MODELING THE IMPACT OF ENVIRONMENTAL FACTORS, DIAPAUSE AND CONTROL STRATEGIES ON TICK AND TICK-BORNE DISEASE DYNAMICS
Bibliographic record
Abstract
In this thesis, we focus on mathematical formulation and analyses for a specific vector responsible for a wide variety of diseases: ticks. Due to climate change, various tick species are rapidly spreading northward from the United States and have increasingly affected the Canadian population through a variety of tick-borne diseases including Lyme disease. In order to address this problem, the Public Health Agency of Canada has dedicated an entire section of its website to discuss the risks and the possibility of preventing, recognising and treating tick-bites. It is therefore important to analyse tick and tick-borne disease dynamics in order to better understand, study and prevent possible new outbreaks. \n\nWe aim to achieve this by using mathematical and epidemiological tools including dynamical systems, ordinary and delay differential equations, basic reproduction numbers and Hopf bifurcation theory. For this purpose, we produce three different models to study the effect of physiological features such as diapause, control strategies and the effect of environmental conditions on tick and tick-borne disease persistence and periodicity.\n\nThe first model we propose is a two-patch tick population model in which we show how the tick reproduction number $R_{T,c}$ affects the long-term behaviours of tick population and how it might lead to extinction, convergence to a coexistent or to a periodic solution. The second model is a single tick population model with switching delays. In this, we prove how oscillations of different frequencies caused by two delays might produce multi-cycle periodic solutions. The last model we analyse is a tick-host model including host control strategies. In this work, we find that there are situations in which the improper application of repellent and acaricide may lead to unexpected results and improve disease spread instead of reducing it.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".