Optimal interventions in networks during a pandemic
Bibliographic record
Abstract
We develop a model of optimal lockdown policy for a social planner who balances population health with short-term wealth accumulation. The unique solution depends on tolerable infection incidence and social network structure. We then use unique data on nursing home networks in the US to calibrate the model and quantify state-level preference for prioritizing health over wealth. We also empirically validate simulation results derived from comparative statics analyses. Our findings suggest that policies that tolerate more virus spread (laissez-faire) increase state GDP growth and COVID-19 deaths in nursing homes. The detrimental effects of laissez-faire policies are more potent for nursing homes that are more peripheral in networks, nursing homes in poorer counties, and nursing homes that operate on a for-profit basis. We also find that US states with Republican governors have a higher tolerable incidence level, but these policies tend to converge with a high death count. Supplementary Information: The online version contains supplementary material available at 10.1007/s00148-022-00916-y.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.010 | 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".