CONVENIENT LINKS BETWEEN TIME VARYING INCIDENCE RATES AND CURRENT STATUS INFORMATION FOR EPIDEMIOLOGICAL MODELS WITH HETEROGENEITY
Bibliographic record
Abstract
Many compartment based epidemiological models are written as differential equation systems for various status subpopulation sizes with per person-time transfer rates between compartments. However, field data obtained by sampling at chosen times is usually provided in terms of status proportions from the total observable population (e.g., relative prevalence). Relationships between per person-time transfer rates (incidence, mortality, intervention rates) and proportions are not obvious when heterogeneity is at work because the various subpopulation sizes undergo different attrition rates and are not evolving in synchrony with the corresponding proportions. Rules are proposed to write sets of differential equations for compartment models, directly in terms of the proportions of the total observable at any time. To facilitate the writing of relationships between per person-time transfer rates and proportions, the systems are cast in network equivalent forms satisfying rules analogous to those of electrical networks (Kirchhoff's law for currents). The method is also extended to variability in the rates within a status subpopulation, considering either a fixed set of compartmental subdivisions or an inner continuum of differences in rates.
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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 | 0.010 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.013 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".