Phylogenetic inference of the transmission and migration dynamics of SARS-CoV-2 and HIV-1 in Canada
Bibliographic record
Abstract
Viral genomes sampled through epidemics illuminate transmission dynamics and evolution, facilitated by bioinformatics, phylogenetics, and genomic epidemiology tools to reconstruct evolutionary trees. Robust inference of large phylogenetic trees depicting viruses’ shared ancestry remains challenging due to computational burden, data biases, model selection, and appropriate use and interpretation of tree-derived metrics. I applied phylogenetics to reconstruct the epidemiological dynamics and evolution of human viruses, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and human immunodeficiency virus 1 (HIV-1), in Canada to evaluate the effectiveness of public health interventions in reducing human disease burden. For SARS-CoV-2, I developed a phylogeographic pipeline that reduced sampling bias to reconstruct the timing, origin, destination, and spread of SARS-CoV-2 introductions ancestral to samples in Canada during the first two waves and since the predomination of variants of concern (VOCs) up to early Omicron BA.1 and BA.2. These analyses support that increased stringency of non-pharmaceutical interventions (NPIs) including travel restrictions effectively reduced viral importation rates into Canada and in particular contexts, also case burden. For HIV-1 in British Columbia, we compared growth and drug resistance among phylogenetic clusters, which represent individuals linked through recent outbreaks, to evaluate how effectively and heterogeneously pre-exposure prophylaxis (PrEP) has reduced the effective reproductive number (Re). Newly diagnosed PrEP users were more likely than non-PrEP users to join clusters and were at increased risk of carrying baseline M184IV mutation, conferring drug resistance to nucleoside reverse transcriptase inhibitor (NRTI) drugs commonly prescribed in combined antiretroviral therapies. Widespread PrEP availability since 2018 in BC has been successful, with an associated reduction of Re in the gay, bisexual, and other men who have sex with men (GBM) community, but several GBM-predominant clusters had no reduction in Re since PrEP, highlighting groups who could benefit from prioritized treatment and prevention resources. This body of work contributes novel applications of phylogenetics to reconstruct viral epidemics, including subsampling, bootstrapping, and counterfactual stochastic modeling, which were interpreted within the context of interventions, informing infectious disease dynamics and policy.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".