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 the first reported coronavirus case in Washington State on Jan. 21, 2020, 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.

Context

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

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

content

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

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

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

2020

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.

Daily updated dataset of all Coronavirus (COVID-19) Cases in all countries in the world. See the README file and the Codebook for more information including data dictionary.

• Confirmed cases and deaths: this data is collected from the World Health Organization Coronavirus Dashboard. The cases & deaths dataset is updated daily.
  • Note 1: Time/date stamps reflect when the data was last updated by WHO. Due to the time required to process and validate the incoming data, there is a delay between reporting to WHO and the update of the dashboard.
  • Note 2: Counts and corrections made after these times will be carried forward to the next reporting cycle for that specific region. Delayed reporting for any specific country, territory or area may result in pooled counts for multiple days being presented, with a retrospective update to counts on previous days to accurately reflect trends. Significant data errors detected or reported to WHO may be corrected at more frequent intervals.
• 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 "https://ourworldindata.org/coronavirus-testing#our-checklist-for-covid-19-testing-data">checklist of questions to understand testing data, information on "https://ourworldindata.org/coronavirus-testing#which-countries-do-we-have-testing-data-for">geographical and temporal coverage, and "https://ourworldindata.org/coronavirus-testing#source-information-country-by-country">detailed country-by-country source information. On 23 June 2022, we stopped adding new datapoints to our COVID-19 testing dataset. You can read more here.
• 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.

On March 10, 2023, the Johns Hopkins Coronavirus Resource Center ceased collecting and reporting of global COVID-19 data. For updated cases, deaths, and vaccine data please visit the following sources:Global: World Health Organization (WHO)U.S.: U.S. Centers for Disease Control and Prevention (CDC)For more information, visit the Johns Hopkins Coronavirus Resource Center.This feature layer contains the most up-to-date COVID-19 cases and the latest trend plot. It covers the US (county or state level), China, Canada, Australia (province/state level), and the rest of the world (country/region level, represented by either the country centroids or their capitals). Data sources are WHO, CDC, ECDC, NHC, DXY, 1point3acres, Worldometers.info, BNO, the COVID Tracking Project (testing and hospitalizations), state and national government health departments, and local media reports. This layer is created and maintained by the Center for Systems Science and Engineering (CSSE) at the Johns Hopkins University. This feature layer is supported by Esri Living Atlas team, JHU APL and JHU Data Services. This layer is opened to the public and free to share. Contact us.

On January 21, 2020, the U.S. Centers for Disease Control and Prevention (CDC) and Washington State Department of Health (DOH) announced the first case of 2019 Novel Coronavirus (COVID-19) in the United States, in Washington state. The link below provides access to DOH daily updates of confirmed Washington State COVID-19 cases and deaths, along with essential information about the virus and guidance on prevention and risk management. The link includes Frequently Asked Questions, as well as resources for specific groups such as parents, caregivers, employers, schools and health care providers.

As global communities responded to COVID-19, we heard from public health officials that the same type of aggregated, anonymized insights we use in products such as Google Maps would be helpful as they made critical decisions to combat COVID-19. These Community Mobility Reports aimed to provide insights into what changed in response to policies aimed at combating COVID-19. The reports charted movement trends over time by geography, across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential.

This story map explores the partnership between the City of Tempe and Arizona State University to study city wastewater for Coronavirus/COVID-19. Featured sections include: What is Coronavirus/COVID-19Analyzing Wastewater DataData-Driven Decision MakingWhat You Can DoFrequently Asked Questions Important Contacts Please also see the Spanish language version.

Read the associated blogpost for a detailed description of how this dataset was prepared; plus extra code for producing animated maps.

Context

The 2019 Novel Coronavirus (COVID-19) continues to spread in countries around the world. This dataset provides daily updated number of reported cases & deaths in Germany on the federal state (Bundesland) and county (Landkreis/Stadtkreis) level. In April 2021 I added a dataset on vaccination progress. In addition, I provide geospatial shape files and general state-level population demographics to aid the analysis.

Content

The dataset consists of thre main csv files: covid_de.csv, demgraphics_de.csv, and covid_de_vaccines.csv. The geospatial shapes are included in the de_state.* files. See the column descriptions below for more detailed information.

• covid_de.csv: COVID-19 cases and deaths which will be updated daily. The original data are being collected by Germany's Robert Koch Institute and can be download through the National Platform for Geographic Data (the latter site also hosts an interactive dashboard). I reshaped and translated the data (using R tidyverse tools) to make it better accessible. This blogpost explains how I prepared the data, and describes how to produces animated maps.

• demographics_de.csv: General Demographic Data about Germany on the federal state level. Those have been downloaded from Germany's Federal Office for Statistics (Statistisches Bundesamt) through their Open Data platform GENESIS. The data reflect the (most recent available) estimates on 2018-12-31. You can find the corresponding table here.

• covid_de_vaccines.csv: In April 2021 I added this file that contains the Covid-19 vaccination progress for Germany as a whole. It details daily doses, broken down cumulatively by manufacturer, as well as the cumulative number of people having received their first and full vaccination. The earliest data are from 2020-12-27.

• de_state.*: Geospatial shape files for Germany's 16 federal states. Downloaded via Germany's Federal Agency for Cartography and Geodesy . Specifically, the shape file was obtained from this link.

Column Description

COVID-19 dataset covid_de.csv:

• state: Name of the German federal state. Germany has 16 federal states. I removed converted special characters from the original data.

• county: The name of the German Landkreis (LK) or Stadtkreis (SK), which correspond roughly to US counties.

• age_group: The COVID-19 data is being reported for 6 age groups: 0-4, 5-14, 15-34, 35-59, 60-79, and above 80 years old. As a shortcut the last category I'm using "80-99", but there might well be persons above 99 years old in this dataset. This column has a few NA entries.

• gender: Reported as male (M) or female (F). This column has a few NA entries.

• date: The calendar date of when a case or death were reported. There might be delays that will be corrected by retroactively assigning cases to earlier dates.

• cases: COVID-19 cases that have been confirmed through laboratory work. This and the following 2 columns are counts per day, not cumulative counts.

• deaths: COVID-19 related deaths.

• recovered: Recovered cases.

Demographic dataset demographics_de.csv:

• state, gender, age_group: same as above. The demographic data is available in higher age resolution, but I have binned it here to match the corresponding age groups in the covid_de.csv file.

• population: Population counts for the respective categories. These numbers reflect the (most recent available) estimates on 2018-12-31.

Vaccination progress dataset covid_de_vaccines.csv:

• date: calendar date of vaccination

• doses, doses_first, doses_second: Daily count of administered doses: total, 1st shot, 2nd shot.

• pfizer_cumul, moderna_cumul, astrazeneca_cumul: Daily cumulative number of administered vaccinations by manufacturer.

• persons_first_cumul, persons_full_cumul: Daily cumulative number of people having received their 1st shot and full vaccination, respectively.

Acknowledgements

All the data have been extracted from open data sources which are being gratefully acknowledged:

• The [Robert ...

On March 10, 2023, the Johns Hopkins Coronavirus Resource Center ceased its collecting and reporting of global COVID-19 data. For updated cases, deaths, and vaccine data please visit: World Health Organization (WHO)For more information, visit the Johns Hopkins Coronavirus Resource Center.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.DOI: https://doi.org/10.6084/m9.figshare.125529863/7/2022 - Adjusted the rate of active cases calculation in the U.S. to reflect the rates of serious and severe cases due nearly completely dominant Omicron variant.6/24/2020 - Expanded Case Rates discussion to include fix on 6/23 for calculating active cases.6/22/2020 - Added Executive Summary and Subsequent Outbreaks sectionsRevisions on 6/10/2020 based on updated CDC reporting. This affects the estimate of active cases by revising the average duration of cases with hospital stays downward from 30 days to 25 days. The result shifted 76 U.S. counties out of Epidemic to Spreading trend and no change for national level trends.Methodology 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.Correction on 6/1/2020Discussion of our assertion of an abundance of caution in assigning trends in rural counties added 5/7/2020. Revisions added on 4/30/2020 are highlighted.Revisions added on 4/23/2020 are highlighted.Executive SummaryCOVID-19 Trends is a methodology for characterizing the current trend for places during the COVID-19 global pandemic. Each day we assign one of five trends: Emergent, Spreading, Epidemic, Controlled, or End Stage to geographic areas to geographic areas based on the number of new cases, the number of active cases, the total population, and an algorithm (described below) that contextualize the most recent fourteen days with the overall COVID-19 case history. Currently we analyze the countries of the world and the U.S. Counties. The purpose is to give policymakers, citizens, and analysts a fact-based data driven sense for the direction each place is currently going. When a place has the initial cases, they are assigned Emergent, and if that place controls the rate of new cases, they can move directly to Controlled, and even to End Stage in a short time. However, if the reporting or measures to curtail spread are not adequate and significant numbers of new cases continue, they are assigned to Spreading, and in cases where the spread is clearly uncontrolled, Epidemic trend.We analyze the data reported by Johns Hopkins University to produce the trends, and we report the rates of cases, spikes of new cases, the number of days since the last reported case, and number of deaths. We also make adjustments to the assignments based on population so rural areas are not assigned trends based solely on case rates, which can be quite high relative to local populations.Two key factors are not consistently known or available and should be taken into consideration with the assigned trend. First is the amount of resources, e.g., hospital beds, physicians, etc.that are currently available in each area. Second is the number of recoveries, which are often not tested or reported. On the latter, we provide a probable number of active cases based on CDC guidance for the typical duration of mild to severe cases.Reasons for undertaking this work in March of 2020: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 15-25 days + 5% from past 26-49 days - total deaths. On 3/17/2022, the U.S. calculation was adjusted to: Active Cases = 100% of new cases in past 14 days + 6% from past 15-25 days + 3% from past 26-49 days - total deaths. Sources: https://www.cdc.gov/mmwr/volumes/71/wr/mm7104e4.htm https://covid.cdc.gov/covid-data-tracker/#variant-proportions If a new variant arrives and appears to cause higher rates of serious cases, we will roll back this adjustment. 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

Abstract

JHU Coronavirus COVID-19 Global Cases, by country

Documentation

PHS is updating the Coronavirus Global Cases dataset weekly, Monday, Wednesday and Friday from Cloud Marketplace.

This data comes from the data repository for the 2019 Novel Coronavirus Visual Dashboard operated by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). This database was created in response to the Coronavirus public health emergency to track reported cases in real-time. The data include the location and number of confirmed COVID-19 cases, deaths, and recoveries for all affected countries, aggregated at the appropriate province or state. It was developed to enable researchers, public health authorities and the general public to track the outbreak as it unfolds. Additional information is available in the blog post.

Visual Dashboard (desktop): https://www.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6

Section 2

Included Data Sources are:

• World Health Organization (WHO): https://www.who.int/
• DXY.cn. Pneumonia. 2020. http://3g.dxy.cn/newh5/view/pneumonia.
• BNO News: https://bnonews.com/index.php/2020/02/the-latest-coronavirus-cases/
• National Health Commission of the People’s Republic of China (NHC): http://www.nhc.gov.cn/xcs/yqtb/list_gzbd.shtml
• China CDC (CCDC): http://weekly.chinacdc.cn/news/TrackingtheEpidemic.htm
• Hong Kong Department of Health: https://www.chp.gov.hk/en/features/102465.html
• Macau Government: https://www.ssm.gov.mo/portal/
• Taiwan CDC: https://sites.google.com/cdc.gov.tw/2019ncov/taiwan?authuser=0
• US CDC: https://www.cdc.gov/coronavirus/2019-ncov/index.html
• Government of Canada: https://www.canada.ca/en/public-health/services/diseases/coronavirus.html
• Australia Government Department of Health: https://www.health.gov.au/news/coronavirus-update-at-a-glance
• European Centre for Disease Prevention and Control (ECDC): https://www.ecdc.europa.eu/en/geographical-distribution-2019-ncov-cases
• Ministry of Health Singapore (MOH): https://www.moh.gov.sg/covid-19
• Italy Ministry of Health: http://www.salute.gov.it/nuovocoronavirus
• 1Point3Arces: https://coronavirus.1point3acres.com/en
• WorldoMeters: https://www.worldometers.info/coronavirus/

%3C!-- --%3E

Section 3

**Terms of Use: **

This GitHub repo and its contents herein, including all data, mapping, and analysis, copyright 2020 Johns Hopkins University, all rights reserved, is provided to the public strictly for educational and academic research purposes. The Website relies upon publicly available data from multiple sources, that do not always agree. The Johns Hopkins University hereby disclaims any and all representations and warranties with respect to the Website, including accuracy, fitness for use, and merchantability. Reliance on the Website for medical guidance or use of the Website in commerce is strictly prohibited.

Section 4

**U.S. county-level characteristics relevant to COVID-19 **

Chin, Kahn, Krieger, Buckee, Balsari and Kiang (forthcoming) show that counties differ significantly in biological, demographic and socioeconomic factors that are associated with COVID-19 vulnerability. A range of publicly available county-specific data identifying these key factors, guided by international experiences and consideration of epidemiological parameters of importance, have been combined by the authors and are available for use:

https://github.com/mkiang/county_preparedness/

Technical and methodological data from the Coronavirus (COVID-19) Infection Survey, England, Wales, Northern Ireland and Scotland.

Dataset no longer updated: Due to changes in the collection and availability of data on COVID-19, this dataset is no longer updated. Latest information about COVID-19 is available via the UKHSA data dashboard. The UK government publish daily data, updated weekly, on COVID-19 cases, vaccinations, hospital admissions and deaths. This note provides a summary of the key data for London from this release. Data are published through the UK Coronavirus Dashboard, last updated on 23 March 2023. This update contains: Data on the number of cases identified daily through Pillar 1 and Pillar 2 testing at the national, regional and local authority level Data on the number of people who have been vaccinated against COVID-19 Data on the number of COVID-19 patients in Hospital Data on the number of people who have died within 28 days of a COVID-19 diagnosis Data for London and London boroughs and data disaggregated by age group Data on weekly deaths related to COVID-19, published by the Office for National Statistics and NHS, is also available. Key Points On 23 March 2023 the daily number of people tested positive for COVID-19 in London was reported as 2,775 On 23 March 2023 it was newly reported that 94 people in London died within 28 days of a positive COVID-19 test The total number of COVID-19 cases identified in London to date is 3,146,752 comprising 15.2 percent of the England total of 20,714,868 cases In the most recent week of complete data (12 March 2023 - 18 March 2023) 2,951 new cases were identified in London, a rate of 33 cases per 100,000 population. This compares with 2,883 cases and a rate of 32 for the previous week In England as a whole, 29,426 new cases were identified in the most recent week of data, a rate of 52 cases per 100,000 population. This compares with 26,368 cases and a rate of 47 for the previous week Up to and including 22 March 2023 6,452,895 people in London had received the first dose of a COVID-19 vaccine and 6,068,578 had received two doses Up to and including 22 March 2023 4,435,586 people in London had received either a third vaccine dose or a booster dose On 22 March 2023 there were 1,370 COVID-19 patients in London hospitals. This compares with 1,426 patients on 15 March 2023. On 22 March 2023 there were 70 COVID-19 patients in mechanical ventilation beds in London hospitals. This compares with 72 patients on 15 March 2023. Update: From 1st July updates are weekly From Friday 1 July 2022, this page will be updated weekly rather than daily. This change results from a change to the UK government COVID-19 Dashboard which will move to weekly reporting. Weekly updates will be published every Thursday. Daily data up to the most recent available will continue to be added in each weekly update. Data summary Local authority data Demographics Notes on data sources Source: UK Coronavirus Dashboard. For more information see: Coronavirus (COVID-19) in the UK - About the Data. Cases Data UK Health Security Agency (UKHSA) reports new and cumulative cases identified by Pillar 1 and Pillar 2 testing. Pillar 1 testing relates to tests carried out in UKHSA laboratories or NHS Hospitals for those with clinical need, and health and care workers. Pillar 2 testing relates to tests carried out on the wider population in Lighthouse laboratories, public, private, and academic sector laboratories or using lateral flow devices. The cases data is published by day for Countries within the UK, and Regions, Upper Tier Local Authority (UTLA) and Lower Tier Local Authority (LTLA) within England. The data used here is taken from the regional and UTLA level cases data. Notice: Changes to COVID-19 case reporting As of 31 January 2022, UKHSA moved all COVID-19 case reporting in England to use an episode-based definition which includes possible reinfections. Those testing positive beyond 90 days of a previous infection are now counted as a separate infection episode (a possible reinfection episode). Previously people who tested positive for COVID-19 were only counted once in case numbers published on the daily dashboard, at the date of the first infection. Full details of the changes can be found here Changes to COVID-19 testing in England The availability of free COVID-19 tests in England changed on 1 April 2022. Information on who can access free tests has been published by UKHSA. Changes to patient testing in the NHS in England have also been published by NHS England. Deaths data Data on COVID-19 associated deaths in England are produced by UKHSA from multiple sources linked to confirmed case data. Deaths are only included if the deceased had a positive test for COVID-19 and died within 28 days of the first positive test. Postcode of residence for deaths is collected at the time of testing. This is supplemented, where available, with information from ONS mortality records, Health Protection Team reports and NHS Digital Patient Demographic Service records. Full details of the methodology are available in the technical summary of the PHE data series on deaths in people with COVID-19. Hospital admissions data UKHSA publish the daily total number of patients admitted to hospital, patients in hospital and patients in beds which can deliver mechanical ventilation with COVID-19. In England this includes COVID-19 patients being treated in NHS acute hospitals, mental health and learning disability trusts, and independent service providers commissioned by the NHS. Vaccination data UKHSA publish the number of people who have received a COVID-19 vaccination, by day on which the vaccine was administered. Data are reported daily and can be updated for historical dates as vaccinations given are recorded on the relevant system. Therefore, data for recent dates may be incomplete. Vaccinations that were carried out in England are reported in the National Immunisation Management Service which is the system of record for the vaccination programme in England. Only people aged 12 and over who have an NHS number and are currently alive are included. Age is defined as a person's age at 31 August 2021. The data includes counts of vaccinations by age band, dose, region, and local authority. Additional analysis of the vaccine roll out in London can be found here. ONS population estimates The counts of vaccines given has been converted to percentage of the population vaccinated using the ONS 2020 mid-year population estimates. This is a different population estimate to that used on the UK Coronavirus Dashboard for sub-national data. The UK Coronavirus Dashboard uses people aged 16 and over in the National Immunisation Management Service (NIMS), which is based on GP registrations. In more urban areas like London, NIMS is likely to give an overestimate of the population due to increased population mobility increasing the likelihood duplicate or out of date GP records. Due to the differences in population estimates the percentage of the population vaccinated given here will be higher than the figures included for London on the UK Coronavirus Dashboard. Data and Resources phe_deaths_age_london.csv Source: https://coronavirus.data.gov.uk/ phe_deaths_london_boroughs.csv Source: https://coronavirus.data.gov.uk/ phe_vaccines_age_london_boroughs.csv

Antibody data, by UK country and age, from the Coronavirus (COVID-19) Infection Survey.

Replaced by http://data.europa.eu/88u/dataset/covid-19-coronavirus-data-daily-up-to-14-december-2020

As of April 26, 2023, the number of both confirmed and presumptive positive cases of the COVID-19 disease reported in the United States had reached over 104 million with over 1.1 million deaths reported among these cases.

Coronavirus deaths by age in the U.S. Daily new cases of COVID-19 hit record highs in the United States at the beginning of 2022. Underlying health conditions can worsen cases of coronavirus, and case fatality rates among confirmed COVID-19 patients increase with age. The highest number of deaths from COVID-19 have been among those aged 85 years and older, with this age group accounting for over 300 thousand deaths.

Where has this coronavirus come from? Coronaviruses are a large group of viruses transmitted between animals and people that cause illnesses ranging from the common cold to more severe diseases. The novel coronavirus that is currently infecting humans was already circulating among certain animal species. The first human case of this new coronavirus strain was reported in China at the end of December 2019. The coronavirus was named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and its associated disease is known as COVID-19.