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.

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.

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.

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

This dataset shows daily confirmed and probable cases of COVID-19 in New York City by date of specimen collection. Total cases has been calculated as the sum of daily confirmed and probable cases. Seven-day averages of confirmed, probable, and total cases are also included in the dataset. A person is classified as a confirmed COVID-19 case if they test positive with a nucleic acid amplification test (NAAT, also known as a molecular test; e.g. a PCR test). A probable case is a person who meets the following criteria with no positive molecular test on record: a) test positive with an antigen test, b) have symptoms and an exposure to a confirmed COVID-19 case, or c) died and their cause of death is listed as COVID-19 or similar. As of June 9, 2021, people who meet the definition of a confirmed or probable COVID-19 case >90 days after a previous positive test (date of first positive test) or probable COVID-19 onset date will be counted as a new case. Prior to June 9, 2021, new cases were counted ≥365 days after the first date of specimen collection or clinical diagnosis. Any person with a residence outside of NYC is not included in counts. Data is sourced from electronic laboratory reporting from the New York State Electronic Clinical Laboratory Reporting System to the NYC Health Department. All identifying health information is excluded from the dataset. These data are used to evaluate the overall number of confirmed and probable cases by day (seven day average) to track the trajectory of the pandemic. Cases are classified by the date that the case occurred. NYC COVID-19 data include people who live in NYC. Any person with a residence outside of NYC is not included.

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.

Note: This dataset is no longer updated. This dataset includes cumulative and weekly counts of the number of new COVID-19 cases reported, number of cases reached, percent cases reached, total contacts elicited, total elicited contacts reached, and percent contacts reached by each week. Please note: In the earlier days of the program, the number of cases represented the numbers reported by selected LHDs. Therefore, the volume could be much lower than all new COVID cases.

This dataset includes information on the number of positive tests of individuals for COVID-19 infection 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 daily, reflecting tests reported by 12:00 am (midnight) three days prior. Data are published on a three-day lag in order to allow all test results to be reported.

Reporting of SARS-CoV2 laboratory testing results is mandated under Part 2 of the New York State Sanitary Code. Clinical laboratories, as defined in Public Health Law (PHL) § 571 electronically report test results to the New York State Department of Health (DOH) via the Electronic Clinical Laboratory Reporting System (ECLRS). The DOH Division of Epidemiology’s Bureau of Surveillance and Data System (BSDS) monitors ECLRS reporting and ensures that all results are accurate.

Test counts are based on specimen collection date. A person may have multiple specimens tested on one day, these would be counted one time, i.e., if two specimens are collected from an individual at the same time and then evaluated, the outcome of the evaluation of those two samples to diagnose the individual is counted as a single test of one person, even though the specimens may be tested separately. All positive test results that are at least 90 days apart are counted as cases/new positives.

New positive test counts are assigned to a county based on this order of preference: 1) the patient’s address, 2) the ordering healthcare provider/campus address, or 3) the ordering facility/campus address.

Archived versions of the reinfections dataset are also available: First infections - https://health.data.ny.gov/d/xdss-u53e Reinfections - https://health.data.ny.gov/d/7aaj-cdtu

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

The COVID Tracking Project collects information from 50 US states, the District of Columbia, and 5 other US territories to provide the most comprehensive testing data we can collect for the novel coronavirus, SARS-CoV-2. We attempt to include positive and negative results, pending tests, and total people tested for each state or district currently reporting that data.

Testing is a crucial part of any public health response, and sharing test data is essential to understanding this outbreak. The CDC is currently not publishing complete testing data, so we’re doing our best to collect it from each state and provide it to the public. The information is patchy and inconsistent, so we’re being transparent about what we find and how we handle it—the spreadsheet includes our live comments about changing data and how we’re working with incomplete information.

From here, you can also learn about our methodology, see who makes this, and find out what information states provide and how we handle it.

Dataset

This dataset was created by Ben Lebovitz

Contents

Context

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. They 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.

Content

As described on the NYTimes Github page.

For each date, we show the cumulative number of confirmed cases and deaths as reported that day in that county or state. All cases and deaths are counted on the date they are first announced.

In some instances, we report data from multiple counties or other non-county geographies as a single county. For instance, we report a single value for New York City, comprising the cases for New York, Kings, Queens, Bronx and Richmond Counties. In these instances the FIPS code field will be empty. (We may assign FIPS codes to these geographies in the future.) See the list of geographic exceptions.

Cities like St. Louis and Baltimore that are administered separately from an adjacent county of the same name are counted separately.

“Unknown” Counties Many state health departments choose to report cases separately when the patient’s county of residence is unknown or pending determination. In these instances, we record the county name as “Unknown.” As more information about these cases becomes available, the cumulative number of cases in “Unknown” counties may fluctuate.

Sometimes, cases are first reported in one county and then moved to another county. As a result, the cumulative number of cases may change for a given county.

Geographic Exceptions New York City All cases for the five boroughs of New York City (New York, Kings, Queens, Bronx and Richmond counties) are assigned to a single area called New York City.

Kansas City, Mo. Four counties (Cass, Clay, Jackson and Platte) overlap the municipality of Kansas City, Mo. The cases and deaths that we show for these four counties are only for the portions exclusive of Kansas City. Cases and deaths for Kansas City are reported as their own line.

Joplin, Mo. Joplin is reported separately from Jasper and Newton Counties.

Chicago All cases and deaths for Chicago are reported as part of Cook County.

Acknowledgements

Thanks to the New York Times for providing this data. The Gitbub repository can be found here: https://github.com/nytimes/covid-19-data

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.

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.

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 sex. These data can also be accessed here: https://github.com/nychealth/coronavirus-data/blob/master/totals/antibody-by-sex.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

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 tracks the updates made on the dataset "New York State (Outside New York City) COVID-19 Cases and Contacts Contact Tracing Initiative" as a repository for previous versions of the data and metadata.

Context:

The fate of the world changed in 2020.

Daily activities were impacted, impeded, and wouldn't be the same forever.

In partnership with Microsoft and the University of Oxford, A Tale of Two Cities is a Data AI hackathon that aims to address trends during and after the pandemic.

I will present my work at this hackathon through my association with the University of Oxford as an AI Tutor for the Artificial Intelligence: Cloud and Edge Implementations course.

Acknowledgments:

I'd like to thank the original authors of these data sources!

Relevant data was extracted from these sources and split into two phases: - COVID era (before 1st February, 2022), and - Post COVID era (after 1st February, 2022)

This helped me to compare trends in New York and London over time. https://i.imgur.com/KFRaB51.png" alt="">

Results of sampling to determine the SARS-CoV-2 N gene levels in NYC DEP Wastewater Resource Recovery Facility (WRRF) influent, disaggregated by the WRRF where the sample was collected, date sample was collected, and date sample was tested. RT-qPCR was changed to digital PCR in April of 2023, resulting values are about 10-20 times higher than those of RT-qPCR. Please refer to this supporting documentation for more technical information Data may be used to track trends in SARS-CoV-2 concentrations in NYC WRRF influent. Dataset does not include COVID-19 case rates.

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.