The Impact of Demographic Variables on Disease Spread: Influenza in Remote Communities
Bibliographic record
Abstract
The role of demographic variables in disease spread in remote and isolated communities is poorly understood. We developed an agent-based model of a small indigenous community to qualitatively study the impact of pre-existing immunity in both young and elderly populations. We observed that in crowded living conditions, the age distribution of the population is a critical factor influencing epidemic spread. As the average age of the population increases, the effect of the pre-existing immunity in older individuals becomes more pronounced in decreasing disease incidence, even when pre-existing immunity levels in young individuals are low. However, in a non-crowded setting with relatively low average persons-per-household, the pre-existing immunity levels of young individuals remains a determining factor, regardless of the age distribution of the population. We suggest that for optimizing public health policies, social and demographic complexities of the remote and vulnerable communities should be carefully evaluated in modeling intervention strategies.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".