On the estimation of the time-dependent transmission rate in epidemiological models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The COVID-19 pandemic highlighted the need to improve the modeling, estimation, and prediction of how infectious diseases spread. Susceptible-exposed-infected-removed (SEIR)-like models have been particularly successful in providing accurate short-term predictions. this study fills a notable literature gap by exploring the following question: is it possible to incorporate a nonparametric SEIR COVID-19 model into the inverse-problem regularization framework when the transmission coefficient varies over time? our positive response considers varying degrees of disease severity, vaccination, and other time-dependent parameters. in addition, we demonstrate the continuity, differentiability, and injectivity of the operator that link the transmission parameter to the observed infection numbers. by employing Tikhonov-type regularization to the corresponding inverse problem, we establish the existence and stability of regularized solutions. numerical examples using both synthetic and real infection data from Chicago and Canada illustrate the accuracy of the model estimation and its ability to fit the data effectively.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it