Updates

• Notice of data discontinuation: Since the start of the pandemic, AP has reported case and death counts from data provided by Johns Hopkins University. Johns Hopkins University has announced that they will stop their daily data collection efforts after March 10. As Johns Hopkins stops providing data, the AP will also stop collecting daily numbers for COVID cases and deaths. The HHS and CDC now collect and visualize key metrics for the pandemic. AP advises using those resources when reporting on the pandemic going forward.

  • CDC Weekly case and death counts (national and state level)
  • CDC County level cases and deaths
  • HHS New hospital admissions
  • CDC NowCast COVID variant proportions (national and regional level)

• April 9, 2020

  • The population estimate data for New York County, NY has been updated to include all five New York City counties (Kings County, Queens County, Bronx County, Richmond County and New York County). This has been done to match the Johns Hopkins COVID-19 data, which aggregates counts for the five New York City counties to New York County.

• April 20, 2020

  • Johns Hopkins death totals in the US now include confirmed and probable deaths in accordance with CDC guidelines as of April 14. One significant result of this change was an increase of more than 3,700 deaths in the New York City count. This change will likely result in increases for death counts elsewhere as well. The AP does not alter the Johns Hopkins source data, so probable deaths are included in this dataset as well.

• April 29, 2020

  • The AP is now providing timeseries data for counts of COVID-19 cases and deaths. The raw counts are provided here unaltered, along with a population column with Census ACS-5 estimates and calculated daily case and death rates per 100,000 people. Please read the updated caveats section for more information.

• September 1st, 2020

  • Johns Hopkins is now providing counts for the five New York City counties individually.

• February 12, 2021

  • The Ohio Department of Health recently announced that as many as 4,000 COVID-19 deaths may have been underreported through the state’s reporting system, and that the "daily reported death counts will be high for a two to three-day period."
  • Because deaths data will be anomalous for consecutive days, we have chosen to freeze Ohio's rolling average for daily deaths at the last valid measure until Johns Hopkins is able to back-distribute the data. The raw daily death counts, as reported by Johns Hopkins and including the backlogged death data, will still be present in the new_deaths column.

• February 16, 2021

  - Johns Hopkins has reconciled Ohio's historical deaths data with the state.

  Overview

The AP is using data collected by the Johns Hopkins University Center for Systems Science and Engineering as our source for outbreak caseloads and death counts for the United States and globally.

The Hopkins data is available at the county level in the United States. The AP has paired this data with population figures and county rural/urban designations, and has calculated caseload and death rates per 100,000 people. Be aware that caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.

This data is from the Hopkins dashboard that is updated regularly throughout the day. Like all organizations dealing with data, Hopkins is constantly refining and cleaning up their feed, so there may be brief moments where data does not appear correctly. At this link, you’ll find the Hopkins daily data reports, and a clean version of their feed.

The AP is updating this dataset hourly at 45 minutes past the hour.

To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

Queries

Use AP's queries to filter the data or to join to other datasets we've made available to help cover the coronavirus pandemic

• Filter cases by state here

• Rank states by their status as current hotspots. Calculates the 7-day rolling average of new cases per capita in each state: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=481e82a4-1b2f-41c2-9ea1-d91aa4b3b1ac

• Find recent hotspots within your state by running a query to calculate the 7-day rolling average of new cases by capita in each county: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=b566f1db-3231-40fe-8099-311909b7b687&showTemplatePreview=true

• Join county-level case data to an earlier dataset released by AP on local hospital capacity here. To find out more about the hospital capacity dataset, see the full details.

• Pull the 100 counties with the highest per-capita confirmed cases here

• Rank all the counties by the highest per-capita rate of new cases in the past 7 days here. Be aware that because this ranks per-capita caseloads, very small counties may rise to the very top, so take into account raw caseload figures as well.

Interactive

The AP has designed an interactive map to track COVID-19 cases reported by Johns Hopkins.

@(https://datawrapper.dwcdn.net/nRyaf/15/)

Interactive Embed Code

<iframe title="USA counties (2018) choropleth map Mapping COVID-19 cases by county" aria-describedby="" id="datawrapper-chart-nRyaf" src="https://datawrapper.dwcdn.net/nRyaf/10/" scrolling="no" frameborder="0" style="width: 0; min-width: 100% !important;" height="400"></iframe><script type="text/javascript">(function() {'use strict';window.addEventListener('message', function(event) {if (typeof event.data['datawrapper-height'] !== 'undefined') {for (var chartId in event.data['datawrapper-height']) {var iframe = document.getElementById('datawrapper-chart-' + chartId) || document.querySelector("iframe[src*='" + chartId + "']");if (!iframe) {continue;}iframe.style.height = event.data['datawrapper-height'][chartId] + 'px';}}});})();</script>

Caveats

• This data represents the number of cases and deaths reported by each state and has been collected by Johns Hopkins from a number of sources cited on their website.
• In some cases, deaths or cases of people who've crossed state lines -- either to receive treatment or because they became sick and couldn't return home while traveling -- are reported in a state they aren't currently in, because of state reporting rules.
• In some states, there are a number of cases not assigned to a specific county -- for those cases, the county name is "unassigned to a single county"
• This data should be credited to Johns Hopkins University's COVID-19 tracking project. The AP is simply making it available here for ease of use for reporters and members.
• Caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.
• Population estimates at the county level are drawn from 2014-18 5-year estimates from the American Community Survey.
• The Urban/Rural classification scheme is from the Center for Disease Control and Preventions's National Center for Health Statistics. It puts each county into one of six categories -- from Large Central Metro to Non-Core -- according to population and other characteristics. More details about the classifications can be found here.

Johns Hopkins timeseries data - Johns Hopkins pulls data regularly to update their dashboard. Once a day, around 8pm EDT, Johns Hopkins adds the counts for all areas they cover to the timeseries file. These counts are snapshots of the latest cumulative counts provided by the source on that day. This can lead to inconsistencies if a source updates their historical data for accuracy, either increasing or decreasing the latest cumulative count. - Johns Hopkins periodically edits their historical timeseries data for accuracy. They provide a file documenting all errors in their timeseries files that they have identified and fixed here

Attribution

This data should be credited to Johns Hopkins University COVID-19 tracking project

Peru is the country with the highest mortality rate due to the coronavirus disease (COVID-19) in Latin America. As of November 13, 2023, the country registered over 672 deaths per 100,000 inhabitants. It was followed by Brazil, with around 331.5 fatal cases per 100,000 population. In total, over 1.76 million people have died due to COVID-19 in Latin America and the Caribbean.

Are these figures accurate? Although countries like Brazil already rank among the countries most affected by the coronavirus disease (COVID-19), there is still room to believe that the number of cases and deaths in Latin American countries are underreported. The main reason is the relatively low number of tests performed in the region. For example, Brazil, one of the most impacted countries in the world, has performed approximately 63.7 million tests as of December 22, 2022. This compared with over one billion tests performed in the United States, approximately 909 million tests completed in India, or around 522 million tests carried out in the United Kingdom.

Capacity to deal with the outbreak With the spread of the Omicron variant, the COVID-19 pandemic is putting health systems around the world under serious pressure. The lack of equipment to treat acute cases, for instance, is one of the problems affecting Latin American countries. In 2019, the number of ventilators in hospitals in the most affected countries ranged from 25.23 per 100,000 inhabitants in Brazil to 5.12 per 100,000 people in Peru.

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

Background and ObjectivesThe official number of daily cases and deaths are the most prominent indicators used to plan actions against the COVID-19 pandemic but are insufficient to see the real impact. Official numbers vary due to testing policy, reporting methods, etc. Therefore, critical interventions are likely to lose their effectiveness and better-standardized indicators like excess deaths/mortality are needed. In this study, excess deaths in Istanbul were examined and a web-based monitor was developed.MethodsDaily all-cause deaths data between January 1, 2015- November 11, 2021 in Istanbul is used to estimate the excess deaths. Compared to the pre-pandemic period, the % increase in the number of deaths was calculated as the ratio of excess deaths to expected deaths (P-Scores). The ratio of excess deaths to official figures (T) was also examined.ResultsThe total number of official and excess deaths in Istanbul are 24.218 and 37.514, respectively. The ratio of excess deaths to official deaths is 1.55. During the first three death waves, maximum P-Scores were 71.8, 129.0, and 116.3% respectively.ConclusionExcess mortality in Istanbul is close to the peak scores in Europe. 38.47% of total excess deaths could be considered as underreported or indirect deaths. To re-optimize the non-pharmaceutical interventions there is a need to monitor the real impact beyond the official figures. In this study, such a monitoring tool was created for Istanbul. The excess deaths are more reliable than official figures and it can be used as a gold standard to estimate the impact more precisely.

Background: Mainland China experienced a major surge in SARS-CoV-2 infections in December 2022-January 2023, but its impact on mortality was unclear given the underreporting of coronavirus disease 2019 deaths. Methods: Using obituary data from the Chinese Academy of Engineering (CAE), we estimated the excess death rate among senior CAE members by taking the difference between the observed rate of all-cause death in December 2022-January 2023 and the expected rate for the same months in 2017-2022, by age groups. We used this to extrapolate an estimate of the number of excess deaths in December 2022-January 2023 among urban dwellers in Mainland China. Results: In December 2022-January 2023, we estimated excess death rates of 0.94 per 100 persons (95% confidence interval [CI] = -0.54, 3.16) in CAE members aged 80-84 years, 3.95 (95% CI = 0.50, 7.84) in 85-89 years, 10.35 (95% CI = 3.59, 17.71) in 90-94 years, and 16.88 (95% CI = 0.00, 34.62) in 95 years and older. Using our baseline assumptions, this extrapolated to 917,000 (95% CI = 425,000, 1.45 million) excess deaths among urban dwellers in Mainland China, much higher than the 81,000 in-hospital deaths officially reported from 9 December 2022 to 30 January 2023. Conclusions: As in many jurisdictions, we estimate that the coronavirus disease 2019 pandemic had a much wider impact on mortality than what was officially documented in Mainland China.

Coronavirus disease 2019 (COVID-19) mortality can be estimated based on reliable mortality data. Variable testing procedures and heterogeneous disease course suggest that a substantial number of COVID-19 deaths is undetected. To address this question, we screened an unselected autopsy cohort for the presence of SARS-CoV-2 and a panel of common respiratory pathogens. Lung tissues from 62 consecutive autopsies, conducted during the first and second COVID-19 pandemic waves in Switzerland, were analyzed for bacterial, viral and fungal respiratory pathogens including SARS-CoV-2. SARS-CoV-2 was detected in 28 lungs of 62 deceased patients (45%), although only 18 patients (29%) were reported to have COVID-19 at the time of death. In 23 patients (37% of all), the clinical cause of death and/or autopsy findings together with the presence of SARS-CoV-2 suggested death due to COVID-19. Our autopsy results reveal a 16% higher SARS-CoV-2 infection rate and an 8% higher SARS-CoV-2 related mortality rate than reported by clinicians before death. The majority of SARS-CoV-2 infected patients (75%) did not suffer from respiratory co-infections, as long as they were treated with antibiotics. In the lungs of 5 patients (8% of all), SARS-CoV-2 was found, yet without typical clinical and/or autopsy findings. Our findings suggest that underreporting of COVID-19 contributes substantially to excess mortality. The small percentage of co-infections in SARS-CoV-2 positive patients who died with typical COVID-19 symptoms strongly suggests that the majority of SARS-CoV-2 infected patients died from and not with the virus.

Seroprevalence of 67.6% is used with 765 million infectionsa from an age-adjusted population as of 14 Jun-6 Jul 2021 from the 4th nationwide serosurvey [6].

Effective June 7th, 2024, this dataset will no longer be updated.This file contains data on:

1. Cumulative count of Ottawa residents with laboratory-confirmed COVID-19 by episode date (i.e. the earliest of symptom onset, testing or reported date), including active cases and resolved cases.

2. Cumulative count of Ottawa residents with laboratory-confirmed COVID-19 who died by date of death.

3. Daily count of Ottawa residents with laboratory-confirmed COVID-19 by reported date and episode date.

4. Daily count of Ottawa residents with laboratory-confirmed COVID-19 by outbreak association and episode date.

5. Daily count of Ottawa residents with laboratory-confirmed COVID-19 newly admitted to the hospital, currently in hospital, and currently in the intensive care unit (ICU).

6. Cumulative rate of confirmed COVID-19 for Ottawa residents by age group and episode date.

7. Cumulative rate of confirmed COVID-19 for Ottawa residents by gender and episode date.

8. Daily count of Ottawa residents with laboratory-confirmed COVID-19 by source of infection and episode date.

Data are from the Ontario Ministry of Health Public Health Case and Contact Management Solution (CCM).

Accuracy: Points of consideration for interpretation of the data:

The percent of cases with no known epidemiological (epi) link, during the current day and previous 13 days, is calculated as the number of cases with no known epi link among all cases. The percent of cases with no known epi link is unstable during time periods with few cases.

Source of infection is based on a case's epidemiologic linkage. If no epidemiologic linkage is identified, source of infection is allocated using a hierarchy of risk factors: related to travel prior to April 1, 2020 > part of an outbreak > close or household contact of a known case > related to travel since April 1, 2020 > unspecified epidemiological link > no known source of infection > no information available.

Data are entered into and extracted by Ottawa Public Health from the Ontario Ministry of Health Public Health Case and Contact Management Solution (CCM). The CCM is a dynamic disease reporting system that allows for ongoing updates; data represent a snapshot at the time of extraction and may differ from previous or subsequent reports.

As the cases are investigated and more information is available, the dates are updated.

A person’s exposure may have occurred up to 14 days prior to onset of symptoms. Symptomatic cases occurring in approximately the last 14 days are likely under-reported due to the time for individuals to seek medical assessment, availability of testing, and receipt of test results.

Confirmed cases are those with a confirmed COVID-19 laboratory result as per the Ministry of Health Public health management of cases and contacts of COVID-19 in Ontario. March 25, 2020 version 6.0.

Counts will be subject to varying degrees of underreporting due to a variety of factors, such as disease awareness and medical care seeking behaviours, which may depend on severity of illness, clinical practice, changes in laboratory testing, and reporting behaviours.

Data on hospital admissions, ICU admissions and deaths are likely under-reported as these events may occur after the completion of public health follow up of cases. Cases that were admitted to hospital or died after follow-up was completed may not be captured in iPHIS or local health unit reporting tools.

Cases are associated with a specific, isolated community outbreak; an institutional outbreak (e.g. healthcare, childcare, education); or no known outbreak (i.e., sporadic).

The distribution of the source of infection among confirmed cases is impacted by the provincial guidance on testing.

Surveillance testing for COVID-19 began in long term care facilities on April 25, 2020.

Source of infection is allocated using a hierarchy: Related to travel prior to April 1, 2020 > Close contact of a known case or part of a community outbreak or source of infection is an institutional outbreak > Related to travel since April 1, 2020 > No known source of infection > Missing.

The percent of cases with unknown source, during the current day and previous 13 days, is calculated as the number of cases with no known source among cases who source of infection is not an institutional outbreak. Calculated over a 14 day period (i.e. the day of interest and the preceding 13 days). The percent of cases with no known source is unstable during time periods with few cases.

Update Frequency: Wednesdays

Attributes: Data fields:

Data fields:

Date – Date in format YYYY-MM-DD H:MM. The date type varies based on the column of interest and could be:

 - Episode date – Earliest of

symptom onset, test or reported date for cases;

 - Date of death – The date

the person was reported to have died

 - Reported date – Date the

confirmed laboratory results were reported to Ottawa Public Health

 - Hospitalization date

Cumulative Cases by Episode Date – cumulative number of Ottawa residents with laboratory-confirmed COVID-19 by episode date. Cumulative Resolved Cases by Episode Date – cumulative number of Ottawa residents with laboratory-confirmed COVID-19 that have not died and are either (1) assessed as ‘recovered’ in The CCM or (2) 14 days past their episode date and not currently hospitalized. Cumulative Active Cases by Episode Date– cumulative number of Ottawa residents with an active COVID-19 infection. Calculated as the total number of Ottawa residents with COVID-19 excluding resolved and deceased cases. Cumulative Deaths by Date of Death - cumulative number of Ottawa residents with laboratory-confirmed COVID-19 who died by date of death. Deaths are included whether or not COVID-19 was determined to be a contributing or underlying cause of death. Daily Cases by Reported Date – number of Ottawa residents with laboratory-confirmed COVID-19 by reported date 7-Day Average of Newly Reported Cases by Reported Date – number of Ottawa residents with laboratory-confirmed COVID-19 by reported date. Calculated over a 7 day period (i.e. the day of interest and the preceding 6 days). Daily Cases by Episode Date - number of Ottawa residents with laboratory-confirmed COVID-19 by episode date. Daily Cases Linked to a Community Outbreak by Episode Date – number of Ottawa residents with laboratory-confirmed COVID-19 associated with a specific isolated community outbreak by episode date. Daily Cases Linked to an Institutional Outbreak – number of Ottawa residents with laboratory-confirmed COVID-19 associated with a COVID-19 outbreak in a healthcare, childcare or educational establishment by case episode date. Healthcare institutions include places such as long-term care homes, retirement homes, hospitals, other healthcare institutions (e.g. group homes, shelters). Daily Cases Not Linked to an Institutional Outbreak (i.e. Sporadic Cases) – number of Ottawa residents with laboratory-confirmed COVID-19 not associated to an outbreak of COVID-19. Cases Newly Admitted to Hospital – Daily number of Ottawa residents with confirmed COVID-19 admitted to hospital. Emergency room visits are not included in the number of hospital admissions. Cases Currently in Hospital – Number of Ottawa residents with confirmed COVID-19 currently in hospital, includes patients in intensive care. Emergency room visits are not included in the number of hospitalizations. Cases Currently in ICU - Number of Ottawa residents with confirmed COVID-19 currently being treated in the intensive care unit (ICU). It is a subset of the count of hospitalized cases. Cumulative Rate of COVID-19 by 10-year Age Groupings (per 100,000 pop) and Episode Date – The number of Ottawa residents with confirmed COVID-19 within an age group (e.g. 0-9 years) divided by the total Ottawa population for that age group. This fraction is then multiplied by 100,000 to get a rate of COVID-19 per 100,000 population for that age group. Cumulative Rate of COVID-19 by Gender (per 100,000 pop) and Episode Date – The number of Ottawa residents with confirmed COVID-19 of a given gender (e.g. female) divided by the total Ottawa population for that gender. This fraction is then multiplied by 100,000 to get a rate of COVID-19 per 100,000 population for that gender. Source of infection is travel by episode date: individuals who are most likely to have acquired their infection during out-of-province travel. Number of cases with missing information on source of infection by episode date: assessment for source of infection was not completed. Number of cases with no known epidemiological link by episode date: individuals who did not travel outside Ontario, are not part of an outbreak, and are not able to identify someone with COVID-19 from whom they might have acquired infection. The assessment for source of infection was completed, but no sources were identified. Source of infection is a close contact by episode date: individuals presumed to have acquired their infection following close contact (e.g. household member, friend, relative) with an individual with confirmed COVID-19. Source of infection is an outbreak by episode date: individuals who are most likely to have acquired their infection as part of a confirmed COVID-19 outbreak. Source of Infection is Unknown by Episode Date: Ottawa residents with confirmed COVID-19 who did not travel outside

Replication file (datasets and do files) for: Encouraged to Cheat? Federal Incentives and Career Concerns at the Sub-National Level as Determinants of Under-Reporting of COVID-19 Mortality in Russia

Although existing literature reveals that autocrats underreported the death toll during the Covid-19 pandemic, few studies have explored how, why, and under what conditions autocrats faked the data. By analysing cross-national daily data (from 164 countries between January 2020 and March 2023), this article reveals that, unlike closed autocracies, which relied on strict restrictions of information and collective actions, electoral autocracies strategically fabricated the mortality statistics with electoral cycles, which coincided with protest cycles. In contrast, such cycles were not salient in democracies. This study argues that, in electoral autocracies, the limited accountability and looser political restrictions induced people and opposition parties to invest in collective actions during electoral seasons, whereas autocrats tried to discourage mass uprisings by well-timed, nuanced statistical tampering. The article also demonstrates that electoral statistical cycles become salient when autocrats can command the co-operation of local politico-bureaucratic agents. Moreover, it provides preliminary evidence that underreporting of casualties helped discourage protests and opposition mobilization in electoral autocracies.

BackgroundThe COVID-19 is a significant public health issue, and monitoring confirmed cases and deaths is an essential epidemiologic tool. We evaluated the features in Brazilian hospitalized patients due to severe acute respiratory infection (SARI) during the COVID-19 pandemic in Brazil. We grouped the patients into the following categories: Influenza virus infection (G1), other respiratory viruses' infection (G2), other known etiologic agents (G3), SARS-CoV-2 infection (patients with COVID-19, G4), and undefined etiological agent (G5).MethodsWe performed an epidemiological study using data from DataSUS (https://opendatasus.saude.gov.br/) from December 2019 to October 2021. The dataset included Brazilian hospitalized patients due to SARI. We considered the clinical evolution of the patients with SARI during the COVID-19 pandemic according to the SARI patient groups as the outcome. We performed the multivariate statistical analysis using logistic regression, and we adopted an Alpha error of 0.05.ResultsA total of 2,740,272 patients were hospitalized due to SARI in Brazil, being the São Paulo state responsible for most of the cases [802,367 (29.3%)]. Most of the patients were male (1,495,416; 54.6%), aged between 25 and 60 years (1,269,398; 46.3%), and were White (1,105,123; 49.8%). A total of 1,577,279 (68.3%) patients recovered from SARI, whereas 701,607 (30.4%) died due to SARI, and 30,551 (1.3%) did not have their deaths related to SARI. A major part of the patients was grouped in G4 (1,817,098; 66.3%) and G5 (896,207; 32.7%). The other groups account for

Percentual absolute error of Covid-19 deaths forecasting outbreaks in Brazil.