Timothy G. Vaughan

Last Name

Vaughan

First Name

Timothy G.

ORCID

0000-0001-6220-2239

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09490 - Stadler, Tanja / Stadler, Tanja

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Publications 1 - 10 of 42

Taming the BEAST - A Community Teaching Material Resource for BEAST 2
Barido-Sottani, Joëlle; Bošková, Veronika; Du Plessis, Louis; et al. (2018)
Systematic Biology
Phylogenetics and phylodynamics are central topics in modern evolutionary biology. Phylogenetic methods reconstruct the evolutionary relationships among organisms, whereas phylodynamic approaches reveal the underlying diversification processes that lead to the observed relationships. These two fields have many practical applications in disciplines as diverse as epidemiology, developmental biology, palaeontology, ecology, and linguistics. The combination of increasingly large genetic data sets and increases in computing power is facilitating the development of more sophisticated phylogenetic and phylodynamic methods. Big data sets allow us to answer complex questions. However, since the required analyses are highly specific to the particular data set and question, a black-box method is not sufficient anymore. Instead, biologists are required to be actively involved with modeling decisions during data analysis. The modular design of the Bayesian phylogenetic software package BEAST 2 enables, and in fact enforces, this involvement. At the same time, the modular design enables computational biology groups to develop new methods at a rapid rate. A thorough understanding of the models and algorithms used by inference software is a critical prerequisite for successful hypothesis formulation and assessment. In particular, there is a need for more readily available resources aimed at helping interested scientists equip themselves with the skills to confidently use cutting-edge phylogenetic analysis software. These resources will also benefit researchers who do not have access to similar courses or training at their home institutions. Here, we introduce the “Taming the Beast” (https://taming-the-beast.github.io/) resource, which was developed as part of a workshop series bearing the same name, to facilitate the usage of the Bayesian phylogenetic software package BEAST 2.
BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis
Bouckaert, Remco; Vaughan, Timothy G.; Barido-Sottani, Joëlle; et al. (2019)
PLoS Computational Biology
Elaboration of Bayesian phylogenetic inference methods has continued at pace in recent years with major new advances in nearly all aspects of the joint modelling of evolutionary data. It is increasingly appreciated that some evolutionary questions can only be adequately answered by combining evidence from multiple independent sources of data, including genome sequences, sampling dates, phenotypic data, radiocarbon dates, fossil occurrences, and biogeographic range information among others. Including all relevant data into a single joint model is very challenging both conceptually and computationally. Advanced computational software packages that allow robust development of compatible (sub-)models which can be composed into a full model hierarchy have played a key role in these developments. Developing such software frameworks is increasingly a major scientific activity in its own right, and comes with specific challenges, from practical software design, development and engineering challenges to statistical and conceptual modelling challenges. BEAST 2 is one such computational software platform, and was first announced over 4 years ago. Here we describe a series of major new developments in the BEAST 2 core platform and model hierarchy that have occurred since the first release of the software, culminating in the recent 2.5 release.
Swiss public health measures associated with reduced SARS-CoV-2 transmission using genome data
Nadeau, Sarah Ann; Vaughan, Timothy G.; Beckmann, Christiane; et al. (2023)
Science Translational Medicine
Genome sequences from evolving infectious pathogens allow quantification of case introductions and local transmission dynamics. We sequenced 11,357 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes from Switzerland in 2020 - the sixth largest effort globally. Using a representative subset of these data, we estimated viral introductions to Switzerland and their persistence over the course of 2020. We contrasted these estimates with simple null models representing the absence of certain public health measures. We show that Switzerland’s border closures de-coupled case introductions from incidence in neighboring countries. Under a simple model, we estimate an 86–98% reduction in introductions during Switzerland’s strictest border closures. Furthermore, the Swiss 2020 partial lockdown roughly halved the time for sampled introductions to die out. Last, we quantified local transmission dynamics once introductions into Switzerland occurred, using a phylodynamic model. We found that transmission slowed 35–63% upon outbreak detection in summer 2020, but not in fall. This finding may indicate successful contact tracing over summer before overburdening in fall. The study highlights the added value of genome sequencing data for understanding transmission dynamics.
Bayesian Phylodynamic Inference of Multitype Population Trajectories Using Genomic Data
Vaughan, Timothy G.; Stadler, Tanja (2025)
Molecular Biology and Evolution
Phylodynamic methods provide a coherent framework for the inference of population parameters directly from genetic data. They are an important tool for understanding both the spread of epidemics as well as long-term macroevolutionary trends in speciation and extinction. In particular, phylodynamic methods based on multitype birth-death models have been used to infer the evolution of discrete traits, the movement of individuals or pathogens between geographic locations or host types, and the transition of infected individuals between disease stages. In these models, population heterogeneity is treated by assigning individuals to different discrete types. Typically, methods which allow inference of parameters under multitype birth-death models integrate over the possible birth-death trajectories (i.e. the type-specific population size functions) to reduce the computational demands of the inference. As a result, it has not been possible to use these methods to directly infer the dynamics of trait-specific population sizes, infected host counts or other such demographic quantities. In this article, we present a method which infers these multitype trajectories with minimal additional computational cost beyond that of existing methods. We demonstrate the practicality of our approach by applying it to a previously published set of Middle East respiratory syndrome coronavirus genomes, inferring the numbers of human and camel cases through time, together with the number and timing of spillovers from the camel reservoir. This application highlights the multitype population trajectory's ability to elucidate properties of the population which are not directly ancestral to its sampled members.
Quantifying SARS-CoV-2 spread in Switzerland based on genomic sequencing data
Nadeau, Sarah Ann; Beckmann, Christiane; Topolsky, Ivan; et al. (2020)
medRxiv
Pathogen genomes provide insights into their evolution and epidemic spread. We sequenced 1,439 SARS-CoV-2 genomes from Switzerland, representing 3-7% of all confirmed cases per week. Using these data, we demonstrate that no one lineage became dominant, pointing against evolution towards general lower virulence. On an epidemiological level, we report no evidence of cryptic transmission before the first confirmed case. We find many early viral introductions from Germany, France, and Italy and many recent introductions from Germany and France. Over the summer, we quantify the number of non-traceable infections stemming from introductions, quantify the effective reproductive number, and estimate the degree of undersampling. Our framework can be applied to quantify evolution and epidemiology in other locations or for other pathogens based on genomic data.
EpiFusion: Joint inference of the effective reproduction number by integrating phylodynamic and epidemiological modelling with particle filtering
Judge, Ciara; Vaughan, Timothy G.; Russell, Timothy; et al. (2024)
PLoS Computational Biology
Accurately estimating the effective reproduction number (Rt) of a circulating pathogen is a fundamental challenge in the study of infectious disease. The fields of epidemiology and pathogen phylodynamics both share this goal, but to date, methodologies and data employed by each remain largely distinct. Here we present EpiFusion: a joint approach that can be used to harness the complementary strengths of each field to improve estimation of outbreak dynamics for large and poorly sampled epidemics, such as arboviral or respiratory virus outbreaks, and validate it for retrospective analysis. We propose a model of Rt that estimates outbreak trajectories conditional upon both phylodynamic (time-scaled trees estimated from genetic sequences) and epidemiological (case incidence) data. We simulate stochastic outbreak trajectories that are weighted according to epidemiological and phylodynamic observation models and fit using particle Markov Chain Monte Carlo. To assess performance, we test EpiFusion on simulated outbreaks in which transmission and/or surveillance rapidly changes and find that using EpiFusion to combine epidemiological and phylodynamic data maintains accuracy and increases certainty in trajectory and Rt estimates, compared to when each data type is used alone. We benchmark EpiFusion's performance against existing methods to estimate Rt and demonstrate advances in speed and accuracy. Importantly, our approach scales efficiently with dataset size. Finally, we apply our model to estimate Rt during the 2014 Ebola outbreak in Sierra Leone. EpiFusion is designed to accommodate future extensions that will improve its utility, such as explicitly modelling population structure, accommodations for phylogenetic uncertainty, and the ability to weight the contributions of genomic or case incidence to the inference.
Mechanistic phylodynamic models do not provide conclusive evidence that non-avian dinosaurs were in decline before their final extinction
Allen, Bethany; Volkova Oliveira, Maria V.; Stadler, Tanja; et al. (2024)
Cambridge Prisms: Extinction
Phylodynamic models can be used to estimate diversification trajectories from time-calibrated phylogenies. Here we apply two such models to phylogenies of non-avian dinosaurs, a clade whose evolutionary history has been widely debated. While some authors have suggested that the clade experienced a decline in diversity, potentially starting millions of years before the end-Cretaceous mass extinction, others have suggested that the group remained highly diverse right up until the Cretaceous-Paleogene (K-Pg) boundary. Our results show that model assumptions, likely with respect to incomplete sampling, have a large impact on whether dinosaurs appear to have experienced a long-term decline or not. The results are also highly sensitive to the topology and branch lengths of the phylogeny used. Developing comprehensive models of sampling bias, and building larger and more accurate phylogenies, are likely to be necessary steps for us to determine whether dinosaur diversity was or was not in decline prior to the end-Cretaceous mass extinction.
Bayesian inference of reassortment networks reveals fitness benefits of reassortment in human influenza viruses
Müller, Nicola F.; Stolz, Ugne; Dudas, Gytis; et al. (2020)
Proceedings of the National Academy of Sciences of the United States of America
Reassortment is an important source of genetic diversity in segmented viruses and is the main source of novel pathogenic influenza viruses. Despite this, studying the reassortment process has been constrained by the lack of a coherent, model-based inference framework. Here, we introduce a coalescent-based model that allows us to explicitly model the joint coalescent and reassortment process. In order to perform inference under this model, we present an efficient Markov chain Monte Carlo algorithm to sample rooted networks and the embedding of phylogenetic trees within networks. This algorithm provides the means to jointly infer coalescent and reassortment rates with the reassortment network and the embedding of segments in that network from full-genome sequence data. Studying reassortment patterns of different human influenza datasets, we find large differences in reassortment rates across different human influenza viruses. Additionally, we find that reassortment events predominantly occur on selectively fitter parts of reassortment networks showing that on a population level, reassortment positively contributes to the fitness of human influenza viruses.
Decoding Genomes
Stadler, Tanja; Magnus, Carsten; Vaughan, Timothy G.; et al. (2024)
Efficient Bayesian inference under the structured coalescent
Vaughan, Timothy G.; Kühnert, Denise; Popinga, Alex; et al. (2014)
Bioinformatics

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Timothy G. Vaughan

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