Genomic epidemiology seeks to better understand the transmission dynamics of infectious pathogens using molecular sequence data. Phylodynamic methods have given genomic epidemiology new power to track the transmission dynamics of pathogens by combining phylogenetic analyses with epidemiological modeling. In recent year, applications of phylodynamics to chronic viral infections such as HIV and hepatitis C virus (HVC) have provided some of the best examples of how phylodynamic inference can provide valuable insights into transmission dynamics within and between different subpopulations or risk groups, allowing for more targeted interventions.
However, conducting phylodynamic inference under complex epidemiological models comes with many challenges. In some cases, it is not always straightforward or even possible to perform likelihood-based inference. Structured SIR-type models where infected individuals can belong to different subpopulations provide a classic example. In this case, the model is both nonlinear and has a high-dimensional state space due to tracking different types of hosts. Computing the likelihood of a phylogeny under such a model involves complex numerical integration or data augmentation methods [1]. In these situations, Approximate Bayesian Computation (ABC) provides an attractive alternative, as Bayesian inference can be performed without computing likelihoods as long as one can efficiently simulate data under the model to compare against empirical observations [2].
Previous work has shown how ABC approaches can be applied to fit epidemiological models to phylogenies [3,4]. Danesh et al. [5] further demonstrate the real world merits of ABC by fitting a structured SIR model to HCV data from Lyon, France. Using this model, they infer viral transmission dynamics between “classical” hosts (typically injected drug users) and “new” hosts (typically young MSM) and show that a recent increase in HCV incidence in Lyon is due to considerably higher transmission rates among “new” hosts . This study provides another great example of how phylodynamic analysis can help epidemiologists understand transmission patterns within and between different risk groups and the merits of expanding our toolkit of statistical methods for phylodynamic inference.

References

[1] Rasmussen, D. A., Volz, E. M., and Koelle, K. (2014). Phylodynamic inference for structured epidemiological models. PLoS Comput Biol, 10(4), e1003570. doi: https://doi.org/10.1371/journal.pcbi.1003570
[2] Beaumont, M. A., Zhang, W., and Balding, D. J. (2002). Approximate Bayesian computation in population genetics. Genetics, 162(4), 2025-2035.
[3] Ratmann, O., Donker, G., Meijer, A., Fraser, C., and Koelle, K. (2012). Phylodynamic inference and model assessment with approximate bayesian computation: influenza as a case study. PLoS Comput Biol, 8(12), e1002835. doi: https://doi.org/10.1371/journal.pcbi.1002835
[4] Saulnier, E., Gascuel, O., and Alizon, S. (2017). Inferring epidemiological parameters from phylogenies using regression-ABC: A comparative study. PLoS computational biology, 13(3), e1005416. doi: https://doi.org/10.1371/journal.pcbi.1005416
[5] Danesh, G., Virlogeux, V., Ramière, C., Charre, C., Cotte, L. and Alizon, S. (2020) Quantifying transmission dynamics of acute hepatitis C virus infections in a heterogeneous population using sequence data. bioRxiv, 689158, ver. 5 peer-reviewed and recommended by PCI Evol Biol. doi: https://doi.org/10.1101/689158