Which COVID policies are most effective? A Bayesian analysis of COVID-19 by jurisdiction
Bibliographic record
Abstract
This paper reports the results of a Bayesian analysis on large-scale empirical data to assess the effectiveness of eleven types of COVID-control policies that have been implemented at various levels of intensity in 40 countries and U.S. states since the onset of the pandemic. The analysis estimates the marginal impact of each type and level of policy as implemented in concert with other policies. The purpose is to provide policymakers and the general public with an estimate of the relative effectiveness of various COVID-control strategies. We find that a set of widely implemented core policies reduces the spread of virus but not by enough to contain the pandemic except in a few highly compliant jurisdictions. The core policies include the cancellation of public events, restriction of gatherings to fewer than 100 people, recommendation to stay at home, recommended restrictions on internal movement, implementation of a partial international travel ban, and coordination of information campaigns. For the median jurisdiction, these policies reduce growth rate in new infections from an estimated 270% per week to approximately 49% per week, but this impact is insufficient to prevent eventual transmission throughout the population because containment occurs only when a jurisdiction reduces growth in COVID infection to below zero. Most jurisdictions must also implement additional policies, each of which has the potential to reduce weekly COVID growth rate by 10 percentage points or more. The slate of these additional high-impact policies includes targeted or full workplace closings for all but essential workers, stay-at-home requirements, and targeted school closures.
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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 | 0.001 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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".