The table below compares the daily averages for the last seven days to the daily averages for the last 28 days.
Defining Confirmed and Probable Cases and Deaths
COVID-19 cases and deaths are categorized as probable or confirmed.
Learn more about these case definitions.
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 charts below show the daily number of cases, hospitalizations and deaths over the past three months citywide and for each borough. This data includes both confirmed and probable cases and deaths, based on molecular and antigen testing, respectively. Due to delays in reporting, which can take as long as a week, recent data are incomplete.
These data show the percent of people given a molecular test who tested positive, by ZIP code, for the most recent seven days of available data. The borough comparison charts include data by ZIP code from the past three months.
The data also show the rate of people given a molecular test during the most recent seven days. A neighborhood is considered to have adequate testing when at least 260 residents per 100,000 have been tested in the past week. This metric of adequate testing may change depending on future testing data.
This map and table show hospitalization rates and death rates over 28 days. To accommodate standard reporting delays for hospitalization and death data, these are published at a 14-day lag.
This chart show the number of people tested by molecular tests and antigen tests.
These charts show percent positivity and test rate for molecular tests.
These charts show people who visited the emergency department with clinical signs and symptoms consistent with COVID-19 illness (including flu-like illnesses and pneumonia) during the past three months, and those who were then admitted to the hospital. While some of these people did not have a positive molecular or antigen test, these charts can be an early warning sign for community transmission of COVID-19.
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 Time: 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.
Review how we are working to address inequities during this public health emergency (PDF).
See the rest here:
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