As of December 22, 2022, the United States had performed around 1.15 billion tests for COVID-19, the highest number of any country worldwide. Russia has conducted over 273 million tests.

High demand leads to testing problems The COVID-19 pandemic has put health systems around the world under severe strain. Towards the beginning of the pandemic there was a huge demand for test kits, but production capacity was stretched thin. In the United States, faulty diagnostic kits produced by the Centers for Disease Control and Prevention meant the early spread of the disease went undetected for weeks. Elsewhere, concerns were raised regarding the accuracy of some rapid diagnostic tests (RDT). In April 2020, officials in India canceled a large order of test kits due to a low accuracy rate.

What are rapid diagnostic tests? Widespread coronavirus testing has helped to track the spread of the disease. RDTs are a point-of-care test that can deliver results in around 30 minutes – more traditional diagnostic tests conducted in laboratory settings are more time consuming but provide greater reliability. One type of RDT detects the presence of antibodies in a patient’s blood sample. Immune system cells produce antibodies to fight pathogens, and the detection of them may mean the patient has developed some natural immunity to the virus.

Private laboratories conducted approximately 74 million tests in Ukraine in 2019. Starting from approximately 46 million in 2016, the number of tests gradually increased over the time period under consideration. Despite the constant increase in numbers, the test count at private labs was lower compared to that at public laboratories, which exceeded 630 million in 2019.

This dataset includes COVID-19 self-test result data voluntarily reported by users of tests through the MakeMyTestCount website (makemytestcount.org). All fields are self-reported by the user with the exception of fields derived from the self-reported zip code. This dataset will be updated monthly. If there are any questions, please direct them to the data steward, Jasmine Chaitram zoa6@cdc.gov.

This dataset includes the following self-reported data:
- Date (by week)– date of test shown by week starting date
- Age group (years) – age of individual taking the test, categorized into the following: 2-4, 5-11, 12-15, 16-17, 18-29, 30-39, 40-49, 50-64, 65-74, 75+
- Race – race of individual taking the test: American Indian or Alaska Native, Asian, Black, Native Hawaiian or Other Pacific Islander, White, Multiple or Other, missing - Ethnicity – ethnicity of individual taking the test: Hispanic, Non-Hispanic, missing - Sex – sex of individual taking the test: male, female, missing - Test result – positive, negative, inconclusive

The dataset also includes the following columns to support analyses. These columns are based on the self-reported zip code:
- State abbreviation
- State name
- State FIPS code - FEMA region

Please note that there are limitations with these data, including:

1. Data are not comprehensive of all self-tests performed. Data represent results voluntarily reported by an individual via the MakeMyTestCount website. These data do not include self-test results that were reported to state and local health departments if they were not also reported through the MakeMyTestCount website. The true denominator (known number of tests completed in the US) cannot be ascertained and reflects a small fraction of the number of self-tests used.

2. Data are not verified. The quality of specimen, appropriate execution of self-test, result produced, and person tested are unverified; therefore, reported interpretation of results cannot be confirmed. All results and accompanying demographic information are also self-reported and cannot be verified.

3. Data reports are not complete. Individual submissions vary widely in terms of the data elements collected. Not all data elements are required (only date, age, and zip code), and some results are missing demographic information.

4. Data are not representative. Based on the limited number of self-reported test results, this dataset is not representative of the use of self-testing by demographic, nor is the dataset inclusive of all self-testing completed within each jurisdiction. This dataset represents a small proportion of overall COVID-19 testing conducted and reported volumes are much lower than testing conducted in point of care and laboratory settings.

5. Data represent individual test results, not persons tested. Data in this dataset are not linkable and do not allow for analyses around serial testing. Data also cannot be disaggregated to identify multiple reports by the same individual.

All analyses should be completed with these limitations in mind.

For more information about the challenges and opportunities around self-test data, please refer to the following article: Ritchey MD, Rosenblum HG, Del Guercio K, et al. COVID-19 Self-Test Data: Challenges and Opportunities — United States, October 31, 2021–June 11, 2022. MMWR Morb Mortal Wkly Rep 2022;71:1005–1010. DOI: http://dx.doi.org/10.15585/mmwr.mm7132a1

Number of services delivered for Magnetic Resonance Imaging (MRI), Computed Tomography (CT) scans, and ultrasounds by type of billing and patient provincial insurance coverage.

Correcting for multiple-testing is critical for high-dimensional omics studies, such as genomics and metabolomics, where there are numerous measurements per sample. One strategy is to estimate the number of statistically independent tests, called the effective number of tests, based on the eigen-analysis of the correlation matrix between the features. This effective number is then used for a subsequent single-step adjustment to obtain the pointwise significance level. Such practice is commonplace in Genome-Wide Association Studies (GWAS) but is also becoming increasingly relevant to Metabolome-Wide Association Studies (MWAS). However, many procedures for estimating the effective number of tests may be too conservative or too lenient, only assume a linear association between features, or have not been evaluated on metabolomics data. Therefore, we propose a modification to the p-value adjustment based on a more general measure of association between two predictors, the Distance Correlation, with specific focus on MWAS. We assessed common GWAS \textit{p}-value adjustment procedures and one tailored for MWAS, which rely on eigen-analysis of the Pearson's correlation matrix. Our study, including varying sample size-to-feature ratios, response types, and metabolite groupings, highlights the superior performance of the Distance Correlation. We introduce the Distance Correlation-based p-value adjustment (DisCo P-ad) as a novel modification that can enhance existing eigen-analysis based procedures by increasing power or reducing false positives. While our focus is on metabolomics, DisCo P-ad can readily be applied to other high-dimensional omics studies. Keywords: Multiple-testing; Effective number of tests; Correlated tests; Eigen-analysis; Pointwise error rate; Metabolome-wide association study

The number of tests at public laboratories of Ukraine reached the minimum at 630.2 million in 2019. Over the past four years, the test count decreased by 23.4 million. An opposite trend was observed for tests in private laboratories across the country, which marked an increase over the same time period.

AbstractThe dataset provided here contains the efforts of independent data aggregation, quality control, and visualization of the University of Arizona (UofA) COVID-19 testing programs for the 2019 novel Coronavirus pandemic. The dataset is provided in the form of machine-readable tables in comma-separated value (.csv) and Microsoft Excel (.xlsx) formats.Additional InformationAs part of the UofA response to the 2019-20 Coronavirus pandemic, testing was conducted on students, staff, and faculty prior to start of the academic year and throughout the school year. These testings were done at the UofA Campus Health Center and through their instance program called "Test All Test Smart" (TATS). These tests identify active cases of SARS-nCoV-2 infections using the reverse transcription polymerase chain reaction (RT-PCR) test and the Antigen test. Because the Antigen test provided more rapid diagnosis, it was greatly used three weeks prior to the start of the Fall semester and throughout the academic year.As these tests were occurring, results were provided on the COVID-19 websites. First, beginning in early March, the Campus Health Alerts website reported the total number of positive cases. Later, numbers were provided for the total number of tests (March 12 and thereafter). According to the website, these numbers were updated daily for positive cases and weekly for total tests. These numbers were reported until early September where they were then included in the reporting for the TATS program.For the TATS program, numbers were provided through the UofA COVID-19 Update website. Initially on August 21, the numbers provided were the total number (July 31 and thereafter) of tests and positive cases. Later (August 25), additional information was provided where both PCR and Antigen testings were available. Here, the daily numbers were also included. On September 3, this website then provided both the Campus Health and TATS data. Here, PCR and Antigen were combined and referred to as "Total", and daily and cumulative numbers were provided.At this time, no official data dashboard was available until September 16, and aside from the information provided on these websites, the full dataset was not made publicly available. As such, the authors of this dataset independently aggregated data from multiple sources. These data were made publicly available through a Google Sheet with graphical illustration provided through the spreadsheet and on social media. The goal of providing the data and illustrations publicly was to provide factual information and to understand the infection rate of SARS-nCoV-2 in the UofA community.Because of differences in reported data between Campus Health and the TATS program, the dataset provides Campus Health numbers on September 3 and thereafter. TATS numbers are provided beginning on August 14, 2020.Description of Dataset ContentThe following terms are used in describing the dataset.1. "Report Date" is the date and time in which the website was updated to reflect the new numbers2. "Test Date" is to the date of testing/sample collection3. "Total" is the combination of Campus Health and TATS numbers4. "Daily" is to the new data associated with the Test Date5. "To Date (07/31--)" provides the cumulative numbers from 07/31 and thereafter6. "Sources" provides the source of information. The number prior to the colon refers to the number of sources. Here, "UACU" refers to the UA COVID-19 Update page, and "UARB" refers to the UA Weekly Re-Entry Briefing. "SS" and "WBM" refers to screenshot (manually acquired) and "Wayback Machine" (see Reference section for links) with initials provided to indicate which author recorded the values. These screenshots are available in the records.zip file.The dataset is distinguished where available by the testing program and the methods of testing. Where data are not available, calculations are made to fill in missing data (e.g., extrapolating backwards on the total number of tests based on daily numbers that are deemed reliable). Where errors are found (by comparing to previous numbers), those are reported on the above Google Sheet with specifics noted.For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to data-management@arizona.edu

The dataset comprises developer test results of Maven projects with flaky tests across a range of consecutive commits from the projects' git commit histories. The Maven projects are a subset of those investigated in an OOPSLA 2020 paper. The commit range for this dataset has been chosen as the flakiness-introducing commit (FIC) and iDFlakies-commit (see the OOPSLA paper for details). The commit hashes have been obtained from the IDoFT dataset.

The dataset will be presented at the 1st International Flaky Tests Workshop 2024 (FTW 2024). Please refer to our extended abstract for more details about the motivation for and context of this dataset.

The following table provides a summary of the data.

Slug (Module) FIC Hash Tests Commits Av. Commits/Test Flaky Tests Tests w/ Consistent Failures Total Distinct Histories

TooTallNate/Java-WebSocket 822d40 146 75 75 24 1 2.6x10^9

apereo/java-cas-client (cas-client-core) 5e3655 157 65 61.7 3 2 1.0x10^7

eclipse-ee4j/tyrus (tests/e2e/standard-config) ce3b8c 185 16 16 12 0 261

feroult/yawp (yawp-testing/yawp-testing-appengine) abae17 1 191 191 1 1 8

fluent/fluent-logger-java 5fd463 19 131 105.6 11 2 8.0x10^32

fluent/fluent-logger-java 87e957 19 160 122.4 11 3 2.1x10^31

javadelight/delight-nashorn-sandbox d0d651 81 113 100.6 2 5 4.2x10^10

javadelight/delight-nashorn-sandbox d19eee 81 93 83.5 1 5 2.6x10^9

sonatype-nexus-community/nexus-repository-helm 5517c8 18 32 32 0 0 18

spotify/helios (helios-services) 23260 190 448 448 0 37 190

spotify/helios (helios-testing) 78a864 43 474 474 0 7 43

The columns are composed of the following variables:

Slug (Module): The project's GitHub slug (i.e., the project's URL is https://github.com/{Slug}) and, if specified, the module for which tests have been executed.

FIC Hash: The flakiness-introducing commit hash for a known flaky test as described in this OOPSLA 2020 paper. As different flaky tests have different FIC hashes, there may be multiple rows for the same slug/module with different FIC hashes.

Tests: The number of distinct test class and method combinations over the entire considered commit range.

Commits: The number of commits in the considered commit range

Av. Commits/Test: The average number of commits per test class and method combination in the considered commit range. The number of commits may vary for each test class, as some tests may be added or removed within the considered commit range.

Flaky Tests: The number of distinct test class and method combinations that have more than one test result (passed/skipped/error/failure + exception type, if any + assertion message, if any) across 30 repeated test suite executions on at least one commit in the considered commit range.

Tests w/ Consistent Failures: The number of distinct test class and method combinations that have the same error or failure result (error/failure + exception type, if any + assertion message, if any) across all 30 repeated test suite executions on at least one commit in the considered commit range.

Total Distinct Histories: The number of distinct test results (passed/skipped/error/failure + exception type, if any + assertion message, if any) for all test class and method combinations along all commits for that test in the considered commit range.

Table of the pool size as a function of the number of tests for a prevalence of 3% measured with a precision of 0.2% at a 95% confidence interval, for both perfect tests (with no false negatives, see Sec III.1) and imperfect tests (with false negatives estimated using the Jones et al. dataset; model parameters defined in Table 2); computed using Eqs 17 and 19.

Updates

April 29, 2020

• The AP is now providing historical time series data for testing counts and death counts from The COVID Tracking Project. The counts are provided here unaltered, along with a population column with Census ACS-1 estimates and calculated testing rate and death rate columns.

October 13, 2020

The COVID Tracking Project is releasing more precise total testing counts, and has changed the way it is distributing the data that ends up on this site. Previously, total testing had been represented by positive tests plus negative tests. As states are beginning to report more specific testing counts, The COVID Tracking Project is moving toward reporting those numbers directly.

This may make it more difficult to compare your state against others in terms of positivity rate, but the net effect is we now have more precise counts:

• Total Test Encounters: Total tests increase by one for every individual that is tested that day. Additional tests for that individual on that day (i.e., multiple swabs taken at the same time) are not included

• Total PCR Specimens: Total tests increase by one for every testing sample retrieved from an individual. Multiple samples from an individual on a single day can be included in the count

• Unique People Tested: Total tests increase by one the first time an individual is tested. The count will not increase in later days if that individual is tested again – even months later

These three totals are not all available for every state. The COVID Tracking Project prioritizes the different count types for each state in this order:

1. Total Test Encounters

2. Total PCR Specimens

3. Unique People Tested

If the state does not provide any of those totals directly, The COVID Tracking Project falls back to the initial calculation of total tests that it has provided up to this point: positive + negative tests.

One of the above total counts will be the number present in the cumulative_total_test_results and total_test_results_increase columns.

• The positivity rates provided on this site will divide confirmed cases by one of these total_test_results columns.

  • Due to these changes, we advise comparing positivity rates between states only if the states being compared have the same type of total test count.

Overview

The AP is using data collected by the COVID Tracking Project to measure COVID-19 testing across the United States.

The COVID Tracking Project data is available at the state level in the United States. The AP has paired this data with population figures and has calculated testing rates and death rates per 1,000 people.

This data is from The COVID Tracking Project API that is updated regularly throughout the day. Like all organizations dealing with data, The COVID Tracking Project 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 COVID Tracking Project daily data reports, and a clean version of their feed.

A Note on timing: - The COVID Tracking Project updates regularly throughout the day, but state numbers will come in at different times. The entire Tracking Project dataset will be updated between 4-5pm EDT daily. Keep this time in mind when reporting on stories comparing states. At certain times of day, one state may be more up to date than another. We have included the date_modified timestamp for state-level data, which represents the last time the state updated its data. The date_checked value in the state-level data reflects the last time The COVID Tracking Project checked the state source. We have also included the last_modified timestamp for the national-level data, which marks the last time the national data was updated.

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.

About the data

• State-level population data is sourced from the census ACS 1-year survey for 2018.
• Historical data for each state is available on The COVID Tracking Project's main data page.

Caveats

• The total_people_tested counts do not include pending tests. They are the total number of tests that have returned positive or negative.
• The process for collecting testing data is different for each state. The COVID Tracking Project makes note of the difficulties specific to each state on their main data page.

Attribution

This data should be credited to The COVID Tracking Project

Contact

Nicky Forster — nforster@ap.org

Analysis of ‘Covid-19 Tests by Race Ethnicity and Date’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/68410b4b-052f-4ce3-8d0c-873b5664f1a4 on 27 January 2022.

--- Dataset description provided by original source is as follows ---

Note: As of April 16, 2021, this dataset will update daily with a five-day data lag.

A. SUMMARY This dataset includes San Francisco COVID-19 tests by race/ ethnicity and date. For each day, this dataset represents the daily count of tests collected by race/ethnicity, and how many of those were positive, negative, and indeterminate. Tests in this dataset include all tests collected from San Francisco residents who listed a San Francisco home address at the time of testing, and tests that were collected in San Francisco but had a missing home address. Data are based on information collected at the time of testing.

For recent data, about 25-30% of tests are missing race/ ethnicity information. Tests where the race/ ethnicity of the patient is unknown are included in the dataset under the "Unknown" category.

This data was de-duplicated by individual and date, so if a person gets tested multiple times on different dates, all tests will be included in this dataset (on the day each test was collected).

The total number of positive test results is not equal to the total number of COVID-19 cases in San Francisco. Each positive test result is investigated. During this investigation, some test results are found to be for persons living outside of San Francisco and some people in San Francisco may be tested multiple times. In both cases, these results are not included in San Francisco’s total COVID-19 case count. To track the number of cases by race/ ethnicity, see this dashboard: https://data.sfgov.org/stories/s/w6za-6st8

B. HOW THE DATASET IS CREATED COVID-19 laboratory test data is based on electronic laboratory test reports. Deduplication, quality assurance measures and other data verification processes maximize accuracy of laboratory test information.

C. UPDATE PROCESS Updates automatically at 05:00 Pacific Time each day. Redundant runs are scheduled at 07:00 and 09:00 in case of pipeline failure.

D. HOW TO USE THIS DATASET Due to the high degree of variation in the time needed to complete tests by different labs there is a delay in this reporting. On March 24 the Health Officer ordered all labs in the City to report complete COVID-19 testing information to the local and state health departments.

In order to track trends over time, a data user can analyze this data by "specimen_collection_date".

Calculating Percent Positivity: The positivity rate is the percentage of tests that return a positive result for COVID-19 (positive tests divided by the sum of positive and negative tests). Indeterminate results, which could not conclusively determine whether COVID-19 virus was present, are not included in the calculation of percent positive. When there are fewer than 20 positives tests for a given race/ethnicity and time period, the positivity rate is not calculated for the public tracker because rates of small test counts are less reliable.

Calculating Testing Rates: To calculate the testing rate per 10,000 residents, divide the total number of tests collected (positive, negative, and indeterminate results) for the specified race/ ethnicity by the total number of residents who identify as that race/ ethnicity (according to the 2018 5-year estimates from the American Community Survey), then multiply by 10,000. When there are fewer than 20 total tests for a given race/ethnicity and time period, the testing rate is not calculated for the public tracker because rates of small test counts are less reliable.

Read more about how this data is updated and validated daily: https://data.sfgov.org/stories/s/nudz-9tg2

There are two other datasets related to tests: 1. COVID-19 Tests 2. <a href="https://data.sfgov.org/dataset/Covid-19-Testing-by

--- Original source retains full ownership of the source dataset ---

A. SUMMARY This dataset includes COVID-19 tests by resident neighborhood and specimen collection date (the day the test was collected). Specifically, this dataset includes tests of San Francisco residents who listed a San Francisco home address at the time of testing. These resident addresses were then geo-located and mapped to neighborhoods. The resident address associated with each test is hand-entered and susceptible to errors, therefore neighborhood data should be interpreted as an approximation, not a precise nor comprehensive total.

In recent months, about 5% of tests are missing addresses and therefore cannot be included in any neighborhood totals. In earlier months, more tests were missing address data. Because of this high percentage of tests missing resident address data, this neighborhood testing data for March, April, and May should be interpreted with caution (see below)

Percentage of tests missing address information, by month in 2020 Mar - 33.6% Apr - 25.9% May - 11.1% Jun - 7.2% Jul - 5.8% Aug - 5.4% Sep - 5.1% Oct (Oct 1-12) - 5.1%

To protect the privacy of residents, the City does not disclose the number of tests in neighborhoods with resident populations of fewer than 1,000 people. These neighborhoods are omitted from the data (they include Golden Gate Park, John McLaren Park, and Lands End).

Tests for residents that listed a Skilled Nursing Facility as their home address are not included in this neighborhood-level testing data. Skilled Nursing Facilities have required and repeated testing of residents, which would change neighborhood trends and not reflect the broader neighborhood's testing data.

This data was de-duplicated by individual and date, so if a person gets tested multiple times on different dates, all tests will be included in this dataset (on the day each test was collected).

The total number of positive test results is not equal to the total number of COVID-19 cases in San Francisco. During this investigation, some test results are found to be for persons living outside of San Francisco and some people in San Francisco may be tested multiple times (which is common). To see the number of new confirmed cases by neighborhood, reference this map: https://sf.gov/data/covid-19-case-maps#new-cases-maps

B. HOW THE DATASET IS CREATED COVID-19 laboratory test data is based on electronic laboratory test reports. Deduplication, quality assurance measures and other data verification processes maximize accuracy of laboratory test information. All testing data is then geo-coded by resident address. Then data is aggregated by analysis neighborhood and specimen collection date.

Data are prepared by close of business Monday through Saturday for public display.

C. UPDATE PROCESS Updates automatically at 05:00 Pacific Time each day. Redundant runs are scheduled at 07:00 and 09:00 in case of pipeline failure.

D. HOW TO USE THIS DATASET San Francisco population estimates for geographic regions can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS).

Due to the high degree of variation in the time needed to complete tests by different labs there is a delay in this reporting. On March 24 the Health Officer ordered all labs in the City to report complete COVID-19 testing information to the local and state health departments.

In order to track trends over time, a data user can analyze this data by "specimen_collection_date".

Calculating Percent Positivity: The positivity rate is the percentage of tests that return a positive result for COVID-19 (positive tests divided by the sum of positive and negative tests). Indeterminate results, which could not conclusively determine whether COVID-19 virus was present, are not included in the calculation of pe

This file contains: - The total number of Covid-19 tests taken by the GGDs for which the results are known and the number of positive tests per day by safety region of registration of the tested person, by date of the test appointment. The numbers relate to decreased Covid-19 tests, from the expansion of the test policy in the Netherlands on 1 June 2020 to 2 days ago. Tests that were erroneously duplicated could not be duplicated due to missing test-identifying data. Tests for which the results were not yet known will be included in the statistics at a later date. This allows the numbers to change slightly. The file is structured as follows: - A set of records per date of the test appointment, with for each date: - A record per safety region, even if there are no reports for the relevant safety region. The numbers are 0 (zero).

Description of variables: Version: Version number of the dataset. When the content of the dataset is structurally changed (i.e. not the daily update or a correction at record level), the version number will be adjusted (+1) and also the corresponding metadata in RIVMdata (https://data.rivm.nl). Version 2 update (10 August 2021): Vanaf 1 juli 2021 nemen de GGD’en testen voor uitgaande reizigers af. Tests for this purpose were reported in this dataset until 10 August. As of 10 August, these tests will no longer be reported retroactively in this dataset due to the difference in test population. Version 3 update (24 March 2022): In versie 3 van deze dataset zijn records samengesteld volgens de gemeente herindeling van 24 maart 2022. For more information, see Description of the Security_region_code variable. Door een technisch probleem ontbrak bij een deel van de records van de gemeenten Hengelo (Overijssel), Bergen (Limburg) en Bergen (Noord-Holland) vanaf najaar 2021 tot en met 23 maart 2022 de veiligheidsregio. From the municipalities of Dijk en Waard, Purmerend, Land van Cuijk and Maashorst, the safety region has been missing for some records since the municipal reorganisation of 22 January 2022. This resulted in a lower number of reported tests for the above municipalities and their associated safety regions. As of 24 March 2022, this issue has been resolved and the correct number of tests performed for all safety regions is shown in this file.

Version 4 update (1 September 2022): Vanaf 1 september 2022 wordt de data niet meer iedere werkdag geüpdatet, maar op dinsdagen en vrijdagen. The dates will be updated retroactively on these days for the other days. Vanaf 1 september 2022 is deze dataset opgesplitst in twee delen. The first part contains the dates from the beginning of the pandemic until 3 October 2021 (week 39) and contains ‘tm’ in the file name. This data will no longer be updated. The second part contains the dates from 4 October 2021 (week 40) and will be updated every Tuesday and Friday.

Date_of_report: Date and time when the database was created by RIVM. Date_of_statistics : Date of the Covid-19 test appointment. Security_region_code: Security region code. Safety region based on the place of residence of the tested person. As of 24 March 2022, this file has been compiled according to the municipality classification of 24 March 2022. The municipality of Weesp is merged into the municipality of Amsterdam. With this classification, the safety region Gooi- en Vechtstreek has become smaller and the safety region Amsterdam-Amstelland larger; GGD Amsterdam has become larger and GGD Gooi- en Vechtstreek has become smaller (https://www.cbs.nl/en-en/our-services/methods/classifications/other/municipal-classifications-per-year/municipal-classification-per-year/municipal-classification-on-1-January-2022).

Security_region_name: This is the name of the safety regions as used so far in various RIVM reports and reports, and may differ slightly from the naming as indicated in the CBS code list. See also: https://www.rijksoverheid.nl/topics/security regions-and-crisis management/security regions. If residence is not known, the security_region is ‘Unknown’. Tested_with_result: Number of Covid-19 tests taken for which the results are known, by date of the test appointment, [Date_of_statistics]. Tested_positive: Number of Covid-19 tests taken with a positive result, by date of the test appointment, [Date_of_statistics].

Number of laboratory tests in the Ministry of Health for the year 2021

Dataset

This dataset was created by Alberto Maria Falletta

Contents

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 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 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).

Note: On November 14, 2020, only 14 hours of laboratory data was collected and shared. A 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.

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. Because of this, as of April 5, 2022, test percent positivity is calculated using PCR tests only. Reporting of total new daily cases (positive results) will continue to include PCR and antigen tests.

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 positives and negatives are accurate. Starting September 30, 2020, this data also includes pooled/batch tests reported by institutions of higher education. This is also known as surveillance testing and not performed by a clinical laboratory.

Test counts reflect those reported to DOH each day. 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. Conversely, if an individual is tested on more than one day, the data will show two tests of an individual, one for each date the person was tested. An individual will only be counted positive one time.

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.

COVID-19: No. of Tests: Mild to Moderate Cases: New: Rapid Tests: Antigen: by State: North: Amapá: Undefined data was reported at 0.000 Unit in 31 May 2024. This stayed constant from the previous number of 0.000 Unit for 30 May 2024. COVID-19: No. of Tests: Mild to Moderate Cases: New: Rapid Tests: Antigen: by State: North: Amapá: Undefined data is updated daily, averaging 0.000 Unit from Jan 2020 (Median) to 31 May 2024, with 1613 observations. The data reached an all-time high of 1.000 Unit in 29 Jul 2020 and a record low of 0.000 Unit in 31 May 2024. COVID-19: No. of Tests: Mild to Moderate Cases: New: Rapid Tests: Antigen: by State: North: Amapá: Undefined data remains active status in CEIC and is reported by Ministry of Health. The data is categorized under Brazil Premium Database’s Health Sector – Table BR.HLA002: Disease Outbreaks: COVID-19: Number of Tests: Mild to Moderate Cases.

*** The County of Santa Clara Public Health Department discontinued updates to the COVID-19 data tables effective June 30, 2025. The COVID-19 data tables will be removed from the Open Data Portal on December 30, 2025. For current information on COVID-19 in Santa Clara County, please visit the Respiratory Virus Dashboard [sccphd.org/respiratoryvirusdata]. For any questions, please contact phinternet@phd.sccgov.org ***

The data set summarizes the number of COVID-19 tests completed among Santa Clara County residents by major healthcare systems in the county. Each ‘test’ or ‘testing incident’ represents at least one specimen tested per person, per day. This does not represent the number of individuals tested, as some people are tested multiple times over time because of the risk of frequent exposure. Source: California Reportable Disease Information Exchange. Data notes: The daily average rate of tests is the daily average number of tests completed over the past 7 days per 100,000 people served by the individual healthcare system. The State of California has defined an initial goal of at least 150 tests per day per 100,000 people. Bay Area County Health Officers set a goal of 200 tests per day per 100,000 people.

This table was updated for the last time on May 20, 2021.