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

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

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

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

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

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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

Purpose: The coronavirus disease 2019 (COVID-19) outbreak, which began in December 2019, has not been completely controlled; therefore, COVID-19 has received much attention from countries around the world. Many related clinical studies, such as clinical trials, have been published, but to the knowledge of the authors, there has been no bibliometric analysis of these publications focusing on clinical research studies on COVID-19.Methods: Global publications on COVID-19 from January 2020 to December 2020 were extracted from the Web of Science (WOS) collection database. The VOSviewer software and CiteSpace were employed to perform a bibliometric study. In addition, we obtained information on relevant clinical trials from the website http://clinicaltrials.gov.Results: China published most of the articles in this field and had the highest number of citations and H-index. The Journal of Medical Virology published most of the articles related to COVID-19. In terms of institutions, Huazhong University of Science and Technology had the most publications, and Wang, JW received the highest number of citations.Conclusion: The diagnosis, prevention, and prognosis of COVID-19 are still the focus of attention at present. The overall analysis of the disease were identified as the emerging topics from the perspectives of epidemiology and statistics. However, finding an effective treatment remains the focus of clinical trials.

As of March 10, 2023, the state with the highest number of COVID-19 cases was California. Almost 104 million cases have been reported across the United States, with the states of California, Texas, and Florida reporting the highest numbers.

From an epidemic to a pandemic The World Health Organization declared the COVID-19 outbreak a pandemic on March 11, 2020. The term pandemic refers to multiple outbreaks of an infectious illness threatening multiple parts of the world at the same time. When the transmission is this widespread, it can no longer be traced back to the country where it originated. The number of COVID-19 cases worldwide has now reached over 669 million.

The symptoms and those who are most at risk Most people who contract the virus will suffer only mild symptoms, such as a cough, a cold, or a high temperature. However, in more severe cases, the infection can cause breathing difficulties and even pneumonia. Those at higher risk include older persons and people with pre-existing medical conditions, including diabetes, heart disease, and lung disease. People aged 85 years and older have accounted for around 27 percent of all COVID-19 deaths in the United States, although this age group makes up just two percent of the U.S. population

As of March 10, 2023, the state with the highest rate of COVID-19 cases was Rhode Island followed by Alaska. Around 103.9 million cases have been reported across the United States, with the states of California, Texas, and Florida reporting the highest numbers of infections.

From an epidemic to a pandemic The World Health Organization declared the COVID-19 outbreak as a pandemic on March 11, 2020. The term pandemic refers to multiple outbreaks of an infectious illness threatening multiple parts of the world at the same time; when the transmission is this widespread, it can no longer be traced back to the country where it originated. The number of COVID-19 cases worldwide is roughly 683 million, and it has affected almost every country in the world.

The symptoms and those who are most at risk Most people who contract the virus will suffer only mild symptoms, such as a cough, a cold, or a high temperature. However, in more severe cases, the infection can cause breathing difficulties and even pneumonia. Those at higher risk include older persons and people with pre-existing medical conditions, including diabetes, heart disease, and lung disease. Those aged 85 years and older have accounted for around 27 percent of all COVID deaths in the United States, although this age group makes up just two percent of the total population

The unique count is the unique number of groups flagged, and the total count is the total number of groups flagged (may be flagged more than once) across a 15-week semester. Using time window k, LS,k and LH,k are the likelihoods of a hotspot group for sections and housing, respectively. The relative risk of the two groups RRk (from Eq 8) is also shown with a 95% confidence interval (CI).

Attributes of prospective space-time clusters (hotspots) for COVID-19 from 1/23-5/20/2020 at the county level.

COVID-19 Cases in England

Tracking Infection Risk at a Local Level

By Dan Winchester [source]

About this dataset

This dataset contains the total number of confirmed COVID-19 cases in each English Upper Tier Local Authority over the past eight days. Aggregated from Public Health England data, this dataset provides unprecedented insight into how quickly the virus has been able to spread in local communities throughout England. Despite testing limitations, understanding these localized patterns of infection can help inform important public health decisions by local authorities and healthcare workers alike.

It is essential to bear in mind that this data is likely an underestimation of true infection rates due to limited testing -- it is critical not to underestimate the risk the virus poses on a local scale! Use this dataset at your own discretion with caution and care; consider supplementing it with other health and socio-economic metrics for a holistic picture of regional trends over time.

This dataset features information surrounding GSS codes and names as well as total numbers of recorded COVID-19 cases per English Upper Tier Local Authority on January 5th 2023 (TotalCases_2023-01-05)

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For more datasets, click here.

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Research Ideas

• Comparing the total cases in each local authority to population density of the region, to identify areas with higher incidence of virus
• Tracking changes in total cases over a period of time to monitor trend shifts and detect possible outbreak hotspots
• Establishing correlations between the spread of COVID-19 and other non-coronavirus related health issues, such as mental health or cardiovascular risk factors

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: utla_by_day.csv | Column name | Description | |:--------------------------|:------------------------------------------------------------------------------------------------------| | GSS_CD | Government Statistical Service code for the local authority. (String) | | GSS_NM | Name of the local authority. (String) | | TotalCases_2023-01-05 | Total number of confirmed COVID-19 cases in the local authority on the 5th of January 2023. (Integer) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Dan Winchester.

Authoritative COVID-19 information and not violating patient rights—Can we do both?With the coronavirus disease 2019 (COVID-19) pandemic now affecting people everywhere on the globe, the sheer scale means we are facing problems we have never before faced in modern times. Health authorities are more than ever in need of authoritative information like where current and upcoming hotspots are in order to decide on how best to prepare and respond. To get this information, collection of actual, accurate, authentic, and location-based patient information is a must. However, legislation to protect citizens’ rights puts restrictions on how and what data collection, analysis, and dissemination of personal information can be done. So, we have a situation where health authorities need good, reliable patient data but face difficulties in obtaining, processing, and distributing it. The challenge we have is how to collect, analyze, and disseminate localized patient information while at the same time ensuring that we protect the individual’s rights._Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...

Description: The COVID-19 dataset used for this EDA project encompasses comprehensive data on COVID-19 cases, deaths, and recoveries worldwide. It includes information gathered from authoritative sources such as the World Health Organization (WHO), the Centers for Disease Control and Prevention (CDC), and national health agencies. The dataset covers global, regional, and national levels, providing a holistic view of the pandemic's impact.

Purpose: This dataset is instrumental in understanding the multifaceted impact of the COVID-19 pandemic through data exploration. It aligns perfectly with the objectives of the EDA project, aiming to unveil insights, patterns, and trends related to COVID-19. Here are the key objectives: 1. Data Collection and Cleaning: • Gather reliable COVID-19 datasets from authoritative sources (such as WHO, CDC, or national health agencies). • Clean and preprocess the data to ensure accuracy and consistency. 2. Descriptive Statistics: • Summarize key statistics: total cases, recoveries, deaths, and testing rates. • Visualize temporal trends using line charts, bar plots, and heat maps. 3. Geospatial Analysis: • Map COVID-19 cases across countries, regions, or cities. • Identify hotspots and variations in infection rates. 4. Demographic Insights: • Explore how age, gender, and pre-existing conditions impact vulnerability. • Investigate disparities in infection rates among different populations. 5. Healthcare System Impact: • Analyze hospitalization rates, ICU occupancy, and healthcare resource allocation. • Assess the strain on medical facilities. 6. Economic and Social Effects: • Investigate the relationship between lockdown measures, economic indicators, and infection rates. • Explore behavioral changes (e.g., mobility patterns, remote work) during the pandemic. 7. Predictive Modeling (Optional): • If data permits, build simple predictive models (e.g., time series forecasting) to estimate future cases.

Data Sources: The primary sources of the COVID-19 dataset include the Johns Hopkins CSSE COVID-19 Data Repository, Google Health’s COVID-19 Open Data, and the U.S. Economic Development Administration (EDA). These sources provide reliable and up-to-date information on COVID-19 cases, deaths, testing rates, and other relevant variables. Additionally, GitHub repositories and platforms like Medium host supplementary datasets and analyses, enriching the available data resources.

Data Format: The dataset is available in various formats, such as CSV and JSON, facilitating easy access and analysis. Before conducting the EDA, the data underwent preprocessing steps to ensure accuracy and consistency. Data cleaning procedures were performed to address missing values, inconsistencies, and outliers, enhancing the quality and reliability of the dataset.

License: The COVID-19 dataset may be subject to specific usage licenses or restrictions imposed by the original data sources. Proper attribution is essential to acknowledge the contributions of the WHO, CDC, national health agencies, and other entities providing the data. Users should adhere to any licensing terms and usage guidelines associated with the dataset.

Attribution: We acknowledge the invaluable contributions of the World Health Organization (WHO), the Centers for Disease Control and Prevention (CDC), national health agencies, and other authoritative sources in compiling and disseminating the COVID-19 data used for this EDA project. Their efforts in collecting, curating, and sharing data have been instrumental in advancing our understanding of the pandemic and guiding public health responses globally.

Overview

As coronavirus cases have exploded across the country, states have struggled to obtain sufficient personal protective equipment such as masks, face shields, gloves and ventilators to meet the needs of healthcare workers. FEMA began distributing PPE from the national stockpile as well as PPE obtained from private manufacturers to states in March.

Initially, FEMA distributed materials based primarily on population. By late March, Its methods changed to send more PPE to hotspot locations, and FEMA claimed these decisions were data-driven and need-based. By late spring, the agency was considering requests from states as well.

Although all U.S. states and territories have received some amount of PPE from FEMA, the amounts of PPE states have per capita and per positive COVID-19 case vary widely.

The AP used this data in a story that ran July 7.

Findings

• Overall, low population, rural states have the most PPE per positive case as of mid-June. This generally held true across types of equipment.
• The states that had the highest number of total PPE items per coronavirus case as of mid-May were, in descending order: Alaska, Montana, Vermont, Hawaii, Wyoming, and North Dakota. The highest was Alaska with 1,579 PPE items per coronavirus case.
• The states that had the highest number of total items per case as of mid-June were largely the same states — Montana, Alaska, Hawaii, Vermont, Wyoming, and West Virginia. The highest was Montana with 1,125 PPE items per coronavirus case.
• Conversely, the states that had the lowest amounts of PPE per positive case in mid-May included hotspot states — Massachusetts, New York, Virginia, California, Nebraska, and Iowa. New Jersey was just a couple spots further down. The lowest was Massachusetts with 36 PPE items per coronavirus case.
• The states that had the lowest amounts of PPE per case as of mid-June were largely the same as well — Massachusetts, New York, Iowa, California, and Nebraska. The lowest was Massachusetts with 32 PPE items per coronavirus case.
• When evaluated on a per-capita basis rather than per positive coronavirus case, the picture is different. The District of Columbia received the most PPE per capita in both May and June, although the vast majority of the PPE it received was distributed as of mid-May. Vermont, Kansas, New Jersey, and North Dakota had the next highest numbers of PPE per capita as of both mid-May and mid-June.
• There is no clear pattern of FEMA distribution by party control of states.

About the data

These numbers include material distributed by FEMA and also those sold by private distributors under direction from FEMA. They include materials both delivered to and en route to states.

States have purchased PPE directly in addition to receiving PPE from FEMA or directed there by the agency, and this data only includes the latter categories.

FEMA also distributed and directed the distribution of gear to U.S. territories in addition to states, which are included in FEMA’s release linked below, but not are not included in this data.

FEMA has publicly distributed its breakdown of PPE delivery by state for May and June. FEMA did not provide comprehensive numbers for each state before May.

These numbers are cumulative, meaning that the numbers for May include items of PPE distributed prior to May 14, dating to when the agency began allocations on March 1. The June numbers include the May numbers and any new PPE distributions since then.

The population column, which was used to calculate the numbers of PPE items per state, came from data from the U.S Census Bureau. Since the Census releases annual population data, population data from 2019 was used for each state.

The numbers of coronavirus cases were pulled from the data released daily by Johns Hopkins University as of the dates that FEMA released its distribution numbers — May 14 and June 10.

Caveats

The data includes amounts of gear that had been delivered to the states or were en route as of the reporting dates.

All PPE item numbers above 1 million were rounded to the nearest hundred thousand by FEMA, but numbers lower than that were not rounded.

In some cases, gear headed to a state was rerouted because it was needed more somewhere else or a state decided it did not need it. In some instances, that resulted in states having higher numbers for certain supplies in May than in June.

The dataset includes all data collected and analysed for the study on "Physical distancing and risk of COVID-19 in small-scale fisheries: A remote sensing assessment in coastal Ghana." The novel coronavirus is predicted to have dire implications on global food systems including fisheries value chains due to restrictions imposed on human movements in many countries. In Ghana, food production, both agriculture and fisheries, is exempted from restrictions as an essential service. We employed an Unmanned Aerial Vehicle (UAV) in assessing the risk of artisanal fishers to the pandemic using physical distancing as a proxy. From analysis of cumulative distribution function (G-function) of the nearest-neighbour distances (NND), this study underscored crowding at all surveyed fish landing beaches and identified potential “hotspots” for disease transmission. Aerial images were obtined. The locations of people in orthomosaic images were manually extracted as point data in ESRI ArcMap v.10.3 using the editor tool. From the point data, the distance from each point to the nearest other point, that is the nearest-neighbour distance (NND), was measured for all individuals presents in each of the six landing beaches in this study. The median distances were compared to the World Health Organisation (WHO) and Centre for Disease Control (CDC) standards on physical (social) distancing.

Discover the latest trends in the skincare industry and learn how it's projected to reach $180 billion by 2024. Explore the key drivers, geographical hotspots, and how COVID-19 has impacted the industry. Stay on top of your skincare game with this informative article.

Sum of variable importances for all levels of a given categorical type.

Simulation scenarios studied using three levels of the number of courses spiked and two levels of the spike factor.

Number of problematic courses found, false discovery rate and sensitivity for competing FDR methods when the spike factor equals 4. “FDR (33%)” and “Sensitivity (33%)” describe the performance of FDR methods among courses with at least 33% of students spiked. Numbers that are italicized indicate a significant difference (0.001 level) from the SimBa method using a t-test.

Comparison of users never vs. ever tested SARS-CoV-2 positive.

The COVID-19 pandemic resulting from the spread of SARS-CoV-2 spurred devastating health and economic crises around the world. Neutralizing antibodies and licensed vaccines were developed to combat COVID-19, but progress was slow. In addition, variants of the receptor-binding domain (RBD) of the spike protein confer resistance of SARS-CoV-2 to neutralizing antibodies, nullifying the possibility of human immunity. Therefore, investigations into the RBD mutations that disrupt neutralization through convalescent antibodies are urgently required. In this study, we comprehensively and systematically investigated the binding stability of RBD variants targeting convalescent antibodies and revealed that the RBD residues F456, F490, L452, L455, and K417 are immune-escaping hotspots, and E484, F486, and N501 are destabilizing residues. Our study also explored the possible modes of actions of emerging SARS-CoV-2 variants. All results are consistent with experimental observations of attenuated antibody neutralization and clinically emerging SARS-CoV-2 variants. We identified possible immune-escaping hotspots that could further promote resistance to convalescent antibodies. The results provide valuable information for developing and designing novel monoclonal antibody drugs to combat emerging SARS-CoV-2 variants.

Hypothesized risk indicators informing the transmission scenarios, their rationale for inclusion, description and sources. Original rasters were warped to 0.25 decimal degrees and World Geodetic System (WGS 84). Complete data description available in Table S2. from Muylaert et al.

The COVID-19 epidemic in December 2019 had a significant negative impact on people’s health and economies all across the world. The most effective preventive measure against COVID-19 is vaccination. Therefore, the development and production of COVID-19 vaccines is booming worldwide. This study aimed to analyze the current state of that research and its development tendency by bibliometrics. We conducted a thorough search of the Web of Science Core Collection. VOSviewer1.6.18 was used to perform the bibliometric analysis of these papers. A total of 6,325 papers were finally included. The USA maintained a top position worldwide. Shimabukuro Tom T and Harvard University were the most prolific author and institution. The Vaccines was the most published journal. The research hotspots of COVID-19 vaccines can be classified into vaccine hesitancy, vaccine safety and effectiveness, vaccine immunogenicity, and adverse reactions to vaccines. Studies on various vaccination types have also concentrated on efficacy against continuously developing virus strains, immunogenicity, side effects, and safety.