A Patient-Initiated Digital COVID-19 Contact Notification Tool (TellYourContacts): Evaluation Study
Bibliographic record
Abstract
BACKGROUND: Contact notification is a method used to control the spread of infectious disease. In this process, a patient who tests positive for an infectious disease and public health officials work to identify the patient's close contacts, notify them of their risk of possible exposure to the disease, and provide resources to facilitate the decreased spreading of disease. Contact notification can be done physically in person, via phone call, or digitally through the use of media such as SMS text messages and email. When alerts are made through the latter, it is called digital contact notification. OBJECTIVE: For this study, we aim to perform a preliminary evaluation of the use of the TellYourContacts website, a digital contact notification tool for COVID-19 that can be used confidentially and anonymously. We will gather information about the number of website users and message senders, the types of messages sent, and the geographic distribution of senders. METHODS: Patients who chose to get tested for COVID-19 and subsequently tested positive for the disease were alerted of their positive results through Curative Inc (a COVID-19 testing laboratory) and Healthvana (a results disclosure app). Included in the notification was a link to the TellYourContacts website and a message encouraging the person who tested positive for COVID-19 to use the website to alert their close contacts of exposure risk. Over the course of three months, from May 18, 2020, to August 17, 2020, we used Google Analytics and Microsoft Excel to record data on the number of website users and message senders, types of messages sent, and geographic distribution of the senders. RESULTS: Over the course of three months, 9130 users accessed the website and 1474 unique senders sent a total of 1957 messages, which included 1820 (93%) SMS text messages and 137 (7%) emails. Users sent messages from 40 US states, with the majority of US senders residing in California (49%). CONCLUSIONS: We set out to determine if individuals who test positive for COVID-19 will use the TellYourContacts website to notify their close contacts of COVID-19 exposure risk. Our findings reveal that, during the observation period, each unique sender sent an average of 1.33 messages. The TellYourContacts website offers an additional method that individuals can and will use to notify their close contacts about a recent COVID-19 diagnosis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".