The Timing and Intensity of Social Distancing to Flatten the COVID-19 Curve: The Case of Spain
Bibliographic record
Abstract
The continued spread of COVID-19 suggests a significant possibility of reimposing the lockdowns and stricter social distancing similar to the early phase of pandemic control. We present a dynamic model to quantify the impact of isolation for the contagion curves. The model is calibrated to the COVID-19 outbreak in Spain to study the effects of the isolation enforcement following the declaration of the state of alarm (14 March 2020). The simulations indicate that both the timing and the intensity of the isolation enforcement are crucial for the COVID-19 spread. For example, a 4-day earlier intervention for social distancing would have reduced the number of COVID-19 infected people by 67%. The model also informs us that the isolation enforcement does not delay the peak day of the epidemic but slows down its end. When relaxing social distancing, a reduction of the contagion probability (with the generalization of preventive actions, such as face mask wearing and hands sanitizing) is needed to overcome the effect of a rise in the number of interpersonal encounters. We report a threshold level for the contagion pace to avoid a second COVID-19 outbreak in Spain.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".