Context

From World Health Organization - On 31 December 2019, WHO was alerted to several cases of pneumonia in Wuhan City, Hubei Province of China. The virus did not match any other known virus. This raised concern because when a virus is new, we do not know how it affects people.

So daily level information on the affected people can give some interesting insights when it is made available to the broader data science community.

The European CDC publishes daily statistics on the COVID-19 pandemic. Not just for Europe, but for the entire world. We rely on the ECDC as they collect and harmonize data from around the world which allows us to compare what is happening in different countries.

Content

This dataset has daily level information on the number of affected cases, deaths and recovery etc. from coronavirus. It also contains various other parameters like average life expectancy, population density, smocking population etc. which users can find useful in further prediction that they need to make.

The data is available from 31 Dec,2019.

Inspiration

Give people weekly data so that they can use it to make accurate predictions.

"COVID-19 mortality correlation with cloudiness, sunlight, latitude in European countries"

Dataset for article titled
"COVID-19 mortality: positive correlation with cloudiness, sunlight and no correlation with latitude in Europe"

by SECIL OMER, ADRIAN IFTIME, VICTOR BURCEA

Corresponding author: A. Iftime, University of Medicine and Pharmacy "Carol Davila", Biophysics Department, 8 Blvd. Eroii Sanitari, 050474 Bucharest, Romania. Email address: adrian.iftime [at] umfcd.ro.

Preprint corresponding to this dataset: https://doi.org/10.1101/2021.01.27.21250658

===========
Dataset file:
1.0.0.COVID-19_Mortality_Cloudiness_Insolation_EUROPE_March_August_2020.csv

Dataset graphical preview:
1.0.0.INFOGRAFIC_CloudFraction_vs_COVID-19_mortality_Europe_March-August_2020.png

DATASET fields:
"Country" :
Country name; 37 European countries included.

"Date":
Date stamp at the collection time.
Data collection was performed in the last day of every month.
Date format: YYYY-MM-DD

"Month_Key" :
Date stamp at the collection time, formatted for easier monthly time series analysis.
Date format: YYYY-MM

"Month_Fct2020"
Date stamp at the collection time,formatted for easier graphing, as a string with names of the months
(in English).

"Deaths_per_1Mpop" :
Monthly mortality from COVID-19 raported in the country,
reported as number of COVID-19 deaths per 1 million population of the country,
in that particular month / country.
NB: it is reported as million population, not patients.

"LogDeaths_per_1Mpop" :
Log10 transformation of "Deaths_per_1Mpop"

"Insolation_Average" :
Insolation average (solar irradiance at ground level),
in that particular month / country.
It is expressed in Watt / square meter of the ground surface.
Data derived from data avaialble at NASA Langley Research Center, NASA’s Earth Observatory,
CERES / FLASHFlux team, 2020,
https://neo.sci.gsfc.nasa.gov/view.php?datasetId=CERES_INSOL_M

"Cloud_Fraction" :
Cloudiness (also known as cloud fraction, cloud cover, cloud amount or sky cover),
as decimal fraction of the sky obscured by clouds,
in that particular month / country.
Data derived from NASA Goddard Space Flight Center, NASA’s Earth Observatory,
MODIS Atmosphere Science Team, 2020,
https://neo.sci.gsfc.nasa.gov/view.php?datasetId=MODAL2_M_CLD_FR

"CENTR_latitude" and
"CENTR_longitude" :
Latitude and Longitude of the country centroid, for each country.
Data derived from Google LLC, "Dataset publishing language: country centroids",
https://developers.google.com/public-data/docs/canonical/countries_csv
NOTE: This is identical in every month (obviuously);
it is redundantly included for easier monthly sectional analysis of the data.

===========
Versioning: 1.0.0.COVID-19_Mortality_Cloudiness_Insolation_EUROPE_March_August_2020.csv

MAJOR: changes yearly; 1 = 2020
MINOR: changes if new monthly data is added in that particular year.
PATCH: Changes only if errors or minor edits were performed.

DOI for this version: 10.5281/zenodo.4266758

Dataset file source for this version (internal analysis source file):
db_covid_all-ANALYSIS.2020-09-22_r10.csv

Following the emergence of a novel coronavirus (SARS-CoV-2) and its spread outside of China, Europe has experienced large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions such as closure of schools and national lock downs. We study the impact of major interventions across 11 European countries for the period from the start of COVID-19 until the 4th of May 2020 when lock downs started to be lifted. Our model calculates backwards from observed deaths to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. We use partial pooling of information between countries with both individual and shared effects on the reproduction number. Pooling allows more information to be used, helps overcome data idiosyncrasies, and enables more timely estimates. Our model relies on fixed estimates of some epidemiological parameters such as the infection fatality rate, does not include importation or sub-national variation and assumes that changes in the reproduction number are an immediate response to interventions rather than gradual changes in behaviour. Amidst the ongoing pandemic, we rely on death data that is incomplete, with systematic biases in reporting, and subject to future consolidation. We estimate that, for all the countries we consider, current interventions have been sufficient to drive the reproduction number R_t below 1 (probability R_t < 1.0 is 99.9%) and achieve epidemic control. We estimate that, across all 11 countries between 12 and 15 million individuals have been infected with SARS-CoV-2 up to 4th May, representing between 3.2% and 4.0% of the population. Our results show that major non-pharmaceutical interventions and lock down in particular have had a large effect on reducing transmission. Continued intervention should be considered to keep transmission of SARS-CoV-2 under control. Data comes from the European Centre for Disease Control for 11 countries. Reported daily death counts were used for the period covered by the study (February 2020-May 2020). In addition to this data, we provide the posterior draws from our statistical model giving estimates of the number of infections and the time-varying reproduction number R(t) of the disease for each of the 11 countries during the time period covered by the study.

The number of COVID-19 deaths reported from European countries has varied more than 100-fold. In terms of coronavirus transmission, the relatively low death rates in some countries could be due to low intrinsic (e.g. low population density) or imposed contact rates (e.g. non-pharmaceutical interventions) among individuals, or because fewer people were exposed or susceptible to infection (e.g. smaller populations). Here we develop a flexible empirical model (skew-logistic) to distinguish among these possibilities. We find that countries reporting fewer deaths did not generally have intrinsically lower rates of transmission and epidemic growth, and flatter epidemic curves. Rather, countries with fewer deaths locked down earlier, had shorter epidemics that peaked sooner, and smaller populations. Consequently, as lockdowns are eased we expect, and are starting to see, a resurgence of COVID-19 across Europe.

Abstract copyright UK Data Service and data collection copyright owner. The English Longitudinal Study of Ageing (see the main study under SN 5050) is a longitudinal household survey for the study of health, economic position, and quality of life among the elderly. The ELSA COVID-19 Study, Waves 1-2, 2020 (SN 8688) is a follow-up substudy based on the sample of the main ELSA study. Within the context of the Coronavirus Disease 2019 (COVID-19) outbreak, all participants for the COVID-19 substudy were selected from the existing ELSA sample to measure the socio-economic effects/psychological impact of the lockdown on the 50+ population of England. The ELSA COVID-19 substudy allows a cross-sectional analysis of the dynamics of the lockdown, enabling too the possibility to link the data collected with previous and future waves of ELSA for longitudinal analysis. Subsequent to that, the University of Southern California (USC) Gateway to Global Aging Data team has created the Harmonized ELSA COVID data file, along with a codebook, to facilitate cross-country comparisons across international harmonized COVID studies, including the harmonized Health and Retirement Study (HRS) COVID study and the harmonized Survey of Health, Ageing and Retirement in Europe (SHARE) COVID study. This is a separate data product which is a user-friendly version of a subset of the ELSA COVID-19 substudy. The Harmonized ELSA COVID dataset uses data from the third edition of the ELSA COVID-19 substudy, released in February 2022. It follows the conventions of variable naming and data structure first developed by the RAND Center for the Study of Aging. The harmonized ELSA COVID data file is built using variables from the harmonized ELSA data file (SN 5050) and the ELSA COVID-19 substudy data files (SN 8688). It does not include any data which is not released under UKDS End User Licence access conditions. Main Topics: Topic areas include: demographics; mental health; financial security; COVID-19-related health; employment and work; financial situation; volunteering and care; physical health and health behaviours; social connection isolation and technological inclusion; income, pensions and retirement.

The English Longitudinal Study of Ageing (see the main study under SN 5050) is a longitudinal household survey for the study of health, economic position, and quality of life among the elderly.

The ELSA COVID-19 Study, Waves 1-2, 2020 (SN 8688) is a follow-up substudy based on the sample of the main ELSA study. Within the context of the Coronavirus Disease 2019 (COVID-19) outbreak, all participants for the COVID-19 substudy were selected from the existing ELSA sample to measure the socio-economic effects/psychological impact of the lockdown on the 50+ population of England. The ELSA COVID-19 substudy allows a cross-sectional analysis of the dynamics of the lockdown, enabling too the possibility to link the data collected with previous and future waves of ELSA for longitudinal analysis.

Subsequent to that, the University of Southern California (USC) Gateway to Global Aging Data team has created the Harmonized ELSA COVID data file, along with a codebook, to facilitate cross-country comparisons across international harmonized COVID studies, including the harmonized Health and Retirement Study (HRS) COVID study and the harmonized Survey of Health, Ageing and Retirement in Europe (SHARE) COVID study. This is a separate data product which is a user-friendly version of a subset of the ELSA COVID-19 substudy. The Harmonized ELSA COVID dataset uses data from the third edition of the ELSA COVID-19 substudy, released in February 2022. It follows the conventions of variable naming and data structure first developed by the RAND Center for the Study of Aging.

The harmonized ELSA COVID data file is built using variables from the harmonized ELSA data file (SN 5050) and the ELSA COVID-19 substudy data files (SN 8688). It does not include any data which is not released under UKDS End User Licence access conditions.

The data correspond to the selected national public response measures presented in the weekly report COVID-19 Country overviews.Response measures collected include mass gathering cancellations (for specific events or a ban on gatherings of a particular size); closure of public spaces (including restaurants, entertainment venues, non-essential shops, partial or full closure of public transport etc.); closure of educational institutions (including daycare or nursery, primary schools, and secondary schools and higher education); ‘stay-at-home’ recommendations for risk groups or vulnerable populations (such as the elderly, people with underlying health conditions, physically disabled people etc.); ‘stay-athome’ recommendations for the general population (which are voluntary or not enforced); and ‘stay-at-home’ orders for the general population (these are enforced and also referred to as ‘lockdown’), use of protective masks in public spaces/on public transport (mutually exclusive voluntary recommendations and mandatory obligations shown separately) and also teleworking recommendations/closure of workplaces It is based on data originally downloaded by the site https://www.ecdc.europa.eu/en/covid-19. Raw data from ECDC, harmonization and homogenization of data from UNIPV - Laboratory of Geomatics

Purpose: We analyzed the effects of COVID-19 as well as its accompanying epidemiological control measures on health-related outcomes (physical and mental health) and unmet care needs of both caregivers and care recipients across Europe and Israel by taking into account country differences.Methods: We applied comparisons of adjusted predictions, controlling for a large set of relevant respondent characteristics, to investigate changes in the physical and mental health of caregivers and care recipients due to COVID-19. Furthermore, multilevel regression models were used to analyze the effect of individual and contextual indicators on the probability of reporting difficulties in receiving care. For the analyses, we used data from 26 countries with 51,983 respondents over 50 years based on the eighth wave of the Survey of Health, Aging and Retirement in Europe (SHARE), which had to be suspended in March 2020, and the SHARE Corona Survey fielded from June to August 2020.Results: During the first phase of the pandemic in spring/summer 2020, the frequency of providing personal care to parents increased in almost all European countries, while care to children, in turn, decreased. Parental caregivers who increased the frequency of providing personal care reported significantly more mental health strains, that is, feeling sad/depressed and anxious/nervous more often since the outbreak of the pandemic. With respect to receiving care, about one out of five care recipients had difficulty in obtaining adequate care from outside the household during the pandemic. The perception of unmet care needs was significantly associated with country differences regarding the duration of the stay-at-home orders. In contrast, the number of confirmed deaths did not have a significant effect on perceiving difficulties related to receiving care.Conclusions: Our findings show the extent of the burden to which caregivers and care recipients were exposed with respect to the unintended consequences of COVID-19-related epidemiological control measures. There is a great need within this population for interventions, which effectively reduce the burden as well as the symptoms of anxiety or depression for caregivers as well as care recipients. This should be recognized by (health) policymakers and social organizations.

The survey covers the summer period with a very good epidemiological situation and further declines in COVID-19 hospitalization. Vaccination was available for all ages 12+ and interest for vaccination waned. Several vaccination centers started offering vaccines without prior registration. Fears of the disease, agreement and compliance with the introduced quarantine measures, changes of behavior at times of the epidemic and the approval of government strategies are surveyed. Several questions are dedicated to vaccination plans and factors influencing the decision to (not)vaccinate. Surveyed are also topics of compulsory vaccination, evaluation of politicians and their stances towards vaccination and experiences of side effects from vaccination. The survey also included questions on being informed about the Recovery plan for Europe. This is the tenth survey from the “How are you, Slovakia?” survey series. How Are You, Slovakia? Online interviews - CAWI Adult inhabitants of Slovakia (18+) with access to the internet The survey used a quota sample from the MNFORCE online panel. The sample was designed as representative for the following socio-demographic variables: gender, age, county (kraj), size of settlement and education of respondent. Only population with access to the internet is covered by the survey. This means that mostly older persons without internet access are missing from the sample.

Abstract copyright UK Data Service and data collection copyright owner.The European Working Conditions Survey (EWCS) is conducted by Eurofound (the European Foundation for the Improvement of Living and Working Conditions). Since its launch in 1990, the EWCS has provided an overview of working conditions in Europe. The main objectives of the survey are to:assess and quantify working conditions of both employees and the self-employed across Europe on a harmonised basis;analyse relationships between different aspects of working conditions;identify groups at risk and issues of concern as well as of progress;monitor trends by providing homogeneous indicators on these issues; andcontribute to European policy development in particular on quality of work and employment issues.Themes covered include employment status, working time duration and organisation, work organisation, learning and training, physical and psychosocial risk factors, health and safety, work-life balance, worker participation, earnings and financial security, as well as work and health.The EWCS paints a wide-ranging picture of Europe at work across countries, occupations, sectors and age groups. Its findings highlight actions for policy actors to help them address the challenges facing Europe today. The EWCS is generally conducted once every five years, although an extra wave was conducted in 2001 to cover the new acceding and candidate EU countries. The survey is based on a questionnaire which is administered face-to-face to a random sample of 'persons in employment' (i.e. employees and the self-employed), representative of the working population in each EU country. An integrated dataset is also available (see SN 7363) which combines data from the first five waves of the survey in one file. Before working with the EWCS data, users are recommended to read the latest supplementary supporting documentation on the Eurofound European Working Conditions Survey webpages. Further information about the series can be found there, including methodological information, technical reports and reports on translation, sampling implementation, sampling evaluation and weighting, coding, quality control, quality assurance and other publications. EWCTS 2021 The regular face-to-face EWCS had to be prematurely terminated in 2020 due to the Covid pandemic so, in 2021, Eurofound carried out a once-off European Working Conditions Telephone Survey (EWCTS) using computer-assisted telephone interviewing (CATI). The EWCTS 2021 included over 70,000 workers in 36 European countries: the EU Member States, Norway, Switzerland, the United Kingdom as well as Albania, Bosnia and Herzegovina, Kosovo, Montenegro, North Macedonia and Serbia. Changing the survey mode to CATI is in line with other similar surveys in the context of the COVID pandemic. The EWCTS 2021 allows Eurofound to provide comparable and representative information on job quality at a time when working lives have undergone considerable changes and the capacity of people at work to contribute to the recovery is critical. Due to the change in interviewing mode, comparison with previous editions of the EWCS may not be possible so the options for analysis of trends over time are limited.DocumentationUsers should note that the only methodological documentation currently available with the study is a Readme file. Further documentation will be provided by the depositor in due course. Users should also note that the UKDS data filenames may differ slightly from those currently quoted in the Readme file, but there is no difference in the content.Latest edition informationFor the third edition (January 2024), NUTS codes now give the code and not the label, so there are no issues with the encoding of non-Latin characters), and some changes have been made to the labels of NACE level 2 for Agriculture and Mining).

Covid-19 Voice Detection Market Outlook

The global Covid-19 Voice Detection market size was valued at USD 1.2 billion in 2023 and is projected to reach USD 3.6 billion by 2032, growing at a compound annual growth rate (CAGR) of 12.5% from 2024 to 2032. This market is witnessing robust growth primarily due to the increased need for rapid, scalable, and non-invasive diagnostic tools amid the global health crisis. The unprecedented surge in demand for early and accurate detection of Covid-19 infections, combined with advancements in artificial intelligence (AI) and machine learning (ML) technologies, are key growth drivers for the market.

One of the foremost growth factors propelling the Covid-19 Voice Detection market is the urgency for early and accurate diagnostic solutions. Traditional diagnostic methods, such as RT-PCR and antigen tests, while accurate, are time-consuming and resource-intensive. In contrast, voice-based detection systems offer a quick and non-invasive alternative, making them highly suitable for large-scale screening and monitoring. Additionally, these systems can be integrated into mobile applications and telehealth platforms, facilitating remote diagnosis and reducing the burden on healthcare facilities.

Another significant growth factor is the rapid advancements in AI and ML technologies. These technologies have shown immense potential in analyzing voice samples to detect respiratory anomalies associated with Covid-19. AI algorithms can process vast amounts of voice data, identifying subtle changes and patterns that may indicate infection. The continuous improvement in AI models and the availability of large datasets for training these models are expected to further enhance the accuracy and reliability of voice-based detection systems.

Furthermore, the increasing adoption of telehealth and remote monitoring solutions has fueled the demand for voice-based diagnostic tools. The Covid-19 pandemic has accelerated the shift towards telemedicine, with many patients and healthcare providers opting for remote consultations to minimize the risk of infection. Voice detection technology seamlessly integrates with telehealth platforms, enabling real-time monitoring and diagnosis. This not only enhances patient convenience but also ensures timely intervention and better health outcomes.

The regional outlook for the Covid-19 Voice Detection market indicates significant growth potential across various geographies. North America, with its advanced healthcare infrastructure and high adoption of AI technologies, is expected to dominate the market. Europe is also poised for substantial growth, driven by increasing government initiatives and investments in healthcare innovation. The Asia Pacific region, with its large population and growing healthcare expenditure, presents lucrative opportunities for market players. In addition, Latin America and the Middle East & Africa are gradually embracing voice-based diagnostics, supported by international collaborations and funding.

The implementation of a Covid 19 Health Code system has been instrumental in managing public health during the pandemic. This system, often integrated into mobile applications, assigns a health status to individuals based on their recent Covid-19 test results, vaccination status, and potential exposure to the virus. By using a color-coded system, such as green for safe, yellow for caution, and red for high risk, the Covid 19 Health Code facilitates the safe movement of people in public spaces. It has been particularly effective in countries where digital infrastructure is robust, allowing for real-time updates and seamless integration with other health monitoring tools. This system not only aids in contact tracing but also empowers individuals to make informed decisions about their daily activities, thereby reducing the spread of the virus.

Technology Analysis

The Covid-19 Voice Detection market can be segmented by technology into AI-based, machine learning, and deep learning categories. AI-based technology is at the forefront of this market, leveraging sophisticated algorithms to analyze voice recordings for signs of Covid-19 infection. AI models are trained on vast datasets, enabling them to identify specific vocal biomarkers associated with respiratory conditions. The continuous refinement of AI algorithms and increasing computational powe

The European Working Conditions Survey (EWCS) is conducted by Eurofound (the European Foundation for the Improvement of Living and Working Conditions). Since its launch in 1990, the EWCS has provided an overview of working conditions in Europe. The main objectives of the survey are to:

• assess and quantify working conditions of both employees and the self-employed across Europe on a harmonised basis;
• analyse relationships between different aspects of working conditions;
• identify groups at risk and issues of concern as well as of progress;
• monitor trends by providing homogeneous indicators on these issues; and
• contribute to European policy development in particular on quality of work and employment issues.

Themes covered include employment status, working time duration and organisation, work organisation, learning and training, physical and psychosocial risk factors, health and safety, work-life balance, worker participation, earnings and financial security, as well as work and health.

The EWCS paints a wide-ranging picture of Europe at work across countries, occupations, sectors and age groups. Its findings highlight actions for policy actors to help them address the challenges facing Europe today. The EWCS is generally conducted once every five years, although an extra wave was conducted in 2001 to cover the new acceding and candidate EU countries. The survey is based on a questionnaire which is administered face-to-face to a random sample of 'persons in employment' (i.e. employees and the self-employed), representative of the working population in each EU country. An integrated dataset is also available (see SN 7363) which combines data from the first five waves of the survey in one file.

Before working with the EWCS data, users are recommended to read the latest supplementary supporting documentation on the Eurofound European Working Conditions Survey webpages. Further information about the series can be found there, including methodological information, technical reports and reports on translation, sampling implementation, sampling evaluation and weighting, coding, quality control, quality assurance and other publications.

EWCTS 2021

The regular face-to-face EWCS had to be prematurely terminated in 2020 due to the Covid pandemic so, in 2021, Eurofound carried out a once-off European Working Conditions Telephone Survey (EWCTS) using computer-assisted telephone interviewing (CATI).

The EWCTS 2021 included over 70,000 workers in 36 European countries: the EU Member States, Norway, Switzerland, the United Kingdom as well as Albania, Bosnia and Herzegovina, Kosovo, Montenegro, North Macedonia and Serbia. Changing the survey mode to CATI is in line with other similar surveys in the context of the COVID pandemic.

The EWCTS 2021 allows Eurofound to provide comparable and representative information on job quality at a time when working lives have undergone considerable changes and the capacity of people at work to contribute to the recovery is critical. Due to the change in interviewing mode, comparison with previous editions of the EWCS may not be possible so the options for analysis of trends over time are limited.

Documentation

Users should note that the only methodological documentation currently available with the study is a Readme file. Further documentation will be provided by the depositor in due course. Users should also note that the UKDS data filenames may differ slightly from those currently quoted in the Readme file, but there is no difference in the content.

Latest edition information

For the third edition (January 2024), NUTS codes now give the code and not the label, so there are no issues with the encoding of non-Latin characters), and some changes have been made to the labels of NACE level 2 for Agriculture and Mining).