COVID-19 confirmed cases and deaths by state as of July 28, 2020 from https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/cases-in-us.html and https://usafacts.org/visualizations/coronavirus-covid-19-spread-map The state numbers listed by the CDC are aggregated from the USAFact county data.The CDC reports healthcare personnel cases and infections (120,467 and 587 as of August 1, 2020; accessed August 2, 2020) but does not disaggregate the numbers by state.Healthcare worker deaths by state as of July 28, 2020 pulled from https://www.medscape.com/viewarticle/927976#vp_1

COVID-19 Deaths Among Healthcare Personnel, by week

The dataset consists of quantitative data derived mainly from international datasets (ILO, WHO), supplemented by data from national datasets and modelled data to complete missing values. It shows the statistical data we collated and used to calculate estimates of Covid-19 deaths among migrant health care workers and includes details on how missing information was imputed. It includes spreadsheet estimates for India, Nigeria, Mexico, and the UK for excess and reported Covid-19 deaths amongst foreign-born workers and for all workers in the human health and social work sector and in three specific health occupations: doctors, nurses, and midwives. For each group the spreadsheets provide a basic estimate and an age-sex standardised estimate.

This project aims to give proper attention to the place of migrant workers in health care systems during the Covid-19 pandemic. Migrant workers are of substantial and growing significance in many countries' health and care systems and are key to realising the global goal of universal health care, so it is vital that we understand much better how Covid-19 is impacting on them.

The project's overarching research questions are, in the relation to Covid-19, what risks do migrant health care workers experience, what are the pressures on resilient and sustainable health care workforces, and how are stakeholders responding to these risks and pressures?

We develop a research method to estimate Covid-19 migrant health care worker mortality rates and trial the method, undertaking statistical analysis and modelling using quantitative data drawn from WHO and OECD data and other demographic and bio-statistical data as available.

In addition to strengthening the methodological techniques and empirical evidence base on the risks of Covid-19 infection and death among migrant health care workers our project also tracks, through documentary analysis, collective responses to such risks and challenges to sustainable health workforces for universal health coverage.

This project is attuned to the urgent need for high quality data and for 'real world' solutions-focused Covid-19 research forged from collaboration. We are focused on the immediate application of proof-of concept findings to a rapidly-evolving global health crisis.

To estimate county of residence of Filipinx healthcare workers who died of COVID-19, we retrieved data from the Kanlungan website during the month of December 2020.22 In deciding who to include on the website, the AF3IRM team that established the Kanlungan website set two standards in data collection. First, the team found at least one source explicitly stating that the fallen healthcare worker was of Philippine ancestry; this was mostly media articles or obituaries sharing the life stories of the deceased. In a few cases, the confirmation came directly from the deceased healthcare worker's family member who submitted a tribute. Second, the team required a minimum of two sources to identify and announce fallen healthcare workers. We retrieved 86 US tributes from Kanlungan, but only 81 of them had information on county of residence. In total, 45 US counties with at least one reported tribute to a Filipinx healthcare worker who died of COVID-19 were identified for analysis and will hereafter be referred to as “Kanlungan counties.” Mortality data by county, race, and ethnicity came from the National Center for Health Statistics (NCHS).24 Updated weekly, this dataset is based on vital statistics data for use in conducting public health surveillance in near real time to provide provisional mortality estimates based on data received and processed by a specified cutoff date, before data are finalized and publicly released.25 We used the data released on December 30, 2020, which included provisional COVID-19 death counts from February 1, 2020 to December 26, 2020—during the height of the pandemic and prior to COVID-19 vaccines being available—for counties with at least 100 total COVID-19 deaths. During this time period, 501 counties (15.9% of the total 3,142 counties in all 50 states and Washington DC)26 met this criterion. Data on COVID-19 deaths were available for six major racial/ethnic groups: Non-Hispanic White, Non-Hispanic Black, Non-Hispanic Native Hawaiian or Other Pacific Islander, Non-Hispanic American Indian or Alaska Native, Non-Hispanic Asian (hereafter referred to as Asian American), and Hispanic. People with more than one race, and those with unknown race were included in the “Other” category. NCHS suppressed county-level data by race and ethnicity if death counts are less than 10. In total, 133 US counties reported COVID-19 mortality data for Asian Americans. These data were used to calculate the percentage of all COVID-19 decedents in the county who were Asian American. We used data from the 2018 American Community Survey (ACS) five-year estimates, downloaded from the Integrated Public Use Microdata Series (IPUMS) to create county-level population demographic variables.27 IPUMS is publicly available, and the database integrates samples using ACS data from 2000 to the present using a high degree of precision.27 We applied survey weights to calculate the following variables at the county-level: median age among Asian Americans, average income to poverty ratio among Asian Americans, the percentage of the county population that is Filipinx, and the percentage of healthcare workers in the county who are Filipinx. Healthcare workers encompassed all healthcare practitioners, technical occupations, and healthcare service occupations, including nurse practitioners, physicians, surgeons, dentists, physical therapists, home health aides, personal care aides, and other medical technicians and healthcare support workers. County-level data were available for 107 out of the 133 counties (80.5%) that had NCHS data on the distribution of COVID-19 deaths among Asian Americans, and 96 counties (72.2%) with Asian American healthcare workforce data. The ACS 2018 five-year estimates were also the source of county-level percentage of the Asian American population (alone or in combination) who are Filipinx.8 In addition, the ACS provided county-level population counts26 to calculate population density (people per 1,000 people per square mile), estimated by dividing the total population by the county area, then dividing by 1,000 people. The county area was calculated in ArcGIS 10.7.1 using the county boundary shapefile and projected to Albers equal area conic (for counties in the US contiguous states), Hawai’i Albers Equal Area Conic (for Hawai’i counties), and Alaska Albers Equal Area Conic (for Alaska counties).20

COVID-19 deaths among health care workers, clients and family members due to transmission during access to HIV services and HIV-related deaths that could be averted by these services per 10,000 clients.

Note: Reporting of new COVID-19 Case Surveillance data will be discontinued July 1, 2024, to align with the process of removing SARS-CoV-2 infections (COVID-19 cases) from the list of nationally notifiable diseases. Although these data will continue to be publicly available, the dataset will no longer be updated.

Authorizations to collect certain public health data expired at the end of the U.S. public health emergency declaration on May 11, 2023. The following jurisdictions discontinued COVID-19 case notifications to CDC: Iowa (11/8/21), Kansas (5/12/23), Kentucky (1/1/24), Louisiana (10/31/23), New Hampshire (5/23/23), and Oklahoma (5/2/23). Please note that these jurisdictions will not routinely send new case data after the dates indicated. As of 7/13/23, case notifications from Oregon will only include pediatric cases resulting in death.

This case surveillance public use dataset has 19 elements for all COVID-19 cases shared with CDC and includes demographics, geography (county and state of residence), any exposure history, disease severity indicators and outcomes, and presence of any underlying medical conditions and risk behaviors.

Currently, CDC provides the public with three versions of COVID-19 case surveillance line-listed data: this 19 data element dataset with geography, a 12 data element public use dataset, and a 33 data element restricted access dataset.

The following apply to the public use datasets and the restricted access dataset:

• Data elements can be found on the COVID-19 case report form located at www.cdc.gov/coronavirus/2019-ncov/downloads/pui-form.pdf.
• Data are considered provisional by CDC and are subject to change until the data are reconciled and verified with the state and territorial data providers.
• Some data are suppressed to protect individual privacy.
• Datasets will include all cases with the earliest date available in each record (date received by CDC or date related to illness/specimen collection) at least 14 days prior to the creation of the current datasets. This 14-day lag allows case reporting to be stabilized and ensure that time-dependent outcome data are accurately captured.
• Datasets are updated monthly.
• Datasets are created using CDC’s Policy on Public Health Research and Nonresearch Data Management and Access and include protections designed to protect individual privacy.
• For more information about data collection and reporting, please see https://www.cdc.gov/coronavirus/2019-ncov/covid-data/about-us-cases-deaths.html.
• For more information about the COVID-19 case surveillance data, please see https://www.cdc.gov/coronavirus/2019-ncov/covid-data/faq-surveillance.html
  

Overview

The COVID-19 case surveillance database includes individual-level data reported to U.S. states and autonomous reporting entities, including New York City and the District of Columbia (D.C.), as well as U.S. territories and affiliates. On April 5, 2020, COVID-19 was added to the Nationally Notifiable Condition List and classified as “immediately notifiable, urgent (within 24 hours)” by a Council of State and Territorial Epidemiologists (CSTE) Interim Position Statement (Interim-20-ID-01). CSTE updated the position statement on August 5, 2020, to clarify the interpretation of antigen detection tests and serologic test results within the case classification (Interim-20-ID-02). The statement also recommended that all states and territories enact laws to make COVID-19 reportable in their jurisdiction, and that jurisdictions conducting surveillance should submit case notifications to CDC. COVID-19 case surveillance data are collected by jurisdictions and reported voluntarily to CDC.

For more information: NNDSS Supports the COVID-19 Response | CDC.

COVID-19 Case Reports COVID-19 case reports are routinely submitted to CDC by public health jurisdictions using nationally standardized case reporting forms. On April 5, 2020, CSTE released an Interim Position Statement with national surveillance case definitions for COVID-19. Current versions of these case definitions are available at: https://ndc.services.cdc.gov/case-definitions/coronavirus-disease-2019-2021/. All cases reported on or after were requested to be shared by public health departments to CDC using the standardized case definitions for lab-confirmed or probable cases. On May 5, 2020, the standardized case reporting form was revised. States and territories continue to use this form.

Data are Considered Provisional

• The COVID-19 case surveillance data are dynamic; case reports can be modified at any time by the jurisdictions sharing COVID-19 data with CDC. CDC may update prior cases shared with CDC based on any updated information from jurisdictions. For instance, as new information is gathered about previously reported cases, health departments provide updated data to CDC. As more information and data become available, analyses might find changes in surveillance data and trends during a previously reported time window. Data may also be shared late with CDC due to the volume of COVID-19 cases.
• Annual finalized data: To create the final NNDSS data used in the annual tables, CDC works carefully with the reporting jurisdictions to reconcile the data received during the year until each state or territorial epidemiologist confirms that the data from their area are correct.

Access Addressing Gaps in Public Health Reporting of Race and Ethnicity for COVID-19, a report from the Council of State and Territorial Epidemiologists, to better understand the challenges in completing race and ethnicity data for COVID-19 and recommendations for improvement.

Data Limitations

To learn more about the limitations in using case surveillance data, visit FAQ: COVID-19 Data and Surveillance.

Data Quality Assurance Procedures

CDC’s Case Surveillance Section routinely performs data quality assurance procedures (i.e., ongoing corrections and logic checks to address data errors). To date, the following data cleaning steps have been implemented:

• Questions that have been left unanswered (blank) on the case report form are reclassified to a Missing value, if applicable to the question. For example, in the question "Was the individual hospitalized?" where the possible answer choices include "Yes," "No," or "Unknown," the blank value is recoded to "Missing" because the case report form did not include a response to the question.
• Logic checks are performed for date data. If an illogical date has been provided, CDC reviews the data with the reporting jurisdiction. For example, if a symptom onset date in the future is reported to CDC, this value is set to null until the reporting jurisdiction updates the date appropriately.
• Additional data quality processing to recode free text data is ongoing. Data on symptoms, race, ethnicity, and healthcare worker status have been prioritized.

Data Suppression

To prevent release of data that could be used to identify people, data cells are suppressed for low frequency (<11 COVID-19 case records with a given values). Suppression includes low frequency combinations of case month, geographic characteristics (county and state of residence), and demographic characteristics (sex, age group, race, and ethnicity). Suppressed values are re-coded to the NA answer option; records with data suppression are never removed.

Additional COVID-19 Data

COVID-19 data are available to the public as summary or aggregate count files, including total counts of cases and deaths by state and by county. These and other COVID-19 data are available from multiple public locations: COVID Data Tracker; United States COVID-19 Cases and Deaths by State; COVID-19 Vaccination Reporting Data Systems; and COVID-19 Death Data and Resources.

Notes:

March 1, 2022: The "COVID-19 Case Surveillance Public Use Data with Geography" will be updated on a monthly basis.

April 7, 2022: An adjustment was made to CDC’s cleaning algorithm for COVID-19 line level case notification data. An assumption in CDC's algorithm led to misclassifying deaths that were not COVID-19 related. The algorithm has since been revised, and this dataset update reflects corrected individual level information about death status for all cases collected to date.

June 25, 2024: An adjustment

As of June 2021, 524 healthcare workers in Poland have died from coronavirus infection. The highest number of COVID-19 infections has been reported among nurses, over 72.4 thousand.

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

As of March, 2020, 55 percent of Hungarians who found the mass spread of coronavirus (COVID-19) possible stated that the healthcare system and its workers were mostly responsible for minimizing the death toll due to the virus. According to 32 percent of respondents, the responsibility was in the hands of the people. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Fact and Figures page.

Abstract Objectives: to analyze the working conditions of health professionals facing the COVID-19 pandemic in Brazil based on online media reports published in prominent news portals. Methods: qualitative analysis of 22 news stories selected from two of the main Brazilian news portals, published between April 20 and 30, 2020. Based on thematic content analysis, we defined five categories: Personal Protective Equipment (PPE) and COVID-19; health workers with comorbidities working on the front line; illness and death due to work; access to treatment and work leave due to COVID-19; resigning from work and professional updating. Results: the news stories reported inadequate working conditions due to lack of and/or inadequate PPE; health care workers with comorbidities remaining at work; sickness and death from COVID-19; strain and fear of being infected, and having to deal with co-workers’ sickness and death; difficulties in getting tested for COVID-19 and obtaining sick leave for treatment; resigning from health care work; need for fast professional updating for COVID-19 health care. Conclusion: the pandemic clearly evidences the need for public investment in health care for workers in charge of caring for the population.

Among COVID-19 patients in the United States from February 12 to March 16, 2020, estimated case-fatality rates were highest for adults aged 85 years and older. Younger people appeared to have milder symptoms, and there were no deaths reported among persons aged 19 years and under.

Tracking the virus in the United States The outbreak of a previously unknown viral pneumonia was first reported in China toward the end of December 2019. The first U.S. case of COVID-19 was recorded in mid-January 2020, confirmed in a patient who had returned to the United States from China. The virus quickly started to spread, and the first community-acquired case was confirmed one month later in California. Overall, there had been approximately 4.5 million coronavirus cases in the country by the start of August 2020.

U.S. health care system stretched California, Florida, and Texas are among the states with the most coronavirus cases. Even the best-resourced hospitals in the United States have struggled to cope with the crisis, and certain areas of the country were dealt further blows by new waves of infections in July 2020. Attention is rightly focused on fighting the pandemic, but as health workers are redirected to care for COVID-19 patients, the United States must not lose sight of other important health care issues.

Morbidity and mortality attributable to COVID-19 is devastating global health systems and economies. Bacillus Calmette Guérin (BCG) vaccination has been in use for many decades to prevent severe forms of tuberculosis in children. Studies have also shown a combination of improved long-term innate or trained immunity (through epigenetic reprogramming of myeloid cells) and adaptive responses after BCG vaccination, which leads to non-specific protective effects in adults. Observational studies have shown that countries with routine BCG vaccination programs have significantly less reported cases and deaths of COVID-19, but such studies are prone to significant bias and need confirmation. To date, in the absence of direct evidence, WHO does not recommend BCG for the prevention of COVID-19. This project aims to investigate in a timely manner whether and why BCG-revaccination can reduce infection rate and/or disease severity in health care workers during the SARS-CoV-2 outbreak in South Africa. These objectives will be achieved with a blinded, randomised controlled trial of BCG revaccination versus placebo in exposed front-line staff in hospitals in Cape Town. Observations will include the rate of infection with COVID-19 as well as the occurrence of mild, moderate or severe ambulatory respiratory tract infections, hospitalisation, need for oxygen, mechanical ventilation or death. HIV-positive individuals will be excluded. Safety of the vaccines will be monitored. A secondary endpoint is the occurrence of latent or active tuberculosis. Initial sample size and follow-up duration is at least 500 workers and 52 weeks. Statistical analysis will be model-based and ongoing in real time with frequent interim analyses and optional increases of both sample size or observation time, based on the unforeseeable trajectory of the South African COVID-19 epidemic, available funds and recommendations of an independent data and safety monitoring board. The study will be supported by a novel 3D lung organoid model of SARS-CoV-2 infection system that can mimic the cascade of immunological events after SARS-CoV-2 infection to determine and analyse the contribution of cellular components to the impact of BCG revaccination in this study. Given the immediate threat of the SARS-CoV-2 epidemic the trial has been designed as a pragmatic study with highly feasible endpoints that can be continuously measured. This allows for the most rapid identification of a beneficial outcome that would lead to immediate dissemination of the results, vaccination of the control group and outreach to the health authorities to consider BCG vaccination for all qualifying health care workers. Methods This dataset was collected in a clinical randomised control trial under the TASK008-BCG CORONA protocol. The trial was conducted in South Africa. This trial was registered with ClinicalTrials.gov, NCT04379336.

Adjusted estimated infection and death counts among hospital workers in the US.

Understanding the social determinants of Covid-19 infection and death is vital for effective Covid-19 early detection and mitigation strategies. This study aims to examine social determinants of Covid-19 infection and death in the context of rural Indonesia. We used Malang district government Covid-19 contact tracing data from 14,264 individuals, spanning the period from March 1, 2020 to July 29, 2020. The contact tracing data was merged with administrative data from 390 villages to determine whether village characteristics (i.e., the number of health workers, number of community-based healthcare interventions, access to Covid-19 referred hospitals, number of indigenous socio-cultural activities, poverty level and distance to a Covid-19 epicentre city) are associated with Covid-19 infection and death. We used multilevel logistic regression to take advantage of the nested structure of data at the village level. We found among the 14,264 samples, 551 individuals were confirmed infected with Covid-19, and 62 died of Covid-19. Individuals aged 18 and older, civil servants (non-health workers), and those having close contact with people with confirmed cases had a higher likelihood of infection with Covid-19. Greater numbers of community-based healthcare interventions and a lesser distance to a pandemic epicentre reduced the likelihood of infection with the virus. Males, older people, individuals with hypertension, individuals diagnosed with pneumonia, and those diagnosed with respiratory failure had a higher likelihood of death due to Covid-19. A greater number of community-based healthcare interventions seems to reduce the likelihood of Covid-19 infection, while better access to a Covid-19 referred hospital seems to reduce the risk of death among Covid-19 patients. The findings suggest the government to prioritise strategies to control the pandemic in rural area through empowering rural community in health education to prevent Covid-19 and in monitoring people mobility, while providing Covid-19 emergency services for rural areas for reducing mortality.

Context

Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. Most people infected with COVID-19 virus will experience mild to moderate respiratory illness and recover without requiring special treatment. Older people, and those with underlying medical problems like cardiovascular disease, diabetes, chronic respiratory disease, and cancer are more likely to develop serious illness. During the entire course of the pandemic, one of the main problems that healthcare providers have faced is the shortage of medical resources and a proper plan to efficiently distribute them. In these tough times, being able to predict what kind of resource an individual might require at the time of being tested positive or even before that will be of immense help to the authorities as they would be able to procure and arrange for the resources necessary to save the life of that patient.

The main goal of this project is to build a machine learning model that, given a Covid-19 patient's current symptom, status, and medical history, will predict whether the patient is in high risk or not.

content

The dataset was provided by the Mexican government (link). This dataset contains an enormous number of anonymized patient-related information including pre-conditions. The raw dataset consists of 21 unique features and 1,048,576 unique patients. In the Boolean features, 1 means "yes" and 2 means "no". values as 97 and 99 are missing data.

• sex: 1 for female and 2 for male.
• age: of the patient.
• classification: covid test findings. Values 1-3 mean that the patient was diagnosed with covid in different degrees. 4 or higher means that the patient is not a carrier of covid or that the test is inconclusive.
• patient type: type of care the patient received in the unit. 1 for returned home and 2 for hospitalization.
• pneumonia: whether the patient already have air sacs inflammation or not.
• pregnancy: whether the patient is pregnant or not.
• diabetes: whether the patient has diabetes or not.
• copd: Indicates whether the patient has Chronic obstructive pulmonary disease or not.
• asthma: whether the patient has asthma or not.
• inmsupr: whether the patient is immunosuppressed or not.
• hypertension: whether the patient has hypertension or not.
• cardiovascular: whether the patient has heart or blood vessels related disease.
• renal chronic: whether the patient has chronic renal disease or not.
• other disease: whether the patient has other disease or not.
• obesity: whether the patient is obese or not.
• tobacco: whether the patient is a tobacco user.
• usmr: Indicates whether the patient treated medical units of the first, second or third level.
• medical unit: type of institution of the National Health System that provided the care.
• intubed: whether the patient was connected to the ventilator.
• icu: Indicates whether the patient had been admitted to an Intensive Care Unit.
• date died: If the patient died indicate the date of death, and 9999-99-99 otherwise.

BackgroundThe COVID-19 pandemic is one of the most devastating public health emergencies of international concern to have occurred in the past century. To ensure a safe, scalable, and sustainable response, it is imperative to understand the burden of disease, epidemiological trends, and responses to activities that have already been implemented. We aimed to analyze how COVID-19 tests, cases, and deaths varied by time and region in the general population and healthcare workers (HCWs) in Ethiopia.MethodsCOVID-19 data were captured between October 01, 2021, and September 30, 2022, in 64 systematically selected health facilities throughout Ethiopia. The number of health facilities included in the study was proportionally allocated to the regional states of Ethiopia. Data were captured by standardized tools and formats. Analysis of COVID-19 testing performed, cases detected, and deaths registered by region and time was carried out.ResultsWe analyzed 215,024 individuals’ data that were captured through COVID-19 surveillance in Ethiopia. Of the 215,024 total tests, 18,964 COVID-19 cases (8.8%, 95% CI: 8.7%– 9.0%) were identified and 534 (2.8%, 95% CI: 2.6%– 3.1%) were deceased. The positivity rate ranged from 1% in the Afar region to 15% in the Sidama region. Eight (1.2%, 95% CI: 0.4%– 2.0%) HCWs died out of 664 infected HCWs, of which 81.5% were from Addis Ababa. Three waves of outbreaks were detected during the analysis period, with the highest positivity rate of 35% during the Omicron period and the highest rate of ICU beds and mechanical ventilators (38%) occupied by COVID-19 patients during the Delta period.ConclusionsThe temporal and regional variations in COVID-19 cases and deaths in Ethiopia underscore the need for concerted efforts to address the disparities in the COVID-19 surveillance and response system. These lessons should be critically considered during the integration of the COVID-19 surveillance system into the routine surveillance system.

Healthcare workers’ infection and death by region and facility type.

Status of COVID-19 cases in Ontario

This dataset compiles daily snapshots of publicly reported data on 2019 Novel Coronavirus (COVID-19) testing in Ontario.

Learn how the Government of Ontario is helping to keep Ontarians safe during the 2019 Novel Coronavirus outbreak.

Effective April 13, 2023, this dataset will be discontinued. The public can continue to access the data within this dataset in the following locations updated weekly on the Ontario Data Catalogue:

• Ontario COVID-19 testing percent positive by age group
• Confirmed positive cases of COVID-19 in Ontario
• Ontario COVID-19 testing metrics by Public Health Unit (PHU)
• Ontario COVID-19 testing percent positive by age group
• COVID-19 cases in hospital and ICU, by Ontario Health (OH) region
• Cumulative deaths (new methodology)
• Deaths Involving COVID-19 by Fatality Type

For information on Long-Term Care Home COVID-19 Data, please visit: Long-Term Care Home COVID-19 Data.

Data includes:

• reporting date
• daily tests completed
• total tests completed
• test outcomes
• total case outcomes (resolutions and deaths)
• current tests under investigation
• current hospitalizations
  • current patients in Intensive Care Units (ICUs) due to COVID-related critical Illness
  • current patients in Intensive Care Units (ICUs) testing positive for COVID-19
  • current patients in Intensive Care Units (ICUs) no longer testing positive for COVID-19
  • current patients in Intensive Care Units (ICUs) on ventilators due to COVID-related critical illness
  • current patients in Intensive Care Units (ICUs) on ventilators testing positive for COVID-19
  • current patients in Intensive Care Units (ICUs) on ventilators no longer testing positive for COVID-19
• Long-Term Care (LTC) resident and worker COVID-19 case and death totals
• Variants of Concern case totals
• number of new deaths reported (occurred in the last month)
• number of historical deaths reported (occurred more than one month ago)
• change in number of cases from previous day by Public Health Unit (PHU).

This dataset is subject to change. Please review the daily epidemiologic summaries for information on variables, methodology, and technical considerations.

Cumulative Deaths

**Effective November 14, 2024 this page will no longer be updated. Information about COVID-19 and other respiratory viruses is available on Public Health Ontario’s interactive respiratory virus tool: https://www.publichealthontario.ca/en/Data-and-Analysis/Infectious-Disease/Respiratory-Virus-Tool **

The methodology used to count COVID-19 deaths has changed to exclude deaths not caused by COVID. This impacts data captured in the columns “Deaths”, “Deaths_Data_Cleaning” and “newly_reported_deaths” starting with data for March 11, 2022. A new column has been added to the file “Deaths_New_Methodology” which represents the methodological change.

The method used to count COVID-19 deaths has changed, effective December 1, 2022. Prior to December 1, 2022, deaths were counted based on the date the death was updated in the public health unit’s system. Going forward, deaths are counted on the date they occurred.

On November 30, 2023 the count of COVID-19 deaths was updated to include missing historical deaths from January 15, 2020 to March 31, 2023. A small number of COVID deaths (less than 20) do not have recorded death date and will be excluded from this file.

CCM is a dynamic disease reporting system which allows ongoing update to data previously entered. As a result, data extracted from CCM represents a snapshot at the time of extraction and may differ from previous or subsequent results. Public Health Units continually clean up COVID-19 data, correcting for missing or overcounted cases and deaths. These corrections can result in data spikes and current totals being different from previously reported cases and deaths. Observed trends over time should be interpreted with caution for the most recent period due to reporting and/or data entry lags.

Related dataset(s)

• Confirmed positive cases of COVID-19 in Ontario

As of January 12, 2023, COVID-19 has been responsible for 202,157 deaths in the UK overall. The North West of England has been the most affected area in terms of deaths at 28,116, followed by the South East of England with 26,221 coronavirus deaths. Furthermore, there have been 22,264 mortalities in London as a result of COVID-19.

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

A Phase II, randomized, placebo-controlled clinical trial (Clinicaltrials.gov NCT04359537) was conducted at SZABMU/PIMS to evaluate the comparative efficacy of various HCQ doses in preventing COVID-19 among high-risk healthcare workers. Enrolment began on 1st May 2020, and the intervention continued for a total of 12 weeks. A total of 228 participants were initially enrolled (Figure 1); of them, 28 were ineligible and excluded. Participants fulfilling the eligibility criteria were randomized into the four treatment groups. Group 1 participants (n=48) were intervened with HCQ 400 mg (locally manufactured by Getz Pharma) twice a day on day 1 followed by 400 mg weekly. Group 2 (n=51) participants were given HCQ 400 mg once every 3 weeks, group 3 (n=55) administered HCQ 200 mg once every 3 weeks and participants in the control group received placebo (n=46). The baseline characteristics of all participants, including age, gender, role, comorbidities, and drug records, were obtained. COVID-19 related symptoms and adverse events (AEs) from the drug were self-reported by the enrolled participant during the study period. The COVID-19 exposure and preventive practices were monitored on a monthly basis. Disease severity was assessed through ordinal scale i.e. no illness (score=1), illness with outpatient observation (score=2), hospitalization (or post-hospital discharge) (score=3), hospitalization with ICU stay (score=4) and death from COVID 19 (score=5). All participants exhibiting COVID-19 symptoms were tested for SARS-CoV-2 during the study and also by the end of the 12th week, with PCR or IgM and IgG serology (as per accessibility).The primary endpoint was to evaluate the COVID-19-free survival among the participants by the end of the study. The secondary endpoints were to evaluate the proportion of rRT-PCR positive COVID-19 cases, the role of exposure and preventive practices, the frequency of COVID-related symptoms, treatment-related side effects, the incidence of all-cause study medicine discontinuation, and maximum disease severity during the study treatment.The study protocol was approved by the ethical review board of Shaheed Zulfiqar Ali Bhutto Medical University (Reference no. 1-1/2015/ERB/SZABMU/549; Dated 20 April 2020), and written informed consents were acquired from the participants before inclusion.

BackgroundIn the context of the COVID-19 pandemic that originated from China in December 2019 and spread around the world, Kampala City witnessed a high number of infections and deaths among healthcare workers (HCWs). This study assessed the level of compliance with Infection Prevention and Control (IPC) measures and its associated factors among HCWs during the COVID-19 pandemic, in Kampala City, Uganda.MethodologyA cross-sectional study was conducted in Nakawa Division, Kampala City, among 240 HCWs and used multistage sampling in government and private not-for-profit (PNFP) healthcare facilities. The outcome variable was self-reported IPC compliance which was composed of the use of masks, gloves, and hand hygiene. These were assessed using a 4-scale tool: always as recommended, most of the time, occasionally, and rarely. Only HCWs who responded “always as recommended” were considered compliant while the rest were considered non-compliant. Data was analyzed in STATA 14.0 using Modified Poisson regression to obtain factors associated with IPC compliance at 95% confidence interval (CI).ResultsForty-six (19.2%) respondents were compliant with all the three IPC measures, and this was associated with the presence of a COVID-19 patients’ ward in the healthcare facility (Adjusted Prevalence Ratio, APR: 2.51, 95%CI: 1.24–5.07). Factors associated with the use of masks were being of the Muslim religion (APR: 1.31, CI: 1.05–1.65), and working in a healthcare facility that has COVID-19 patients’ ward (APR: 1.29, CI: 1.06–1.59). Factors associated with the use of gloves were the age of the HCW, those above 40 years old being less complaint (APR: 0.47, CI: 0.24–0.93), working in the diagnosis department (APR: 2.08, CI: 1.17–3.70), and working in a healthcare facility that has COVID-19 patients’ ward (APR: 1.73, CI: 1.13–2.64). Factors associated with hand hygiene were working in a health center (HC) IV (PR: 1.7, CI: 1.26–2.30) or a HC II (PR: 1.68, CI: 1.28–2.21).ConclusionConsidering the elevated risk of disease transmission in health settings, IPC compliance was low; indicating an increased risk of COVID-19 infection among health care workers in Kampala City.