Effective Vaccination Strategy for Infectious Diseases by Analyzing the Age and Comorbidity Attributes of Individuals on Social Network
Bibliographic record
Abstract
Infectious diseases have a profound impact on human society, and although they can cause serious consequences, including loss of life and economic impacts, vaccinations can prevent or minimize their impact. Vaccination strategies, including applying limited vaccines to a population to minimize outbreaks, are crucial to maximize vaccination effectiveness. An evolutionary computation system is designed for generating vaccination strategies based on a social contact network’s topology, as well as individual’s age and comorbidity characteristics, to examine how age and comorbidities impact vaccination strategies. After testing the candidate strategies on multiple graphs and analyzing the results, the results indicated that the base strategies (e.g. vaccinating high degree nodes, vaccinating the population randomly) perform the worst when minimizing the maximum and total number of infected, while normal strategies (derived from general Power-Law Cluster Graphs) were effective, and aged-derived strategies were more effective than comorbidity-derived strategies. It was observed that by implementing these attributes in the graph topology, rather than considering them as graph measures, the effectiveness of the regular strategies could be increased.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".