Behavioral regimes in the evolution of extremal epidemic graphs
Bibliographic record
Abstract
Models of epidemic spread often incorporate contact networks along which the epidemic can spread. The character of the network can have a substantial impact on the course of the epidemic. In this study networks are optimized to yield long-lasting epidemics. These networks represent an upper bound on one type of network behavior. The evolutionary algorithm used searches the space of networks with a specified degree sequence, with degrees representing the number of sexual partners of each member of the population. The representation used is a linear chromosome specifying a series of editing moves applied to an initial network. The initial network specifies the degree sequence of the searched networks implicitly and the editing moves preserve the degree sequence. The evolutionary algorithm uses a non-standard type of restart in which the currently best network in the population replaces the initial network. This restart operator is called a recentering operator. The recentering operator moves the evolving population to successively higher fitness portions of the network space. In this study the algorithm is applied to networks with average degree from 2.5 to 7. In low-degree networks, short epidemics result from failure of the disease to spread through the relatively sparse links of the network. In high-degree networks, short epidemics result from the rapid infection of the entire population. The evolutionary algorithm is able to optimize both high and low degree networks to significantly increase the epidemic duration.
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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 | 0.000 | 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) | 0.000 | 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".