Pandemic policy assessment by artificial intelligence
Bibliographic record
Abstract
Mobility-control policy is a controversial nonpharmacological approach to pandemic control due to its restriction on people's liberty and economic impacts. Due to the computational complexity of mobility control, it is challenging to assess or compare alternative policies. Here, we develop a pandemic policy assessment system that employs artificial intelligence (AI) to evaluate and analyze mobility-control policies. The system includes three components: (1) a general simulation framework that models different policies to comparable network-flow control problems; (2) a reinforcement-learning (RL) oracle to explore the upper-bound execution results of policies; and (3) comprehensive protocols for converting the RL results to policy-assessment measures, including execution complexity, effectiveness, cost and benefit, and risk. We applied the system to real-world metropolitan data and evaluated three popular policies: city lockdown, community quarantine, and route management. For each policy, we generated mobility-pandemic trade-off frontiers. The results manifest that the smartest policies, such as route management, have high execution complexity but limited additional gain from mobility retention. In contrast, a moderate-level intelligent policy such as community quarantine has acceptable execution complexity but can effectively suppress infections and largely mitigate mobility interventions. The frontiers also show one or two turning points, reflecting the safe threshold of mobility retention when considering policy-execution errors. In addition, we simulated different policy environments and found inspirations for the current policy debates on the zero-COVID policy, vaccination policy, and relaxing restrictions.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".