The outbreak of coronavirus in 2020 will have a massive impact on the functioning of most companies in Poland. More than 80 percent of entrepreneurs considered the exemption from social security contributions (ZUS) to be the most effective type of state aid as a result of the coronavirus outbreak. Nearly 70 percent of investors also expect exemption from tax payments, and every second company would like the government to subsidize sick pay. If the state of the epidemic in Poland continues until mid-April, the number of companies that will be forced to lay off all their employees as a result of the outbreak of coronavirus in 2020 will almost double.

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

Important: This dataset is updated regularly and the latest version for download can be found here.

In response to the COVID-19 pandemic, the Allen Institute for AI has partnered with leading research groups to prepare and distribute the COVID-19 Open Research Dataset (CORD-19), a free resource of scholarly articles, including full text content, about COVID-19 and the coronavirus family of viruses for use by the global research community.

This dataset is intended to mobilize researchers to apply recent advances in natural language processing to generate new insights in support of the fight against this infectious disease. The corpus will be updated weekly as new research is published in peer-reviewed publications and archival services like bioRxiv, medRxiv, and others.

By downloading this dataset you are agreeing to the Dataset license. Specific licensing information for individual articles in the dataset is available in the metadata file.

Additional licensing information is available on the PMC website, medRxiv website and bioRxiv website.

Dataset content:

• Commercial use subset
• Non-commercial use subset
• PMC custom license subset
• bioRxiv/medRxiv subset (pre-prints that are not peer reviewed)
• Metadata file
• Readme

Each paper is represented as a single JSON object (see schema file for details).

Description:

The dataset contains all COVID-19 and coronavirus-related research (e.g. SARS, MERS, etc.) from the following sources:

• PubMed's PMC open access corpus using this query (COVID-19 and coronavirus research)
• Additional COVID-19 research articles from a corpus maintained by the WHO
• bioRxiv and medRxiv pre-prints using the same query as PMC (COVID-19 and coronavirus research)

We also provide a comprehensive metadata file of coronavirus and COVID-19 research articles with links to PubMed, Microsoft Academic and the WHO COVID-19 database of publications (includes articles without open access full text).

We recommend using metadata from the comprehensive file when available, instead of parsed metadata in the dataset. Please note the dataset may contain multiple entries for individual PMC IDs in cases when supplementary materials are available.

This repository is linked to the WHO database of publications on coronavirus disease and other resources, such as Microsoft Academic Graph, PubMed, and Semantic Scholar. A coalition including the Chan Zuckerberg Initiative, Georgetown University’s Center for Security and Emerging Technology, Microsoft Research, and the National Library of Medicine of the National Institutes of Health came together to provide this service.

Citation:

When including CORD-19 data in a publication or redistribution, please cite our arXiv pre-print.

The Allen Institute for AI and particularly the Semantic Scholar team will continue to provide updates to this dataset as the situation evolves and new research is released.

Listing of Washoe County COVID-19 case data, by day posted to public dashboard. This table is based on best available information from the Washoe County Health District. Not all fields are populated for all dates.Name FieldName FieldType Comment

OBJECTID OBJECTID ObjectID System generated unique ID

Date Reported reportdt Date Effective date of this row of data

Confirmed confirmed Integer Total number of confirmed cases to date

Recovered recovered Integer Number of recoveries to date

Deaths deaths Integer Number of deaths to date

Active active Integer Current number of active cases

Male Male Small Integer Total confirmed cases to date: Male

Female Female Small Integer Total confirmed cases to date: Female

OtherGender GenderOther Small Integer Total confirmed cases to date: OtherGender

Total Cases 0-9 Age0to9 Small Integer Total confirmed cases to date: Total Cases 0-9

Total Cases 10-19 Age10to19 Small Integer Total confirmed cases to date: Total Cases 10-19

Total Cases 20-29 Age20to29 Small Integer Total confirmed cases to date: Total Cases 20-29

Total Cases 30-39 Age30to39 Small Integer Total confirmed cases to date: Total Cases 30-39

Total Cases 40-49 Age40to49 Small Integer Total confirmed cases to date: Total Cases 40-49

Total Cases 50-59 Age50to59 Small Integer Total confirmed cases to date: Total Cases 50-59

Total Cases 60-69 Age60to69 Small Integer Total confirmed cases to date: Total Cases 60-69

Total Cases 70-79 Age70to79 Small Integer Total confirmed cases to date: Total Cases 70-79

Total Cases 80-89 Age80to89 Small Integer Total confirmed cases to date: Total Cases 80-89

Total Cases 90-99 Age90to99 Small Integer Total confirmed cases to date: Total Cases 90-99

Total Cases 100+ Age100plus Small Integer Total confirmed cases to date: Total Cases 100+

UnknownAge AgeNA Small Integer Total confirmed cases to date: UnknownAge

Native American E_NativeAmerican Integer Total Cases to date: Native American

Asian E_Asian Integer Total Cases to date: Asian

African American E_Black Integer Total Cases to date: African American

Hispanic E_Hispanic Integer Total Cases to date: Hispanic

Hawaiian or Pacific Islander E_HawaiianPacific Integer Total Cases to date: Hawaiian or Pacific Islander

Caucasian E_White Integer Total Cases to date: Caucasian

Multiple E_Multiple Integer Total Cases to date: Multiple

OtherEthnicity E_Other Integer Total Cases to date: OtherEthnicity

EthnicityUnknown E_Unknown Integer Total Cases to date: EthnicityUnknown

New Cases 7 Day Moving Average NewCases7DMA Double Average New Cases over last 7 days

NewCases NewCases Integer New Cases in last day

ActiveCasesAge0to9per100K Age0to9_100K Double Active Cases per 100,000: Age0to9

ActiveCasesAge10to19per100K Age10to19_100K Double Active Cases per 100,000: Age10to19

ActiveCasesAge20to29per100K Age20to29_100K Double Active Cases per 100,000: Age20to29

ActiveCasesAge30to39per100K Age30to39_100K Double Active Cases per 100,000: Age30to39

ActiveCasesAge40to49per100K Age40to49_100K Double Active Cases per 100,000: Age40to49

ActiveCasesAge50to59per100K Age50to59_100K Double Active Cases per 100,000: Age50to59

ActiveCasesAge60to69per100K Age60to69_100K Double Active Cases per 100,000: Age60to69

ActiveCasesAge70to79per100K Age70to79_100K Double Active Cases per 100,000: Age70to79

ActiveCasesAge80to89per100K Age80to89_100K Double Active Cases per 100,000: Age80to89

ActiveCasesAge90to99per100K Age90to99_100K Double Active Cases per 100,000: Age90to99

ActiveCasesAge100plusper100K Age100plus_100K Double Active Cases per 100,000: Age100plus

Context

The 2019–20 coronavirus pandemic is an ongoing pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Source: https://en.wikipedia.org/wiki/2019%E2%80%9320_coronavirus_pandemic.

Content

Coronavirus COVID-19 confirmed cases, deaths, case mortality ratios, country, latitude, and longitude.

Disclaimer: Data will be more accurate as more data comes in. Deaths/Infections will be a better measure of mortality rate after a pandemic is over, when the estimates of the number of infections start to get closer to the true number of infected individuals. Note discussion of case mortality ratio (numbers as they are reported) vs infection mortality ratio (estimates of the actual numbers). This dataset discusses case mortality ratios.

Acknowledgements

Banner photo by Adhy Savala on Unsplash.

Data generated from the notebook https://www.kaggle.com/paultimothymooney/does-latitude-impact-the-spread-of-covid-19 using data from https://www.kaggle.com/paultimothymooney/latitude-and-longitude-for-every-country-and-state and https://www.kaggle.com/sudalairajkumar/novel-corona-virus-2019-dataset, all of which were released under open data licenses.

Note: In these datasets, a person is defined as up to date if they have received at least one dose of an updated COVID-19 vaccine. The Centers for Disease Control and Prevention (CDC) recommends that certain groups, including adults ages 65 years and older, receive additional doses. Starting on July 13, 2022, the denominator for calculating vaccine coverage has been changed from age 5+ to all ages to reflect new vaccine eligibility criteria. Previously the denominator was changed from age 16+ to age 12+ on May 18, 2021, then changed from age 12+ to age 5+ on November 10, 2021, to reflect previous changes in vaccine eligibility criteria. The previous datasets based on age 12+ and age 5+ denominators have been uploaded as archived tables. Starting June 30, 2021, the dataset has been reconfigured so that all updates are appended to one dataset to make it easier for API and other interfaces. In addition, historical data has been extended back to January 5, 2021. This dataset shows full, partial, and at least 1 dose coverage rates by zip code tabulation area (ZCTA) for the state of California. Data sources include the California Immunization Registry and the American Community Survey’s 2015-2019 5-Year data. This is the data table for the LHJ Vaccine Equity Performance dashboard. However, this data table also includes ZTCAs that do not have a VEM score. This dataset also includes Vaccine Equity Metric score quartiles (when applicable), which combine the Public Health Alliance of Southern California’s Healthy Places Index (HPI) measure with CDPH-derived scores to estimate factors that impact health, like income, education, and access to health care. ZTCAs range from less healthy community conditions in Quartile 1 to more healthy community conditions in Quartile 4. The Vaccine Equity Metric is for weekly vaccination allocation and reporting purposes only. CDPH-derived quartiles should not be considered as indicative of the HPI score for these zip codes. CDPH-derived quartiles were assigned to zip codes excluded from the HPI score produced by the Public Health Alliance of Southern California due to concerns with statistical reliability and validity in populations smaller than 1,500 or where more than 50% of the population resides in a group setting. These data do not include doses administered by the following federal agencies who received vaccine allocated directly from CDC: Indian Health Service, Veterans Health Administration, Department of Defense, and the Federal Bureau of Prisons. For some ZTCAs, vaccination coverage may exceed 100%. This may be a result of many people from outside the county coming to that ZTCA to get their vaccine and providers reporting the county of administration as the county of residence, and/or the DOF estimates of the population in that ZTCA are too low. Please note that population numbers provided by DOF are projections and so may not be accurate, especially given unprecedented shifts in population as a result of the pandemic.

Regarding all Vaccination Data The date of Last Update is 4/21/2023. Additionally on 4/27/2023 several COVID-19 datasets were retired and no longer included in public COVID-19 data dissemination. See this link for more information https://imap.maryland.gov/pages/covid-data Summary The cumulative number of COVID-19 vaccinations among Maryland residents by age groupings: 0-9; 10-19; 20-29; 30-39; 40-49; 50-59; 60-69; 70-79; 80+; unknown Description MD COVID-19 - Vaccinations by Age Distribution data layer is a collection of COVID-19 vaccinations that have been reported each day into ImmuNet. Terms of Use The Spatial Data, and the information therein, (collectively the "Data") is provided "as is" without warranty of any kind, either expressed, implied, or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted, nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct, indirect, incidental, consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data, nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

Note: DPH is updating and streamlining the COVID-19 cases, deaths, and testing data. As of 6/27/2022, the data will be published in four tables instead of twelve. The COVID-19 Cases, Deaths, and Tests by Day dataset contains cases and test data by date of sample submission. The death data are by date of death. This dataset is updated daily and contains information back to the beginning of the pandemic. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Cases-Deaths-and-Tests-by-Day/g9vi-2ahj. The COVID-19 State Metrics dataset contains over 93 columns of data. This dataset is updated daily and currently contains information starting June 21, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-State-Level-Data/qmgw-5kp6 . The COVID-19 County Metrics dataset contains 25 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-County-Level-Data/ujiq-dy22 . The COVID-19 Town Metrics dataset contains 16 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Town-Level-Data/icxw-cada . To protect confidentiality, if a town has fewer than 5 cases or positive NAAT tests over the past 7 days, those data will be suppressed. COVID-19 cases and associated deaths that have been reported among Connecticut residents, broken down by race and ethnicity. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. Deaths reported to the either the Office of the Chief Medical Examiner (OCME) or Department of Public Health (DPH) are included in the COVID-19 update. The following data show the number of COVID-19 cases and associated deaths per 100,000 population by race and ethnicity. Crude rates represent the total cases or deaths per 100,000 people. Age-adjusted rates consider the age of the person at diagnosis or death when estimating the rate and use a standardized population to provide a fair comparison between population groups with different age distributions. Age-adjustment is important in Connecticut as the median age of among the non-Hispanic white population is 47 years, whereas it is 34 years among non-Hispanic blacks, and 29 years among Hispanics. Because most non-Hispanic white residents who died were over 75 years of age, the age-adjusted rates are lower than the unadjusted rates. In contrast, Hispanic residents who died tend to be younger than 75 years of age which results in higher age-adjusted rates. The population data used to calculate rates is based on the CT DPH population statistics for 2019, which is available online here: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Population/Population-Statistics. Prior to 5/10/2021, the population estimates from 2018 were used. Rates are standardized to the 2000 US Millions Standard population (data available here: https://seer.cancer.gov/stdpopulations/). Standardization was done using 19 age groups (0, 1-4, 5-9, 10-14, ..., 80-84, 85 years and older). More information about direct standardization for age adjustment is available here: https://www.cdc.gov/nchs/data/statnt/statnt06rv.pdf Categories are mutually exclusive. The category “multiracial” includes people who answered ‘yes’ to more than one race category. Counts may not add up to total case counts as data on race and ethnicity may be missing. Age adjusted rates calculated only for groups with more than 20 deaths. Abbreviation: NH=Non-Hispanic. Data on Connecticut deaths were obtained from the Connecticut Deaths Registry maintained by the DPH Office of Vital Records. Cause of death was determined by a death certifier (e.g., physician, APRN, medical

Why was there considerable variation in initial COVID-19 mortality impact across countries? Through a configurational lens, this paper examines which configurations of five conditions—a delayed public-health response, past epidemic experience, proportion of elderly in population, population density, and national income per capita—influence early COVID-19 mortality impact measured by years of life lost (YLL). A fuzzy-set qualitative comparative analysis (fsQCA) of 80 countries identifies four distinctive pathways associated with high YLL rate and four other different pathways leading to low YLL rate. Results suggest that there is no singular “playbook”—a set of policies that countries can follow. Some countries failed differently, whereas others succeeded differently. Countries should take into account their situational contexts to adopt a holistic response strategy to combat any future public-health crisis. Regardless of the country’s past epidemic experience and national income levels, a speedy public-health response always works well. For high-income countries with high population density or past epidemic experience, they need to take extra care to protect elderly populations who may otherwise overstretch healthcare capacity.

WARNING: This asset has been deprecated and will no longer be updated (Last Updated April 14, 2022). Summary The cumulative number of COVID-19 vaccinations by age groupings: 0-9; 10-19; 20-29; 30-39; 40-49; 50-59; 60-69; 70-79; 80+; Unknown. Description MD COVID-19 - Vaccinations by Age Distribution data layer is a collection of COVID-19 vaccinations that have been reported each day into ImmuNet. Terms of Use The Spatial Data, and the information therein, (collectively the "Data") is provided "as is" without warranty of any kind, either expressed, implied, or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted, nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct, indirect, incidental, consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data, nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

Please see FAQ for latest information on COVID-19 Data Hub Data Flows. https://covid-19.geohive.ie/pages/helpfaqs Category Field label Field Name Explanation ExtractDate Extract Date Date the data is Extracted Latitude Latitude Longitude Longitude VaccinationDate Vaccination Date Date the Vaccination occurred Week Week Details of epidemiological weeks available here https://www.hpsc.ie/notifiablediseases/resources/epidemiologicalweeks/ TotalDailyVaccines Total Daily Vaccines Gender Male Female NA Dose Number Dose1 Dose 1 Dose2 Dose 2 SingleDose Single Dose Vaccine Brand Moderna Pfizer Janssen AstraZeneca Age Group Partial_Age0to9 At Least One Dose Age 0 to 11 Dose 1 of Astrazenenca, MRNA or Single Dose Vaccine Partial_Age10to19 At Least One Dose Age 12 to 19 Partial_Age20to29 At Least One Dose Age 20 to 29 Partial_Age30to39 At Least One Dose Age 30 to 39 Partial_Age40to49 At Least One Dose Age 40 to 49 Partial_Age50to59 At Least One Dose Age 50 to 59 Partial_Age60to69 At Least One Dose Age 60 to 69 Partial_Age70to79 At Least One Dose Age 70 to 79 Partial_Age80+ At Least One Dose Age80+ Partial_NA At Least One Dose Not Assigned Age Group Cumulative ParCum_Age0to9 Cumulative Age 0 to 11 Cumulative At least One Dose Age 0 to 11 ParCum_Age10to19 Cumulative Age 12 to 19 Cumulative At least One Dose Age 12 to 19 ParCum_Age20to29 Cumulative Age 20 to 29 Cumulative At least One Dose Age 20 to 29 ParCum_Age30to39 Cumulative Age 30 to 39 Cumulative At least One Dose Age 30 to 39 ParCum_Age40to49 Cumulative Age 40 to 49 Cumulative At least One Dose Age 40 to 49 ParCum_Age50to59 Cumulative Age50 to 59 Cumulative At least One Dose Age 50 to 59 ParCum_Age60to69 Cumulative Age 60 to 69 Cumulative At least One Dose Age 60 to 69 ParCum_Age70to79 Cumulative Age 70 to 79 Cumulative At least One Dose Age 70 to 79 ParCum_80+ Cumulative Age 80+ Cumulative At least One Dose Age 80+ Age Group Cumulative Percent ParCum_NA Cumulative Age Not Assigned Cumulative At least One Dose Age Not Assigned ParPer_Age0to9 At Least One Dose Percent Age 0 to 11 Cumulative At least One Dose Age cohort/ Age cohort population ParPer_Age10to19 At Least One Dose Percent Age 12 to 19 ParPer_Age20to29 At Least One Dose Percent Age 20 to 29 ParPer_Age30to39 At Least One Dose Percent Age 30 to 39 ParPer_Age40to49 At Least One Dose Percent Age 40 to 49 ParPer_Age50to59 At Least One Dose Percent Age 50 to 59 ParPer_Age60to69 At Least One Dose Percent Age 60 to 69 ParPer_Age70to79 At Least One Dose Percent Age 70 to 79 ParPer_80+ At Least One Dose Percent 80+ ParPer_NA At Least One Dose Percent Not Assigned Age Group Fully_Age0to9 Fully vaccinated Age 0 to 11 Dose 2 of An MRNA or AztraZeneca Vaccine or a single dose vaccine of a Janssen Fully_Age10to19 Fully vaccinated Age 12 to 19 Fully_Age20to29 Fully vaccinated Age 20 to 29 Fully_Age30to39 Fully vaccinated Age 30 to 39 Fully_Age40to49 Fully vaccinated Age 40 to 49 Fully_Age50to59 Fully vaccinated Age 50 to 59 Fully_Age60to69 Fully vaccinated Age 60 to 69 Fully_Age70to79 Fully vaccinated Age 70 to 79 Fully_Age80+ Fully vaccinated Age 80+ Fully_NA Fully vaccinated Age Not Available Age Group Cumulative FullyCum_Age0to9 Cumulative Fully vaccinated Age 0 to 11 FullyCum_Age10to19 Cumulative Fully vaccinated Age 12 to 19 FullyCum_Age20to29 Cumulative Fully vaccinated Age 20 to 29 FullyCum_Age30to39 Cumulative Fully vaccinated Age 30 to 39 FullyCum_Age40to49 Cumulative Fully vaccinated Age 40 to 49 FullyCum_Age50to59 Cumulative Fully vaccinated Age 50 to 59 FullyCum_Age60to69 Cumulative Fully vaccinated Age 60 to 69 FullyCum_Age70to79 Cumulative Fully vaccinated Age 70 to 79 FullyCum_80+ Cumulative Fully vaccinated Age 80+ Age Group Cumulative Percent FullyCum_NA Cumulative Fully vaccinated Age Not Available FullyPer_Age0to9 Cumulative Percent Fully vaccinated Age 0 to 11 Cumulative Fully Vaccinated Age cohort/ Age cohort population FullyPer_Age10to19 Cumulative Percent Fully vaccinated Age 12 to 19 FullyPer_Age20to29 Cumulative Percent Fully vaccinated Age 20 to 29 FullyPer_Age30to39 Cumulative Percent Fully vaccinated Age 30 to 39 FullyPer_Age40to49 Cumulative Percent Fully vaccinated Age 40 to 49 FullyPer_Age50to59 Cumulative Percent Fully vaccinated Age 50 to 59 FullyPer_Age60to69 Cumulative Percent Fully vaccinated Age 60 to 69 FullyPer_Age70to79 Cumulative Percent Fully vaccinated Age 70 to 79 FullyPer_80+ Cumulative Percent Fully vaccinated Age 80+ FullyPer_NA Cumulative Percent Fully vaccinated Age Not Available

ObjectivesTo identify evidence-based strategies to improve adherence to the preventive measures against the coronavirus disease (COVID-19) at the community level.MethodThis is an evidence brief for policy, combining research evidence specific to contextual knowledge from stakeholders. A systematic search was performed in 18 electronic databases, gray literature, and a handle search, including only secondary and tertiary studies that focused on the adherence of the general population to COVID-19 preventive measures in the community. Two reviewers, independently, performed the study selection, data extraction, and assessment of the quality of the studies. Relevant evidence has been synthesized to draft evidence-based strategies to improve adherence. These strategies were circulated for external endorsement by stakeholders and final refinement. Endorsement rates >80%, 60–80% and

Abstract

The fourth edition of the Global Findex offers a lens into how people accessed and used financial services during the COVID-19 pandemic, when mobility restrictions and health policies drove increased demand for digital services of all kinds.

The Global Findex is the world's most comprehensive database on financial inclusion. It is also the only global demand-side data source allowing for global and regional cross-country analysis to provide a rigorous and multidimensional picture of how adults save, borrow, make payments, and manage financial risks. Global Findex 2021 data were collected from national representative surveys of about 128,000 adults in more than 120 economies. The latest edition follows the 2011, 2014, and 2017 editions, and it includes a number of new series measuring financial health and resilience and contains more granular data on digital payment adoption, including merchant and government payments.

The Global Findex is an indispensable resource for financial service practitioners, policy makers, researchers, and development professionals.

Geographic coverage

National coverage

Analysis unit

Individual

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most developing economies, Global Findex data have traditionally been collected through face-to-face interviews. Surveys are conducted face-to-face in economies where telephone coverage represents less than 80 percent of the population or where in-person surveying is the customary methodology. However, because of ongoing COVID-19 related mobility restrictions, face-to-face interviewing was not possible in some of these economies in 2021. Phone-based surveys were therefore conducted in 67 economies that had been surveyed face-to-face in 2017. These 67 economies were selected for inclusion based on population size, phone penetration rate, COVID-19 infection rates, and the feasibility of executing phone-based methods where Gallup would otherwise conduct face-to-face data collection, while complying with all government-issued guidance throughout the interviewing process. Gallup takes both mobile phone and landline ownership into consideration. According to Gallup World Poll 2019 data, when face-to-face surveys were last carried out in these economies, at least 80 percent of adults in almost all of them reported mobile phone ownership. All samples are probability-based and nationally representative of the resident adult population. Phone surveys were not a viable option in 17 economies that had been part of previous Global Findex surveys, however, because of low mobile phone ownership and surveying restrictions. Data for these economies will be collected in 2022 and released in 2023.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households. Each eligible household member is listed, and the hand-held survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

In traditionally phone-based economies, respondent selection follows the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies where mobile phone and landline penetration is high, a dual sampling frame is used.

The same respondent selection procedure is applied to the new phone-based economies. Dual frame (landline and mobile phone) random digital dialing is used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digital dialing is used in economies with limited to no landline presence (less than 20 percent).

For landline respondents in economies where mobile phone or landline penetration is 80 percent or higher, random selection of respondents is achieved by using either the latest birthday or household enumeration method. For mobile phone respondents in these economies or in economies where mobile phone or landline penetration is less than 80 percent, no further selection is performed. At least three attempts are made to reach a person in each household, spread over different days and times of day.

Sample size for United States is 1007.

Mode of data collection

Landline and mobile telephone

Research instrument

Questionnaires are available on the website.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar. 2022. The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. Washington, DC: World Bank.

Deprecated as of 4/21/2023On 4/27/2023 several COVID-19 datasets were retired and no longer included in public COVID-19 data dissemination. For more information, visit https://imap.maryland.gov/pages/covid-dataSummaryThe cumulative number of COVID-19 vaccinations among Maryland residents by age groupings: 0-9; 10-19; 20-29; 30-39; 40-49; 50-59; 60-69; 70-79; 80+; unknownDescriptionMD COVID-19 - Vaccinations by Age Distribution data layer is a collection of COVID-19 vaccinations that have been reported each day into ImmuNet.COVID-19 is a disease caused by a respiratory virus first identified in Wuhan, Hubei Province, China in December 2019. COVID-19 is a new virus that hasn't caused illness in humans before. Worldwide, COVID-19 has resulted in thousands of infections, causing illness and in some cases death. Cases have spread to countries throughout the world, with more cases reported daily. The Maryland Department of Health reports daily on COVID-19 cases by county.

Data from a Covid-19 data extraction from Johns Hopkins University (JHU)

The data were processed with the project script world-datas-analysis in order to add additional columns, including the ratio of cases in relation to the number of inhabitants, it was then exported in CSV format.

Initial source: https://github.com/CSSEGISandData/COVID-19 File exported from world-datas-analysis: CSV file

The project world-datas-analysis can export in gnuplot format filtered data according to your needs, see example below

Example rendered

Example rendering with gnuplot

enter image description here

Objective: To study the differences in clinical characteristics, risk factors, and complications across age-groups among the inpatients with the coronavirus disease 2019 (COVID-19).Methods: In this population-based retrospective study, we included all the positive hospitalized patients with COVID-19 at Wuhan City from December 29, 2019 to April 15, 2020, during the first pandemic wave. Multivariate logistic regression analyses were used to explore the risk factors for death from COVID-19. Canonical correlation analysis (CCA) was performed to study the associations between comorbidities and complications.Results: There are 36,358 patients in the final cohort, of whom 2,492 (6.85%) died. Greater age (odds ration [OR] = 1.061 [95% CI 1.057–1.065], p < 0.001), male gender (OR = 1.726 [95% CI 1.582–1.885], p < 0.001), alcohol consumption (OR = 1.558 [95% CI 1.355–1.786], p < 0.001), smoking (OR = 1.326 [95% CI 1.055–1.652], p = 0.014), hypertension (OR = 1.175 [95% CI 1.067–1.293], p = 0.001), diabetes (OR = 1.258 [95% CI 1.118–1.413], p < 0.001), cancer (OR = 1.86 [95% CI 1.507–2.279], p < 0.001), chronic kidney disease (CKD) (OR = 1.745 [95% CI 1.427–2.12], p < 0.001), and intracerebral hemorrhage (ICH) (OR = 1.96 [95% CI 1.323–2.846], p = 0.001) were independent risk factors for death from COVID-19. Patients aged 40–80 years make up the majority of the whole patients, and them had similar risk factors with the whole patients. For patients aged

final_all_cntry_a2

As of 5/18/2023 this dataset will be updated weekly on Tuesdays with a weekly granularity.

This dataset includes the VA health planning region, sewershed (i.e., wastewater treatment facilityservice area), start of collection week, percentile, percentile groups (Highest: 80-100th, Higher: 60-79.9th, Middle: 40-59.9th, Lower: 20-39.9th, Lowest: 0-19.9th), and report date. This dataset was first published on 05/18/2023. The data set increases in size weekly and as a result, the dataset may take longer to update; however, it is expected to be available by 12:00 noon. When you download the data set, the sewersheds will be sorted in ascending alphabetical order by health region. The sample collection dates will be sorted in ascending order, meaning that the earliest date will be at the top. The most recent date will be at the bottom of each sewershed’s data.