A theory of voluntary testing and self‐isolation in an ongoing pandemic
Why this work is in the frame
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Bibliographic record
Abstract
Beyond Covid-19, there is a growing interest in what economic structures will be needed to face ongoing pandemics. In this paper, we focus on the diagnostic problem and examine a new paradigm of voluntary self-testing by private individuals. We develop a dynamic model where individuals without symptoms face daily choices of either taking the risk of going out (to work and socialize), staying at home in self-isolation, or using a test to verify whether they are infected before going out. Our central insight is that the equilibrium public infection risk falls when home-based testing becomes cheaper and easier to use, even if they generate both false-positive (type I error) and false-negative (type II error) test outcomes. We also show that the presence of naïve individuals actually reduces the equilibrium infection risk in the economy. Overall our model shows that, even if inaccurate, home-based tests are vital for an economy facing an ongoing pandemic.
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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 | 0.016 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it