Phylogenetic and Phylodynamic Analyses of HCV Strains Circulating among Patients Using Injectable Drugs in Central Italy

Scalable gradients enable Hamiltonian Monte Carlo sampling for phylodynamic inference under episodic birth-death-sampling models

Birth-death models play a key role in phylodynamic analysis for their interpretation in terms of key epidemiological parameters. In particular, models with piecewise-constant rates varying at different epochs in time, to which we refer as episodic birth-death-sampling (EBDS) models, are valuable for their reflection of changing transmission dynamics over time. A challenge, however, that persists with current time-varying model inference procedures is their lack of computational efficiency. This limitation hinders the full utilization of these models in large-scale phylodynamic analyses, especially when dealing with high-dimensional parameter vectors that exhibit strong correlations. We present here a linear-time algorithm to compute the gradient of the birth-death model sampling density with respect to all time-varying parameters, and we implement this algorithm within a gradient-based Hamiltonian Monte Carlo (HMC) sampler to alleviate the computational burden of conducting inference under a wide variety of structures of, as well as priors for, EBDS processes. We assess this approach using three different real world data examples, including the HIV epidemic in Odesa, Ukraine, seasonal influenza A/H3N2 virus dynamics in New York state, America, and Ebola outbreak in West Africa. HMC sampling exhibits a substantial efficiency boost, delivering a 10- to 200-fold increase in minimum effective sample size per unit-time, in comparison to a Metropolis-Hastings-based approach. Additionally, we show the robustness of our implementation in both allowing for flexible prior choices and in modeling the transmission dynamics of various pathogens by accurately capturing the changing trend of viral effective reproductive number.

Scalable gradients enable Hamiltonian Monte Carlo sampling for phylodynamic inference under episodic birth-death-sampling models

Birth-death models play a key role in phylodynamic analysis for their interpretation in terms of key epidemiological parameters. In particular, models with piecewise-constant rates varying at different epochs in time, to which we refer as episodic birth-death-sampling (EBDS) models, are valuable for their reflection of changing transmission dynamics over time. A challenge, however, that persists with current time-varying model inference procedures is their lack of computational efficiency. This limitation hinders the full utilization of these models in large-scale phylodynamic analyses, especially when dealing with high-dimensional parameter vectors that exhibit strong correlations. We present here a linear-time algorithm to compute the gradient of the birth-death model sampling density with respect to all time-varying parameters, and we implement this algorithm within a gradient-based Hamiltonian Monte Carlo (HMC) sampler to alleviate the computational burden of conducting inference under a wide variety of structures of, as well as priors for, EBDS processes. We assess this approach using three different real world data examples, including the HIV epidemic in Odesa, Ukraine, seasonal influenza A/H3N2 virus dynamics in New York state, America, and Ebola outbreak in West Africa. HMC sampling exhibits a substantial efficiency boost, delivering a 10- to 200-fold increase in minimum effective sample size per unit-time, in comparison to a Metropolis-Hastings-based approach. Additionally, we show the robustness of our implementation in both allowing for flexible prior choices and in modeling the transmission dynamics of various pathogens by accurately capturing the changing trend of viral effective reproductive number.

Molecular Epidemiology and Baseline Resistance of Hepatitis C Virus to Direct Acting Antivirals in Croatia

Molecular epidemiology of hepatitis C virus (HCV) is exceptionally complex due to the highly diverse HCV genome. Genetic diversity, transmission dynamics, and epidemic history of the most common HCV genotypes were inferred by population sequencing of the HCV NS3, NS5A, and NS5B region followed by phylogenetic and phylodynamic analysis. The results of this research suggest high overall prevalence of baseline NS3 resistance associate substitutions (RAS) (33.0%), moderate prevalence of NS5A RAS (13.7%), and low prevalence of nucleoside inhibitor NS5B RAS (8.3%). Prevalence of RAS significantly differed according to HCV genotype, with the highest prevalence of baseline resistance to NS3 inhibitors and NS5A inhibitors observed in HCV subtype 1a (68.8%) and subtype 1b (21.3%), respectively. Phylogenetic tree reconstructions showed two distinct clades within the subtype 1a, clade I (62.4%) and clade II (37.6%). NS3 RAS were preferentially associated with clade I. Phylogenetic analysis demonstrated that 27 (9.0%) HCV sequences had a presumed epidemiological link with another sequence and classified into 13 transmission pairs or clusters which were predominantly comprised of subtype 3a viruses and commonly detected among intravenous drug users (IDU). Phylodynamic analyses highlighted an exponential increase in subtype 1a and 3a effective population size in the late 20th century, which is a period associated with an explosive increase in the number of IDU in Croatia.