Using cultural, historical, and epidemiological data to inform, calibrate, and verify model structures in agent-based simulations
Bibliographic record
Abstract
Agent-based simulation models are excellent tools for addressing questions about the spread of infectious diseases in human populations because realistic, complex behaviors as well as random factors can readily be incorporated. Agent-based models are flexible and allow for a wide variety of behaviors, time-related variables, and geographies, making the calibration process an extremely important step in model development. Such calibration procedures, including verification and validation, may be complicated, however, and usually require incorporation of substantial empirical data and theoretical knowledge of the populations and processes under study. This paper describes steps taken to build and calibrate an agent-based model of epidemic spread in an early 20th century fishing village in Newfoundland and Labrador, including a description of some of the detailed ethnographic and historical data available. We illustrate how these data were used to develop the structure of specific parts of the model. The resulting model, however, is designed to reflect a generic small community during the early 20th century and the spread of a directly transmitted disease within such a community, not the specific place that provided the data. Following the description of model development, we present the results of a replication study used to confirm the model behaves as intended. This study is also used to identify the number of simulations necessary for high confidence in average model output. We also present selected results from extensive sensitivity analyses to assess the effect that variation in parameter values has on model outcomes. After careful calibration and verification, the model can be used to address specific practical questions of interest. We provide an illustrative example of this process.
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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 | 0.014 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".