A Dynamical Framework for Modeling Fear of Infection and Frustration\n with Social Distancing in COVID-19 Spread
Bibliographic record
Abstract
In this paper, we introduce a novel modeling framework for incorporating fear\nof infection and frustration with social distancing into disease dynamics. We\nshow that the resulting SEIR behavior-perception model has three principal\nmodes of qualitative behavior---no outbreak, controlled outbreak, and\nuncontrolled outbreak. We also demonstrate that the model can produce transient\nand sustained waves of infection consistent with secondary outbreaks. We fit\nthe model to cumulative COVID-19 case and mortality data from several regions.\nOur analysis suggests that regions which experience a significant decline after\nthe first wave of infection, such as Canada and Israel, are more likely to\ncontain secondary waves of infection, whereas regions which only achieve\nmoderate success in mitigating the disease's spread initially, such as the\nUnited States, are likely to experience substantial secondary waves or\nuncontrolled outbreaks.\n
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".