SEAIR Framework Accounting for a Personalized Risk Prediction Score: Application to the Covid-19 Epidemic
Bibliographic record
Abstract
The aim of the present work is to provide an SEAIR framework which takes a personalized risk prediction score as an additional input. Each individual is categorized depending on his actual status with respect to the disease - moderate or severe symptoms -, and the level of risk predicted - low or high. This idea leads to a 4-fold extension of the ODE model in classical SEAIR. This model offers the possibility for policy-makers to explore differentiated containment strategies, by varying sizes for the low risk segment and varying dates for 'progressive release' of the population, while exploring the discriminative capacity of the risk score, for instance through its AUC. Differential contact rates for low-risk/high-risk compartments are also included in the model. The demo allows to select contact rates and time-depending exit strategies. The hard-coded parameters correspond to the data for the Covid-19 epidemic in France, and the risk refers to the probability of being admitted in ICU upon infection. Some examples of simulations are provided.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".