Representing Tuberculosis Transmission with Complex Contagion: An Agent-Based Simulation Modeling Approach
Bibliographic record
Abstract
ObjectiveA recent study reported a tuberculosis (TB) outbreak in which, among newly infected individuals, exposure to additional active infections was associated with a higher probability of developing active disease. Referred to as <i>complex contagion</i>, multiple reexposures to TB within a short period after initial infection is hypothesized to confer a greater likelihood of developing active infection in 1 y. The purpose of this article is to develop and validate an agent-based simulation model (ABM) to study the effect of complex contagion on population-level TB transmission dynamics.MethodsWe built an ABM of a TB epidemic using data from a series of outbreaks recorded in the 20th century in Saskatchewan, Canada. We fit 3 dynamical schemes: base, with no complex contagion; additive, in which each reexposure confers an independent risk of activated infection; and threshold, in which a small number of reexposures confers a low risk and a high number of reexposures confers a high risk of activation.ResultsWe find that the base model fits the mortality and incidence output targets best, followed by the threshold and then the additive models. The threshold model fits the incidence better than the base model does but overestimates mortality. All 3 models produce qualitatively realistic epidemic curves.ConclusionWe find that complex contagion qualitatively changes the trajectory of a TB epidemic, although data from a high-incidence setting are reproduced better with the base model. Results from this model demonstrate the feasibility of using ABM to capture nuances in TB transmission.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 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".