An August 2020 survey of fraud examiners worldwide revealed increases in different types of fraud risks after the start of the coronavirus pandemic. In May 2020, 29 percent of respondents reported a significant increase in identity theft risk. Additionally, 43 percent of respondents expected a significant increase in identity theft risk over the next twelve months.

An August 2020 survey of fraud examiners worldwide revealed increases in different types of fraud risks after the start of the coronavirus pandemic. In May 2020, ** percent of respondents reported a significant increase in cyber fraud risk. Additionally, ** percent of respondents expected a significant increase in cyber fraud risk over the next twelve months.

(1 January—30 June 2020).

The methods and results of the publication "COVID-19 test fraud detection: Findings from a pilot study comparing conventional and statistical approaches" are described in more detail in this appendix. The R-syntax for the calculation is provided, as well as a pseudo data set with which the syntax can also be tested.

Geographic coverage

National, regional

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2020 Vietnam COVID-19 High Frequency Phone Survey of Households (VHFPS) uses a nationally representative household survey from 2018 as the sampling frame. The 2018 baseline survey includes 46,980 households from 3132 communes (about 25% of total communes in Vietnam). In each commune, one EA is randomly selected and then 15 households are randomly selected in each EA for interview. We use the large module of to select the households for official interview of the VHFPS survey and the small module households as reserve for replacement.

After data processing, the final sample size for Round 3 is 4,560 households. Round 3 includes a larger expanded sample to provinces affected by the August/September 2020 outbreak.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire consists of the following sections

Section 2. Behavior Section 3. Health Section 5. Employment (main respondent) Section 6. Coping Section 7. Safety Nets Section 8. FIES Section 10. Opinion

Note: Some categorical responses have been merged in the anonymized data set for confidentiality.

Cleaning operations

Data cleaning began during the data collection process. Inputs for the cleaning process include available interviewers’ note following each question item, interviewers’ note at the end of the tablet form as well as supervisors’ note during monitoring. The data cleaning process was conducted in following steps: • Append households interviewed in ethnic minority languages with the main dataset interviewed in Vietnamese. • Remove unnecessary variables which were automatically calculated by SurveyCTO • Remove household duplicates in the dataset where the same form is submitted more than once. • Remove observations of households which were not supposed to be interviewed following the identified replacement procedure. • Format variables as their object type (string, integer, decimal, etc.) • Read through interviewers’ note and make adjustment accordingly. During interviews, whenever interviewers find it difficult to choose a correct code, they are recommended to choose the most appropriate one and write down respondents’ answer in detail so that the survey management team will justify and make a decision which code is best suitable for such answer. • Correct data based on supervisors’ note where enumerators entered wrong code. • Recode answer option “Other, please specify”. This option is usually followed by a blank line allowing enumerators to type or write texts to specify the answer. The data cleaning team checked thoroughly this type of answers to decide whether each answer needed recoding into one of the available categories or just keep the answer originally recorded. In some cases, that answer could be assigned a completely new code if it appeared many times in the survey dataset.
• Examine data accuracy of outlier values, defined as values that lie outside both 5th and 95th percentiles, by listening to interview recordings. • Final check on matching main dataset with different sections, where information is asked on individual level, are kept in separate data files and in long form. • Label variables using the full question text. • Label variable values where necessary.

Archived as of 9/25/2025: The datasets will no longer receive updates but the historical data will continue to be available for download. Note: 11/1/2023: Publication of the COVID data will be delayed because of technical difficulties. Note: 9/20/2023: With the end of the federal emergency and reporting requirements continuing to evolve, the Indiana Department of Health will no longer publish and refresh the COVID-19 datasets after November 15, 2023 - one final dataset publication will continue to be available. Note: 5/10/2023: Due to a technical issue updates are delayed for COVID data. New files will be published as soon as they are available. Note: 3/22/2023: Due to a technical issue updates are delayed for COVID data. New files will be published as soon as they are available. Note: 3/15/2023 test data will be removed from the COVID dashboards and HUB files in recognition of the fact that widespread use of at-home tests and a decrease in lab testing no longer provides an accurate representation of COVID-19 spread. Historical Changes: 1/11/2023: Due to a technical issue updates are delayed for COVID data. New files will be published as soon as they are available. 1/5/2023: Due to a technical issue the COVID datasets were not updated on 1/4/23. Updates will be published as soon as they are available. 9/29/22: Due to a technical difficulty, the weekly COVID datasets were not generated yesterday. They will be updated with current data today - 9/29 - and may result in a temporary discrepancy with the numbers published on the dashboard until the normal weekly refresh resumes 10/5. 9/27/2022: As of 9/28, the Indiana Department of Health (IDOH) is moving to a weekly COVID update for the dashboard and all associated datasets to continue to provide trend data that is applicable and usable for our partners and the public. This is to maintain alignment across the nation as states move to weekly updates. 8/19/2022 - The first and second dose columns are being removed as of 8/22/22 as the Health department has transitioned to reporting on Fully/Partially vaccinated. The final historical file including these columns from 8/19 will continue to be available. 2/10/2022: Data was not published on 2/9/2022 due to a technical issue, but updated data was released 2/10/2022. 10/13/2021: This dataset now includes columns for new and total booster shots administered. Please see the data dictionary for additional details. 08/06/2021: There are updates today to county-level vaccination rates to reflect a correction to records that were assigned to the wrong location based on ZIP code. 06/23/2021: COVID Hub files will no longer be updated on Saturdays. The normal refresh of these files has been changed to Mon-Fri. 06/10/2021: COVID Hub files will no longer be updated on Sundays. The normal refresh of these files has been changed to Mon-Sat. 06/07/2021: Today’s new counts include doses newly reported to the Indiana Department of Health on Saturday and Sunday. 06/03/2021: Individuals are able to update their personal and demographic information during the vaccination registration process. Today’s data reflects changes made by individuals to their race, ethnicity, or county of residence over the course of their vaccination series. 05/06/2021: On Monday 5/3, individuals classified as "Unknown" county of residence were inadvertently converted to "Out of State." These individuals have been corrected in today's dataset. 03/17/2021: This dataset has been updated to include zeros for dates where there is no reported data. 03/11/2021: This dataset has been updated to include totals and newly administered single dose vaccination data.

A survey in 2023 found that the proportion of adults in the United States who believed in COVID-19 misinformation doesn't always vary by their race or ethnic background. White adults were less likely than Blacks and Hispanics to believe in the false claims that "More people have died from the COVID-19 vaccines than have died from the COVID-19 virus" and "The COVID-19 vaccines have caused thousands of sudden deaths in otherwise healthy people." However, with the other two false claims, all adults were more or less likely to be wrong.

This statistic shows the share of adults who thought select false claims about COVID-19 were definitely or probably true in the United States as of 2023, by race and ethnicity.

A study held in early 2024 found that more than a third of surveyed consumers in selected countries worldwide had witnessed false news about politics in the week running to the survey. Suspicious or false COVID-19 news was also a problem. False news False news is often at its most insidious when it distorts or misrepresents information about key topics, such as public health, global conflicts, and elections. With 2024 set to be a significant year of political change, with elections taking place worldwide, trustworthy and verifiable information will be crucial. In the U.S., trust in news sources for information about the 2024 presidential election is patchy. Republicans and Independents are notably less trusting of news about the topic than their Democrat-voting peers, with only around 40 percent expressing trust in most news sources in the survey. Social media fared the least well in this respect with just a third of surveyed adults saying that they had faith in such sites to deliver trustworthy updates on the 2024 election. A separate survey revealed that older adults were the least likely to trust the news media for election news. This is something that publishers can bear in mind when targeting audiences with updates and campaign information. Distorting the truth: the impact of false news Aside from reading (and potentially believing) false information, consumers are also at risk of accidentally sharing false news and therefore contributing to its spread. One way in which the dissemination of false news could be stemmed is by consumers educating themselves on how to identify suspicious content, however government intervention has also been tabled. Consumers are split on whether or not governments should take steps to restrict false news, partly due to concerns about the need to protect freedom of information.

On January 12, 2021, over 4.5 thousand individuals in the UK were admitted to hospital with coronavirus (COVID-19), the highest single amount since the start of the pandemic. The daily hospital cases started to rise significantly at the end of 2020 and into January 2021, however since then the number of hospitalizations fell dramatically as the UK managed to vaccinate millions against COVID-19. Overall, since the pandemic started around 994 thousand people in the UK have been hospitalized with the virus.

The total number of cases in the UK can be found here. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

The dataset is composed of rumors and non-rumors related to COVID-19 collected from three different online sources: (a) the Brazilian Ministry of Health official website, (b) a journalistic initiative named boatos.org focused on debunking online rumors, including COVID-19, and (c) the O Globo news that provides a special track to follow rumors about COVID-19.

The Brazilian Ministry of Health dataset (saude.gov.csv) is composed of 79 rumors and 05 non-rumors texts classified by the own Brazilian government; the boatos.org dataset (boatos.org.csv) is composed of 951 rumors classified and debunked by a team of journalists; and the O Globo dataset (oglobo.com.csv) is composed of 261 rumors and 03 non-rumors also classified by journalists. In total, the COVID-19 RUMOR dataset has 1291 rumors and 08 non-rumors.

Remote working has increased dramatically due to the coronavirus (COVID-19) pandemic, bringing new challenges related to cyber security. ** percent of large companies in Finland agreed in a 2020 survey that the decentralized working model increased information and cyber security risks. Meanwhile, only seven percent of the companies disagreed.

Online safety during COVID-19

While the world has been focused on the health and economic threats caused by the COVID-19 pandemic, cyber criminals have clearly attempted to take advantage of the crisis since the start of 2020. Both the likelihood and impact of cyber attacks is on the rise, as criminals are increasingly targeting major corporations, governments, and critical infrastructure. Online threats to businesses can take various forms, such as data breaches, phishing, malware and malicious spam emails that are used to mislead employees and customers. For instance, the risk of cyber fraud has significantly increased worldwide based on the 2020 data. It is also estimated that cyber crime will remain a major risk in the future, as both individuals and organizations become more technology dependent.

Preparedness for cyber security risks in Finnish companies

Finns made the quickest shift to remote working in Europe, and even 75 percent of employees moved entirely to home office during spring 2020. At the same time, digital transformation also creates new potential for cyber security vulnerabilities. Based on survey results from 2020, ** percent of major Finnish companies perceived risks related to information and cyber security significant. However, over ** percent of companies claimed in the same survey that they are prepared for information and cyber security risks.

During the COVID-19 lockdown, Instacart became an essential service for millions of Americans trapped at home. Even as early as February, Instacart started noticing unusual demand for items such as...

Background

The COVID-19 pandemic is a global healthcare emergency. Prediction models for COVID-19 imaging are rapidly being developed to support medical decision making in imaging. However, inadequate availability of a diverse annotated dataset has limited the performance and generalizability of existing models.

Purpose

The Radiological Society of North America (RSNA) assembled the RSNA International COVID-19 Open Radiology Database (RICORD), a collection of COVID-related imaging datasets and expert annotations to support research and education. The RICORD datasets are made freely available to the research community and will be incorporated in the Medical Imaging and Data Resource Center (MIDRC), a multi-institutional research data repository funded by the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health.

Materials and Methods

MIDRC-RICORD dataset 1a was created through a collaboration between the RSNA and the Society of Thoracic Radiology (STR). Pixel-level volumetric segmentation with clinical annotations by thoracic radiology subspecialists was performed for all COVID positive thoracic computed tomography (CT) imaging studies in a labeling schema coordinated with other international consensus panels and COVID data annotation efforts.

Results

MIDRC-RICORD dataset 1a consists of 120 thoracic computed tomography (CT) scans from four international sites annotated with detailed segmentation and diagnostic labels.

Patient Selection: Patients at least 18 years in age receiving positive diagnosis for COVID-19.

Data Abstract

1. 120 Chest CT examinations (axial series only, any protocol).

2. Annotations comprised of

• a) Detailed segmentation of affected regions;
• b) Image-level labels (Infectious opacity, Infectious TIB/micronodules, Infectious cavity, Noninfectious nodule/mass, Atelectasis, Other noninfectious opacity)
• c) Exam-level diagnostic labels (Typical, Indeterminate, Atypical, Negative for pneumonia, Halo sign, Reversed halo sign, Reticular pattern w/o parenchymal opacity, Perilesional vessel enlargement, Bronchial wall thickening, Bronchiectasis, Subpleural curvilinear line, Effusion, Pleural thickening, Pneumothorax, Pericardial effusion, Lymphadenopathy, Pulmonary embolism, Normal lung, Infectious lung disease, Emphysema, Oncologic lung disease, Non-infectious inflammatory lung disease, Non-infectious interstitial, Fibrotic lung disease, Other lung disease)
• d) Exam-level procedure labels (With IV contrast, Without IV contrast, QA- inadequate motion/breathing, QA- inadequate insufficient inspiration, QA- inadequate low resolution, QA- inadequate incomplete lungs, QA- inadequate wrong body part/modality, Endotracheal tube, Central venous/arterial line, Nasogastric tube, Sternotomy wires, Pacemaker, Other support apparatus).

3. Supporting clinical variables: MRN*, Age, Study Date*, Exam Description, Sex, Study UID*, Image Count, Modality, Testing Result, Specimen Source (* pseudonymous values).

Research Benefits

As this is a public dataset, RICORD is available for non-commercial use (and further enrichment) by the research and education communities which may include development of educational resources for COVID-19, use of RICORD to create AI systems for diagnosis and quantification, benchmarking performance for existing solutions, exploration of distributed/federated learning, further annotation or data augmentation efforts, and evaluation of the examinations for disease entities beyond COVID-19 pneumonia. Deliberate consideration of the detailed annotation schema, demographics, and other included meta-data will be critical when generating cohorts with RICORD, particularly as more public COVID-19 imaging datasets are made available via complementary and parallel efforts. It is important to emphasize that there are limitations to the clinical “ground truth” as the SARS-CoV-2 RT-PCR tests have widely documented limitations and are subject to both false-negative and false-positive results which impact the distribution of the included imaging data, and may have led to an unknown epidemiologic distortion of patients based on the inclusion criteria. These limitations notwithstanding, RICORD has achieved the stated objectives for data complexity, heterogeneity, and high-quality expert annotations as a comprehensive COVID-19 thoracic imaging data resource.

Abstract

The main objective of this project is to collect household data for the ongoing assessment and monitoring of the socio-economic impacts of COVID-19 on households and family businesses in Vietnam. The estimated field work and sample size of households in each round is as follows:

Round 1 June fieldwork- approximately 6300 households (at least 1300 minority households) Round 2 August fieldwork - approximately 4000 households (at least 1000 minority households) Round 3 September fieldwork- approximately 4000 households (at least 1000 minority households) Round 4 December- approximately 4000 households (at least 1000 minority households) Round 5 - pending discussion

Geographic coverage

National, regional

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2020 Vietnam COVID-19 High Frequency Phone Survey of Households (VHFPS) uses a nationally representative household survey from 2018 as the sampling frame. The 2018 baseline survey includes 46980 households from 3132 communes (about 25% of total communes in Vietnam). In each commune, one EA is randomly selected and then 15 households are randomly selected in each EA for interview. Out of the 15 households, 3 households have information collected on both income and expenditure (large module) as well as many other aspects. The remaining 12 other households have information collected on income, but do not have information collected on expenditure (small module). Therefore, estimation of large module includes 9396 households and are representative at regional and national levels, while the whole sample is representative at the provincial level.

We use the large module of to select the households for official interview of the VHFPS survey and the small module households as reserve for replacement. The sample size of large module has 9396 households, of which, there are 7951 households having phone number (cell phone or line phone).

After data processing, the final sample size is 6,213 households.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire for Round 1 consisted of the following sections Section 2. Behavior Section 3. Health Section 4. Education & Child caring Section 5A. Employment (main respondent) Section 5B. Employment (other household member) Section 6. Coping Section 7. Safety Nets Section 8. FIES

Cleaning operations

Data cleaning began during the data collection process. Inputs for the cleaning process include available interviewers’ note following each question item, interviewers’ note at the end of the tablet form as well as supervisors’ note during monitoring. The data cleaning process was conducted in following steps: • Append households interviewed in ethnic minority languages with the main dataset interviewed in Vietnamese. • Remove unnecessary variables which were automatically calculated by SurveyCTO • Remove household duplicates in the dataset where the same form is submitted more than once. • Remove observations of households which were not supposed to be interviewed following the identified replacement procedure. • Format variables as their object type (string, integer, decimal, etc.) • Read through interviewers’ note and make adjustment accordingly. During interviews, whenever interviewers find it difficult to choose a correct code, they are recommended to choose the most appropriate one and write down respondents’ answer in detail so that the survey management team will justify and make a decision which code is best suitable for such answer. • Correct data based on supervisors’ note where enumerators entered wrong code. • Recode answer option “Other, please specify”. This option is usually followed by a blank line allowing enumerators to type or write texts to specify the answer. The data cleaning team checked thoroughly this type of answers to decide whether each answer needed recoding into one of the available categories or just keep the answer originally recorded. In some cases, that answer could be assigned a completely new code if it appeared many times in the survey dataset.
• Examine data accuracy of outlier values, defined as values that lie outside both 5th and 95th percentiles, by listening to interview recordings. • Final check on matching main dataset with different sections, where information is asked on individual level, are kept in separate data files and in long form. • Label variables using the full question text. • Label variable values where necessary.

Response rate

The target for Round 1 is to complete interviews for 6300 households, of which 1888 households are located in urban area and 4475 households in rural area. In addition, at least 1300 ethnic minority households are to be interviewed. A random selection of 6300 households was made out of 7951 households for official interview and the rest as for replacement. However, the refusal rate of the survey was about 27 percent, and households from the small module in the same EA were contacted for replacement and these households are also randomly selected.

Inflation was the most worrying topic worldwide as of May 2025, with ********* of the respondents choosing that option. Crime and violence, as well as poverty and social inequality, followed behind. Moreover, following Russia's invasion of Ukraine and the war in Gaza, *** percent of the respondents were worried about military conflict between nations. Only *** percent were worried about the COVID-19 pandemic, which dominated the world after its outbreak in 2020. Global inflation and rising prices Inflation rates have spiked substantially since the beginning of the COVID-19 pandemic in 2020. From 2020 to 2021, the worldwide inflation rate increased from *** percent to *** percent, and from 2021 to 2022, the rate increased sharply from *** percent to *** percent. While rates are predicted to fall by 2025, many are continuing to struggle with price increases on basic necessities. Poverty and global development Poverty and social inequality were the third most worrying issues for respondents. While poverty and inequality are still prominent, global poverty rates have been on a steady decline over the years. In 1994, ** percent of people in low-income countries and around one percent of people in high-income countries lived on less than 2.15 U.S. dollars per day. By 2018, this had fallen to almost ** percent of people in low-income countries and 0.6 percent in high-income countries. Moreover, fewer people globally are dying of preventable diseases, and people are living longer lives. Despite these aspects, issues such as wealth inequality have global prominence.

This study provides the profiles and success predictions of students considering data before, during, and after the COVID-19 pandemic. Using a field experiment of 396 students and more than 7400 instances, we have analyzed students’ performance considering the temporal distribution of autonomous learning during courses from 2016/2017 to 2020/2021. After applying unsupervised learning, results show 3 main profiles from the clusters obtained in the simulations: students who work continuously, those who do it in the last-minute, and those with a low performance in the whole autonomous learning. We have found that the highest success ratio is related to students that work in a continuous basis. However, last-minute working is not necessarily linked to failure. We have also found that students’ marks can be predicted successfully taking into account the whole data sets. However, predictions are worse when removing data from the month before the final exam. These predictions are useful to prevent students’ wrong learning strategies, and to detect malpractices such as copying. We have done all these analyses taking into account the effect of the COVID-19 pandemic, founding that students worked in a more continuous basis in the confinement. This effect was still present one year after. Finally, We have also included an analysis of the techniques that could be more effective to keep in a future non-pandemic scenario the good habits that were detected in the confinement.

A survey conducted in Britain in August 2024 found that 49 percent of men in Britain and 44 percent of women thought that social media networks did a very bad job tackling misinformation during the recent riots in England. Roughly one quarter of women felt that social networks did a fairly bad job, as did just under one quarter of men. Far-right riots occurred throughout the United Kingdom during the period of late July to early August 2024, and a total of 466 individuals have been charged with various offenses.