The highest share of polled catering business owners stated that under present quarantine conditions, their businesses' safety margin was one month. However, one quarter of respondents had rather positive expectations and reported that they would not close their restaurants despite the critical situation caused by the COVID-19 outbreak in Russia.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

Due to the COVID-19 pandemic, in the first quarter of 2021, hospital margins in the United States decreased by 77 percent compared to Q1 2019. For the rest of 2021 hospital margins were projected to be 10 to 11 percent below pre-pandemic levels. This statistic shows changes in hospital margins in the United States due to COVID-19 in 2021 compared to 2019, by quarter.

Updated Data Regarding COVID-19

This U.S. County COVID-19 Mapping Dashboard shows the county-by-county impact of the coronavirus across the U.S., including percentages of the population infected. https://covid.woolpert.com The link to the desktop version is on the left of this home page, and the mobile version on the right.

By clicking on any state in the left column, state data by county will appear. The map can also be used to navigate to an area of interest and the statistics for all counties within the map will update. There are links to each state’s data and surveillance dashboard and to the Twitter accounts of each state’s department of health.

This information will be refreshed daily as data becomes available.

For additional data, check out the COVID-19 GIS Hub by our partner Esri at https://coronavirus-disasterresponse.hub.arcgis.com/ #covid19

This dashboard provides insights into the state of COVID-19 testing in the United States.

While some testing site data is provided directly by state and local governments and healthcare providers, much of this data was sourced by GISCorps volunteers from the websites of local governments and healthcare providers and is not authoritative or comprehensive. Please contact testing sites or state and local agencies directly for official information and testing requirements.

To submit updated information about a testing site or to suggest one that isn't on this map, please fill out and submit this form. GISCorps can also supply a spreadsheet template for bulk data uploads; please contact info@giscorps.org to discuss that option.

Find the COVID-19 Testing Sites in the United States public ArcGIS REST service at https://services.arcgis.com/8ZpVMShClf8U8dae/arcgis/rest/services/TestingLocations_public2/FeatureServer.

A study published in May 2020 revealed the extent of the impact that the COVID-19 could have on the economy in Italy based on three different scenarios (soft, intermediate, and hard). The average EBITDA margin of pharmaceutical manufacturers in Italy could increase under a soft and an intermediate scenario. On the other hand, it was estimated that the EBITDA could decrease to ten percent based on a hard scenario. The scenarios are described by the source as follows: soft scenario includes 1.5 months of lockdown; intermediate scenario assumes a longer period of shut down within a range of 2 to 4 months with some activities that will reopen before others as a consequence of a new wave of contagion; worst case scenario assumes a lockdown of up 6 months.

Abstract

The World Bank has launched a fast-deploying high-frequency phone-based survey of households to generate near real time insights into the socio-economic impact of COVID-19 on households which hence to be used to support evidence-based policy responses to the crisis. At a time when conventional modes of data collection are not feasible, this phone-based rapid data collection method offers a way to gather granular information on the transmission mechanisms of the crisis on the populations, to identify gaps in policy responses, and to generate insights to inform scaling up or redirection of resources as the crisis unfolds.

Geographic coverage

National

Analysis unit

Individual, Household-level

Sampling procedure

A mobile frame was generated via random digit dialing (RDD), based on the National Numbering Plans from the Malaysian Communications and Multimedia Commission (MCMC). All possible subscriber combinations were generated in DRUID (D Force Sampling's Reactive User Interface Database), an SQL database interface which houses the complete sampling frame. From this database, complete random telephone numbers were sampled. For Round 1, a sample of 33,894 phone numbers were drawn (without replacement within the survey wave) from a total of 102,780,000 possible mobile numbers from more than 18 mobile providers in the sampling frame, which were not stratified. Once the sample was drawn in the form of replicates (subsamples) of n = 10.000, the numbers were filtered by D-Force Sampling using an auto-dialer to determine each numbers' working status. All numbers that yield a working call disposition for at least one of the two filtering attempts were then passed to the CATI center human interviewing team. Mobile devices were assumed to be personal, and therefore the person who answered the call was the selected respondent. Screening questions were used to ensure that the respondent was at least 18 years old and within the capacity of either contributing, making or with knowledge of household finances. Respondents who had participated in Round 1 were sampled for Round 2. Fresh respondents were introduced in Round 3 in addition to panel respondents from Round 2; fresh respondents in Round 3 were selected using the same procedure for sampling respondents in Round 1.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire is available in three languages, including English, Bahasa Melayu, and Mandarin Chinese. It can be downloaded from the Downloads section.

Response rate

In Round 1, the survey successfully interviewed 2,210 individuals out of 33,894 sampled phone numbers. In Round 2, the survey successfully re-interviewed 1,047 individuals, recording a 47% response rate. In Round 3, the survey successfully re-interviewed 667 respondents who had been previously interviewed in Round 2, recording a 64% response rate. The panel respondents in Round 3 were added with 446 fresh respondents.

Sampling error estimates

In Round 1, assuming a simple random sample, with p=0.5 and n=2,210 at the 95% CI level, yields a margin of sampling error (MOE) of 2.09 percentage points. Incorporating the design effect into this estimate yields a margin of sampling error of 2.65% percentage points.

In Round 2, the complete weight was for the entire sample adjusted to the 2021 population estimates from DOSM’s annual intercensal population projections. Assuming a simple random sample with p=0.5 and n=1,047 at the 95% CI level, yields a margin of sampling error (MOE) of 3.803 percentage points. Incorporating the design effect into this estimate yields a margin of sampling error of 3.54 percentage points.

Among both fresh and panel samples in Round 3, assuming a simple random sample, with p=0.5 and n=1,113 at the 95% CI level yields a margin of sampling error (MOE) of 2.94 percentage points. Incorporating the design effect into this estimate yields a margin of sampling error of 3.34 percentage points.

Among panel samples in Round 3, with p=0.5 and n=667 at the 95% CI level yields a margin of sampling error (MOE) of 3.80 percentage points. Incorporating the design effect into this estimate yields a margin of sampling error of 4.16 percentage points.

The COVID-19 outbreak caused a massive setback to the stability of financial system due to emergence of several other risks with COVID, which significantly influenced the continuity of profitable banking operations. Therefore, this study aims to see that how differently the liquidity risk and credit risk influenced the banking profitability during Covid-19 (Q12020 to Q42021) than before COVID (Q12018 to Q42019). The study employs pooled OLS, and OLS fixed & random effects models, to analyze the panel data on a sample of 37 banks currently operating in Pakistan. The results depict that liquidity risk has a positive and significant relationship with return on assets and return on equity, but insignificant relationship with net interest margin. Credit risk has a negative and significant relationship with return on assets, return on equity, and net interest margin. The study also applies quantile regression to address the normality issue in data. The quantile regression results are consistent with pooled OLS, and OLS fixed and random effects results. The study makes valuable suggestions for regulators, policymakers, and others users of financial institutional data. The current study will help to set policies for efficient management of LR and CR.

This site provides historical data related to COVID-19 booster dose eligibility presented on two CDC COVID Data Tracker sites: Vaccinations in the US and Vaccination Equity. Data are updated weekly on Thursdays.

Some COVID-19 vaccine recipients are eligible to receive booster doses, and criteria for booster eligibility may change over time. Data and footnotes will be updated to align with the current recommendations.

CDC counts people as having “received a booster dose” if they are fully vaccinated and received another dose of any COVID-19 vaccine on or after August 13, 2021. This does not distinguish whether the recipient is immunocompromised and received an additional dose

Data Limitations:

• The booster eligibility metric excludes fully vaccinated recipients who have an “Other” primary series vaccine type. 
• Booster eligibility counts and percentages exclude vaccine administrations reported by Texas as the primary series cannot be linked to booster dose in the aggregate data submitted by Texas.

Footnotes:

CDC counts people as being “eligible to get a booster dose” if it has been at least 5 months since their completed Pfizer-BioNTech or Moderna primary series or at least 2 months since their completed Janssen (Johnson & Johnson) single-dose vaccine. 

• Beginning December 9, 2021, adolescents ages 16 and 17 years are authorized and recommended to get a Pfizer-BioNTech booster dose at least 6 months after completing the Pfizer-BioNTech primary series. 
• Beginning January 4, 2022, people ages 16 years and older who have completed the Pfizer-BioNTech  primary series can get a booster dose at least 5 months after completing the primary series. 
• Beginning January 5, 2022, adolescents ages 12–15 years who have completed the Pfizer-BioNTech primary series can get a Pfizer-BioNTech booster dose at least 5 months after completing the primary series. 
• Beginning January 7, 2022, adults ages 18 years and older who have completed the Moderna primary series can get an mRNA booster dose at least 5 months after completing the primary series.

In 2023, the gross profit margin of Sinovac Biotech Ltd., one of China's leading vaccine producers, increased slightly to almost 60 percent. Thanks to the development and wide adaption of CoronaVac, the company's COVID-19 vaccine, Sinovac's annual revenue increased almost 40 times in 2021.

This layer shows population broken down by race and Hispanic origin. This is shown by tract, county, and state centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the predominant race living within an area, and the total population in that area. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B03002Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census: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 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 Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Cumulative number of specimens tested by Irish laboratories for SARS-CoV - number and percentage positive. Data is available for all laboratories, hospitals and other labs (NVRL and Cherry Orchard) - total tests and total positive results. Data is provided to the HPSC by the HSE COVID-19 Daily lab tracker system. Based on data reported to HSE by 15:00 (Date_HPSC) but refers to data collected as of midnight the previous day.

After October 13, 2022, this dataset will no longer be updated as the related CDC COVID Data Tracker site was retired on October 13, 2022.

This dataset contains historical trends in vaccinations and cases by age group, at the US national level. Data is stratified by at least one dose and fully vaccinated. Data also represents all vaccine partners including jurisdictional partner clinics, retail pharmacies, long-term care facilities, dialysis centers, Federal Emergency Management Agency and Health Resources and Services Administration partner sites, and federal entity facilities.

Probit (margins) results for stockpiling and general concern with the pandemic.

A feature layer containing COVID-19 case data for Minnehaha County, South Dakota.

About this dataset

Context

The dataset tabulates the Antonito median household income by race. The dataset can be utilized to understand the racial distribution of Antonito income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Antonito, CO median household income breakdown by race betwen 2011 and 2021
• Median Household Income by Racial Categories in Antonito, CO (2021, in 2022 inflation-adjusted dollars)

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Antonito median household income by race. You can refer the same here

Indonesia Commercial Banks: Net Interest Margin data was reported at 4.718 % in Dec 2024. This records an increase from the previous number of 4.691 % for Nov 2024. Indonesia Commercial Banks: Net Interest Margin data is updated monthly, averaging 4.907 % from Jan 2012 (Median) to Dec 2024, with 156 observations. The data reached an all-time high of 6.059 % in Jan 2012 and a record low of 4.060 % in Feb 2015. Indonesia Commercial Banks: Net Interest Margin data remains active status in CEIC and is reported by Indonesia Financial Services Authority. The data is categorized under Global Database’s Indonesia – Table ID.KBE013: Bank Performance: Commercial Bank. Significant changes from mid-2013 until early 2015 was caused by regulation changes. [COVID-19-IMPACT]

This layer shows health insurance coverage by type and by age group. This is shown by tract, county, and state centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the count and percent uninsured. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B27010 (Not all lines of this ACS table are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census: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 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 Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.