Bibliographic record
Abstract
THE RECENT EMERGENCE OF SEVERE ACUTE RESPIRATORY syndrome (SARS) has drawn attention to the strategies of isolation and quarantine (I&Q) as a method of disease control. The fundamental dilemma associated with the implementation of I&Q is how to predict the populationlevel efficacy of individual quarantine: Which and how many individuals need to be quarantined to achieve effective control at the population level? Although some forms of I&Q have proven effective in SARS, 1,2 they are not appropriate for all infectious diseases. Diseases like varicella, for which costs of quarantine may be high (many work and school days are lost when noninfected contacts are kept at home) and the return minimal (a relatively mild disease is avoided), require a different approach. Furthermore, in some cases, I&Q may be not only costly but harmful. An I&Q policy for varicella, in the long run, may actually increase the average age (and therefore the severity) of first infection. Using I&Q to control rubella in China could actually lead to higher levels of disease because under the current system (of no control), about 97% of the population has rubella antibodies obtained from direct exposure to infectious individuals. 2 Such a level of natural immunity would be impossible to accomplish under the current effective US and Canadian vaccination policies. Mathematical modeling can help determine when I&Q arethebeststrategiesfordiseasecontrolaswellashowthey might affect short- and long-term disease dynamics. Mathematical modeling offers ways of integrating populationlevel knowledge based on previous epidemics with availableindividualandpopulationdatatopredicttheoutcomes of several alternative scenarios. This kind of mathematical epidemiologyisparticularlywellsuitedtoproblemsforwhich formal experimentation is impossible for logistical or ethical reasons. In these situations, mathematical models can play a role in planning and experimental design in epidemiology, ecology, and immunology. Mathematical disease modeling is an attempt to fit empirical data to abstract processes. Decisions must always be made about which variables to exclude from the model. Although inclusion of more variables (for example, the baselinehealthstatusofeveryindividual)wouldmakethemodel more accurate, such models would be impossibly complex. The balance between predictive power and its level of detail depends on the questions the model is intended to answer. Variables that can influence the outcome of I&Q policies include the number of contacts an infected person has per unit of time, the probability of infection per contact, and the proportion of the population that is vacci
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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) | 0.000 | 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 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".