This dataset provides values for CORONAVIRUS DEATHS reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.

Since late January, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.

We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak.

The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository.

About Dataset: WHO COVID-19 Global Data

This dataset provides comprehensive information on the global COVID-19 pandemic as reported to the World Health Organization (WHO). The dataset is available in comma-separated values (CSV) format and includes the following fields:

Daily cases and deaths by date reported to WHO: WHO-COVID-19-global-data.csv

• Date_reported (Date): The date of reporting to WHO.
• Country_code (String): The ISO Alpha-2 country code.
• Country (String): The name of the country, territory, or area.
• WHO_region (String): The WHO regional office to which the country belongs. WHO Member States are grouped into six WHO regions, including AFRO (Regional Office for Africa), AMRO (Regional Office for the Americas), SEARO (Regional Office for South-East Asia), EURO (Regional Office for Europe), EMRO (Regional Office for the Eastern Mediterranean), and WPRO (Regional Office for the Western Pacific).
• New_cases (Integer): The number of new confirmed cases reported on a given day. This is calculated by subtracting the previous cumulative case count from the current cumulative case count.
• Cumulative_cases (Integer): The total cumulative confirmed cases reported to WHO up to the specified date.
• New_deaths (Integer): The number of new confirmed deaths reported on a given day. Similar to new cases, this is calculated by subtracting the previous cumulative death count from the current cumulative death count.- Cumulative_deaths (Integer): The total cumulative confirmed deaths reported to WHO up to the specified date.

In addition to the COVID-19 case and death data, this dataset also includes valuable information related to COVID-19 vaccinations. The vaccination data consists of the following fields:

Vaccination Data Fields: vaccination-data.csv

• COUNTRY (String): Country, territory, or area.
• ISO3 (String): ISO Alpha-3 country code.
• WHO_REGION (String): The WHO regional office to which the country belongs.
• DATA_SOURCE (String): Indicates the data source, which can be either "REPORTING" (Data reported by Member States or sourced from official reports) or "OWID" (Data sourced from Our World in Data COVID-19 Vaccinations).
• DATE_UPDATED (Date): Date of the last update.
• TOTAL_VACCINATIONS (Integer): Cumulative total vaccine doses administered.
• PERSONS_VACCINATED_1PLUS_DOSE (Decimal): Cumulative number of persons vaccinated with at least one dose.
• TOTAL_VACCINATIONS_PER100 (Integer): Cumulative total vaccine doses administered per 100 population.
• PERSONS_VACCINATED_1PLUS_DOSE_PER100 (Decimal): Cumulative persons vaccinated with at least one dose per 100 population.
• PERSONS_LAST_DOSE (Integer): Cumulative number of persons vaccinated with a complete primary series.
• PERSONS_LAST_DOSE_PER100 (Decimal): Cumulative number of persons vaccinated with a complete primary series per 100 population.
• VACCINES_USED (String): Combined short name of the vaccine in the format "Company - Product name."
• FIRST_VACCINE_DATE (Date): Date of the first vaccinations, equivalent to the start/launch date of the first vaccine administered in a country.
• NUMBER_VACCINES_TYPES_USED (Integer): Number of vaccine types used per country, territory, or area.
• PERSONS_BOOSTER_ADD_DOSE (Integer): Cumulative number of persons vaccinated with at least one booster or additional dose.
• PERSONS_BOOSTER_ADD_DOSE_PER100 (Decimal): Cumulative number of persons vaccinated with at least one booster or additional dose per 100 population.

In addition to the vaccination data, a separate dataset containing vaccination metadata is available, including information about vaccine names, product names, company names, authorization dates, start and end dates of vaccine rollout, and more.

Vaccination metadata Fields: vaccination-metadata.csv

• ISO3 (String): ISO Alpha-3 country code
• VACCINE_NAME (String): Combined short name of vaccine: "Company - Product name" (see below)
• PRODUCT_NAME (String): Name or label of vaccine product, or type of vaccine (if unnamed).
• COMPANY_NAME (String): Marketing authorization holder of vaccine product.
• FIRST_VACCINE_DATE (Date): Date of first vaccinations. Equivalent to start/launch date of the first vaccine administered in a country.
• AUTHORIZATION_DATE (Date): Date vaccine product was authorized for use in the country, territory, area.
• START_DATE (Date): Start/launch date of vaccination with vaccine type (excludes vaccinations during clinical trials).
• END_DATE (Date): End date of vaccine rollout
• COMMENT (String): Comments related to vaccine rollout
• DATA_SOURCE (String): Indicates data source - REPORTING: Data reported by Member States, or sourced from official re...

Method

The dataset contains several confirmed COVID-19 cases, number of deaths, and death rate in six regions. The objective of the study is to compare the number of confirmed cases in Africa to other regions.

Death rate = Total number of deaths from COVID-19 divided by the Total Number of infected patients.

The study provides evidence for the country-level in six regions by the World Health Organisation's classification.

Findings

Based on the descriptive data provided above, we conclude that the lack of tourism is one of the key reasons why COVID-19 reported cases are low in Africa compared to other regions. We also justified this claim by providing evidence from the economic freedom index, which indicates that the vast majority of African countries recorded a low index for a business environment. On the other hand, we conclude that the death rate is higher in the African region compared to other regions. This points to issues concerning health-care expenditure, low capacity for testing for COVID-19, and poor infrastructure in the region.

Apart from COVID-19, there are significant pre-existing diseases, namely; Malaria, Flu, HIV/AIDS, and Ebola in the continent. This study, therefore, invites the leaders to invest massively in the health-care system, infrastructure, and human capital in order to provide a sustainable environment for today and future generations. Lastly, policy uncertainty has been a major issue in determining a sustainable development goal on the continent. This uncertainty has differentiated Africa to other regions in terms of stepping up in the time of global crisis.

The aim of this study was to compare the clinical characteristics and outcomes of COVID-19 survivors and non-survivors who were transferred from general wards to critical care units in four tertiary hospitals in Nepal. The study employed a retrospective observational design, utilizing data from the ICU registry managed by the Nepal Intensive Care Research Foundation (NICRF) which utilized Case Report Forms that contained comprehensive information on patients admitted to the ICU. Demographic data, clinical characteristics, laboratory parameters, treatments, and outcomes were analyzed. The statistical analysis involved the use of the Mann-Whitney U test for continuous variables and Pearson's chi-squared test for categorical variables. Fisher's exact test was applied when expected frequencies were less than 5. IBM Statistical Package for the Social Sciences (SPSS) software, version 25, was used for analysis. Ethical clearance for the study was obtained from the Nepal Health Research Council on January 23, 2023 (Ref No. 1698/2022). The study received exemption from the review of secondary data, as the information was de-identified and did not involve direct patient participation. The ethical approval ensured compliance with ethical standards and protected the rights and confidentiality of the study participants.

The International Registry of Healthcare Workers Exposed to COVID-19 Patients (UNITY Global), is an international registry of approximately 10,000 healthcare workers in low- and middle-income countries experiencing increasing numbers of COVID-19 cases and commensurate increased exposure to the SARS-CoV-2 virus among their healthcare worker populations.

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COVID-19 Worldwide Daily Data

Daily global COVID-19 data for all countries, provided by Johns Hopkins University (JHU) Center for Systems Science and Engineering (CSSE). If you want to use the update version of the data, you can use our daily updated data with the help of api key by entering it via Altadata.

Overview

In this data product, you may find the latest and historical global daily data on the COVID-19 pandemic for all countries.

The COVID‑19 pandemic, also known as the coronavirus pandemic, is an ongoing global pandemic of coronavirus disease 2019 (COVID‑19), caused by severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2). The outbreak was first identified in December 2019 in Wuhan, China. The World Health Organization declared the outbreak a Public Health Emergency of International Concern on 30 January 2020 and a pandemic on 11 March. As of 12 August 2020, more than 20.2 million cases of COVID‑19 have been reported in more than 188 countries and territories, resulting in more than 741,000 deaths; more than 12.5 million people have recovered.

The Johns Hopkins Coronavirus Resource Center is a continuously updated source of COVID-19 data and expert guidance. They aggregate and analyze the best data available on COVID-19 - including cases, as well as testing, contact tracing and vaccine efforts - to help the public, policymakers and healthcare professionals worldwide respond to the pandemic.

Methodology

• Cases and Death counts include confirmed and probable (where reported)
• Recovered cases are estimates based on local media reports, and state and local reporting when available, and therefore may be substantially lower than the true number. US state-level recovered cases are from COVID Tracking Project.
• Active cases = total cases - total recovered - total deaths
• Incidence Rate = cases per 100,000 persons
• Case-Fatality Ratio (%) = Number recorded deaths / Number cases
• Country Population represents 2019 projections by UN Population Division, integrated to the JHU CSSE's COVID-19 data by ALTADATA

Data Source

• Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE)
• United Nations Population Division

Related Data Products

• COVID-19 US Daily Data
• OECD, EU28, G20 Life Expectancy and Mortality Indicators

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Data Dictionary

• Reported Date (reported_date) : Covid-19 Report Date
• Country_Region (country_region) : Country, region or sovereignty name
• Population (population) : Country populations as per United Nations Population Division
• Confirmed Case (confirmed) : Confirmed cases include presumptive positive cases and probable cases
• Active cases (active) : Active cases = total confirmed - total recovered - total deaths
• Deaths (deaths) : Death cases counts
• Recovered (recovered) : Recovered cases counts
• Mortality Rate (mortality_rate) : Number of recorded deaths * 100 / Number of confirmed cases
• Incident Rate (incident_rate) : Confirmed cases per 100,000 persons

COVID-19Plots and analysis relating to the coronavirus pandemic. Includes five sets of plots and associated R code to generate them.1) HeatmapsUpdated every few days - heatmaps of COVID-19 case and death trajectories for Local Authorities (or equivalent) in England, Wales, Scotland, Ireland and Germany.2) All cause mortalityUpdated on Tuesday (for England & Wales), Wednesday (for Scotland) and Friday (for Northern Ireland) - analysis and plots of weekly all-cause deaths in 2020 compared to previous years by country, age, sex and region. Also a set of international comparisons using data from mortality.org3) ExposuresNo longer updated - mapping of potential COVID-19 mortality exposure at local levels (LSOAs) in England based on the age-sex structure of the population and levels of poor health.There is also a Shiny app which creates slightly lower resolution versions of the same plots online, which you can find here: https://victimofmaths.shinyapps.io/covidmapper/, on GitHub https://github.com/VictimOfMaths/COVIDmapper and uploaded to this record4) Index of Multiple Deprivation No longer updated - preliminary analysis of the inequality impacts of COVID-19 based on Local Authority level cases and levels of deprivation. 5) Socioeconomic inequalities. No longer updated (unless ONS release more data) - Analysis of published ONS figures of COVID-19 and other cause mortality in 2020 compared to previous years by deprivation decile.Latest versions of plots and associated analysis can be found on Twitter: https://twitter.com/victimofmathsThis work is described in more detail on the UK Data Service Impact and Innovation Lab blog: https://blog.ukdataservice.ac.uk/visualising-high-risk-areas-for-covid-19-mortality/Adapted from data from the Office for National Statistics licensed under the Open Government Licence v.1.0.http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/

BackgroundThe global COVID-19 pandemic is still ongoing, and cross-country and cross-period variation in COVID-19 age-adjusted case fatality rates (CFRs) has not been clarified. Here, we aimed to identify the country-specific effects of booster vaccination and other features that may affect heterogeneity in age-adjusted CFRs with a worldwide scope, and to predict the benefit of increasing booster vaccination rate on future CFR.MethodCross-temporal and cross-country variations in CFR were identified in 32 countries using the latest available database, with multi-feature (vaccination coverage, demographic characteristics, disease burden, behavioral risks, environmental risks, health services and trust) using Extreme Gradient Boosting (XGBoost) algorithm and SHapley Additive exPlanations (SHAP). After that, country-specific risk features that affect age-adjusted CFRs were identified. The benefit of booster on age-adjusted CFR was simulated by increasing booster vaccination by 1–30% in each country.ResultsOverall COVID-19 age-adjusted CFRs across 32 countries ranged from 110 deaths per 100,000 cases to 5,112 deaths per 100,000 cases from February 4, 2020 to Jan 31, 2022, which were divided into countries with age-adjusted CFRs higher than the crude CFRs and countries with age-adjusted CFRs lower than the crude CFRs (n = 9 and n = 23) when compared with the crude CFR. The effect of booster vaccination on age-adjusted CFRs becomes more important from Alpha to Omicron period (importance scores: 0.03–0.23). The Omicron period model showed that the key risk factors for countries with higher age-adjusted CFR than crude CFR are low GDP per capita and low booster vaccination rates, while the key risk factors for countries with higher age-adjusted CFR than crude CFR were high dietary risks and low physical activity. Increasing booster vaccination rates by 7% would reduce CFRs in all countries with age-adjusted CFRs higher than the crude CFRs.ConclusionBooster vaccination still plays an important role in reducing age-adjusted CFRs, while there are multidimensional concurrent risk factors and precise joint intervention strategies and preparations based on country-specific risks are also essential.

Deprecated as of 4/27/2023On 4/27/2023 several COVID-19 datasets were retired and no longer included in public COVID-19 data dissemination. For more information, visit https://imap.maryland.gov/pages/covid-dataSummaryThe total number of residents who have been administered at least one COVID-19 test in each Maryland jurisdiction.DescriptionData represent the number of Maryland residents, both in number and by percent of the population, who have been tested for COVID-19 at least once each Maryland jurisdiction.COVID-19 is a disease caused by a respiratory virus first identified in Wuhan, Hubei Province, China in December 2019. COVID-19 is a new virus that hasn't caused illness in humans before. Worldwide, COVID-19 has resulted in thousands of infections, causing illness and in some cases death. Cases have spread to countries throughout the world, with more cases reported daily. The Maryland Department of Health reports daily on COVID-19 cases by county.

This dataset reports the daily reported number of the 7-day moving average rates of Deaths involving COVID-19 by vaccination status and by age group. Learn how the Government of Ontario is helping to keep Ontarians safe during the 2019 Novel Coronavirus outbreak. 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 Data includes: * Date on which the death occurred * Age group * 7-day moving average of the last seven days of the death rate per 100,000 for those not fully vaccinated * 7-day moving average of the last seven days of the death rate per 100,000 for those fully vaccinated * 7-day moving average of the last seven days of the death rate per 100,000 for those vaccinated with at least one booster ##Additional notes As of June 16, all COVID-19 datasets will be updated weekly on Thursdays by 2pm. As of January 12, 2024, data from the date of January 1, 2024 onwards reflect updated population estimates. This update specifically impacts data for the 'not fully vaccinated' category. 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. 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. The data does not include vaccination data for people who did not provide consent for vaccination records to be entered into the provincial COVaxON system. This includes individual records as well as records from some Indigenous communities where those communities have not consented to including vaccination information in COVaxON. “Not fully vaccinated” category includes people with no vaccine and one dose of double-dose vaccine. “People with one dose of double-dose vaccine” category has a small and constantly changing number. The combination will stabilize the results. Spikes, negative numbers and other data anomalies: Due to ongoing data entry and data quality assurance activities in Case and Contact Management system (CCM) file, Public Health Units continually clean up COVID-19, correcting for missing or overcounted cases and deaths. These corrections can result in data spikes, negative numbers and current totals being different from previously reported case and death counts. Public Health Units report cause of death in the CCM based on information available to them at the time of reporting and in accordance with definitions provided by Public Health Ontario. The medical certificate of death is the official record and the cause of death could be different. Deaths are defined per the outcome field in CCM marked as “Fatal”. Deaths in COVID-19 cases identified as unrelated to COVID-19 are not included in the Deaths involving COVID-19 reported. Rates for the most recent days are subject to reporting lags All data reflects totals from 8 p.m. the previous day. This dataset is subject to change.

The ongoing COVID-19 pandemic has posed a significant global challenge to healthcare systems. Every country has seen multiple waves of this disease, placing a considerable strain on healthcare resources. Across the world, the pandemic has motivated diligent data collection, with an enormous amount of data being available in the public domain. In this manuscript, we collate COVID-19 case data from around the world (available on the World Health Organization (WHO) website), and provide various definitions for waves. Using these definitions to define labels, we create a labelled dataset, which can be used while building supervised learning classifiers. We also use a simple eXtreme Gradient Boosting (XGBoost) model to provide a minimum standard for future classifiers trained on this dataset and demonstrate the utility of our dataset for the prediction of (future) waves. This dataset will be a valuable resource for epidemiologists and others interested in the early prediction of future waves. The datasets are available from https://github.com/RamanLab/COWAVE/.

Here we investigated whether the dengue fever pandemic of 2019-2020 may have influenced COVID-19 incidence and spread around the world. In Brazil, the geographic distribution of dengue fever was highly complementary to that of COVID-19. This was accompanied by an inverse correlation between COVID-19 and dengue fever incidence that could not be explained by socioeconomic factors. This inverse correlation was observed for 5,016 Brazilian municipalities reporting COVID-19 cases, 558 micro- and 137 meso-regions, 27 states and 5 regions. Brazilian states with high population levels of dengue IgM in 2020 exhibited: (i) lower COVID-19 case and death incidence, (ii) slower infection growth rates, and (iii) took longer to accumulate COVID-19 cases. No such inverse correlations were observed for the chikungunya virus, which is also transmitted by the Aedes aegypti mosquito. The same inverse correlation between COVID-19 and dengue fever incidence was observed for 145 locations (66 countries and the 64 states of Mexico and Colombia) in Latin America, the Caribbean, and Asia. Countries with high dengue incidence took longer to accumulate COVID-19 cases than those without dengue. Although the dataset considered has quality and availability limitations, these findings raise the possibility of an immunological cross-reaction between dengue virus serotypes and SARS-CoV-2, which could have led to partial immunological protection for COVID-19 in dengue infected communities. However, further studies are necessary to better test this hypothesis. Methods COVID-19 incidence in Brazil was obtained from Brasil.io (https://brasil.io/covid19/), which compiles data from all the Brazilian state health agencies and was accessed on 2020-10-06. The period considered in the analysis was from the first COVID-19 case to the 26th epidemiological week of 2020 (which ends on the 27 th of June 2020). The COVID-19 incidence in countries around the world was collected from Coronavirus COVID-19 Global Cases by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU) (https://coronavirus.jhu.edu/).

State-level data were considered for Colombia and Mexico, and a country-level was considered for the other countries under investigation. Dengue epidemiological and serological data was compiled from data published regularly in the official epidemiological bulletins during 2019 and 2020 by the Brazilian Ministry of Health (Ministério da Saúde, 2020a and 2020b). The incidence available via DATASUS (2020) considered the period from the 27th epidemiological week of 2019 to the 26th epidemiological week of 2020. This incidence for Latin American countries was collected from the Pan American Health Organization (www.paho.org), which also provides dengue incidence data on a state level for Mexico. For Colombian states data was collected from bulletins made available by the Colombian Health Ministry (https://www.minsalud.gov.co). For other countries considered data was collected from disease threat reports provided by the European Centre for Disease Prevention and Control - (ECDC - www.ecdc.europa.eu).

IntroductionThe traditional approach to epidemic control has been to slow down the rate of infection while building up healthcare capacity, resulting in a flattened epidemic curve. Advancements in bio-information-communication technology (BICT) have enabled the preemptive isolation of infected cases through efficient testing and contact tracing. This study aimed to conceptualize the BICT-enabled epidemic control (BICTEC) and to document its relationships with epidemic curve shaping and epidemic mitigation performance.MethodsDaily COVID-19 incidences were collected from outbreak to Aug. 12, 2020, for nine countries reporting the first outbreak on or before Feb. 1, 2020. Key epidemic curve determinants–peak height (PH), time to peak (TTP), and area under the curve (AUC)–were estimated for each country, and their relationships were analyzed to test if epidemic curves peak quickly at a shorter height. CFR (Case Fatality Rate) and CI (Cumulative Incidence) were compared across the countries to identify relationships between epidemic curve shapes and epidemic mitigation performance.ResultsChina and South Korea had the quickest TTPs (40.70 and 45.37 days since outbreak, respectively) and the shortest PHs (2.95 and 4.65 cases per day, respectively). Sweden, known for its laissez-faire approach, had the longest TTP (120.36) and the highest PH (279.74). Quicker TTPs were correlated with shorter PHs (ρ = 0·896, p = 0·0026) and lower AUCs (0.790, p = 0.0028), indicating that epidemic curves do not follow a flattened trajectory. During the study period, countries with quicker TTPs tended to have lower CIs (ρ = .855, P = .006) and CFRs (ρ = 0.684, P = .061). For example, South Korea, with the second-quickest TTP, reported the second lowest CI and the lowest CFR.ConclusionsCountries that experienced early COVID-19 outbreaks demonstrated the epidemic curves that quickly peak at a shorter height, indicating a departure from the traditional flattened trajectory. South Korea’s BICTEC was found to be at least as effective as most lockdowns in reducing CI and CFR.

The dataset contains information on hospitalised COVID-19 patients in acute and intensive care as well as number of COVID-19 cases and number of tests for COVID-19 in Europe. The data stem from a broad data collection initiative encompassing data on 18 European countries and two Italian regions. Countries were included in the data collection of hospitalised COVID-19 patients in acute and intensive care, which was kicked off on 20 March, 2020, once they reported at least five positive cases per 100,000 population. The database has been updated daily since. Data stems from official online sources such as websites of ministries of health, national research and public health institutes, official dashboards from national institutions and Our World in Data (OWiD). More information can be found here: https://www.mig.tu-berlin.de/menue/home/akt_de/#c970629

Deprecated as of 4/21/2023On 4/27/2023 several COVID-19 datasets were retired and no longer included in public COVID-19 data dissemination. For more information, visit https://imap.maryland.gov/pages/covid-dataSummaryThe cumulative number of COVID-19 vaccinations for persons aged 65+ within a single Maryland jurisdiction: Persons fully vaccinated and those who have received at least one dose.DescriptionThe MD COVID-19—Persons 65+ Fully Vaccinated layer represents the number of people in each Maryland jurisdiction aged 65 and older who have either received at least one dose of COVID-19 vaccine in a two-dose regimen or are fully vaccinated (have either received a single shot regimen or have completed the second dose in a two-dose regimen), reported each day into ImmuNet.CDC COVID10 Vaccinations in the United States,CountyCOVID-19 is a disease caused by a respiratory virus first identified in Wuhan, Hubei Province, China in December 2019. COVID-19 is a new virus that hasn't caused illness in humans before. Worldwide, COVID-19 has resulted in thousands of infections, causing illness and in some cases death. Cases have spread to countries throughout the world, with more cases reported daily. The Maryland Department of Health reports daily on COVID-19 cases by county.

Changelog:

Update September 20, 2021: Data and overview updated to reflect data used in the September 15 story Over Half of States Have Rolled Back Public Health Powers in Pandemic. It includes 303 state or local public health leaders who resigned, retired or were fired between April 1, 2020 and Sept. 12, 2021. Previous versions of this dataset reflected data used in the Dec. 2020 and April 2021 stories.

Overview

Across the U.S., state and local public health officials have found themselves at the center of a political storm as they combat the worst pandemic in a century. Amid a fractured federal response, the usually invisible army of workers charged with preventing the spread of infectious disease has become a public punching bag.

In the midst of the coronavirus pandemic, at least 303 state or local public health leaders in 41 states have resigned, retired or been fired since April 1, 2020, according to an ongoing investigation by The Associated Press and KHN.

According to experts, that is the largest exodus of public health leaders in American history.

Many left due to political blowback or pandemic pressure, as they became the target of groups that have coalesced around a common goal — fighting and even threatening officials over mask orders and well-established public health activities like quarantines and contact tracing. Some left to take higher profile positions, or due to health concerns. Others were fired for poor performance. Dozens retired. An untold number of lower level staffers have also left.

The result is a further erosion of the nation’s already fragile public health infrastructure, which KHN and the AP documented beginning in 2020 in the Underfunded and Under Threat project.

Findings

The AP and KHN found that:

• One in five Americans live in a community that has lost its local public health department leader during the pandemic
• Top public health officials in 28 states have left state-level departments ## Using this data To filter for data specific to your state, use this query

To get total numbers of exits by state, broken down by state and local departments, use this query

Methodology

KHN and AP counted how many state and local public health leaders have left their jobs between April 1, 2020 and Sept. 12, 2021.

The government tasks public health workers with improving the health of the general population, through their work to encourage healthy living and prevent infectious disease. To that end, public health officials do everything from inspecting water and food safety to testing the nation’s babies for metabolic diseases and contact tracing cases of syphilis.

Many parts of the country have a health officer and a health director/administrator by statute. The analysis counted both of those positions if they existed. For state-level departments, the count tracks people in the top and second-highest-ranking job.

The analysis includes exits of top department officials regardless of reason, because no matter the reason, each left a vacancy at the top of a health agency during the pandemic. Reasons for departures include political pressure, health concerns and poor performance. Others left to take higher profile positions or to retire. Some departments had multiple top officials exit over the course of the pandemic; each is included in the analysis.

Reporters compiled the exit list by reaching out to public health associations and experts in every state and interviewing hundreds of public health employees. They also received information from the National Association of City and County Health Officials, and combed news reports and records.

Public health departments can be found at multiple levels of government. Each state has a department that handles these tasks, but most states also have local departments that either operate under local or state control. The population served by each local health department is calculated using the U.S. Census Bureau 2019 Population Estimates based on each department’s jurisdiction.

KHN and the AP have worked since the spring on a series of stories documenting the funding, staffing and problems around public health. A previous data distribution detailed a decade's worth of cuts to state and local spending and staffing on public health. That data can be found here.

Attribution

Findings and the data should be cited as: "According to a KHN and Associated Press report."

Is Data Missing?

If you know of a public health official in your state or area who has left that position between April 1, 2020 and Sept. 12, 2021 and isn't currently in our dataset, please contact authors Anna Maria Barry-Jester annab@kff.org, Hannah Recht hrecht@kff.org, Michelle Smith mrsmith@ap.org and Lauren Weber laurenw@kff.org.

SummaryThe cumulative number of COVID-19 vaccinations for persons aged 65+ within a single Maryland jurisdiction: Persons fully vaccinated and those who have received at least one dose.DescriptionThe MD COVID-19—Persons 65+ Fully Vaccinated layer represents the number of people in each Maryland jurisdiction aged 65 and older who have either received at least one dose of COVID-19 vaccine in a two-dose regimen or are fully vaccinated (have either received a single shot regimen or have completed the second dose in a two-dose regimen), reported each day into ImmuNet.COVID-19 is a disease caused by a respiratory virus first identified in Wuhan, Hubei Province, China in December 2019. COVID-19 is a new virus that hasn't caused illness in humans before. Worldwide, COVID-19 has resulted in thousands of infections, causing illness and in some cases death. Cases have spread to countries throughout the world, with more cases reported daily. The Maryland Department of Health reports daily on COVID-19 cases by county.

BackgroundDisseminated tuberculosis is frequently associated with delayed diagnosis and a poorer prognosis.ObjectivesTo describe case series of disseminated TB and diagnosis delay in a low TB burden country during the COVID-19 period.MethodologyWe consecutively included all patients with of disseminated TB reported from 2019 to 2021 in the reference hospital of the Northern Crown of the Metropolitan Area of Barcelona. We collected socio-demographic information, clinical, laboratory and radiological findings.ResultsWe included all 30 patients reported during the study period—5, 9, and 16 in 2019, 2020, and 2021 respectively—20 (66.7%) of whom were male and whose mean age was 41 years. Twenty-five (83.3%) were of non-EU origin. The most frequent system involvement was central nervous system (N = 8; 26.7%) followed by visceral (N = 7; 23.3%), gastro-intestinal (N = 6, 20.0%), musculoskeletal (N = 5; 16.7%), and pulmonary (N = 4; 13.3%). Hypoalbuminemia and anemia were highly prevalent (72 and 77%). The median of diagnostic delay was 6.5 months (IQR 1.8–30), which was higher among women (36.0 vs. 3.5 months; p = 0.002). Central nervous system involvement and pulmonary involvement were associated with diagnostic delay among women. We recorded 24 cured patients, two deaths, three patients with post-treatment sequelae, and one lost-to-follow up. We observed a clustering effect of patients in low-income neighborhoods (p < 0.001).ConclusionThere was a substantial delay in the diagnosis of disseminated TB in our study region, which might impacted the prognosis with women affected more negatively. Our results suggest that an increase in the occurrence of disseminated TB set in motion by diagnosis delay may have been a secondary effect of the COVID-19 pandemic.

The first column contains the best hyperparameters for the “Top 13” features, as described in the previous section. The second column contains the best hyperparameters for all features generated.