Evaluation of MyCOVIDRisk App Users: An Updated Risk Evaluation and Mitigation Tool for Public Use
Bibliographic record
Abstract
Background The MyCOVIDRisk app is a free web-based tool for the public to quickly estimate the likelihood of COVID-19 infection based on individual behavior, environmental factors, and local case counts. User input of activities and mitigation measures impact the modifiable risk estimates. Originally launched in October 2020, an updated version was released in November 2021 to account for the transmission dynamics of delta and omicron variants and the protective effects of vaccination. Objective This study aims to assess trends in (1) user characteristics, (2) projected risk level, and (3) mitigation measures selected by users since the app’s inception. Methods We tracked overall site usage with Google Analytics. To describe user inputs (preferred activities, gathering sizes, vaccination status, and other risk mitigation steps), we aggregated back-end app data logging at every run of the risk analysis algorithm. We calculated descriptive statistics. Results As of March 1, 2022, the MyCOVIDRisk app has been used 1,339,940 times (1,231,546 times in v1 and 108,394 times in v2). Multiple characteristics of activities changed across the 2 versions. For example, the top activity in v1 was “Visiting Friend’s House” (22.6%, n=146,399); versus “Family Dinner” in v2 (21.3%, n=19,724). In v1, only 0.7% of users who were originally “high risk” and 10.8% of those who were originally “moderate risk” decreased their predicted risk to “low” using layered mitigation steps. In v2, in comparison, 24.4% of high-risk and 24.8% of moderate-risk activities were decreased to low risk. Conclusions The MyCOVIDRisk App allows individuals to assess in real time risk of being infected by SARS-CoV-2. Using app-directed mitigation steps, users were able to reduce their predicted risk of COVID-19 transmission during daily activities. Patterns of mask use and types of activities changed over time. In v2, users were more likely to report being vaccinated or boosted and wearing masks compared to what national statistics suggest. Future iterations of the app should assess actual change in behavior and should aim to reach those who are not currently vaccinated or masking. Acknowledgments We would like to thank Rebecca Wang, Jason Peres da Silva, and Anusha Rahman. Conflicts of Interest None declared.
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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 | 0.012 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".