Applications of Agent-Based Modelling Techniques to Studies of Historical Epidemics: The 1918 Flu in Newfoundland and Labrador
Bibliographic record
Abstract
The purpose of this article is twofold. First, the study addresses questions related to the spread and impact of the 1918 influenza pandemic in a small Newfoundland community, focusing on the role of large social institutions including an orphanage, school, and churches. Records indicate, for example, that residents of the orphanage in St. Anthony, our study community, experienced an increased risk of infection at different times during the epidemic than did members of the general community. Further, archival sources show that a variety of public health measures including closure of public gathering spaces were implemented throughout Newfoundland, but evidence suggests that the success of these measures varied. Second, this paper presents an argument for the important role computer simulation models can play in historical research, which is demonstrated using results from simulations focusing on social, demographic, and cultural factors, including behaviours and interactions of community residents. These examples highlight how modelling techniques can be used in historical research to address gaps in archival sources and help direct future research paths, and to test counterfactual scenarios to identify important factors influencing observed outcomes.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".