COVID-19 Daily Testing - By Test - Historical by US Government | Healthcare

About this Dataset

COVID-19 Daily Testing - By Test - Historical

NOTE: This dataset has been retired and marked as historical-only.

This dataset contains counts of unique tests and results for COVID-19. This dataset differs from https://data.cityofchicago.org/d/t4hh-4ku9 in that each person is in that dataset only once, even if tested multiple times. In this dataset, each test is counted, even if multiple tests are performed on the same person, although a person should not appear in this dataset more than once on the same day unless he/she had both a positive and not-positive test.

The positivity rate displayed in this dataset uses the method most commonly used by other jurisdictions in the United States.

Only Chicago residents are included based on the home address as provided by the medical provider.

Molecular (PCR) and antigen tests received through electronic lab reporting are included. Individuals may be tested multiple times. Tests are counted on the day the specimen was collected. A small number of tests collected prior to 3/1/2020 are not included in the table.

Not-positive lab results include negative results, invalid results, and tests not performed due to improper collection. Chicago Department of Public Health (CDPH) does not receive all not-positive results.

All data are provisional and subject to change. Information is updated as additional details are received.

Data Source: Illinois Department of Public Health Electronic Lab Reports
Organization: City of Chicago
Last updated: 2020-11-10T16:54:57.402397
Tags: covid-19, health, historical, link-to-article-present, public-health, testing

Tables

Table 1

@usgov.city_of_chicago_covid_19_daily_testing_by_test.table_1

345.08 KB
1540 rows
59 columns


CREATE TABLE table_1 (
  "date" TIMESTAMP,
  "day" VARCHAR,
  "positive_tests" BIGINT,
  "not_positive_tests" BIGINT,
  "total_tests" BIGINT,
  "tests_age_0_17" BIGINT,
  "tests_age_18_29" BIGINT,
  "tests_age_30_39" BIGINT,
  "tests_age_40_49" BIGINT,
  "tests_age_50_59" BIGINT,
  "tests_age_60_69" BIGINT,
  "tests_age_70_79" BIGINT,
  "tests_age_80" BIGINT,
  "tests_age_unknown" BIGINT,
  "tests_female" BIGINT,
  "tests_male" BIGINT,
  "tests_gender_unknown" BIGINT,
  "tests_latinx" BIGINT,
  "tests_asian_non_latinx" BIGINT,
  "tests_black_non_latinx" BIGINT,
  "tests_white_non_latinx" BIGINT,
  "tests_other_race_non_latinx" BIGINT,
  "tests_unknown_race_ethnicity" BIGINT,
  "positive_tests_age_0_17" BIGINT,
  "positive_tests_age_18_29" BIGINT,
  "positive_tests_age_30_39" BIGINT,
  "positive_tests_age_40_49" BIGINT,
  "positive_tests_age_50_59" BIGINT,
  "positive_tests_age_60_69" BIGINT,
  "positive_tests_age_70_79" BIGINT,
  "positive_tests_age_80" BIGINT,
  "positive_tests_age_unknown" BIGINT,
  "positive_tests_female" BIGINT,
  "positive_tests_male" BIGINT,
  "positive_tests_unknown_gender" BIGINT,
  "positive_tests_latinx" BIGINT,
  "positive_tests_asian_non_latinx" BIGINT,
  "positive_tests_black_non_latinx" BIGINT,
  "positive_tests_white_non_latinx" BIGINT,
  "positive_tests_other_race_non_latinx" BIGINT,
  "positive_tests_unknown_race_ethnicity" BIGINT,
  "not_positive_tests_age_0_17" BIGINT,
  "not_positive_tests_age_18_29" BIGINT,
  "not_positive_tests_age_30_39" BIGINT,
  "not_positive_tests_age_40_49" BIGINT,
  "not_positive_tests_age_50_59" BIGINT,
  "not_positive_tests_age_60_69" BIGINT,
  "not_positive_tests_age_70_79" BIGINT,
  "not_positive_tests_age_80" BIGINT,
  "not_positive_tests_age_unknown" BIGINT,
  "not_positive_tests_female" BIGINT,
  "not_positive_tests_male" BIGINT,
  "not_positive_tests_gender_unknown" BIGINT,
  "not_positive_tests_latinx" BIGINT,
  "not_positive_tests_asian_non_latinx" BIGINT,
  "not_positive_tests_black_non_latinx" BIGINT,
  "not_positive_tests_white_non_latinx" BIGINT,
  "not_positive_tests_other_race_non_latinx" BIGINT,
  "not_positive_tests_unknown_race_ethnicity" BIGINT
);