Modeling the spread of the 1918 Influenza pandemic in a Newfoundland community
Bibliographic record
Abstract
Infectious disease epidemics have played and continue to play important roles in human populations. At different geographical levels, the spread of epidemics are affected by multiple demographic, social, cultural, political, economic, and other factors. Variation in these factors often produces different local or regional outcomes, so it is important for researchers to understand how individual behaviors and interactions can produce and explain larger patterns of disease spread. In small, traditional communities, important factors include settlement and household organization, daily behaviors, and relationships among residents. This research uses two computer simulation models to test the relative impact of these factors on disease spread in a small study community in Newfoundland and Labrador in the early 20th century, using data from the 1918 flu pandemic and other archival sources. In the agent-based model, which emphasizes movement to important social spaces, schoolchildren drive the size and timing of epidemics. In the social network model, which reflects important relationships among community residents, epidemics begun by adult women tend to be slower and smaller than epidemics begun by other types of individuals. These results demonstrate that, based on their roles in the community, members of different age and sex groups can strongly affect epidemic outcomes. Further, because simulation models are often used to develop or recommend public health policies or intervention strategies, the different results of the two models indicate the importance of selecting appropriate design features to ensure the best possible recommendations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".