Estimating The Undiagnosed HIV-Positive Population A Mathematical Modeling Study
Bibliographic record
Abstract
In this thesis a mathematical model of HIV transmission and diagnosis is used to estimate the total size of the HIV-positive population and the HIV incidence from HIV case report data for the Province of Alberta. Worldwide, estimates of the size of the HIV-positive population are used to allocate medical resources and target disease prevention efforts, while estimates of HIV incidence are used to evaluate the effectiveness of intervention programs and track changes in risk behaviours. Many HIV surveillance programs are based on reports of newly diagnosed cases. Estimating the total size of the HIV-positive population from this data is challenging as those who are HIV-positive but have not been diagnosed are not included. Furthermore, trends in HIV diagnosis do not reflect trends in incidence as the length of time newly diagnosed HIV patients have been infected is usually unknown. Fitting the model used in this thesis is complicated by the presence of non-identifiable parameters. Non-identifiable parameters occur when all parameter values on a surface in the parameter space have identical model outcomes for the quantities represented in the data. Methods for systematic detection of this behaviour and resolution of nonidentifiabilities are discussed in a general modelling framework and applied to the HIV model for the assessment of the Province of Alberta data. Interval estimates for all parameters are obtained using an iterated Markov chain Monte Carlo (MCMC) method and the resulting fitted model is validated. The validated model is used to produce estimates of the total size of the HIV-positive population including those who have not been diagnosed for the years 2001 to 2020. Estimates of HIV incidence, time from infection to diagnosis, and the size of the undiagnosed population are also computed using the model. Uncertainty and sensitivity analysis are used to determine how much uncertainty remains in these estimates and which parameters are most important to the model outcomes. Finally, the model is used to simulate several potential intervention strategies to reduce HIV incidence in the province. The potential impact of antiretroviral drug resistant strains of HIV on a hypothetical “treatment as prevention” program in the context of a generalized HIV epidemic is studied using another model. This model includes the development and transmission of drug resistant viral strains. Sensitivity and uncertainty analyses are used to explore the potential outcomes. Finally, the asymptotic behaviour of a simple disease model similar to the Alberta HIV model, but using more general forms of population dependent transmission, is analyzed mathematically. It is shown that for some types of population dependence this model can display complicated dynamical behaviours including backward bifurcations and Hopf bifurcations.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".