The data here show COVID-19 trends in New York City since the city’s first confirmed case was diagnosed on February 29, 2020. You can also download our data and technical notes on Github.
These charts shows the daily number of confirmed and probable COVID-19 cases by diagnosis date, hospitalizations by admission date and deaths by date of death. Due to delays in reporting, which can take as long as a week, recent data are incomplete.
Defining Confirmed and Probable Cases and Deaths
COVID-19 cases and deaths are categorized as probable or confirmed.
Types of Tests
Cases are defined differently based on the type of test used to detect COVID-19.
Molecular tests, such as PCR tests, are the most reliable way to test for COVID-19. Someone who tests positive for the virus with a molecular test is classified as a confirmed case. These tests look for genetic material from the virus that causes COVID-19 (SARS-CoV-2). Unless otherwise specified, data on test counts, test rates and percent positivity only reflects molecular testing.
Antigen tests are faster than molecular tests but can be less accurate. These tests look for proteins on the surface of the SARS-CoV-2 virus. Someone who tests positive with an antigen test is classified as a probable case.
Antibody tests check the blood for signs that you have had the virus in the past. An antibody test may not be accurate for someone with active or recent infection. Someone who tests positive with only an antibody test — and not a diagnostic test — is not classified as a probable or confirmed case.
These tables show monthly hospitalization and death rates, by ZIP Code, for the last 6 months. Download data going back to March 2020.
These charts show the number of people tested by molecular tests and antigen tests each day since the start of the pandemic in NYC.
From March to early May in 2020, we discouraged people with mild and moderate symptoms from being tested, so our data from that period represent mostly people with severe illness.
There is a turnaround time between when a person receives a test and when the result is reported to the Health Department. This turnaround time can depend on whether a person is tested at a hospital or at an outpatient clinic, the laboratory used and the total volume of testing locally and nationally, among other factors.
Recently, most results have been reported within two days. The below charts show data from molecular tests only.
An antibody test can show if you have ever had the virus, but it does not show if you currently are infected.
It is not yet clear whether testing positive for antibodies provides long-term protection from COVID-19.
These charts show the number of people tested with antibody tests, the number of people who tested positive and the percent of people tested who had positive results for the week ending on the listed date.
About the Data: All of the data on these pages were collected by the NYC Health Department. Data will be updated daily but are preliminary and subject to change.
Reporting Lag: Our data are published with a three-day lag, meaning that the most recent data in today's update are from three days before.
This lag is due to the standard delays (up to several days) in reporting to the Health Department a new test, case, hospitalization or death. Given the delay, our counts of what has happened in the most recent few days are artificially small. We delay publishing these data until more reports have come in and the data are more complete.
Health Inequities in Data: Differences in health outcomes among racial and ethnic groups are due to long-term structural racism, not biological or personal traits.
Structural racism — centuries of racist policies and discriminatory practices across institutions, including government agencies, and society — prevents communities of color from accessing vital resources (such as health care, housing and food) and opportunities (such as employment and education), and negatively affects overall health and well-being. The disproportionate impact of COVID-19 on New Yorkers of color highlights how these inequities negatively influence health outcomes.