Bibliographic record
Abstract
The susceptible, infected, removed model for epidemics assumes that the population in which the epidemic takes place is well mixed. This strong assumption can be relaxed by permitting the epidemic to spread only along the links of a contact network or graph. This study uses evolutionary computation to search for graphs that exhibit one of two extreme behaviors: maximum epidemic duration or maximal number of individuals catching the disease. The focus of the paper is on comparison of two representations for evolvable networks. The first makes local expansions of the network specified by a linear chromosome. The second, a permutation-based representation, joins a large cycle with another cycle specified by the permutation. The linear chromosome representation, based on iterated simplexification, yields inferior results in both fitness measures but creates networks with a structure more like a personal contact network. Location of such behaviorally extreme networks will provide a set of test cases for intervention strategies as well as providing conjectures to focus standard mathematical investigation of the types of networks that yield extreme behavior. This study also proposes a testing protocol for network representations for epidemic 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 | 0.000 |
| 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) | 0.004 | 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".