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Showing content from https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/quidel.html below:

Quidel | Delphi Epidata API

1. Accessibility: Delphi-internal only
2. Quidel COVID-19 Tests
   1. Overview
   2. Estimation
      1. Standard Error
      2. Smoothing
         1. Temporal Smoothing
         2. Geographical Smoothing
   3. Lag and Backfill
   4. Limitations
   5. Missingness
3. Quidel Flu Tests (inactive)
   1. Overview

• Source name: quidel
• Earliest issue available: July 29, 2020
• Number of data revisions since May 19, 2020: 1
• Date of last change: October 22, 2020
• Available for: county, hrr, msa, state, HHS, nation (see geography coding docs)
• Time type: day (see date format docs)
• License: CC BY

Data source based on COVID-19 Antigen tests, provided to us by Quidel, Inc. When a patient (whether at a doctor’s office, clinic, or hospital) has COVID-like symptoms, doctors may order an antigen test. An antigen test can detect parts of the virus that are present during an active infection. This is in contrast with antibody tests, which detect parts of the immune system that react to the virus, but which persist long after the infection has passed. Quidel began providing us with test data starting May 9, 2020, and data volume increased to statistically meaningful levels starting May 26, 2020.

The source data from which we derive our estimates contains a number of features for every test, including localization at 5-digit Zip Code level, a TestDate and StorageDate, patient age, and unique identifiers for the device on which the test was performed, the individual test, and the result. Multiple tests are stored on each device.

Let \(n\) be the number of total COVID tests taken over a given time period and a given location (the test result can be negative, positive, or invalid). Let \(x\) be the number of tests taken with positive results in this location over the given time period. We are interested in estimating the percentage of positive tests which is defined as:

We estimate p across 6 temporal-spatial aggregation schemes:

• daily, at the county level;
• daily, at the MSA (metropolitan statistical area) level;
• daily, at the HRR (hospital referral region) level;
• daily, at the state level;
• daily, at the HHS level;
• daily, at the US national level.

We assume the estimates for each time point follow a binomial distribution. The estimated standard error then is:

We add two kinds of smoothing to the smoothed signals:

Smoothed estimates are formed by pooling data over time. That is, daily, for each location, we first pool all data available in that location over the last 7 days, and we then recompute everything described in the two subsections above.

Pooling in this way makes estimates available in more geographic areas, as many areas report very few tests per day, but have enough data to report when 7 days are considered.

County, MSA and HRR levels: In a given County, MSA or HRR, suppose \(N\) COVID tests are taken in a certain time period, \(X\) is the number of tests taken with positive results.

For smoothed signals, after taking the temporal pooling,

• if \(N \geq 50\), we still use: \(p = \frac{100 X}{N}\)
• if \(25 \leq N < 50\), we lend \(50 - N\) fake samples from its parent state to shrink the estimate to the state’s mean, which means: \(p = 100 \left( \frac{N}{50} \frac{X}{N} + \frac{50 - N}{50} \frac{X_s}{N_s} \right)\) where \(N_s, X_s\) are the number of COVID tests and the number of COVID tests taken with positive results taken in its parent state in the same time period. A parent state is defined as the state with the largest proportion of the population in this county/MSA/HRR.

Counties with sample sizes smaller than 50 are merged into megacounties for the raw signals; counties with sample sizes smaller than 25 are merged into megacounties for the smoothed signals.

State level, HHS level, National level: locations with fewer than 50 tests are discarded. For the remaining locations, \(p = \frac{100 X}{N}\)

Because testing centers may report their data to Quidel several days after they occur, these signals are typically available with 5-6 days of lag. This means that estimates for a specific day first become available 5-6 days later.

The amount of lag in reporting can vary, and not all tests are reported with the same lag. After we first report estimates for a specific date, further data may arrive about tests that occurred on that date, sometimes six weeks later or more. When this happens, we issue new estimates for those dates. This means that a reported estimate for, say, June 10th may first be available in the API on June 14th and subsequently revised on June 16th.

This data source is based on data provided to us by a lab testing company. They can report on a portion of United States COVID-19 Antigen tests, but not all of them, and so this source only represents those tests known to them. Their coverage may vary across the United States. The coverage of the signals for some age groups (e.g. age 0-4 and age 65+) are extremely limited at HRR and MSA level, and can even be limited at state level on weekends.

When fewer than 50 tests are reported in a state/a HHS region/US on a specific day, no data is reported for that area on that day; an API query for all reported states on that day will not include it.

When fewer than 50 tests are reported in a county, HRR or MSA on a specific day, and not enough samples can be filled in from the parent state for smoothed signals specifically, no data is reported for that area on that day; an API query for all reported geographic areas on that day will not include it.

These signals are inactive. They were updated until May 19, 2020.

• Source name: quidel
• Earliest issue available: April 29, 2020
• Last issued: May 19, 2020
• Number of data revisions since May 19, 2020: 0
• Date of last change: Never
• Available for: msa, state (see geography coding docs)
• Time type: day (see date format docs)

Data source based on flu lab tests, provided to us by Quidel, Inc. When a patient (whether at a doctor’s office, clinic, or hospital) has COVID-like symptoms, doctors may perform a flu test to rule out seasonal flu (influenza), because these two diseases have similar symptoms. Using this lab test data, we estimate the total number of flu tests per medical device (a measure of testing frequency), and the percentage of flu tests that are negative (since ruling out flu leaves open another cause—possibly covid—for the patient’s symptoms), in a given location, on a given day.

The number of flu tests conducted in individual counties can be quite small, so we do not report these signals at the county level.

The flu test data is no longer updated as of May 19, 2020, as the number of flu tests conducted during the summer (outside of the normal flu season) is quite small.

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