Achoo!

Exploring the Phylodynamics of seasonal influenza in NZ

Phylodynamics

  • Term introduced by Grenfell et al. (Science, 2004)
  • Refers to the interplay between "immunodynamics, epidemiology and evolutionary biology", and the effect this has on the shape of pathogen phylogenies.

Influenza

  • Influenza is an RNA virus and mutates very rapidly.
  • Epidemics occur every season (Winter) in NZ.
  • Repeatability: Great system for studying epidemiology!

SHIVERS

  • Southern Hemisphere Influenza and Vaccine Effectiveness, Research and Surveillance
  • Approximately 150 H3N2 genomes sampled over 3 seasons from among the 20 New Zealand district health boards (DHBs)
  • This is a pilot data set - we help determine which data to collect next!

Questions

  • Can the seasonal prevalence dynamics be explained by a compartmental model?
  • How does population structure influence these dynamics?
  • Is there evidence for multiple introductions during a season?

Inferring prevalence curves

The SIR model

  • Susceptible-Infectious-Removed
  • Described graphically as a Stochastic Petri Network:

Relationship between SIR model and genetic data

  • The SIR model + sampling process generates an epidemic trajectory $C$ including sampling events.
  • The generates a sampled transmission tree $T$.
  • Influenza genes evolve down this tree to produce an alignment $A$.

\[ P(T,C|A) \propto P(A|T) P(T|C) P(C|\theta) P(\theta) \]


How do we sample the $(T,C)$ state space?

SIR prevalence from SHIVERS data

  • Analyze all 3 years of sequence data simultaneously.
  • Assume unlinked trees with distinct SIR epidemics conditional on a shared set of model parameters.

Phylogeography of seasonal epidemics

Potential for phylogeographic analyses

  • Each SHIVERS genome tagged with location down to DHB-level.
  • Have three distinct phylogeographic models on hand:
    1. "mugration" model of Lemey et al.
    2. Structured coalescent model of Hudson and Notohara.
    3. BDMM of Kuhnert et al. (in press)
  • None of these are explicitly consider epidemiological models: to do!

Mugration results

Mugration results (North/South)

SC results (North/South)

Detecting multiple introductions

Qualitative evidence

Semi-quantitative evidence

North/South/World model

Vast numbers of introductions?

Where to now?

  • Improve SIR analyses using a more realistic sampling model.
  • Use hierarchical priors and incorporate human movement data to improve SC analyses.
  • Determine the cause of the overestimates of introduction count.
  • ... any others? This is a very rich set of data!