As of December 22, 2022, there have been 2.6 million cases of COVID-19 in New York City, as well as 200,189 hospitalizations, and 37,452 deaths. This statistic shows the number of COVID-19 cases, hospitalizations, and deaths in New York City as of December 22, 2022.

Daily count of NYC residents who tested positive for SARS-CoV-2, who were hospitalized with COVID-19, and deaths among COVID-19 patients.

Note that this dataset currently pulls from https://raw.githubusercontent.com/nychealth/coronavirus-data/master/case-hosp-death.csv on a daily basis.

As of March 6, 2021, there have been around 39.7 million tests for COVID-19 in the state of New York, leading to almost 1.7 million positive cases. New York has been one of the hardest hit U.S. states by the COVID-19 pandemic and accounts for a high amount of cases in the U.S. This statistic shows the cumulative number of COVID-19 tests, cases, hospitalizations, and deaths in New York as of March 6, 2021.

View daily updates and historical trends for New York Coronavirus Cases Currently Hospitalized. Source: US Department of Health & Human Services. Track ec…

Note: This dataset was archived on 10/6/23. Statewide hospitalization data is available in the New York State Statewide COVID-19 Hospitalizations and Beds dataset.

This dataset includes the number of patients hospitalized, and number of patients in the intensive care unit (ICU) among patients with lab-confirmed COVID-19 disease by hospital region and reporting date. The primary goal of publishing this dataset is to provide users with timely information about hospitalizations among patients with lab-confirmed COVID-19 disease.

The data source for this dataset is the daily COVID-19 survey through the New York State Department of Health (NYSDOH) Health Electronic Response Data System (HERDS). Hospitals are required to complete this survey daily and data reflects the number of patients hospitalized and number of patients in the ICU reported by hospitals through the survey each day. These data include NYS resident and non-NYS resident hospitalizations. The information from the survey is used for statewide surveillance, planning, resource allocation, and emergency response activities. Hospitals began reporting for the HERDS COVID-19 survey in mid-March 2020.

To calculate regional totals, the number of patients hospitalized and number of patients in the ICU are each summed by hospital region and reporting date.

The information in this dataset is updated daily on NY Forward; New York State’s resource for COVID-19 testing, early warning monitoring, and regional daily hospitalization dashboards. More information can be found at forward.ny.gov.

NYC Coronavirus (COVID-19) data

This repository contains data on coronavirus (COVID-19) in New York City (NYC), updated daily. Data are assembled by the NYC Department of Health and Mental Hygiene (DOHMH) Incident Command System for COVID-19 Response (Surveillance and Epidemiology Branch in collaboration with Public Information Office Branch). You can view these data on the Department of Health's website. Note that data are being collected in real-time and are preliminary and subject to change as COVID-19 response continues.

Files summary.csv This file contains summary information, including when the dataset was "cut" - the cut-off date and time for data included in this update.

Estimated hospitalization counts reflect the total number of people ever admitted to a hospital, not currently admitted.

case-hosp-death.csv This file includes daily counts of new confirmed cases, hospitalizations, and deaths.

Cases are by date of diagnosis Hospitalizations are by date of admission Deaths are by date of death Because of delays in reporting, the most recent data may be incomplete. Data shown currently will be updated in the future as new cases, hospitalizations, and deaths are reported.

boro.csv This contains rates of confirmed cases, by NYC borough of residence. Rates are:

Cumulative since the start of the outbreak Age adjusted according to the US 2000 standard population Per 100,000 people in the borough by-age.csv This contains age-specific rates of confirmed cases, hospitalizations, and deaths.

by-sex.csv This contains rates of confirmed cases, hospitalizations, and deaths.

testing.csv This file includes counts of New York City residents with specimens collected for SARS-CoV-2 testing by day, the subsets who tested positive as confirmed COVID-19 cases, were ever hospitalized, and who died, as of the date of extraction from the NYC Health Department's disease surveillance database. For each date of extraction, results for all specimen collection dates are appended to the bottom of the dataset. Lags between specimen collection date and report dates of cases, hospitalizations, and deaths can be assessed by comparing counts for the same specimen collection date across multiple data extract dates.

tests-by-zcta.csv This file includes the cumulative count of New York City residents by ZIP code of residence who:

Were ever tested for COVID-19 (SARS-CoV-2) Tested positive The cumulative counts are as of the date of extraction from the NYC Health Department's disease surveillance database. Technical Notes This section may change as data and documentation are updated.

Estimated number of COVID-19 patients ever hospitalized At this time, NYC DOHMH does not have the ability to robustly quantify the number of people currently admitted to a hospital given intense resource and time constraints on hospital reporting systems. Therefore, we have estimated the number of individuals diagnosed with COVID-19 who have ever been hospitalized by matching the list of key fields from known cases that are reported by laboratories to the NYC DOHMH Bureau of Communicable Disease surveillance database to other sources of hospital admission information. These other sources include:

The NYC DOHMH syndromic surveillance database that tracks daily hospital admissions from all 53 emergency departments across NYC The New York State Department of Health Hospital Emergency Response Data System (HERDS). Rates per 100,000 people Annual citywide, borough-specific, and demographic specific intercensal population estimates from 2018 were developed by NYC DOHMH on the basis of the US Census Bureau’s Population Estimates Program, as of November 2019.

Rates of cases at the borough-level were calculated using direct standardization for age at diagnosis and weighting by the US 2000 standard population.

https://github.com/nychealth/coronavirus-data/blob/master/README.md

In the state of New York, there have been 89,995 hospitalizations due to COVID-19 as of June 21, 2020. This statistic shows the cumulative number of hospitalizations due to COVID-19 in New York State from March 21 to June 21, 2020, by day.

Note: This dataset has been archived as of 10/7/25, as HERDS COVID hospitalization shifts from a daily to a weekly reporting cadence.

A new weekly dataset is now available: New York State Statewide Weekly COVID-19 Hospitalizations and Fatalities

This archived dataset includes information at the reporting facility level on patients hospitalized, admitted, discharged and fatalities. It also includes information on staffed beds. Patient information collected as part of the HERDS Hospital Survey are lab-confirmed COVID-19 positive. Hospitalized means patients admitted as inpatients in either inpatient or observation beds and does not include patients that were treated and released from an Emergency Department. The title of this dataset was initially the Hospital Electronic Response Data System (HERDS) Hospital Survey: COVID-19 Hospitalizations and Beds. The dataset was changed to its current title on 11/4/2021.

This dataset includes information at the reporting facility level on patients hospitalized, admitted, discharged and fatalities. It also includes information on staffed beds. Patient information collected as part of the HERDS Hospital Survey are lab-confirmed COVID-19 positive. Hospitalized means patients admitted as inpatients in either inpatient or observation beds and does not include patients that were treated and released from an Emergency Department. The title of this dataset was initially the Hospital Electronic Response Data System (HERDS) Hospital Survey: COVID-19 Hospitalizations and Beds. The dataset was changed to its current title on 11/4/2021.

This dataset includes information at the reporting facility level on patients hospitalized, admitted, discharged and fatalities. It also includes information on staffed beds. Patient information collected as part of the HERDS Hospital Survey are lab-confirmed COVID-19 positive. Hospitalized means patients admitted as inpatients in either inpatient or observation beds and does not include patients that were treated and released from an Emergency Department. The title of this dataset was initially the Hospital Electronic Response Data System (HERDS) Hospital Survey: COVID-19 Hospitalizations and Beds. The dataset was changed to its current title on 11/4/2021.

The dataset shows outcomes (confirmed cases, hospitalizations, and deaths) for cohorts defined by each date of specimen collection (specimen_date). For example, if a NYC resident tested positive for SARS-CoV-2 and was subsequently hospitalized, both events would show under the same specimen_date, indicating the date of specimen collection for the positive test and not the date of the hospitalization. For a comparable dataset showing diagnosis dates for confirmed cases, admission dates for hospitalized patients, and death dates for decedents, see https://data.cityofnewyork.us/Health/COVID-19-Daily-Counts-of-Cases-Hospitalizations-an/rc75-m7u3

IntroductionOur study explores how New York City (NYC) communities of various socioeconomic strata were uniquely impacted by the COVID-19 pandemic.MethodsNew York City ZIP codes were stratified into three bins by median income: high-income, middle-income, and low-income. Case, hospitalization, and death rates obtained from NYCHealth were compared for the period between March 2020 and April 2022.ResultsCOVID-19 transmission rates among high-income populations during off-peak waves were higher than transmission rates among low-income populations. Hospitalization rates among low-income populations were higher during off-peak waves despite a lower transmission rate. Death rates during both off-peak and peak waves were higher for low-income ZIP codes.DiscussionThis study presents evidence that while high-income areas had higher transmission rates during off-peak periods, low-income areas suffered greater adverse outcomes in terms of hospitalization and death rates. The importance of this study is that it focuses on the social inequalities that were amplified by the pandemic.

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

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.

This dataset tracks the updates made on the dataset "New York Forward COVID-19 Daily Hospitalization Summary by Region (Archived)" as a repository for previous versions of the data and metadata.

This dataset shows the COVID-19 outcomes by testing cohorts. It shows the cases, hospitalizations and Deaths in the NYC (New York City). The data is provided by the Department of Health and Mental Hygiene (DOHMH).

Background: In face of the continuing worldwide COVID-19 epidemic, how to reduce the transmission risk of COVID-19 more effectively is still a major public health challenge that needs to be addressed urgently.Objective: This study aimed to develop an age-structured compartment model to evaluate the impact of all diagnosed and all hospitalized on the epidemic trend of COVID-19, and explore innovative and effective releasing strategies for different age groups to prevent the second wave of COVID-19.Methods: Based on three types of COVID-19 data in New York City (NYC), we calibrated the model and estimated the unknown parameters using the Markov Chain Monte Carlo (MCMC) method.Results: Compared with the current practice in NYC, we estimated that if all infected people were diagnosed from March 26, April 5 to April 15, 2020, respectively, then the number of new infections on April 22 was reduced by 98.02, 93.88, and 74.08%. If all confirmed cases were hospitalized from March 26, April 5, and April 15, 2020, respectively, then as of June 7, 2020, the total number of deaths in NYC was reduced by 67.24, 63.43, and 51.79%. When only the 0–17 age group in NYC was released from June 8, if the contact rate in this age group remained below 61% of the pre-pandemic level, then a second wave of COVID-19 could be prevented in NYC. When both the 0–17 and 18–44 age groups in NYC were released from June 8, if the contact rates in these two age groups maintained below 36% of the pre-pandemic level, then a second wave of COVID-19 could be prevented in NYC.Conclusions: If all infected people were diagnosed in time, the daily number of new infections could be significantly reduced in NYC. If all confirmed cases were hospitalized in time, the total number of deaths could be significantly reduced in NYC. Keeping a social distance and relaxing lockdown restrictions for people between the ages of 0 and 44 could not lead to a second wave of COVID-19 in NYC.

To assess the vaccine effectiveness for community-dwelling New York City (NYC) residents following age-based SARS-CoV-2 vaccine eligibility.

This dataset includes information at the report date level on patients admitted for inpatient care to the hospital that are lab-confirmed COVID-19 positive. Admitted means that the patient was newly admitted to the hospital or was confirmed positive after admission. Zip Code information became available for COVID-19 admissions as of May 2, 2020. Hospitalized means patients admitted as inpatients in either inpatient or observation beds and does not include patients that were treated and released from an Emergency Department. The title of this dataset was initially the Hospital Electronic Response Data System (HERDS) Hospital Survey: COVID-19 Admissions by Zip Code. The dataset was changed to its current title on 11/4/2021.

Characteristics and outcomes of hospitalized patients.