Modeling herd immunity: Insights from human behavior during COVID-19
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We present a comprehensive SEIRDI[Formula: see text] model incorporating six compartments to examine the progression of COVID-19 and evaluate control strategies. Our findings reveal that implementing partial restrictions with varying levels of stringency delays the infection peak by approximately 160 days, alleviating pressure on healthcare systems. Under a herd immunity approach with an 85% immunity threshold, at least 425 days (14 months) are required for a gradual return to normalcy. Accelerated vaccination significantly shortens this timeline, emphasizing the critical role of immunization in societal reopening. Additionally, we highlight the importance of rigorously assessing post-vaccine side effects to uphold ethical and scientific standards. These insights provide actionable guidance for policymakers to balance public health interventions with economic recovery.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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