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

2019 Novel Coronavirus COVID-19 (2019-nCoV) Visual Dashboard and Map:
https://www.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6

• Confirmed Cases by Country/Region/Sovereignty
• Confirmed Cases by Province/State/Dependency
• Deaths
• Recovered

Downloadable data:
https://github.com/CSSEGISandData/COVID-19

Additional Information about the Visual Dashboard:
https://systems.jhu.edu/research/public-health/ncov

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F5505749%2F2b83271d61e47e2523e10dc9c28e545c%2F600x200.jpg?generation=1599042483103679&alt=media" alt="">

ALTADATA is a curated data marketplace where our subscribers and our data partners can easily exchange ready-to-analyze datasets and create insights with EPO, our visual data analytics platform.

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

Suggested Blog Posts

• Bayesian Thinking During the Pandemic
• Impact of COVID-19 on California Electricity Demand
• Keep Calm and Look At The Fundamentals
• Markets In The Corona Virus Crisis

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

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F5505749%2F2b83271d61e47e2523e10dc9c28e545c%2F600x200.jpg?generation=1599042483103679&alt=media" alt="">

ALTADATA is a curated data marketplace where our subscribers and our data partners can easily exchange ready-to-analyze datasets and create insights with EPO, our visual data analytics platform.

COVID-19 US Daily Data

State level daily COVID-19 data for United States, provided by Johns Hopkins University (JHU) Center for Systems Science and Engineering (CSSE). If you want to use the updated 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 daily data on the COVID-19 pandemic for United States with the states level breakdown.

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
• US Testing Rate = total test results per 100,000 persons. The "total test results" are equal to "Total test results (Positive + Negative)" from COVID Tracking Project.
• US Hospitalization Rate (%) = Total number hospitalized / Number cases. The "Total number hospitalized" is the "Hospitalized – Cumulative" count from COVID Tracking Project. The "hospitalization rate" and "Total number hospitalized" are only presented for those states which provide cumulative hospital data.
• States Population data is retrieved from U.S. Census Bureau on top of the JHU CSSE's COVID-19 data

Data Source

• Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE)
• U.S. Census Bureau, Population Division

Related Data Products

• COVID-19 Worldwide Daily Data

Suggested Blog Posts

• Bayesian Thinking During the Pandemic
• Impact of COVID-19 on California Electricity Demand
• Keep Calm and Look At The Fundamentals
• Markets In The Corona Virus Crisis

Data Dictionary

• Reported Date (reported_date): Covid-19 Report Date
• Province State (province_state): State name
• Population (population): Estimated state populations as of July 2019, as per U.S. Census Bureau Population Division
• Latitude (lat): Dot locations, not representative of a specific address
• Longitude (lng): Dot locations longitude, not representative of a specific address
• 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
• Hospitalization Rate (hospitalization_rate): Total number of people hospitalized * 100...

COVID-19 Trends MethodologyOur goal is to analyze and present daily updates in the form of recent trends within countries, states, or counties during the COVID-19 global pandemic. The data we are analyzing is taken directly from the Johns Hopkins University Coronavirus COVID-19 Global Cases Dashboard, though we expect to be one day behind the dashboard’s live feeds to allow for quality assurance of the data.Revisions added on 4/23/2020 are highlighted.Revisions added on 4/30/2020 are highlighted.Discussion of our assertion of an abundance of caution in assigning trends in rural counties added 5/7/2020. Correction on 6/1/2020Methodology update on 6/2/2020: This sets the length of the tail of new cases to 6 to a maximum of 14 days, rather than 21 days as determined by the last 1/3 of cases. This was done to align trends and criteria for them with U.S. CDC guidance. The impact is areas transition into Controlled trend sooner for not bearing the burden of new case 15-21 days earlier.Reasons for undertaking this work:The popular online maps and dashboards show counts of confirmed cases, deaths, and recoveries by country or administrative sub-region. Comparing the counts of one country to another can only provide a basis for comparison during the initial stages of the outbreak when counts were low and the number of local outbreaks in each country was low. By late March 2020, countries with small populations were being left out of the mainstream news because it was not easy to recognize they had high per capita rates of cases (Switzerland, Luxembourg, Iceland, etc.). Additionally, comparing countries that have had confirmed COVID-19 cases for high numbers of days to countries where the outbreak occurred recently is also a poor basis for comparison.The graphs of confirmed cases and daily increases in cases were fit into a standard size rectangle, though the Y-axis for one country had a maximum value of 50, and for another country 100,000, which potentially misled people interpreting the slope of the curve. Such misleading circumstances affected comparing large population countries to small population counties or countries with low numbers of cases to China which had a large count of cases in the early part of the outbreak. These challenges for interpreting and comparing these graphs represent work each reader must do based on their experience and ability. Thus, we felt it would be a service to attempt to automate the thought process experts would use when visually analyzing these graphs, particularly the most recent tail of the graph, and provide readers with an a resulting synthesis to characterize the state of the pandemic in that country, state, or county.The lack of reliable data for confirmed recoveries and therefore active cases. Merely subtracting deaths from total cases to arrive at this figure progressively loses accuracy after two weeks. The reason is 81% of cases recover after experiencing mild symptoms in 10 to 14 days. Severe cases are 14% and last 15-30 days (based on average days with symptoms of 11 when admitted to hospital plus 12 days median stay, and plus of one week to include a full range of severely affected people who recover). Critical cases are 5% and last 31-56 days. Sources:U.S. CDC. April 3, 2020 Interim Clinical Guidance for Management of Patients with Confirmed Coronavirus Disease (COVID-19). Accessed online. Initial older guidance was also obtained online. Additionally, many people who recover may not be tested, and many who are, may not be tracked due to privacy laws. Thus, the formula used to compute an estimate of active cases is: Active Cases = 100% of new cases in past 14 days + 19% from past 15-30 days + 5% from past 31-56 days - total deaths.We’ve never been inside a pandemic with the ability to learn of new cases as they are confirmed anywhere in the world. After reviewing epidemiological and pandemic scientific literature, three needs arose. We need to specify which portions of the pandemic lifecycle this map cover. The World Health Organization (WHO) specifies six phases. The source data for this map begins just after the beginning of Phase 5: human to human spread and encompasses Phase 6: pandemic phase. Phase six is only characterized in terms of pre- and post-peak. However, these two phases are after-the-fact analyses and cannot ascertained during the event. Instead, we describe (below) a series of five trends for Phase 6 of the COVID-19 pandemic.Choosing terms to describe the five trends was informed by the scientific literature, particularly the use of epidemic, which signifies uncontrolled spread. The five trends are: Emergent, Spreading, Epidemic, Controlled, and End Stage. Not every locale will experience all five, but all will experience at least three: emergent, controlled, and end stage.This layer presents the current trends for the COVID-19 pandemic by country (or appropriate level). There are five trends:Emergent: Early stages of outbreak. Spreading: Early stages and depending on an administrative area’s capacity, this may represent a manageable rate of spread. Epidemic: Uncontrolled spread. Controlled: Very low levels of new casesEnd Stage: No New cases These trends can be applied at several levels of administration: Local: Ex., City, District or County – a.k.a. Admin level 2State: Ex., State or Province – a.k.a. Admin level 1National: Country – a.k.a. Admin level 0Recommend that at least 100,000 persons be represented by a unit; granted this may not be possible, and then the case rate per 100,000 will become more important.Key Concepts and Basis for Methodology: 10 Total Cases minimum threshold: Empirically, there must be enough cases to constitute an outbreak. Ideally, this would be 5.0 per 100,000, but not every area has a population of 100,000 or more. Ten, or fewer, cases are also relatively less difficult to track and trace to sources. 21 Days of Cases minimum threshold: Empirically based on COVID-19 and would need to be adjusted for any other event. 21 days is also the minimum threshold for analyzing the “tail” of the new cases curve, providing seven cases as the basis for a likely trend (note that 21 days in the tail is preferred). This is the minimum needed to encompass the onset and duration of a normal case (5-7 days plus 10-14 days). Specifically, a median of 5.1 days incubation time, and 11.2 days for 97.5% of cases to incubate. This is also driven by pressure to understand trends and could easily be adjusted to 28 days. Source used as basis:Stephen A. Lauer, MS, PhD *; Kyra H. Grantz, BA *; Qifang Bi, MHS; Forrest K. Jones, MPH; Qulu Zheng, MHS; Hannah R. Meredith, PhD; Andrew S. Azman, PhD; Nicholas G. Reich, PhD; Justin Lessler, PhD. 2020. The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application. Annals of Internal Medicine DOI: 10.7326/M20-0504.New Cases per Day (NCD) = Measures the daily spread of COVID-19. This is the basis for all rates. Back-casting revisions: In the Johns Hopkins’ data, the structure is to provide the cumulative number of cases per day, which presumes an ever-increasing sequence of numbers, e.g., 0,0,1,1,2,5,7,7,7, etc. However, revisions do occur and would look like, 0,0,1,1,2,5,7,7,6. To accommodate this, we revised the lists to eliminate decreases, which make this list look like, 0,0,1,1,2,5,6,6,6.Reporting Interval: In the early weeks, Johns Hopkins' data provided reporting every day regardless of change. In late April, this changed allowing for days to be skipped if no new data was available. The day was still included, but the value of total cases was set to Null. The processing therefore was updated to include tracking of the spacing between intervals with valid values.100 News Cases in a day as a spike threshold: Empirically, this is based on COVID-19’s rate of spread, or r0 of ~2.5, which indicates each case will infect between two and three other people. There is a point at which each administrative area’s capacity will not have the resources to trace and account for all contacts of each patient. Thus, this is an indicator of uncontrolled or epidemic trend. Spiking activity in combination with the rate of new cases is the basis for determining whether an area has a spreading or epidemic trend (see below). Source used as basis:World Health Organization (WHO). 16-24 Feb 2020. Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19). Obtained online.Mean of Recent Tail of NCD = Empirical, and a COVID-19-specific basis for establishing a recent trend. The recent mean of NCD is taken from the most recent fourteen days. A minimum of 21 days of cases is required for analysis but cannot be considered reliable. Thus, a preference of 42 days of cases ensures much higher reliability. This analysis is not explanatory and thus, merely represents a likely trend. The tail is analyzed for the following:Most recent 2 days: In terms of likelihood, this does not mean much, but can indicate a reason for hope and a basis to share positive change that is not yet a trend. There are two worthwhile indicators:Last 2 days count of new cases is less than any in either the past five or 14 days. Past 2 days has only one or fewer new cases – this is an extremely positive outcome if the rate of testing has continued at the same rate as the previous 5 days or 14 days. Most recent 5 days: In terms of likelihood, this is more meaningful, as it does represent at short-term trend. There are five worthwhile indicators:Past five days is greater than past 2 days and past 14 days indicates the potential of the past 2 days being an aberration. Past five days is greater than past 14 days and less than past 2 days indicates slight positive trend, but likely still within peak trend time frame.Past five days is less than the past 14 days. This means a downward trend. This would be an

Our World in Data - COVID-19

▶ About Our World in Data 🏢

• Our World in Data website
• Our World in Data GitHub

▶ Similar Datasets 📄

• COVID-19 World Vaccination Progress
• The Our World in Data COVID Vaccination Data
• Data on COVID-19 (coronavirus)
• COVID-19 dataset by Our World in Data

▶ Context 📝

The complete COVID-19 dataset is a collection of the COVID-19 data maintained and provided by Our World in Data. Our World in Data team will update it daily throughout the duration of the COVID-19 pandemic.

▶ Content 📃

These are the following information that includes in the dataset: | Metrics | Source | Updated | Countries | | --- | --- | | Vaccinations | Official data collated by the Our World in Data team | Daily | 218 | | Tests & positivity | Official data collated by the Our World in Data team | Weekly | 139 | | Hospital & ICU | Official data collated by the Our World in Data team | Weekly | 39 | | Confirmed cases | JHU CSSE COVID-19 Data | Daily | 196 | | Confirmed deaths | JHU CSSE COVID-19 Data | Daily | 196 | | Reproduction rate | Arroyo-Marioli F, Bullano F, Kucinskas S, Rondón-Moreno C | Daily | 185 | | Policy responses | Oxford COVID-19 Government Response Tracker | Daily | 186 | | Other variables of interest | International organizations (UN, World Bank, OECD, IHME…) | Fixed |

Data dictionary is available below ⤵

▶ Acknowledgements 🙏

I'd like to clarify that I'm only making data about vaccines collected by Our World in Data available to Kaggle community. This dataset is gathered, integrated, and posted the new version on a daily basis, as maintained by Our World in Data on their GitHub repository.

▶ Inspiration 💭

• Forecasting daily new confirmed cases of COVID-19 in specific country.
• Perform data analysis/data visualization of COVID-19 cases/death/etc.

📷 Images by Fusion Medical Animation.

COVID cases and deaths for LA County and California State. Updated daily.

Data source: Johns Hopkins University (https://coronavirus.jhu.edu/us-map), Johns Hopkins GitHub (https://github.com/CSSEGISandData/COVID-19/blob/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_US.csv). Code available: https://github.com/CityOfLosAngeles/covid19-indicators.

To date, one million confirmed cases of SARS-CoV-2 virus have been reported worldwide with a death toll of over 50,000 (1). Particular concern has been raised regarding the exposure of healthcare professionals. Early reports from the Wuhan province in China described up to 29% infection rates among healthcare professionals before the use of personal Protection equipment (PPE) was fully established (2). Several measures are being established with regard the correct use of PPE and reduction in aerosol generating procedures. However to the authors’ knowledge, no specific guidance is available regarding the potential risk of aerosolization of SARS-Cov-2 virus via chest drains in patients with active air leak.Viral Spread and Air LeakThe SARS-CoV-2 virus, which leads to COVID-19, has been demonstrated to remain viable in aerosol form and is transmitted by droplets (3). Despite the current coronavirus pandemic, we are still faced with patients requiring chest tube drainage for pneumothorax on cardiothoracic and respiratory wards, as well as in critical care units. Whilst drains may be inserted with lower risk of viral spread for simple pleural effusions, the authors fear there may be a high risk of aerosolization in cases of pneumothorax with active air leak, whether that be primary, secondary, or indeed iatrogenic in mechanically ventilated patients requiring high PEEP ventilation such as in patients with COVID-19.Citing a recent example of a postoperative thoracic surgical patient in the authors’ unit who had a prolonged air leak and who later was found to be positive for SARS-CoV-2, they have considered the implications of aerosolization from the chest drain and in particular the chest drain bottle. This may represent an under-recognised means of viral spread, which may put patients and health care professionals at risk of infection.Chest Drains and Risk of AerosolizationTraditional under water seal chest drain bottles have a port which allows attachment to low pressure wall suction. Most modern drain systems also have a safety valve which opens to air should the suction be accidentally turned off in the presence of an air leak, to avoid creating a closed system effect which could lead to a tension pneumothorax. If the drain bottle is not attached to suction, then the port is open to the atmosphere.When air leaks into a chest drain bottle, it causes the fluid inside to bubble. Given the aerosolization that is likely to occur inside the drain bottle, which then escapes through the suction port or safety valve, this may be a potentially important mode of viral transmission. Alternatives to a traditional chest drain bottle include a number of different digital chest drainage systems. Whilst these do not have a port open to room air, they are not closed systems and the air escapes from the system into the air without any specific viral filter.A number of patients on the authors’ unit’s thoracic ward have since tested positive for COVID-19. Whilst the patient with the air leak may not have been the source of infection, they feel this should be considered. In their patient, a digital chest drainage system was being used.In light of this, and until further robust evidence regarding the volume of aerosolization from a chest drain bottle emerges, the authors would recommend the use of closed drainage systems, i.e. connecting the standard drain bottle to wall suction to avoid the spread of viral load via aerosolization. However, in order to obtain this, the safety valve will have to be occluded with potential risk of increasing intrathoracic pressure and cause tension, should the suction system be switched off whilst still connected to the bottle. Furthermore, keeping the bottle attached to wall suction will significantly limit the mobilization of patients, which is a significant risk factor for postoperative complications in the surgical patient.A Bespoke Chest Drain SystemIn order to overcome this, a possible consideration would be to attach an antimicrobial filter, such as those used in ventilator circuits, to the chest drain suction port leaving the drain off suction and occluding the safety valve. Connecting the filter directly to the chest drain should be discouraged, as fluid and moisture directly from the chest cavity are likely to interfere with the functioning of the filter.Therefore, the authors designed a bespoke drainage system using the Filta-Guard™ ventilator filter from Intersurgical Ltd© 2020 and a segment of endotracheal tube to use in their unit (Figures 1 and 2). The filter guarantees a filtration efficiency of >99.999% as tested on Hepatitis C and Mycobacterium tuberculosis in addition to standard test micro-organisms (4). The SARS-Cov-2 diameter varies from 60 to 140 nm, and therefore is larger than hepatitis C virus, which has an average diameter of about 55 nm. The authors postulated that given the larger size compared to Hep C virus, this filter should be effective in preventing flow of SARS-Cov-2 across the filter, however to their knowledge, this has not been clinically tested. Regarding the possible resistance to the system added by the filter and related risk of building up pressure in the chest cavity, they believe this should be marginal. Published data suggest the above filter would generate a resistance against the passage of 30L/min of 1.0cm H2O and 2.3cm H2O at 60L/min (4).ConclusionsThe efficacy of this chest drain modification clearly needs to be further investigated, however, given the current pandemic, any method of reducing viral spread should be considered.Acknowledgements: The authors would like to acknowledge Mr Panagiotis Theodoropoulos and Mr Duncan Steele, Specialist Registrars in Thoracic Surgery at Hammersmith Hospital, London.ReferencesJohns Hopkins University & Medicine. COVID-19 Map. https://coronavirus.jhu.edu/map.html. Published 2020. Accessed April 2, 2020.Chen W, Huang Y. To protect healthcare workers better, to save more lives. Anesth Analg. 2020:1-15. doi:10.1213/ANE.0000000000004834van Doremalen N, Bushmaker T, Morris DH, et al. Aerosol and surface stability of SARS-CoV-2 as compared with SARS-CoV-1. N Engl J Med. 2020. doi:10.1056/NEJMc2004973Systems ICR. Filta-GuardTM range - high efficiency. https://www.intersurgical.com/products/airway-management/filtaguard-range-high-efficiency#1944000. Published 2020. Accessed April 2, 2020.

Data on COVID-19 (coronavirus) by Our World in Data

🗂️ Download our complete COVID-19 dataset : CSV | XLSX | JSON

Our complete COVID-19 dataset is a collection of the COVID-19 data maintained by Our World in Data. We will update it daily throughout the duration of the COVID-19 pandemic (more information on our updating process and schedule here). It includes the following data:

A specific section of this repository is also dedicated to vaccinations, with a lighter dataset containing only vaccination data.

The data you find here and our data sources

• Confirmed cases and deaths: our data comes from the COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). We discuss how and when JHU collects and publishes this data here. The cases & deaths dataset is updated daily. *Note: the number of cases or deaths reported by any institution—including JHU, the WHO, the ECDC and others—on a given day does not necessarily represent the actual number on that date. This is because of the long reporting chain that exists between a new case/death and its inclusion in statistics. This also means that negative values in cases and deaths can sometimes appear when a country corrects historical data, because it had previously overestimated the number of cases/deaths. Alternatively, large changes can sometimes (although rarely) be made to a country's entire time series if JHU decides (and has access to the necessary data) to correct values retrospectively.*
• Hospitalizations and intensive care unit (ICU) admissions: our data is collected from official sources and collated by Our World in Data. The complete list of country-by-country sources is available here.
• Testing for COVID-19: this data is collected by the Our World in Data team from official reports; you can find further details in our post on COVID-19 testing, including our checklist of questions to understand testing data, information on geographical and temporal coverage, and detailed country-by-country source information. The testing dataset is updated around twice a week.
• Vaccinations against COVID-19: this data is collected by the Our World in Data team from official reports.
• Other variables: this data is collected from a variety of sources (United Nations, World Bank, Global Burden of Disease, Blavatnik School of Government, etc.). More information is available in our codebook.

The complete Our World in Data COVID-19 dataset

**Our complete COVID-19 dataset is available in CSV, XLSX, and JSON formats, and inc...

Data on COVID-19 (coronavirus)

Our complete COVID-19 dataset is a collection of the COVID-19 data maintained by Pritesh Raj. We will update it daily throughout the duration of the COVID-19 pandemic. It includes the following data:

The complete Our World in Data COVID-19 dataset

The CSV and XLSX files follow a format of 1 row per location and date. The JSON version is split by country ISO code, with static variables and an array of daily records.

The variables represent all of our main data related to confirmed cases, deaths, hospitalizations, and testing, as well as other variables of potential interest.

Confirmed cases

Confirmed deaths

Hospital & ICU

Data on COVID-19 (coronavirus) Vaccination

Our complete COVID-19 dataset is a collection of the COVID-19 data. We will update it daily throughout the duration of the COVID-19 pandemic. It includes the following data:

The complete Our World in Data COVID-19 dataset

The CSV and files follow a format of 1 row per location and date. This version is split by country ISO code, with static variables and an array of daily records.

The variables represent all of our main data related to confirmed cases, deaths, hospitalizations, and testing, as well as other variables of potential interest.

Confirmed cases

Confirmed deaths

Hospital & ICU