Decoding the Fundamental Drivers of Phylodynamic Inference.

Despite its increasing role in the understanding of infectious disease transmission at the applied and theoretical levels, phylodynamics lacks a well-defined notion of ideal data and optimal sampling. We introduce a method to visualize and quantify the relative impact of pathogen genome sequence and sampling times-two fundamental sources of data for phylodynamics under birth-death-sampling models-to understand how each drives phylodynamic inference. Applying our method to simulated data and real-world SARS-CoV-2 and H1N1 Influenza data, we use this insight to elucidate fundamental trade-offs and guidelines for phylodynamic analyses to draw the most from sequence data. Phylodynamics promises to be a staple of future responses to infectious disease threats globally. Continuing research into the inherent requirements and trade-offs of phylodynamic data and inference will help ensure phylodynamic tools are wielded in ever more targeted and efficient ways.

Scalable Bayesian phylogenetics.

Recent advances in Bayesian phylogenetics offer substantial computational savings to accommodate increased genomic sampling that challenges traditional inference methods. In this review, we begin with a brief summary of the Bayesian phylogenetic framework, and then conceptualize a variety of methods to improve posterior approximations via Markov chain Monte Carlo (MCMC) sampling. Specifically, we discuss methods to improve the speed of likelihood calculations, reduce MCMC burn-in, and generate better MCMC proposals. We apply several of these techniques to study the evolution of HIV virulence along a 1536-tip phylogeny and estimate the internal node heights of a 1000-tip SARS-CoV-2 phylogenetic tree in order to illustrate the speed-up of such analyses using current state-of-the-art approaches. We conclude our review with a discussion of promising alternatives to MCMC that approximate the phylogenetic posterior. This article is part of a discussion meeting issue 'Genomic population structures of microbial pathogens'.

Phylogenetic and Spatiotemporal Analyses of the Complete Genome Sequences of Avian Coronavirus Infectious Bronchitis Virus in China During 1985-2020: Revealing Coexistence of Multiple Transmission Chains and the Origin of LX4-Type Virus.

Infectious bronchitis (IB) virus (IBV) causes considerable economic losses to poultry production. The data on transmission dynamics of IBV in China are limited. The complete genome sequences of 212 IBV isolates in China during 1985-2020 were analyzed as well as the characteristics of the phylogenetic tree, recombination events, dN/dS ratios, temporal dynamics, and phylogeographic relationships. The LX4 type (GI-19) was found to have the highest dN/dS ratios and has been the most dominant genotype since 1999, and the Taiwan-I type (GI-7) and New type (GVI-1) showed an increasing trend. A total of 59 recombinants were identified, multiple recombination events between the field and vaccine strains were found in 24 isolates, and the 4/91-type (GI-13) isolates were found to be more prone to being involved in the recombination. Bayesian phylogeographic analyses indicated that the Chinese IBVs originated from Liaoning province in the early 1900s. The LX4-type viruses were traced back to Liaoning province in the late 1950s and had multiple transmission routes in China and two major transmission routes in the world. Viral phylogeography identified three spread regions for IBVs (including LX4 type) in China: Northeastern China (Heilongjiang, Liaoning, and Jilin), north and central China (Beijing, Hebei, Shanxi, Shandong, and Jiangsu), and Southern China (Guangxi and Guangdong). Shandong has been the epidemiological center of IBVs (including LX4 type) in China. Overall, our study highlighted the reasons why the LX4-type viruses had become the dominant genotype and its origin and transmission routes, providing more targeted strategies for the prevention and control of IB in China.

The Impacts of Low Diversity Sequence Data on Phylodynamic Inference during an Emerging Epidemic.

Phylodynamic inference is a pivotal tool in understanding transmission dynamics of viral outbreaks. These analyses are strongly guided by the input of an epidemiological model as well as sequence data that must contain sufficient intersequence variability in order to be informative. These criteria, however, may not be met during the early stages of an outbreak. Here we investigate the impact of low diversity sequence data on phylodynamic inference using the birth-death and coalescent exponential models. Through our simulation study, estimating the molecular evolutionary rate required enough sequence diversity and is an essential first step for any phylodynamic inference. Following this, the birth-death model outperforms the coalescent exponential model in estimating epidemiological parameters, when faced with low diversity sequence data due to explicitly exploiting the sampling times. In contrast, the coalescent model requires additional samples and therefore variability in sequence data before accurate estimates can be obtained. These findings were also supported through our empirical data analyses of an Australian and a New Zealand cluster outbreaks of SARS-CoV-2. Overall, the birth-death model is more robust when applied to datasets with low sequence diversity given sampling is specified and this should be considered for future viral outbreak investigations.