# Estimating the impact of direct acting antiviral therapy on the prevalence of hepatitis C virus infection using phylogenetics

**Authors:** Hossain M.S. Sazzad, Hui Li, Behzad Hajarizadeh, Bethany A. Horsburgh, Jason Grebely, Gregory J. Dore, Rowena A. Bull, Andrew R. Lloyd, Chaturaka Rodrigo

PMC · DOI: 10.1016/j.virusres.2025.199566 · Virus Research · 2025-03-26

## TL;DR

This study uses phylogenetics to estimate the impact of hepatitis C treatments in Australia, finding that analyzing acute infections gives results closer to traditional methods.

## Contribution

The study demonstrates that phylogenetic analysis of acute HCV infections better reflects epidemiological trends during DAA scale-up.

## Key findings

- Phylogenetic analysis of acute HCV infections aligns more closely with epidemiological estimates of declining infections.
- GT3a showed a 36% decline in effective population size between 2011–2019, matching non-phylogenetic estimates.
- GT1a did not show a similar decline in effective population size using the same methods.

## Abstract

•Phylogenetic models can complement traditional infectious diseases surveillance.•Antivirals against hepatitis C infection became free-of-charge in Australia in 2016.•Epidemiological surveillance found decline in Australian HCV infections since then.•Phylogenetic modelling has limitations in replicating the same.•The mutation rate differences in acute and chronic infection affects phylogenetic inferences.

Phylogenetic models can complement traditional infectious diseases surveillance.

Antivirals against hepatitis C infection became free-of-charge in Australia in 2016.

Epidemiological surveillance found decline in Australian HCV infections since then.

Phylogenetic modelling has limitations in replicating the same.

The mutation rate differences in acute and chronic infection affects phylogenetic inferences.

Australia has provided unrestricted subsidized access to direct-acting antiviral (DAA) treatment for hepatitis C virus (HCV) infection since 2016. Epidemiological surveillance estimates suggest prevalence of chronic HCV infection has declined since 2016, but these estimates are not separated by genotype and may not capture ‘hidden’ infected populations, notably the most marginalized groups affected, including people who inject drugs and people in prison. This study used phylogenetics to assess whether epidemiological estimates of declining HCV prevalence in the prisons of New South Wales, Australia due to DAA scale up could be reproduced.

Near-full-length 280 HCV consensus sequences (GT1a: n = 140, GT3a: n = 140) sampled between 2006 – 2019 from two prison-based cohort studies in NSW were used for phylogenetic estimates. These included 110 acute infection sequences (GT1a: n = 48, GT3a: n = 62) which were considered in a separate sensitivity analysis given the differences in virus mutation rates in acute and chronic infection. Changes in the effective population size of infected people for each genotype were explored with BEAST software suite (v1.10) using a coalescent Bayesian skyline approach.

Both the main and sensitivity analyses for GT3a showed a reduction in the effective population size with the latter showing a 36 % decline between 2011–2019 which is more concordant with the decline estimated from non-phylogenetic methods. A decline of similar magnitude was not demonstrated for GT1a. Overall, the analyses using acute infection sequences only were closer to the trends of independent epidemiological estimates.

An adequately powered Bayesian evolutionary analysis using acute stage infection sequences may reproduce the decline in HCV infections observed by traditional epidemiological methods during DAA scale up.

Image, graphical abstract

## Linked entities

- **Diseases:** hepatitis C virus infection (MONDO:0005231)

## Full-text entities

- **Diseases:** acute (MESH:D000208), infected (MESH:D007239), HCV infection (MESH:D006526)
- **Species:** Homo sapiens (human, species) [taxon 9606]

## Full text

_Full body text omitted from this summary view._ Fetch the complete paper as Markdown: https://tomesphere.com/paper/PMC11999633/full.md

## Figures

3 figures with captions in the complete paper: https://tomesphere.com/paper/PMC11999633/full.md

## References

40 references — full list in the complete paper: https://tomesphere.com/paper/PMC11999633/full.md

---
Source: https://tomesphere.com/paper/PMC11999633