Coupled models of structured contagion processes in human-environment systems
Bibliographic record
Abstract
Models of infectious processes are a common feature in the landscape of applied mathematics. It is rare that these processes are isolated from other significant dynamics in nature, and therefore we can incorporate some of the complexity inherent in real systems by coupling infections to major features of the ecosystems they inhabit. Infectious processes can take many forms, but in this thesis we consider three: the COVID-19 pandemic, the invasion of eastern North American forests by wood-borne pests, and the outbreak cycles of an endemic forest pest. The first chapter covers a model of Sars-CoV-2 in a structured population, coupled with a replicator equation representing sentiment towards the use of non-pharmaceutical interventions. We use this human-environment model of to compare the efficacy of vulnerable-first and transmission-preventing age structured vaccination strategies. The buildup of natural immunity in a population combined with a low vaccination supply is shown to cause a transmission-preventing vaccination strategy to be more effective. The second chapter considers a spatially structured model of forest pest contagion over an empirically-derived network of forest patches in eastern Canada. Since these pests can frequently be spread long distances by wood transport, we couple this model to the sentiment of local populations towards avoiding firewood transport from outside their area. Three possible countermeasures to the spread of the invasive pest are compared: social incentives, direct interception of infested firewood, and quarantine of patches. The level of effort needed to significantly reduce forest damage with any of these methods is substantial and unlikely to be implemented. The final chapter extends a model of mountain pine beetle (MPB) in western north american pine forests to incorporate tree mortality due to wildfire. We find that wildfire acts as a disturbance that increases the heterogeneity in age structure, and therefore is able to increase the resilience of the forest to outbreaks of MPB. A targeted thinning procedure aimed specifically at increasing heterogeneity in the forest age structure is proposed and shown to be highly effective at reducing the severity of outbreak. The effectiveness of targeted thinning in the manner described further emphasizes the importance of heterogeneity in forest stand structure. Each model tests the importance of indirect protection in preventing the spread of an infectious agent through a specific host population, with respect to key parameters. Models let us use counterfactuals to gain potentially invaluable understanding of these complex human-environment systems.
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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 | 0.006 |
| 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 | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".