of cases data were implemented.
• 1/31/2023 - updated “population_estimate” column to reflect the 2020 Census Bureau American Community Survey (ACS) San Francisco Population estimates.
• 1/5/2023 - data on SNF cases removed. See section ARCHIVED DATA for more detail.
• 3/23/2022 - ‘Native American’ changed to ‘American Indian or Alaska Native’ to align with the census.
• 1/22/2022 - system updates to improve timeliness and accuracy of cases and deaths data were implemented.
• 7/15/2022 - reinfections added to cases dataset. See section SUMMARY for more information on how reinfections are identified.

The 2022 cartographic boundary shapefiles are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. The American Indian/Alaska Native/Native Hawaiian (AIANNH) Areas file includes the following legal entities: federally recognized American Indian reservations and off-reservation trust land areas, state-recognized American Indian reservations, and Hawaiian home lands (HHLs). The statistical entities included are Alaska Native village statistical areas (ANVSAs), Oklahoma tribal statistical areas (OTSAs), tribal designated statistical areas (TDSAs), and state designated tribal statistical areas (SDTSAs). Joint use areas included in this file refer to areas that are administered jointly and/or claimed by two or more American Indian tribes. The Census Bureau designates both legal and statistical joint use areas as unique geographic entities for the purpose of presenting statistical data. Note that tribal subdivisions and Alaska Native Regional Corporations (ANRCs) are additional types of American Indian/Alaska Native areas stored by the Census Bureau, but are displayed in separate files because of how they fall within the Census Bureau's geographic hierarchy. The State of Hawaii's Office of Hawaiian Home Lands provides the legal boundaries for the HHLs. The boundaries for ANVSAs, OTSAs, and TDSAs were delineated for the 2020 Census through the Participant Statistical Areas Program (PSAP) by participants from the federally recognized tribal governments. The Bureau of Indian Affairs (BIA) within the U.S. Department of the Interior (DOI) provides the list of federally recognized tribes and only provides legal boundary information when the tribes need supporting records, if a boundary is based on treaty or another document that is historical or open to legal interpretation, or when another tribal, state, or local government challenges the depiction of a reservation or off-reservation trust land. The generalized boundaries for federally recognized American Indian reservations and off-reservation trust lands are based on those as of January 1, 2022, as reported by the federally recognized tribal governments through the Census Bureau's Boundary and Annexation Survey (BAS). The generalized boundaries for state-recognized American Indian reservations and for SDTSAs are based on those delineated by state governor-appointed liaisons for the 2020 Census through the State American Indian Reservation Program and PSAP respectively.

In a survey conducted in 2022, 64 percent of the opinion leaders and prominent journalists surveyed in Latin America said that job creation and economic growth was the most important problem Latin America would face in the incoming 18 months. The second main issue according to these experts was inflation and economic instability.

Title VI of the Civil Rights Act and the Executive Order on Environmental Justice (#12898) do not provide specific guidance to evaluate EJ issues within a region's transportation planning process. Therefore, MPOs must devise their own methods for ensuring that EJ issues are investigated and evaluated in transportation decision-making. In 2001, DVRPC developed an EJ technical assessment to identify direct and disparate impacts of its plans, programs, and planning process on defined population groups in the Delaware Valley region. This assessment, called the Indicators of Potential Disadvantage Methodology, is utilized in a variety of DVRPC plans and programs. DVRPC currently assesses the following population groups, defined by the U.S. Census Bureau:YouthOlder AdultsFemaleRacial MinorityEthnic MinorityForeign-BornDisabledLimited English ProficiencyLow-IncomeCensus tables used to gather data from the 2018-2022 American Community Survey 5-Year EstimatesUsing U.S. Census American Community Survey data, the population groups listed above are identified and located at the census tract level. Data is gathered at the regional level, combining populations from each of the nine counties, for either individuals or households, depending on the indicator. From there, the total number of persons in each demographic group is divided by the appropriate universe (either population or households) for the nine-county region, providing a regional average for that population group. Any census tract that meets or exceeds the regional average level, or threshold, is considered an EJ-sensitive tract for that group.Census tables used to gather data from the 2018-2022 American Community Survey 5-Year Estimates.For more information and for methodology, visit DVRPC's website:http://www.dvrpc.org/GetInvolved/TitleVI/For technical documentation visit DVRPC's GitHub IPD repo: https://github.com/dvrpc/ipdSource of tract boundaries: 2020 US Census Bureau, TIGER/Line ShapefilesNote: Tracts with null values should be symbolized as "Insufficient or No Data".Data Dictionary for Attributes:(Source = DVRPC indicates a calculated field)FieldAliasDescriptionSourceyearIPD analysis yearDVRPCgeoid2011-digit tract GEOIDCensus tract identifierACS 5-yearstatefp2-digit state GEOIDFIPS Code for StateACS 5-yearcountyfp3-digit county GEOIDFIPS Code for CountyACS 5-yeartractceTract numberTract NumberACS 5-yearnameTract numberCensus tract identifier with decimal placesACS 5-yearnamelsadTract nameCensus tract name with decimal placesACS 5-yeard_classDisabled percentile classClassification of tract's disabled percentage as: well below average, below average, average, above average, or well above averagecalculatedd_estDisabled count estimateEstimated count of disabled populationACS 5-yeard_est_moeDisabled count margin of errorMargin of error for estimated count of disabled populationACS 5-yeard_pctDisabled percent estimateEstimated percentage of disabled populationACS 5-yeard_pct_moeDisabled percent margin of errorMargin of error for percentage of disabled populationACS 5-yeard_pctileDisabled percentileTract's regional percentile for percentage disabledcalculatedd_scoreDisabled percentile scoreCorresponding numeric score for tract's disabled classification: 0, 1, 2, 3, 4calculatedem_classEthnic minority percentile classClassification of tract's Hispanic/Latino percentage as: well below average, below average, average, above average, or well above averagecalculatedem_estEthnic minority count estimateEstimated count of Hispanic/Latino populationACS 5-yearem_est_moeEthnic minority count margin of errorMargin of error for estimated count of Hispanic/Latino populationACS 5-yearem_pctEthnic minority percent estimateEstimated percentage of Hispanic/Latino populationcalculatedem_pct_moeEthnic minority percent margin of errorMargin of error for percentage of Hispanic/Latino populationcalculatedem_pctileEthnic minority percentileTract's regional percentile for percentage Hispanic/Latinocalculatedem_scoreEthnic minority percentile scoreCorresponding numeric score for tract's Hispanic/Latino classification: 0, 1, 2, 3, 4calculatedf_classFemale percentile classClassification of tract's female percentage as: well below average, below average, average, above average, or well above averagecalculatedf_estFemale count estimateEstimated count of female populationACS 5-yearf_est_moeFemale count margin of errorMargin of error for estimated count of female populationACS 5-yearf_pctFemale percent estimateEstimated percentage of female populationACS 5-yearf_pct_moeFemale percent margin of errorMargin of error for percentage of female populationACS 5-yearf_pctileFemale percentileTract's regional percentile for percentage femalecalculatedf_scoreFemale percentile scoreCorresponding numeric score for tract's female classification: 0, 1, 2, 3, 4calculatedfb_classForeign-born percentile classClassification of tract's foreign born percentage as: well below average, below average, average, above average, or well above averagecalculatedfb_estForeign-born count estimateEstimated count of foreign born populationACS 5-yearfb_est_moeForeign-born count margin of errorMargin of error for estimated count of foreign born populationACS 5-yearfb_pctForeign-born percent estimateEstimated percentage of foreign born populationcalculatedfb_pct_moeForeign-born percent margin of errorMargin of error for percentage of foreign born populationcalculatedfb_pctileForeign-born percentileTract's regional percentile for percentage foreign borncalculatedfb_scoreForeign-born percentile scoreCorresponding numeric score for tract's foreign born classification: 0, 1, 2, 3, 4calculatedle_classLimited English proficiency percentile classClassification of tract's limited english proficiency percentage as: well below average, below average, average, above average, or well above averagecalculatedle_estLimited English proficiency count estimateEstimated count of limited english proficiency populationACS 5-yearle_est_moeLimited English proficiency count margin of errorMargin of error for estimated count of limited english proficiency populationACS 5-yearle_pctLimited English proficiency percent estimateEstimated percentage of limited english proficiency populationACS 5-yearle_pct_moeLimited English proficiency percent margin of errorMargin of error for percentage of limited english proficiency populationACS 5-yearle_pctileLimited English proficiency percentileTract's regional percentile for percentage limited english proficiencycalculatedle_scoreLimited English proficiency percentile scoreCorresponding numeric score for tract's limited english proficiency classification: 0, 1, 2, 3, 4calculatedli_classLow-income percentile classClassification of tract's low income percentage as: well below average, below average, average, above average, or well above averagecalculatedli_estLow-income count estimateEstimated count of low income (below 200% of poverty level) populationACS 5-yearli_est_moeLow-income count margin of errorMargin of error for estimated count of low income populationACS 5-yearli_pctLow-income percent estimateEstimated percentage of low income (below 200% of poverty level) populationcalculatedli_pct_moeLow-income percent margin of errorMargin of error for percentage of low income populationcalculatedli_pctileLow-income percentileTract's regional percentile for percentage low incomecalculatedli_scoreLow-income percentile scoreCorresponding numeric score for tract's low income classification: 0, 1, 2, 3, 4calculatedoa_classOlder adult percentile classClassification of tract's older adult percentage as: well below average, below average, average, above average, or well above averagecalculatedoa_estOlder adult count estimateEstimated count of older adult population (65 years or older)ACS 5-yearoa_est_moeOlder adult count margin of errorMargin of error for estimated count of older adult populationACS 5-yearoa_pctOlder adult percent estimateEstimated percentage of older adult population (65 years or older)ACS 5-yearoa_pct_moeOlder adult percent margin of errorMargin of error for percentage of older adult populationACS 5-yearoa_pctileOlder adult percentileTract's regional percentile for percentage older adultcalculatedoa_scoreOlder adult percentile scoreCorresponding numeric score for tract's older adult classification: 0, 1, 2, 3, 4calculatedrm_classRacial minority percentile classClassification of tract's non-white percentage as: well below average, below average, average, above average, or well above averagecalculatedrm_estRacial minority count estimateEstimated count of non-white populationACS 5-yearrm_est_moeRacial minority count margin of errorMargin of error for estimated count of non-white populationACS 5-yearrm_pctRacial minority percent estimateEstimated percentage of non-white populationcalculatedrm_pct_moeRacial minority percent margin of errorMargin of error for percentage of non-white populationcalculatedrm_pctileRacial minority percentileTract's regional percentile for percentage non-whitecalculatedrm_scoreRacial minority percentile scoreCorresponding numeric score for tract's non-white classification: 0, 1, 2, 3, 4calculatedtot_ppTotal population estimateEstimated total population of tract (universe [or denominator] for youth, older adult, female, racial minoriry, ethnic minority, & foreign born)ACS 5-yeartot_pp_moeTotal population margin of errorMargin of error for estimated total population of tractACS 5-yeary_classYouth percentile classClassification of tract's youth percentage as: well below average, below average, average, above average, or well above averagecalculatedy_estYouth count estimateEstimated count of youth population (under 18 years)ACS 5-yeary_est_moeYouth count margin of errorMargin of error for estimated count of youth populationACS 5-yeary_pctYouth population percentage estimateEstimated percentage of youth population (under 18 years)calculatedy_pct_moeYouth population percentage margin of

This public use dataset has 11 data elements reflecting United States COVID-19 community levels for all available counties. This dataset contains the same values used to display information available on the COVID Data Tracker at: https://covid.cdc.gov/covid-data-tracker/#county-view?list_select_state=all_states&list_select_county=all_counties&data-type=CommunityLevels The data are updated weekly.

CDC looks at the combination of three metrics — new COVID-19 admissions per 100,000 population in the past 7 days, the percent of staffed inpatient beds occupied by COVID-19 patients, and total new COVID-19 cases per 100,000 population in the past 7 days — to determine the COVID-19 community level. The COVID-19 community level is determined by the higher of the new admissions and inpatient beds metrics, based on the current level of new cases per 100,000 population in the past 7 days. New COVID-19 admissions and the percent of staffed inpatient beds occupied represent the current potential for strain on the health system. Data on new cases acts as an early warning indicator of potential increases in health system strain in the event of a COVID-19 surge. Using these data, the COVID-19 community level is classified as low, medium, or high. COVID-19 Community Levels can help communities and individuals make decisions based on their local context and their unique needs. Community vaccination coverage and other local information, like early alerts from surveillance, such as through wastewater or the number of emergency department visits for COVID-19, when available, can also inform decision making for health officials and individuals.

See https://www.cdc.gov/coronavirus/2019-ncov/science/community-levels.html for more information.

For the most accurate and up-to-date data for any county or state, visit the relevant health department website. COVID Data Tracker may display data that differ from state and local websites. This can be due to differences in how data were collected, how metrics were calculated, or the timing of web updates.

For more details on the Minnesota Department of Health COVID-19 thresholds, see COVID-19 Public Health Risk Measures: Data Notes (Updated 4/13/22). https://mn.gov/covid19/assets/phri_tcm1148-434773.pdf

Note: This dataset was renamed from "United States COVID-19 Community Levels by County as Originally Posted" to "United States COVID-19 Community Levels by County" on March 31, 2022. March 31, 2022: Column name for county population was changed to “county_population”. No change was made to the data points previous released. March 31, 2022: New column, “health_service_area_population”, was added to the dataset to denote the total population in the designated Health Service Area based on 2019 Census estimate. March 31, 2022: FIPS codes for territories American Samoa, Guam, Commonwealth of the Northern Mariana Islands, and United States Virgin Islands were re-formatted to 5-digit numeric for records released on 3/3/2022 to be consistent with other records in the dataset. March 31, 2022: Changes were made to the text fields in variables “county”, “state”, and “health_service_area” so the formats are consistent across releases. March 31, 2022: The “%” sign was removed from the text field in column “covid_inpatient_bed_utilization”. No change was made to the data. As indicated in the column description, values in this column represent the percentage of staffed inpatient beds occupied by COVID-19 patients (7-day average). March 31, 2022: Data values for columns, “county_population”, “health_service_area_number”, and “health_service_area” were backfilled for records released on 2/24/2022. These columns were added since the week of 3/3/2022, thus the values were previously missing for records released the week prior. April 7, 2022: Updates made to data released on 3/24/2022 for Guam, Commonwealth of the Northern Mariana Islands, and United States Virgin Islands to correct a data mapping error.

BackgroundNinety-eight percent of documented cases of the zoonotic disease human monkeypox (MPX) were reported after 2001, with especially dramatic global spread in 2022. This longitudinal study aimed to assess spatiotemporal risk factors of MPX infection and predict global epidemiological trends.MethodTwenty-one potential risk factors were evaluated by correlation-based network analysis and multivariate regression. Country-level risk was assessed using a modified Susceptible-Exposed-Infectious-Removed (SEIR) model and a risk-factor-driven k-means clustering analysis.ResultsBetween historical cases and the 2022 outbreak, MPX infection risk factors changed from relatively simple [human immunodeficiency virus (HIV) infection and population density] to multiple [human mobility, population of men who have sex with men, coronavirus disease 2019 (COVID-19) infection, and socioeconomic factors], with human mobility in the context of COVID-19 being especially key. The 141 included countries classified into three risk clusters: 24 high-risk countries mainly in West Europe and Northern America, 70 medium-risk countries mainly in Latin America and Asia, and 47 low-risk countries mainly in Africa and South Asia. The modified SEIR model predicted declining transmission rates, with basic reproduction numbers ranging 1.61–7.84 in the early stage and 0.70–4.13 in the current stage. The estimated cumulative cases in Northern and Latin America may overtake the number in Europe in autumn 2022.ConclusionsIn the current outbreak, risk factors for MPX infection have changed and expanded. Forecasts of epidemiological trends from our modified SEIR models suggest that Northern America and Latin America are at greater risk of MPX infection in the future.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2018-2022 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..The 2018-2022 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.