A Web-Based COVID-19 Tool for Testing Residents in Retirement Homes: Development Study
Bibliographic record
Abstract
BACKGROUND: Long-term care facilities have been widely affected by the COVID-19 pandemic. Empirical evidence demonstrated that older people are the most impacted and are at higher risk of mortality after being infected. Regularly testing care facility residents is a practical approach to detecting infections proactively. In many cases, the care staff must perform the tests on the residents while also providing essential care, which in turn causes imbalances in their working time. Once an outbreak occurs, suppressing the spread of the virus in retirement homes (RHs) is challenging because the residents are in contact with each other, and isolation measures cannot be widely enforced. Regular testing strategies, on the other hand, have been shown to effectively prevent outbreaks in RHs. However, high-frequency testing may consume substantial staff working time, which results in a trade-off between the time invested in testing and the time spent providing essential care to residents. OBJECTIVE: We developed a web application (Retirement Home Testing Optimizer) to assist RH managers in identifying effective testing schedules for residents. The outcome of the app, called the "testing strategy," is based on dividing facility residents into groups and then testing no more than 1 group per day. METHODS: We created the web application by incorporating influential factors such as the number of residents and staff, the average rate of contacts, the amount of time spent to test, and constraints on the test interval and size of groups. We developed mixed integer nonlinear programming models for balancing staff workload in long-term care facilities while minimizing the expected detection time of a probable infection inside the facility. Additionally, by leveraging symmetries in the problem, we proposed a fast and efficient local search method to find the optimal solution. RESULTS: Considering the number of residents and staff and other practical constraints of the facilities, the proposed application computes the optimal trade-off testing strategy and suggests the corresponding grouping and testing schedule for residents. The current version of the application is deployed on the server of the Where2Test project and is accessible on their website. The application is open source, and all contents are offered in English and German. We provide comprehensive instructions and guidelines for easy use and understanding of the application's functionalities. The application was launched in July 2022, and it is currently being tested in RHs in Saxony, Germany. CONCLUSIONS: Recommended testing strategies by our application are tailored to each RH and the goals set by the managers. We advise the users of the application that the proposed model and approach focus on the expected scenarios, that is, the expected risk of infection, and they do not guarantee the avoidance of worst-case scenarios.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".