Mathematical and statistical modeling of infection and transmission dynamics of viral diseases
Bibliographic record
Abstract
The modeling of viral disease has recently piqued the interest of researchers. It is fundamental and a source of concern to accurately predict the spread and transmission of a viral disease, considering its evolution with time. It is very complicated to model a subpopulation with underlying diseases, considering population heterogeneity and epidemic-influencing factors.<br/><br/>For this purpose, we hope to be able to gather papers that will use mathematical and statistical models to demonstrate a link between the endemic and epidemic states of a viral disease, as well as take into account population and transmission heterogeneity and some of the mitigation measures to combat the disease, as well as the possibility that some of the population may be infected with other diseases, leading to co-infection.<br/><br/>Potential topics include (but are not limited to) the following: <br/>- Prediction of the transition between endemic state and epidemic wave; <br/>- Estimation of the daily reproduction number; <br/>- Risk-benefit of the vaccinations in age and comorbidity classes; <br/>- Mutation and evolution of viral pathogen modeling; <br/>- Simulation of the mitigation measures; <br/>- Viral co-infections modeling.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".