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/trends/data-by-day.csv on a daily basis.

On December 19, 2022, there were 3,553 new cases of COVID-19 in New York City. The state of New York has been one of the hardest hit U.S. states by the COVID-19 pandemic. This statistic shows the number of new COVID-19 cases in New York City from March 8, 2020 to December 19, 2022, by diagnosis date.

This dataset tracks the updates made on the dataset "New York State Statewide COVID-19 Fatalities by Age Group" as a repository for previous versions of the data and metadata.

As of March 7, 2021, there have been 39,029 deaths due to COVID-19 in the state of New York, with the first 3 deaths reported on March 14, 2020. This statistic shows the cumulative number of deaths related to COVID-19 in New York State from March 14 to March 7, 2021, by day.

This dataset tracks the updates made on the dataset "New York State Statewide COVID-19 Vaccination Data by County (Archived, 2023-2024)" as a repository for previous versions of the data and metadata.

As of December 16, 2022, there had been almost 6.37 million COVID-19 cases in New York State, with 2.97 million cases found in New York City. New York has been one of the U.S. states most impacted by the pandemic, recording the highest number of deaths in the country.

A closer look at the outbreak in New York Towards the middle of December 2022, the number of deaths due to the coronavirus in New York State had reached almost 60 thousand, and almost half of those deaths were in New York City. However, the number of new daily deaths in New York City peaked early in the pandemic and although there have been times when the number of new daily deaths surged, they have not gotten close to reaching the levels seen at the beginning of the pandemic. New York City is made up of five counties, which are more commonly known by their borough names – Staten Island is the borough with the highest rate of COVID-19 cases.

These COVID data were compiled from the New York City collection of COVID data. The data span March - October 2020, and are compiled at the ZIP code level. https://www1.nyc.gov/site/doh/covid/covid-19-data.page

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 the first reported coronavirus case in Washington State on Jan. 21, 2020, 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.

On April 7, 2020, there were 598 new deaths due to COVID-19 in New York City, higher than any other day since the pandemic hit the city. The state of New York has been one of the hardest hit U.S. states by the COVID-19 pandemic. This statistic shows the number of new COVID-19 deaths in New York City from March 3, 2020 to December 19, 2022, by date.

Aggregation of all city-funded encumbrances coded by agencies as being related to the COVID-19 pandemic, allocated to FMS defined expense types. This data was collected from March 10, 2020 through June 29, 2021 and is no longer being updated.

This dataset tracks the updates made on the dataset "New York State Statewide COVID-19 Testing by Age Group (Archived)" as a repository for previous versions of the data and metadata.

This dashboard provides a snapshot of several key measurements of COVID-19's impact on New York City, including total cases, case growth, hospitalizations, deaths, and the distribution of positive COVID-19 tests across the City by ZIP Code.The dashboard features data maintained by the New York City Department of Health and Mental Hygiene (DOHMH) and published for public use at https://github.com/nychealth/coronavirus-data. Please consult the README file for data definitions and notes on proper use and interpretation.DOHMH updates data in this repository on a daily basis, but not all datasets are updated every day.

This dataset contains information on antibody testing for COVID-19: the number of people who received a test, the number of people with positive results, the percentage of people tested who tested positive, and the rate of testing per 100,000 people, stratified by modified ZIP Code Tabulation Area (ZCTA) of residence. Modified ZCTA reflects the first non-missing address within NYC for each person reported with an antibody test result. This unit of geography is similar to ZIP codes but combines census blocks with smaller populations to allow more stable estimates of population size for rate calculation. It can be challenging to map data that are reported by ZIP Code. A ZIP Code doesn’t refer to an area, but rather a collection of points that make up a mail delivery route. Furthermore, there are some buildings that have their own ZIP Code, and some non-residential areas with ZIP Codes. To deal with the challenges of ZIP Codes, the Health Department uses ZCTAs which solidify ZIP codes into units of area. Often, data reported by ZIP code are actually mapped by ZCTA. The ZCTA geography was developed by the U.S. Census Bureau. These data can also be accessed here: https://github.com/nychealth/coronavirus-data/blob/master/totals/antibody-by-modzcta.csv Exposure to COVID-19 can be detected by measuring antibodies to the disease in a person’s blood, which can indicate that a person may have had an immune response to the virus. Antibodies are proteins produced by the body’s immune system that can be found in the blood. People can test positive for antibodies after they have been exposed, sometimes when they no longer test positive for the virus itself. It is important to note that the science around COVID-19 antibody tests is evolving rapidly and there is still much uncertainty about what individual antibody test results mean for a single person and what population-level antibody test results mean for understanding the epidemiology of COVID-19 at a population level.
These data only provide information on people tested. People receiving an antibody test do not reflect all people in New York City; therefore, these data may not reflect antibody prevalence among all New Yorkers. Increasing instances of screening programs further impact the generalizability of these data, as screening programs influence who and how many people are tested over time. Examples of screening programs in NYC include: employers screening their workers (e.g., hospitals), and long-term care facilities screening their residents.

In addition, there may be potential biases toward people receiving an antibody test who have a positive result because people who were previously ill are preferentially seeking testing, in addition to the testing of persons with higher exposure (e.g., health care workers, first responders)

Rates were calculated using interpolated intercensal population estimates updated in 2019. These rates differ from previously reported rates based on the 2000 Census or previous versions of population estimates. The Health Department produced these population estimates based on estimates from the U.S. Census Bureau and NYC Department of City Planning.

Antibody tests are categorized based on the date of specimen collection and are aggregated by full weeks starting each Sunday and ending on Saturday. For example, a person whose blood was collected for antibody testing on Wednesday, May 6 would be categorized as tested during the week ending May 9. A person tested twice in one week would only be counted once in that week. This dataset includes testing data beginning April 5, 2020.

Data are updated daily, and the dataset preserves historical records and source data changes, so each extract date reflects the current copy of the data as of that date. For example, an extract date of 11/04/2020 and extract date of 11/03/2020 will both contain all records as they were as of that extract date. Without filtering or grouping by extract date, an analysis will almost certainly be miscalculating or counting the same values multiple times. To analyze the most current data, only use the latest extract date. Antibody tests that are missing dates are not included in the dataset; as dates are identified, these events are added. Lags between occurrence and report of cases and tests can be assessed by comparing counts and rates across multiple data extract dates.
For further details, visit: • https://www1.nyc.gov/site/doh/covid/covid-19-data.page • https://github.com/nychealth/coronavirus-data • https://data.cityofnewyork.us/Health/Modified-Zip-Code-Tabulation-Areas-MODZCTA-/pri4-ifjk

In the state of New York, Richmond and Rockland have the highest coronavirus case rates when adjusted for the population of a county. Rockland County had around 1,404 positive cases per 10,000 people as of April 19, 2021.

The five boroughs of NYC With around 894,400 positive infections as of mid-April 2021, New York City has the highest number of coronavirus cases in New York State – this means that there were approximately 1,065 cases per 10,000 people. New York City is composed of five boroughs; each borough is coextensive with a county of New York State. Staten Island is the smallest in terms of population, but it is the borough with the highest rate of COVID-19 cases.

Public warned against complacency The number of new COVID-19 cases in New York City spiked for the second time as the winter holiday season led to an increase in social gatherings. New York State is slowly recovering – indoor dining reopened in February 2021 – but now is not the time for people to become complacent. Despite the positive rollout of vaccines, experts have urged citizens to adhere to guidelines and warned that face masks might have to be worn for at least another year.

This dataset shows daily citywide counts of persons tested by nucleic acid amplification tests (NAAT, also known as a molecular test; e.g. a PCR test) for SARS-CoV-2 , counts of persons with positive tests, and the percent positivity. Also included is a calculation of the average percent positivity over a 7-day period. NAAT tests work through direct detection of the virus’s genetic material, and typically involve collecting a nasal swab. These tests are highly accurate and recommended for diagnosing current COVID-19 infection. After specimen collection, molecular tests are processed in a laboratory, and results are electronically reported to the New York State (NYS) Electronic Clinical Laboratory Results System (ECLRS). Test results for NYC residents are then sent electronically to NYC DOHMH. There is typically a lag of a few days between when a specimen is collected and when a result is reported to NYC DOHMH. Data is sourced from electronic laboratory reporting from NYS ECLRS. All identifying health information is excluded from the dataset.

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.

Note: This dataset is no longer being updated as of September 1, 2023. This dataset includes information on the number of tests of individuals for COVID-19 infection by zip code performed in New York State beginning March 1, 2020, when the first case of COVID-19 was identified in the state. The primary goal of publishing this dataset is to provide users timely information about local disease spread and reporting of positive cases. The data will be updated weekly, reflecting tests completed by 2:00 pm on the day prior to the date of the update.

Note: On November 14, 2020, only 14 hours of laboratory data was collected and shared. The 2:00 pm cutoff time was implemented, allowing the NYSDOH to enhance data quality reviews. All other published laboratory data represented 24 hours of data collection. Prior to November 14, 2020 data reflected tests completed by 12:00 am (midnight) the day of the update (i.e., all tests reported by the end of the day on the day before the update).

As of April 4, 2022, the Department of Health and Human Services (HHS) no longer requires entities conducting COVID testing to report negative or indeterminate antigen test results. This may impact the number and interpretation of total test results reported to the state and also impacts calculation of test percent positivity. Total positives continues to include both PCR and antigen positive test results.

This collection of cases was acquired at Stony Brook University from patients who tested positive for COVID-19. The collection includes images from different modalities and organ sites (chest radiographs, chest CTs, brain MRIs, etc.). Radiology imaging data is extremely important in COVID-19 from both a diagnostic and a monitoring perspective, given the crucial nature of COVID-19 pulmonary disease and its rapid phenotypic changes. The datasets are available for building AI systems for diagnostic and prognostic modeling.

This collection also includes associated clinical data for each patient. The clinical data consists of diagnoses, procedures, lab tests, covid19 specific data values (e.g., intubation status, symptoms at admission) and a set of derived data elements, which were used in analyses of this data. The clinical data is stored as a set of csv files which comply with OMOP Common Data Model data elements.

The images on the right show automated identification of regions of prognostic importance on baseline chest radiographs. The regions of highest prognostic importance (as determined by the AI algorithm) are observed primarily in lower lung regions, consistent with clinical findings on the corresponding CXRs.

This dataset shows the number of hospital admissions for influenza-like illness, pneumonia, or include ICD-10-CM code (U07.1) for 2019 novel coronavirus. Influenza-like illness is defined as a mention of either: fever and cough, fever and sore throat, fever and shortness of breath or difficulty breathing, or influenza. Patients whose ICD-10-CM code was subsequently assigned with only an ICD-10-CM code for influenza are excluded. Pneumonia is defined as mention or diagnosis of pneumonia. Baseline data represents the average number of people with COVID-19-like illness who are admitted to the hospital during this time of year based on historical counts. The average is based on the daily avg from the rolling same week (same day +/- 3 days) from the prior 3 years. Percent change data represents the change in count of people admitted compared to the previous day. Data sources include all hospital admissions from emergency department visits in NYC. Data are collected electronically and transmitted to the NYC Health Department hourly. This dataset is updated daily. All identifying health information is excluded from the dataset.

Aggregation of all city-funded encumbrances coded by agencies as being related to the COVID-19 pandemic, allocated to IBO-defined expense types. This data was collected from March 10, 2020 through June 29, 2021 and is no longer being updated.