The data here show cumulative data on COVID-19 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.
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.
The maps and charts below show confirmed cases, deaths, and percent of people tested by a molecular test with a positive result, by a person's ZIP code of residence.
The number of reported confirmed cases in a ZIP code may be affected by that area’s population size, access to health care and access to testing. For example, an area with a low total case number but a high percent of positive cases could reflect more people with mild symptoms not getting tested.
These data show cumulative confirmed case, hospitalization and death rates by group.
This data shows confirmed case, hospitalization and death rates by age group, race/ethnicity and sex for each of New York City's five boroughs.
Due to the small number of cases among transgender and gender-nonconforming people, data on those cases are not included in this table at this time.
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 data show the number of people tested with antibody tests by ZIP code of residence, the testing rate per 100,000 people in that ZIP code and the percent of people tested who had positive results.
These data show antibody testing rates and percent of people tested who tested positive by group. Breakdowns by race/ethnicity are not available because most laboratory records do not include the patient’s race/ethnicity.
The Health Department reports two types of COVID-19 deaths:
Due to delays in lab results, some deaths initially reported as probable may be changed to confirmed. Also, demographic data on probable deaths are incomplete, as some records do not include this information yet. Data on probable deaths that are missing demographic information are classified as "Data Pending".
Data on people identified as other categories, including Native American/Alaska Native or multi-racial, are not provided here. The Hispanic/Latino category includes people of any race. Race and ethnicity information is most complete for people who are hospitalized or have died. There are much less demographic data currently available for non-hospitalized cases.
Underlying conditions can include lung disease, asthma, heart disease, a weakened immune system, obesity, diabetes, kidney disease, liver disease and cancer.
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.