# next on phyloseminar.org

## Viral phylodynamics

Phylodynamics of infectious disease epidemics

The genetic diversity of many pathogens is shaped by epidemiological history. But, the dynamics of infectious disease epidemics differ in important ways from demographic processes that have traditionally been studied by population geneticists. In many epidemics, the population size and birth rate changes rapidly in a nonlinear fashion through time. Mathematical models for describing infectious disease dynamics have a long history that has run parallel to the development of modern population genetics, but until recently, there has been little communication between these fields.Interest has grown in developing a new set of mathematical models for genealogies generated by epidemic processes. These methods reveal how the effective population size of a pathogen depends on transmission rates, the number of infected hosts, and the size of the bottleneck at the time of transmission. These mathematical models have also enabled new applications of pathogen genetic data to public health. Pathogen genetic data can be informative about epidemic processes in ways that standard surveillance data are not, especially regarding the source of infections and risk factors for transmission. I will review several approaches to mathematical modeling of pathogen genealogies and present applications of these methods to HIV-1 and the recent Ebola virus epidemic in Western Africa.

Epidemic reconstruction in a phylogenetics framework

Major recent advances in genome sequencing technology make it feasible that in future epidemics, a sequence will be available for every clinical case that can be identified. In some scenarios, such as agricultural epidemics (where farm-to-farm spread is of more interest than animal-to-animal), diseases such as HIV (where most infected individuals will eventually present themselves to clinicians), and epidemics occurring in well-monitored populations such as hospital inpatients, we will as a consequence be able to acquire a set of sequences representing the pathogens infecting most or all cases in the transmission chain. Genetic data therefore provides an important new tool for the investigation of epidemics, in particular the determination of the epidemic's transmission tree, which describes which case infected which others. As the genetic diversity in a set of sequences taken from the same epidemic will not be enormous even for fast-evolving RNA viruses, the best approach would be to combine both genetic and epidemiological data. I present here a new method for transmission tree reconstruction which is integrated into the Bayesian phylogenetics framework available in BEAST. It is based on the observation that if the phylogeny is know, there is a one-to-one correspondence between possible transmission trees and partitions of the internal nodes of the tree into connected subgraphs. The MCMC procedure in BEAST has been modified to sample from the space of trees with nodes partitioned in this way, simultaneously estimating both phylogenetic tree and transmission tree. Rather than assuming that the entire tree is generated by a single coalescent process, the posterior probability of a phylogeny is now calculated based on an individual-based model of disease transmission, which can take into account epidemiological characteristics of the host cases, such as spatial location. I will outline results using simulated data and sequences from the 2003 Dutch epidemic of H7N7 avian influenza.

Statistical inference for phylodynamics

Phylodynamic methods are widely used to estimate demographic parameters and historical population dynamics from genealogies of individuals sampled from a population. In this phyloseminar, I will describe how we can understand genealogies in terms of basic demographic or ecological processes, and how these concepts can be used to develop statistical models for inference. In particular, I will discuss some similarities and differences between the two main modeling frameworks in phylodynamics: the coalescent and birth-death models. I will also briefly introduce some of the latest statistical methods currently used to fit these models to genealogies. I will end by discussing one of the main challenges facing the field---adequately representing the structure of complex, heterogenous populations in phylodynamic models.