Every day, schools, child care centres and licensed home child care agencies report to the Ministry of Education on children, students and staff that have positive cases of COVID-19. If there is a discrepancy between numbers reported here and those reported publicly by a Public Health Unit, please consider the number reported by the Public Health Unit to be the most up-to-date. Schools and school boards report when a school is closed to the Ministry of Education. Data is current as of 2:00 pm the previous day. This dataset is subject to change. Data is only updated on weekdays excluding provincial holidays Effective June 15, 2022, board and school staff will not be expected to report student/staff absences and closures in the Absence Reporting Tool. The ministry will no longer report absence rates or school/child care closures on Ontario.ca for the remainder of the school year. Learn how the Government of Ontario is helping to keep Ontarians safe during the 2019 Novel Coronavirus outbreak. ##Summary of school closures This is a summary of school closures in Ontario. Data includes: * Number of schools closed * Total number of schools * Percentage of schools closed ##School Absenteeism This report provides a summary of schools and school boards that have reported staff and student absences. Data includes: * School board * School * City or Town * Percentage of staff and students who are absent ##Summary of cases in schools This report provides a summary of COVID-19 activity in publicly-funded Ontario schools. Data includes: * School-related cases (total) * School-related student cases * School-related staff cases * Current number of schools with a reported case * Current number of schools closed Note: In some instances the type of cases are not identified due to privacy considerations. ##Schools with active COVID-19 cases This report lists schools and school boards that have active cases of COVID-19. Data includes : * School Board * School * Municipality * Confirmed Student Cases * Confirmed Staff Cases * Total Confirmed Cases ##Cases in school board partners This report lists confirmed active cases of COVID-19 for other school board partners (e.g. bus drivers, authorized health professionals etc.) and will group boards if there is a case that overlaps. Data includes : * School Board(s) * School Municipality * Confirmed cases – other school board partners ##Summary of targeted testing conducted in schools This data includes all tests that have been reported to the Ministry of Education since February 1, 2021. School boards and other testing partners will report to the Ministry every Wednesday based on data from the previous seven days. Data includes : * School boards or regions * Number of schools invited to participate in the last seven days * Total number of tests conducted in the last seven days * Cumulative number of tests conducted * Number of new cases identified in the last seven days * Cumulative number of cases identified ##Summary of asymptomatic testing at conducted in pharmacies: This is a summary of COVID-19 rapid antigen testing conducted at participating pharmacies in Ontario since March 27, 2021. * Total number of tests conducted in the last seven days * Cumulative number of tests conducted * Number of new cases identified in the last seven days * Cumulative number of cases identified

The Advancing Education Safely dashboard includes information about COVID-19 testing and confirmed cases for SDP students and staff in 2021-22.

BackgroundCOVID-19 testing is critical for identifying cases to prevent transmission. COVID-19 self-testing has the potential to increase diagnostic testing capacity and to expand access to hard-to-reach areas in low-and-middle-income countries. We investigated the feasibility and acceptability of COVID-19 self-sampling and self-testing using SARS-CoV-2 Antigen-Rapid Diagnostic Tests (Ag-RDTs).MethodsFrom July 2021 to February 2022, we conducted a mixed-methods cross-sectional study examining self-sampling and self-testing using Standard Q and Panbio COVID-19 Ag Rapid Test Device in Urban and rural Blantyre, Malawi. Health care workers and adults (18y+) in the general population were non-randomly sampled.ResultsOverall, 1,330 participants were enrolled of whom 674 (56.0%) were female and 656 (54.0%) were male with 664 for self-sampling and 666 for self-testing. Mean age was 30.7y (standard deviation [SD] 9.6). Self-sampling usability threshold for Standard Q was 273/333 (82.0%: 95% CI 77.4% to 86.0%) and 261/331 (78.8%: 95% CI 74.1% to 83.1%) for Panbio. Self-testing threshold was 276/335 (82.4%: 95% CI 77.9% to 86.3%) and 300/332 (90.4%: 95% CI 86.7% to 93.3%) for Standard Q and Panbio, respectively. Agreement between self-sample results and professional test results was 325/325 (100%) and 322/322 (100%) for Standard Q and Panbio, respectively. For self-testing, agreement was 332/333 (99.7%: 95% CI 98.3 to 100%) for Standard Q and 330/330 (100%: 95% CI 99.8 to 100%) for Panbio. Odds of achieving self-sampling threshold increased if the participant was recruited from an urban site (odds ratio [OR] 2.15 95% CI 1.44 to 3.23, P < .01. Compared to participants with primary school education those with secondary and tertiary achieved higher self-testing threshold OR 1.88 (95% CI 1.17 to 3.01), P = .01 and 4.05 (95% CI 1.20 to13.63), P = .02, respectively.ConclusionsOne of the first studies to demonstrate high feasibility and acceptability of self-testing using SARS-CoV-2 Ag-RDTs among general and health-care worker populations in low- and middle-income countries potentially supporting large scale-up. Further research is warranted to provide optimal delivery strategies of self-testing.

This dataset presents information on COVID-19 in children and young people of educational age, education staff and educational settings. This includes: * Testing and cases among children and young people of educational age. * Hospital admissions related to COVID-19 among children and young people of educational age. * Information from contact tracing on cases present in an educational setting in the 7-days before symptom onset, and on cases who work in education or childcare. * Information about COVID-19 cases in registered school pupils. This data is also available on the COVID-19 Education Surveillance Dashboard. Additional data sources relating to this topic area are provided in the Links section of the Metadata below. All publications and supporting material to this topic area can be found on the Enhanced Surveillance of COVID-19 in Education settings section of the Public Health Scotland website. From 11/06/2021 data completeness will be up to the previous Wednesday, so weekly data are aggregated from Thursday to Wednesday. Previously data covered periods from Saturday to Friday. This is due to NHS Boards submitting admission data from Monday to Friday and a three day lag for some boards by the time data is processed for COVID-19 hospital admission. From 2nd of July, information on testing and admissions will be extended to include 20-21 years olds, and admissions will also include 18-19 year olds. From 13th of August, information on PCR testing and admissions has been extended to include 0-1 year olds.

COVID-19 Diagnostic Testing Market Outlook

The COVID-19 diagnostic testing market size was valued at USD 85 billion in 2023, with a forecasted value of USD 75 billion by 2032, growing at a CAGR of -1.3% during the forecast period. The market's decline is primarily driven by the decreasing number of COVID-19 cases and the widespread availability of vaccines. Despite the downward trend, the market is expected to maintain a significant presence due to ongoing testing requirements in various sectors, evolving virus variants, and the need for early detection in future outbreaks.

One of the main growth factors for the COVID-19 diagnostic testing market is the increasing awareness of the importance of early detection and prevention. Governments and health organizations worldwide have emphasized the necessity of mass testing to control the spread of the virus. Investments in healthcare infrastructure and the development of innovative testing methods have also played a crucial role in maintaining the market's momentum. Moreover, the emergence of new variants has underscored the need for continuous testing and monitoring, ensuring that the market remains relevant.

Technological advancements have significantly influenced the growth of the COVID-19 diagnostic testing market. The development of rapid and accurate testing methods, such as molecular and antigen tests, has revolutionized the industry. These technologies have enabled healthcare providers to quickly identify and isolate infected individuals, thereby preventing further transmission. Additionally, advancements in at-home testing kits have made it more convenient for individuals to monitor their health status, leading to increased adoption of these products.

The expanding applications of COVID-19 diagnostic testing beyond healthcare settings have also contributed to market growth. Many industries, including travel, hospitality, and education, have adopted regular testing protocols to ensure the safety of their employees and customers. This widespread adoption has created a sustained demand for diagnostic tests, even as the number of cases fluctuates. Furthermore, the integration of testing with digital health platforms and mobile applications has streamlined the process, making it easier for individuals to access and interpret their results.

Regionally, North America has been a significant market for COVID-19 diagnostic testing, driven by the high number of cases and robust healthcare infrastructure. Europe and Asia Pacific have also exhibited strong growth, supported by government initiatives and increasing awareness about the importance of testing. In contrast, regions like Latin America and the Middle East & Africa have faced challenges due to limited healthcare infrastructure and resources. However, international aid and collaborations have helped to mitigate some of these issues, fostering growth in these markets.

Test Type Analysis

The COVID-19 diagnostic testing market is segmented into molecular tests, antigen tests, and antibody tests. Molecular tests, such as RT-PCR, remain the gold standard due to their high accuracy and reliability. These tests detect the virus's genetic material and are widely used in hospital and laboratory settings. Despite their longer turnaround time, molecular tests are preferred for definitive diagnosis and for confirming cases of COVID-19, especially in symptomatic individuals and high-risk populations.

Antigen tests have gained popularity due to their rapid turnaround time and ease of use. These tests detect specific proteins on the surface of the virus and can provide results within minutes. While they are less accurate than molecular tests, antigen tests are valuable for mass screening and point-of-care testing. Their ability to quickly identify infected individuals makes them crucial in settings where immediate results are needed, such as airports, schools, and workplaces.

Antibody tests, also known as serology tests, detect the presence of antibodies in the blood, indicating a past infection. These tests play a crucial role in understanding the spread of the virus and the population's immunity levels. While not used for diagnosing active infections, antibody tests provide valuable data for epidemiological studies and vaccine efficacy assessments. They have been instrumental in guiding public health strategies and vaccination campaigns.

The implementation of a <a href="https://dataintelo.com/report/global-covid-19-health-code-market" target="_blank"

The study identified the emotional impact of COVID-2019 pandemic among teaching and non-teaching staff in Vocational Enterprises Institutes in Abuja, Nigeria. The research design used for this study was a cross-sectional study. The study was conducted in Abuja, Nigeria. The population of the study was 182 respondents consisting of 91 males and 63 females teaching staff as well as 16 males and 12 females’ non-teaching staff from the six Vocational Enterprises Institutes, one each from the six area councils in Abuja, Nigeria. Total population sampling technique was used to select the whole population of the study. The instruments used for data collection was Pandemic Emotional Impact Scale. Cronbach Alpha statistical method was used to determine the reliability index of the instrument and found to be .90. The study employed the use of weighted mean formula to answer the research questions and z-test to test the null hypotheses using GraphPad online z-test calculator. Findings from the study revealed among others that worried about finances, anxious or ill at ease, difficulty concentrating, being less productive, worried about personal health or safety, being more bored, difficulty sleeping, feeling lonelier or isolated and feeling more down or depressed, worried about getting necessities like medications were emotional impact of COVID-2019 pandemic among teaching and non-teaching staff in Vocational Enterprises Institutes in Abuja, Nigeria. The study recommended among others that, the education secretariat of the Federal Capital Territory, Abuja, Nigeria should develop an emotional intelligence framework for the management of emotional challenges associated with COVID-19 for teaching and non-teaching staff in Vocational Enterprises Institutes in Abuja, Nigeria.

This upload consists of the dataset employed in the publication "Detection of COVID-19 Infection from Routine Blood Exams with Machine Learning: a Feasibility Study". The paper was accepted for publication at Journal of Medical Systems (Springer), and a pre-print version is also available on MedRXiv and has been attached to the upload for further reference. If you decide to use or reference this dataset (or the related work) please cite the journal version (details will be added as soon as available).

The dataset consists of 280 records of patients admitted to the San Raffaele Hospital (Milan, Italy), annotated with a collection of hematochemical values from routine blood exams (namely: white blood cells counts, and the platelets, CRP, AST, ALT, GGT, ALP, LDH plasma levels), and a target variable that describes COVID-19 positivity/negativity (in the target column, class 2 and class 1 can both be treated as COVID-19 positive patients)

The abstract of the supporting publication follows:

Background - The COVID-19 pandemia due to the SARS-CoV-2 coronavirus, in its first 4 months since its outbreak, has to date reached more than 200 countries worldwide with more than 2 million confirmed cases (probably a much higher number of infected), and almost 200,000 deaths. Amplification of viral RNA by (real time) reverse transcription polymerase chain reaction (rRT-PCR) is the current gold standard test for confirmation of infection, although it presents known shortcomings: long turnaround times (3-4 hours to generate results), potential shortage of reagents, false-negative rates as large as 15-20%, the need for certified laboratories, expensive equipment and trained personnel. Thus there is a need for alternative, faster, less expensive and more accessible tests.

Material and methods - We developed two machine learning classification models using hematochemical values from routine blood exams (namely: white blood cells counts, and the platelets, CRP, AST, ALT, GGT, ALP, LDH plasma levels) drawn from 279 patients who, after being admitted to the San Raffaele Hospital (Milan, Italy) emergency-room with COVID-19 symptoms, were screened with the rRT-PCR test performed on respiratory tract specimens. Of these patients, 177 resulted positive, whereas 102 received a negative response.

Results - We have developed two machine learning models, to discriminate between patients who are either positive or negative to the SARS-CoV-2: their accuracy ranges between 82% and 86%, and sensitivity between 92% e 95%, so comparably well with respect to the gold standard. We also developed an interpretable Decision Tree model as a simple decision aid for clinician interpreting blood tests (even off-line) for COVID-19 suspect cases.

Discussion - This study demonstrated the feasibility and clinical soundness of using blood tests analysis and machine learning as an alternative to rRT-PCR for identifying COVID-19 positive patients. This is especially useful in those countries, like developing ones, suffering from shortages of rRT-PCR reagents and specialized laboratories. We made available a Web-based tool for clinical reference and evaluation. This tool is available at https://covid19-blood-ml.herokuapp.com.

Social Work Field Instruction in the Shadow of COVID-19: a scoping review

Objective: The objective of this scoping review is to synthesize the state, nature, extent, and emerging best practices and principles in the current empirical literature on field instruction in social work. Building upon the previous work of Bogo et al. (2020), the aim is to answer the following research questions: 1) What is the state of the empirical literature (i.e., range and extent) on social work field instruction published amid COVID-19? 2) What is the nature (i.e., topics, design, and methods, theoretical frameworks, outcomes measured, and incorporation of culture and diversity) of the social work field instruction empirical literature published during this period? and 3) How have practices and principles in social work field instruction adjusted, adapted, and innovated during the course of the COVID-19 pandemic?

Inclusion criteria: Our inclusion criteria derive from the work of Bogo et al. (2020). The concept of interest is Social Work Field Instruction during COVID-19. We will include literature focused on 1) field instruction; 2) social work disciplinary orientation; 3) undergraduate or graduate-level field education; 4) student or field instructor participants (i.e., field instructors, field educators, practice educators, practice assessors, internal or external supervisors, field supervisors); and 5) COVID-19. Articles will be excluded if they: 1) focus on integrated field seminars or the faculty field liaison role only; 2) include only field coordinators or directors as participants. In terms of context, we will include all empirical literature (inclusive of all study designs), without geographic restriction, published in English between March 01 2020 and December 31 2021 (22 months).

Methods: To identify relevant studies, we will search the peer-reviewed empirical literature published between March 01 2020 and December 31 2021 in three steps. For the first phase we will search databases relevant to social work: Scholars Portal, ProQuest—ASSIA & Social Work abstracts, EBSCO Social Science Abstracts, and OVID Social Work Abstracts. Second, we will hand search of table of contents of relevant journals focusing on social work, field education, and practice learning. Finally, we will search the reference lists of articles included in the sample. Using a piloted screening tool, two members of the team will independently screen all titles and abstracts, followed by full article screening. Data will be extracted using a piloted form. We will synthesize (i.e., collate, summarize, and report) extracted data, presenting in narrative and table formats.

Review questions: Building upon the previous work of Bogo et al. (2020), the aim is to answer the following research questions: 1) What is the state of the empirical literature (i.e., range and extent) on social work field instruction published amid COVID-19? 2) What is the nature (i.e., topics, design, and methods, theoretical frameworks, outcomes measured, and incorporation of culture and diversity) of the social work field instruction empirical literature published during this period? and 3) How have practices and principles in social work field instruction adjusted, adapted, and innovated during the course of the COVID-19 pandemic?

Keywords: field instruction, field education, practice learning, student supervision, social work, best practices, scoping review

Eligibility criteria

Participants: Our population of interest for this review are students of social work. We will include literature focused on 1) field instruction; 2) social work disciplinary orientation; 3) undergraduate or graduate-level field education; 4) student or field instructor participants (i.e., field instructors, field educators, practice educators, practice assessors, internal or external supervisors, field supervisors); and 5) COVID-19. Our exclusion criteria are articles which: 1) focus on integrated field seminars or the faculty field liaison role only; 2) include only field coordinators or directors as participants.

Concept: The concept of interest is Social Work Field Instruction during COVID-19.

Context: Building upon the previous work of Bogo et al. (2020), and given the specific challenges and changes that have been demanded in social work field instruction over the course of the COVID-19 pandemic (CASWE, 2021), we have chosen to adjust our search strategy to focus on literature published between March 01 2020 and December 31 2021 (22 months).

Types of Sources:This scoping review will consider all empirical study designs (i.e., quantitative, qualitative, and mixed methods studies) that meet our inclusion criteria. While we will exclude conceptual, editorial, and theses/dissertations, these exclusions will be flagged, and reference lists will be searched for additional records to screen.

Methods:We will conduct the proposed scoping review following the steps of Arksey and O’Malley’s scoping review framework (2005), methodologically informed by Joanna Brigg’s Institute recommendations and the PRISMA-ScR reporting guidelines (Peters et al., 2020; Tricco et al., 2018).

Search strategy: The initial search strategy was constructed using the three-step framework developed by Bogo et al. (2020). Using search terms deriving from Bogo et al. (2020), using the search terms “‘field instruct*’ OR ‘field educat*’ OR ‘supervis*’ OR ‘practicum’ OR ‘practice teach*’ OR ‘practice learn*’ AND ‘social work’ AND ‘student’” (Bogo et al., 2020), we will search databases relevant to social work. These keywords and index terms will be adapted for each of the following databases relevant to social work: Scholars Portal, ProQuest—ASSIA & Social Work abstracts, EBSCO Social Science Abstracts, and OVID Social Work Abstracts. Next, we will hand search of the table of contents of relevant journals focusing on social work, field education, and practice learning including: Social Work Education, Journal of Social Work Education, The Field Educator, The Clinical Supervisor, Journal of Teaching in Social Work, Journal of Practice Teaching and Learning, Aotearoa New Zealand Social Work, Social Work Education: The international journal, The European Journal of Social Work, The British Journal of Social Work, The International Journal of Social Welfare. Finally, we will search the reference lists of articles included in the sample, and reference lists in excluded literature flagged as relevant conceptual, editorial, and theses/dissertations.

Study/Source of Evidence selection: All records identified through the search will be uploaded into Covidence software for conducting scoping and systematic reviews, and deduplicated. Using a modified version of Bogo et al.’s (2020) previously developed title and abstract screening form, we will pilot a subsample of articles with two members of the team independently screening records against the inclusion and exclusion criteria. Based on results of the pilot, we will determine if additional rounds of piloting will be required to finalize our inclusion criteria and screening form. Two members of the team will independently screen all records by title and abstract using the revised screening form. A third member of the team will resolve any disagreements between screeners. We will then screen full texts for inclusion, recording the reasons for excluding records.

Data Extraction: We will use a modified version of Bogo et al. (2020)’s previously developed extraction form, which will include all relevant data (e.g., date, journal, study location, type of study, design/methods, participants/population, topic category, theoretical framework, models of field instruction, culture and diversity indication, key findings, field outcome measures, best practices and principles). The team will iteratively refine the tool through the pilot process, with modifications reported in the scoping review. Members of the team will independently extract data from all included papers, consulting with the team to clarify and address any questions related to extraction.

Data Analysis and Presentation: We will synthesize (i.e., collate, summarize, and report) extracted data. This will involve presenting frequencies of the quantitative data, and describing the qualitative data (Peters et al., 2020). We will use a table to present the included articles and key characteristics, with additional tables summarizing the outcomes and impacts identified, and measures used across the articles.

References

Arksey, H., & O’Malley, L. (2005). Scoping studies: Towards a methodological framework. International Journal of Social Research Methodology, 8(1), 19–32. https://doi.org/10.1080/1364557032000119616

Bogo, M., Sewell, K. M., Mohamud, F., & Kourgiantakis, T. (2020). Social work field instruction: A scoping review. Social Work Education, 0(0), 1–34. https://doi.org/10.1080/02615479.2020.1842868

Canadian Association of Social Work Education. (2021). Statement on the Continued Critical Role of Field Education in Social Work Education—Updated. https://caswe-acfts.ca/statement-on-the-continued-critical-role-of-field-education-in-social-work-education-updated-2/

Peters, M. D. J., Marnie, C., Tricco, A. C., Pollock, D., Munn, Z., Alexander, L., McInerney, P., Godfrey, C. M., & Khalil, H. (2020). Updated methodological guidance for the conduct of scoping reviews. JBI Evidence Synthesis, 18(10), 2119–2126. https://doi.org/10.11124/JBIES-20-00167

Tricco, A. C., Lillie, E., Zarin, W., O’Brien, K. K., Colquhoun, H., Levac, D., Moher, D., Peters, M. D. J., Horsley, T., Weeks, L., Hempel, S., Akl, E. A., Chang, C., McGowan, J., Stewart, L., Hartling, L., Aldcroft, A., Wilson, M. G., Garritty, C., … Straus, S. E. (2018). Prisma extension for scoping reviews (PRISMA-SCR): Checklist and explanation. Annals of Internal Medicine. https://doi.org/10.7326/M18-0850

Cross-tabulation of risk category and RT-PCR results (n = 1,174).

The Education Endowment Foundation (EEF) has been leading the management of the Tuition Partners (TP) pillar of the National Tutoring Programme (NTP) in 2020/2021, funded as part of the government coronavirus catch-up package. The TP programme allows schools to access subsidised tuition from a list of 33 tuition partners, quality approved by the EEF, to support pupils who have missed out the most as a result of school closures due to the COVID-19 pandemic. The focus is on supporting disadvantaged pupils, in particular those eligible for Pupil Premium, but with flexibility for schools to select those pupils who they feel were most in need of the support. The EEF commissioned the National Foundation for Educational Research (NFER) to run a reach and engagement nimble randomised controlled trial (RCT) with EM Tuition, an approved NTP Tuition Partner. The RCT explored the impact of two distinctive types of recruitment emails on school sign-up to the TP programme provided by EM Tuition: one email included a testimonial from a headteacher on the benefits of tutoring, the other included a summary of the research evidence of the benefits of tutoring. EM Tuition sent recruitment emails during February and March 2021 to 1,949 primary, secondary, and special schools in areas of England where they offer tutoring provision, including Hertfordshire, Essex, North London, the East of England, and Suffolk. Schools were randomly allocated to receive one of the two types of email messages. A team from NFER analysed the impact of the different recruitment emails on the proportion of schools signing a Memorandum of Understanding (MoU) or providing an Expression of Interest (EoI) for their pupils to receive tutoring from EM Tuition as part of the TP programme.

The Education Endowment Foundation (EEF) has been leading the management of the Tuition Partners (TP) pillar of the National Tutoring Programme (NTP) in 2020/2021, funded as part of the government coronavirus catch-up package. The TP programme allows schools to access subsidised tuition from a list of 33 tuition partners, quality approved by the EEF, to support pupils who have missed out the most as a result of school closures due to the COVID 19 pandemic. The focus is on supporting disadvantaged pupils, in particular those eligible for Pupil Premium, but with flexibility for schools to select those pupils who they feel were most in need of the support. The EEF commissioned the National Foundation for Educational Research (NFER) to run a reach and engagement nimble randomised controlled trial (RCT) with EM Tuition, an approved NTP Tuition Partner. The RCT explored the impact of two distinctive types of recruitment emails on school sign-up to the TP programme provided by EM Tuition: one email included a testimonial from a headteacher on the benefits of tutoring, the other included a summary of the research evidence of the benefits of tutoring. EM Tuition sent recruitment emails during February and March 2021 to 1,949 primary, secondary, and special schools in areas of England where they offer tutoring provision, including Hertfordshire, Essex, North London, the East of England, and Suffolk. Schools were randomly allocated to receive one of the two types of email messages. A team from NFER analysed the impact of the different recruitment emails on the proportion of schools signing a Memorandum of Understanding (MoU) or providing an Expression of Interest (EoI) for their pupils to receive tutoring from EM Tuition as part of the TP programme.

Context

Corona - COVID19 virus affects the respiratory system of healthy individual & Respiratory Sound is one of the important testing methods to identify the corona virus.

With the Respiratory Sound dataset, Develop a Machine Learning Model to classify the Respiratory Sound of Healthy vs Corona affected patients & this model powers the AI application to test the Corona Virus in Faster Phase.

Content

Respiratory Sound files of Corona affected users and Users does not have corona is available in CoronaHack-Respiratory-Sound-Dataset

Respiratory sound files available in train and test folders with dates and user ID

• Multiple category of respiratory sound files

  • breathing-deep
  • breathing-shallow
  • cough-heavy
  • cough-shallow
  • counting-fast
  • counting-normal
  • vowel-a
  • vowel-e
  • vowel-o

Respiratory Sound files for each users available in data folders and labels , demographic details and other ailments related to User available in Corona-Hack-Respiratory-Sound-Metadata.csv file

Target variable -

COVID_test_status - ( Possible values 1 - User Has COVID , 0 -User does not have COVID & NA - User does't provided COVID status )

Features :-

• User Personal ID

  • USER_ID - User ID is encrypted actual customer is not stored
  • GENDER - M - Male & F - For Female
  • AGE. - Integer
  • COVID_STATUS - ( Possible values - healthy , resp_illness_not_identified,ect )
  • ENGLISH_PROFICIENCY - Y - Yes or N - No

• User Demographic details :-

  • COUNTRY
  • COUNTY_RO_STATE
  • CITY_LOCALITY

• Data collection dates -

  • DATES - Date format YYYYMMDD

• User Health ailments and Corona Symptoms ( Possible values - 1 True & 0- False )

  • Diabetes
  • Asthma
  • Smoker
  • Hypertension
  • Fever
  • Returning_User
  • Using_Mask
  • Cold
  • Caugh
  • Muscle_Pain
  • loss_of_smell
  • Sore_Throat
  • Fatigue
  • Breathing_Difficulties
  • Chronic_Lung_Disease
  • Ischemic_Heart_Disease
  • Pneumonia
  • Diarrheoa

• Respiratory Sound file category paths ( Below files contain the path with file name & if the .Wav file not available value will be blank )

  • breathing-deep
  • breathing-shallow
  • cough-heavy
  • cough-shallow
  • counting-fast
  • counting-normal
  • vowel-a
  • vowel-e
  • vowel-o

Acknowledgements

Respiratory Data for multiple users is collected from Coswara-Data & credits for data collection deliberately goes to Coswara team , Hope this Kaggle dataset help to speed up building of Machine Learning models and to carry more works on model explainability

Links :-

https://github.com/iiscleap/Coswara-Data https://coswara.iisc.ac.in/?locale=en-US

Inspiration

Corona Testing is one of time consuming & it delays the treatment for patient for corona , Corona testing is mandated in all airpots across globe & other places. AI Machine Learning - Respiratory Sound Model will help human society to Speed up Corona testing from Days to Minutes

Machine Learning - Respiratory Sound Model will be driver to Corona testing mobile apps

The Citizens' Pulse surveys examine Finnish attitudes and opinions in the context of the coronavirus pandemic (COVID-19). Main themes in the surveys include the activity and communication of authorities, compliance with regulations, future expectations, trust, and the respondents' own state of mind. The 14th collection round of 2021 surveyed the respondents' opinions on the reliability of the information provided by various Finnish authorities (e.g. the Finnish Institute for Health and Welfare) and members of various groups (e.g. health care employees, researchers) on the coronavirus crisis. The respondents were asked to evaluate how fair or unfair they thought Finnish society was at present and how well Finnish authorities (including the Government, Ministries, and other authorities such as the police and the Finnish Institute for Health and Welfare) had been prepared for epidemics such as COVID-19. The respondents' state of mind was examined with questions on various matters relating to health and well-being. The questions covered, for example, whether the respondents were worried about their own risk or the risk of people close to them contracting COVID-19, the availability of health care for them and people close to them for issues unrelated to COVID-19, and their own mental well-being. Additionally, the respondents' concerns were charted with questions regarding livelihood and everyday life (e.g. whether they were worried about the adequacy of their income or the income of people close to them, the uncertainty regarding how long the exceptional circumstances would last, and their children's schooling). Next, the respondents' confidence in their future and experiences of stress were surveyed. The respondents were asked to evaluate whether an atmosphere of crisis prevailed amongst Finns. The availability and findability of relevant information on the coronavirus crisis was charted. Compliance with coronavirus restrictions was examined by asking the respondents whether they had followed the restrictions and recommendations set by authorities for preventing transmission of the virus, including maintaining safe distances, washing hands regularly, wearing a face mask, avoiding touching the eyes, nose or mouth with unwashed hands, using a hand sanitiser when it was available in public places, and minimising contacts with people outside immediate family and friends. Finally, the respondents were asked how useful they thought several measures were in preventing the spread of COVID-19 (e.g. testing yourself with rapid antigen at-home tests, avoiding public events, and using a COVID-19 certificate to enter restaurants and events), how easy it had been/would be for the respondents to comply with the measures, and how long they would be willing to comply with the measures. Background variables included the respondent's gender, age group, NUTS3 region of residence, highest level of education, household composition, and perceived financial situation of household.

Overview Cough audio signal classification has been successfully used to diagnose a variety of respiratory conditions, and there has been significant interest in leveraging Machine Learning (ML) to provide widespread COVID-19 screening. The COUGHVID dataset provides over 30,000 crowdsourced cough recordings representing a wide range of subject ages, genders, geographic locations, and COVID-19 statuses. Furthermore, experienced pulmonologists labeled more than 2,000 recordings to diagnose medical abnormalities present in the coughs, thereby contributing one of the largest expert-labeled cough datasets in existence that can be used for a plethora of cough audio classification tasks. As a result, the COUGHVID dataset contributes a wealth of cough recordings for training ML models to address the world’s most urgent health crises. Private Set and Testing Protocol Researchers interested in testing their models on the private test dataset should contact us at coughvid@epfl.ch, briefly explaining the type of validation they wish to make, and their obtained results obtained through cross-validation with the public data. Then, access to the unlabeled recordings will be provided, and the researchers should send the predictions of their models on these recordings. Finally, the performance metrics of the predictions will be sent to the researchers. The private testing data is not included in any file within our Zenodo record, and it can only be accessed by contacting the COUGHVID team at the aforementioned e-mail address. New Semi-Supervised Labeling The third version of the COUGHVID dataset contains thousands of additional recordings obtained through October 2021. Additionally, the recordings containing coughs were re-labeled according to a semi-supervised learning algorithm that combined the user labels with those of the expert physicians, which were modeled using ML and expanded on the previously unlabeled data. These labels can be found in the "status_SSL" column of the "metadata_compiled.csv" file.

Demographic details of school management participants.

Baseline demographics and socioeconomic characteristics and reach of the CHW support component of the T2C Model.

The Coronavirus Disease 2019 (COVID-19) has had devastating effects globally. These effects are likely to result in mental health problems at different levels. Although studies have reported the mental health burden of the pandemic on the general population and frontline health workers, the impact of the disease on the mental health of patients in COVID-19 treatment and isolation centres have been understudied in Africa. We estimated the prevalence of depression and anxiety and associated risk factors in hospitalized persons with COVID-19. A cross-sectional survey was conducted among 489 patients with COVID-19 at the three government-designated treatment and isolation centres in Edo State, Nigeria. The 9-item Patient Health Questionnaire (PHQ-9) and the Generalized Anxiety Disorder-7 (GAD-7) tool were used to assess depression and anxiety respectively. Binary logistic regression was applied to determine risk factors of depression and anxiety. Results Of the 489 participants, 49.1% and 38.0% had depressive and anxiety symptoms respectively. The prevalence of depression, anxiety, and combination of both were 16.2%, 12.9% and 9.0% respectively. Moderate-severe symptoms of COVID-19, ≥14 days in isolation, worrying about the outcome of infection and stigma increased the risk of having depression and anxiety. Additionally, being separated/divorced increased the risk of having depression and having comorbidity increased the risk of having anxiety. A substantial proportion of our participants experienced depression, anxiety and a combination of both especially in those who had the risk factors we identified. The findings underscore the need to address modifiable risk factors for psychiatric manifestations early in the course of the disease and integrate mental health interventions and psychosocial support into COVID-19 management guidelines. -- Methods Setting and study design A descriptive cross-sectional study was conducted from 15th April to 11th November 2020. The participants were COVID-19 Real Time-Reverse Transcriptase -Polymerase Chain Reaction (rRT-PCR) positive persons who were hospitalized at the three government- designated treatment and isolation centres in Edo State, Nigeria. Participants and data collection procedure All eligible and consenting persons who were COVID-19 rRT-PCR positive and hospitalized at any of the study institutions within the period of the survey were recruited. The inclusion criteria comprised of persons with confirmed COVID-19, hospitalized at any of the study institutions who consented to participate in the study and were eleven years and above. Exclusion criteria comprised of hospitalized persons who tested positive for COVID-19 but declined or were unable to give consent to participate in the study and persons below 11 years due to the inappropriateness of the assessment tools for anxiety and depression in this age group. Medical records/registers at the treatment and isolation centres were reviewed daily in order to identify new admissions and discharges in the centres and ineligible patients due to age (less than 11 years). A total of 796 persons with confirmed COVID-19 were hospitalized at the three government designated treatment and isolation centres in Edo State over the study period. All patients were informed and acknowledged a detailed description of the study, eligibility requirements and voluntariness to participate in the study. Nineteen of them were below 11 years and were excluded, and 265 patients either refused to give consent or were too ill (critically ill) to consent and participate in the study. A total of 512 were therefore recruited for the study. Semi-structured and structured questionnaires incorporating socio-demographics, basic clinical history/information and an assessment of anxiety and depression were administered to recruited participants on the fifth day of admission into the treatment and isolation centres. The questionnaires were self - administered except for those who opted for interviewer-administered questionnaires (mainly those with severe COVID-19 infection). Questionnaires were administered in the English language as all participants had some levels of formal education and were literate enough to understand the language. Those who were critically ill with COVID-19 infection were unable to consent and participate in the study. Online survey and hard copies of the questionnaires were made available for completion. All the participants preferred hard copies of the questionnaires and a copy of the signed consent form was retained by each participant and one by the researchers. Clinical information on severity of COVID-19 infection and presence and type of comorbidity were obtained from their medical records (case files). Length of stay in treatment and isolation centres was obtained from their case files after discharge from the centres as the questionnaires were coded for ease of identification. Measurements The socio-demographic/clinical history questionnaire This was designed to provide information about the participant’s age, gender, religion, marital status, employment status and the highest level of formal education. Clinical variables such as COVID-19 rRT-PCR status, previous/family history of mental illness, the severity of COVID-19 infection, the number of days in isolation, comorbidity were ascertained as well. To ascertain the worry factor, the question “what is your greatest worry about being COVID-19 positive” was asked. The 9-item Patient Health Questionnaire (PHQ‑9) This consists of nine items, each of which is scored 0 to 3, providing a 0 to 27 severity score.[15] PHQ‑9 severity is calculated by assigning scores of 0, 1, 2, and 3, to the response categories of: Not at all, several days, more than half the days, and nearly every day, respectively. It consists of the nine criteria for depression from the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM‑IV). The PHQ‑9 is comparable or superior in operating characteristics, and valid as both a diagnostic and severity measure.[16] Scores of 5, 10, 15, and 20 represent cut-off points for mild, moderate, moderately severe, and severe depression respectively. A PHQ-9 score of 10 or greater is recommended if a single screening cut-off is to be used, this cut-off point has a sensitivity for major depression of 88% and a specificity of 88%. The modified version for adolescents PHQ-A was used for participants within the ages of 11 and 17 years. A cut-off score of ≥ 10 was used to represent cases of depression. The PHQ-9 can be self-administered or clinician administered. The Generalized Anxiety Disorder-7 (GAD-7) This is a 7-item self-report questionnaire that allows for the rapid detection of GAD, the validity is not compromised if the clinician reads the questions to the client.[17] Participants are asked if they were bothered by anxiety-related problems over the past two weeks by answering seven items on a 4-point scale. The total scores range from 0 to 21. At a cut-off score of 10, the GAD-7 had a sensitivity of 89 % and a specificity of 82 % for detecting GAD compared with a structured psychiatric interview.[17] Notably, among clinical and general population samples, the GAD-7 has demonstrated good reliability and cross-cultural validity as a measure of GAD (16). Its use has been validated in adolescents.[18] A cut-off score of ≥ 10 was used to represent cases of anxiety. Ethics Ethical clearance was obtained from our Research Ethics Committee of the Irrua Specialist Teaching Hospital, Irrua. Informed written consent was obtained from each participant and from the parents or guardians of participants who were less than 18 years. Participants who were less than 18 years also assented to the study. Confidentiality and anonymity were ensured by not indicating the names of the participants on the questionnaires. Statistical analysis The collected data were analysed using the Statistical Package for Social Sciences (SPSS) version 21. Dependent variables were depression and anxiety. Independent variables were sociodemographic and clinical characteristics. Descriptive statistics were used to summarise socio-demographic and clinical related data and mean with standard deviation for continuous variables. Chi-square (χ2) tests were used to test the association of independent variables with dependent variables. Fisher's exact test was used for cells with expected frequencies < 5. The student's t- test was used to compare means. Binary logistic regression was applied to identify predictors of depression and anxiety that were significant at bivariate analysis. All tests were 2-tailed, and the level of significance was set at a P-value of <0.05.

Chaque jour, les écoles, les centres de garde d’enfants et les agences agréées de garde d’enfants en milieu familial signalent au ministère de l’Éducation les cas positifs de COVID-19 recensés chez les enfants, les élèves et les membres du personnel.

Si vous remarquez une différence entre les données fournies sur cette page et les données publiées par un bureau de santé publique, nous vous conseillons de vous fier aux données publiées par le bureau de santé publique, qui sont les plus à jour.

Les écoles et les conseils scolaires doivent indiquer au ministère de l’Éducation les écoles qui ont été fermées. Les données sont à jour en date de la veille à 14 h.

Cet ensemble de données pourrait changer.

Les données sont actualisées en semaine seulement, exclusion faite des jours fériés. À compter du 15 juin, 2022, le personnel des conseils scolaires ne sera pas tenu de signaler les absences des élèves/du personnel dans l’Outil de signalement des absences. Le ministère ne publiera plus les taux d'absence ou les fermetures d'écoles ou de garderies sur Ontario.ca pour le reste de l'année scolaire.

Apprenez comment le gouvernement de l'Ontario contribue à la sécurité des Ontariens pendant l’épidémie du nouveau coronavirus 2019 en Ontario.

Sommaire des écoles fermées

Consultez le sommaire des écoles fermées en Ontario.

Les données comprennent :

• Nombre d'écoles fermées
• Nombre total d'écoles
• Poucentage d'écoles fermées

Écoles absentéisme

Ces données fournissent un sommaire des écoles et des conseils scolaires qui ont signalé des absences de membres du personnel ou d'élèves

Les données comprennent :

• Conseil scolaire
• École
• Ville
• Pourcentage d'élèves et de membres du personnel signalés comme absents

Sommaire des cas recensés dans les écoles

Ces données fournissent un sommaire de l'activité du COVID-19 dans les écoles financées par les fonds publics de l’Ontario.

Les données comprennent :

• Nombre total de cas en milieu scolaire
• Cas recensés chez un élève en milieu scolaire
• Cas recensés chez un membre du personnel en milieu scolaire
• Nombre d’écoles ayant actuellement un cas déclaré
• Nombre d’écoles actuellement fermées

Remarque : dans certains cas, le type de cas n’est pas identifié pour des raisons de confidentialité.

Écoles ayant des cas actifs de COVID-19

Les données ci-dessous répertorient les écoles et les conseils scolaires ayant actuellement un ou plusieurs cas actifs de COVID-19.

Les données comprennent :

• Conseil scolaire
• École
• Ville
• Cas confirmés chez un élève
• Cas confirmés chez un membre du personnel
• Nombre total de cas

Cas recensés chez des partenaires des conseils scolaires

Les données énumèrent les cas actifs confirmés de COVID-19 pour les autres partenaires des conseils scolaires (par exemple, les chauffeurs de bus, les professionnels de santé autorisés, etc.) et regrouperont les conseils s'il y a un cas qui se chevauche.

Les données comprennent :

• Conseil scolaire(s)
• École
• Municipalité
• Cas confirmés - autres partenaires du conseil scolaire

Aperçu des tests ciblés réalisés dans les écoles

Les données publiées rendent compte des tests déclarés au ministère de l'Éducation depuis le 1er février 2021. Le mercredi de chaque semaine, les conseils scolaires et les autres partenaires effectuant les tests déclareront les données qu'ils ont recueillies au cours des sept jours précédents.

Les données comprennent : * Conseil scolaire ou région * Nombre d'écoles ayant participé au cours des sept derniers jours * Nombre total des tests réalisés au cours des sept derniers jours * Total cumulatif du nombre de tests réalisés * Nombre de nouveaux cas recensés au cours des sept derniers jours * Total cumulatif du nombre de cas recensés

Aperçu des tests asymptomatiques ciblés réalisés dans les pharmacies:

Consultez ici le tableau récapitulatif des tests antigéniques rapides réalisés dans les pharmacies participantes de l’Ontario depuis le 27 mars 2021.

• Nombre total des tests réalisés au cours des sept derniers jours
• Total cumulatif du nombre de tests réalisés
• Nombre de nouveaux cas recensés au cours des sept derniers jours
• Total cumulatif du nombre de cas recensés

Factors associated with self-sampling and self-testing accuracy.

BackgroundWhile COVID-19 has had a wide-ranging impact on individuals and societies, persons with disabilities are uniquely affected largely due to secondary health conditions and challenges in adhering to protective measures. However, research on COVID-19 and vaccine acceptance has primarily focused on the general population and healthcare workers but has specifically not targeted PwDs, who are more vulnerable within societies. Hence, this study assessed PwDs knowledge of COVID-19 and factors associated with COVID-19 vaccine acceptance.MethodsA cross-sectional survey was conducted among PwDs in the Atwima Mponua District in the Ashanti Region of Ghana. Respondents were sampled systematically and data was collected using a structured questionnaire. The data were analyzed with STATA version 16.0. Descriptive analysis was done using means and proportions. The chi-square test and Logistic regression were used to assess Covid-19 vaccine acceptance among the respondents.Results250 PwDs were recruited for the study. A higher proportion of the respondents were females, physically impaired, and between 30–50 years. The majority (74%) of the PwDs had average knowledge about Covid-19. Factors such as age, educational level and type of disability were significantly associated with PwDs’ knowledge of COVID-19. The acceptance rate for COVID-19 among PwDs was 71.2%. Age, religion, knowledge of COVID-19, and educational level were significantly associated with Covid-19 vaccine acceptance. Persons with disabilities with low and average knowledge of COVID-19 were 95% and 65%, respectively, less likely to accept the vaccine compared to those with high knowledge of COVID-19 (AOR = 0.05, 95%CI: 0.01, 0.21; AOR = 0.35, 95%CI: 0.12, 1.03). Older people and those with higher education were more likely to accept the vaccine compared to younger people and those with no or less education.ConclusionPersons with disabilities have average knowledge of COVID-19 and a greater percentage of them were willing to accept the vaccine. The study identified age, religion, knowledge of COVID-19, and educational level as contributing factors to their willingness to accept the COVID-19 vaccine. This suggest that PwDs will lean positive toward COVID-19 vaccine programs and as such, vaccination programs should target them.