Phylodynamic Inference across Epidemic Scales

Abstract

Within-host genetic diversity and large transmission bottlenecks confound phylodynamic inference of epidemiological dynamics. Conventional phylodynamic approaches assume that nodes in a time-scaled pathogen phylogeny correspond closely to the time of transmission between hosts that are ancestral to the sample. However, when hosts harbor diverse pathogen populations, node times can substantially pre-date infection times. Imperfect bottlenecks can cause lineages sampled in different individuals to coalesce in unexpected patterns. To address realistic violations of standard phylodynamic assumptions we developed a new inference approach based on a multi-scale coalescent model, accounting for nonlinear epidemiological dynamics, heterogeneous sampling through time, non-negligible genetic diversity of pathogens within hosts, and imperfect transmission bottlenecks. We apply this method to HIV-1 and Ebola virus (EBOV) outbreak sequence data, illustrating how and when conventional phylodynamic inference may give misleading results. Within-host diversity of HIV-1 causes substantial upwards bias in the number of infected hosts using conventional coalescent models, but estimates using the multi-scale model have greater consistency with reported number of diagnoses through time. In contrast, we find that within-host diversity of EBOV has little influence on estimated numbers of infected hosts or reproduction numbers, and estimates are highly consistent with the reported number of diagnoses through time. The multi-scale coalescent also enables estimation of within-host effective population size using single sequences from a random sample of patients. We find within-host population genetic diversity of HIV-1 p17 to be 2Nμ=0.012 (95% CI 0.0066-0.023), which is lower than estimates based on HIV envelope serial sequencing of individual patients.

Keywords: Ebola; HIV; coalescent; phylodynamics.

Fig. 1

The pretransmission interval and incomplete lineage sorting. The shaded tree represents a transmission chain where each region represents the pathogen population in each of three patients. The width of the shaded regions corresponds to the genetic diversity. In this scenario, A infects B with an imperfect transmission bottleneck, and then B infects C. The genealogy at the bottom is reconstructed from a sample of a single lineage from each patient at three distinct time points. When diversity exists in donor A, a pre-transmission interval will occur at each inferred transmission event (MRCA(A,B) precedes transmission from A to B), and the order of transmission events may become randomized in the virus genealogy. Note that the pre-transmission interval also is a random variable defined by the donor’s diversity at time of each transmission. Terminal branch lengths are also elongated due to these processes.

Fig. 2

Estimation of population size and transmission rates from simulated pathogen genealogies in a stochastic exponentially growing epidemic with large within-host effective population size. Model parameters are described in the text. (A) Example epidemic trajectory (red) and estimated number infected through time (black). Shaded region shows 95% using parametric bootstrap. (B) Distribution of the estimated population size at the last sample point using both traditional coalescent model (CoM) and the new MSCoM. (C) Estimated transmission rates using the MSCoM across all simulation replicates with 95% CIs based on parametric bootstrap. The red line shows the true transmission rate. (D) Comparison of the estimated (MSCoM) and true population size across all simulation replicates. Colors indicate time in the epidemic when the population size comparison is made. Green corresponds to the early epidemic and red corresponds to the late epidemic.

Fig. 3

Phylodynamic analysis of 227 HIV-1 gag p17 sequences from an outbreak in Latvia showing estimated number infections, effective population size, and R₀. Shaded regions show 95% CIs. (A) Estimated cumulative number of infections through time (blue) using the multi-scale coalescent model that accounts for within-host evolution. Points show cumulative reported diagnoses in the outbreak. (B) Estimated cumulative number of infections through time (blue) using the coalescent model developed in Volz (2012) that does not account for within-host evolution. (C) Estimated epidemic effective population size through time using BNPR (Karcher et al. 2016). (D) Estimated posterior reproduction numbers and within-host effective population sizes in units of coalescent time (years). Red dashed lines show 95% interquantile range and solid dash line shows posterior median.

Fig. 4

Phylodynamic analysis of time-scaled phylogenies estimated in Gire et al. (2014) based on 78 whole genome sequences from EBOV patients in Sierra Leone in 2014 using MSCoM (A) and CoM12 (B). Trajectories show estimated cumulative infections through time. The shaded region shows 95% CIs. The red shaded region shows a prediction over a time period where no sequence data were collected. Points show cumulative WHO case reports in Sierra Leone.