Challenges in epidemiological modelling: from socio-virulence dynamics to HIV interventions in MSM populations.
Bibliographic record
Abstract
The spread of infectious diseases is one of the biggest challenges that public health faces nowadays. Their control is often not an easy task. Social behaviour plays an important role in disease prevention. However, the complex interplay between social behaviour and transmission dynamics is still not very well understood. This thesis tackles this topic by introducing a dynamic variable representing social behaviour that is coupled with epidemiological compartmental models. Under certain conditions, social behaviour can greatly impact the outcomes of an emerging virulent pathogen and thus it is necessary to explicitly model social behaviour for better understanding of transmission dynamics, and better and more accurate predictions. \n \nThe Human Immunodeficiency Virus (HIV) is a disease that attacks the immune system of its host. Since its emergence in the 1970s, it remains an epidemic that disproportionately affects gay and bisexual men who have sex with men (GbMSM). While diagnosis and treatment procedures kept improving, it still poses a public health concern. The introduction of Pre-Exposure Prophylaxis (PrEP) as a preventive drug in Canada in 2015 was a crucial step to help with decreasing HIV prevalence. PrEP however doesn't prevent the transmission of bacterial sexually transmitted infections (STIs). Previous studies have found a positive correlation between the increase in PrEP use and the decrease of condom usage. We investigated conditions under which the prevalence of bacterial STIs remains low under a PrEP regimen and we found that population level annual testing is essential for risk mitigation. \n \nWhile PrEP has a great potential in reducing HIV prevalence, its impact might not be as strong as that created by frequent testing. In a final study, we examined different strategies, that are season specific and risk level specific, to derive an optimal strategy that aims to reduce HIV prevalence in Toronto by 2050. Given that higher risk sexual behaviour is more observed during the summer months, we concluded that testing the entire population twice during the summer is the most effective way to get a low HIV prevalence subject to plausible levels of testing and PrEP recruitment.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".