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Article ; Online: Approaches and challenges to inferring the geographical source of infectious disease outbreaks using genomic data.

Chen, Zhiyuan / Lemey, Philippe / Yu, Hongjie

The Lancet. Microbe

2023 Volume 5, Issue 1, Page(s) e81–e92

Abstract: Genomic data hold increasing potential in the elucidation of transmission dynamics and geographical sources of infectious disease outbreaks. Phylogeographic methods that use epidemiological and genomic data obtained from surveillance enable us to infer ... ...

Abstract	Genomic data hold increasing potential in the elucidation of transmission dynamics and geographical sources of infectious disease outbreaks. Phylogeographic methods that use epidemiological and genomic data obtained from surveillance enable us to infer the history of spatial transmission that is naturally embedded in the topology of phylogenetic trees as a record of the dispersal of infectious agents between geographical locations. In this Review, we provide an overview of phylogeographic approaches widely used for reconstructing the geographical sources of outbreaks of interest. These approaches can be classified into ancestral trait or state reconstruction and structured population models, with structured population models including popular structured coalescent and birth-death models. We also describe the major challenges associated with sequencing technologies, surveillance strategies, data sharing, and analysis frameworks that became apparent during the generation of large-scale genomic data in recent years, extending beyond inference approaches. Finally, we highlight the role of genomic data in geographical source inference and clarify how this enhances understanding and molecular investigations of outbreak sources.
MeSH term(s)	Phylogeny ; Disease Outbreaks ; Phylogeography ; Genomics
Language	English
Publishing date	2023-11-30
Publishing country	England
Document type	Journal Article ; Review ; Research Support, Non-U.S. Gov't
ISSN	2666-5247
ISSN (online)	2666-5247
DOI	10.1016/S2666-5247(23)00296-3
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Article: Phycova - a tool for exploring covariates of pathogen spread.

Blokker, Tim / Baele, Guy / Lemey, Philippe / Dellicour, Simon

Virus evolution

2022 Volume 8, Issue 1, Page(s) veac015

Abstract: Genetic analyses of fast-evolving pathogens are frequently undertaken to test the impact of covariates on their dispersal. In particular, a popular approach consists of parameterizing a discrete phylogeographic model as a generalized linear model to ... ...

Abstract	Genetic analyses of fast-evolving pathogens are frequently undertaken to test the impact of covariates on their dispersal. In particular, a popular approach consists of parameterizing a discrete phylogeographic model as a generalized linear model to identify and analyse the predictors of the dispersal rates of viral lineages among discrete locations. However, such a full probabilistic inference is often computationally demanding and time-consuming. In the face of the increasing amount of viral genomes sequenced in epidemic outbreaks, there is a need for a fast exploration of covariates that might be relevant to consider in formal analyses. We here present PhyCovA (short for 'Phylogeographic Covariate Analysis'), a web-based application allowing users to rapidly explore the association between candidate covariates and the number of phylogenetically informed transition events among locations. Specifically, PhyCovA takes as input a phylogenetic tree with discrete state annotations at the internal nodes, or reconstructs those states if not available, to subsequently conduct univariate and multivariate linear regression analyses, as well as an exploratory variable selection analysis. In addition, the application can also be used to generate and explore various visualizations related to the regression analyses or to the phylogenetic tree annotated by the ancestral state reconstruction. PhyCovA is freely accessible at https://evolcompvir-kuleuven.shinyapps.io/PhyCovA/ and also distributed in a dockerized form obtainable from https://hub.docker.com/repository/docker/timblokker/phycova. The source code and tutorial are available from the GitHub repository https://github.com/TimBlokker/PhyCovA.
Language	English
Publishing date	2022-02-18
Publishing country	England
Document type	Journal Article
ZDB-ID	2818949-8
ISSN	2057-1577
ISSN	2057-1577
DOI	10.1093/ve/veac015
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Article ; Online: Bayesian Phylogeographic Analysis Incorporating Predictors and Individual Travel Histories in BEAST.

Hong, Samuel L / Lemey, Philippe / Suchard, Marc A / Baele, Guy

Current protocols

2022 Volume 1, Issue 4, Page(s) e98

Abstract: Advances in sequencing technologies have tremendously reduced the time and costs associated with sequence generation, making genomic data an important asset for routine public health practices. Within this context, phylogenetic and phylogeographic ... ...

Abstract	Advances in sequencing technologies have tremendously reduced the time and costs associated with sequence generation, making genomic data an important asset for routine public health practices. Within this context, phylogenetic and phylogeographic inference has become a popular method to study disease transmission. In a Bayesian context, these approaches have the benefit of accommodating phylogenetic uncertainty, and popular implementations provide the possibility to parameterize the transition rates between locations as a function of epidemiological and ecological data to reconstruct spatial spread while simultaneously identifying the main factors impacting the spatial spread dynamics. Recent developments enable researchers to make use of travel history data of infected individuals in the reconstruction of pathogen spread, offering increased inference accuracy and mitigating sampling bias. Here, we describe a detailed workflow to reconstruct the spatial spread of a pathogen through Bayesian phylogeographic analysis in discrete space using these novel approaches, implemented in BEAST. The individual protocols focus on how to incorporate molecular data, covariates of spread, and individual travel history data into the analysis. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Creating a SARS-CoV-2 MSA using sequences from GISAID Basic Protocol 2: Setting up a discrete trait phylogeographic reconstruction in BEAUti Basic Protocol 3: Phylogeographic reconstruction incorporating travel history information Basic Protocol 4: Visualizing ancestral spatial trajectories for specific taxa.
MeSH term(s)	Bayes Theorem ; COVID-19/epidemiology ; COVID-19/genetics ; COVID-19/transmission ; COVID-19/virology ; Computational Biology/methods ; Databases, Nucleic Acid ; Humans ; Phylogeny ; Phylogeography/methods ; SARS-CoV-2/classification ; SARS-CoV-2/genetics ; SARS-CoV-2/isolation & purification ; Sequence Analysis, DNA/methods ; Software ; Travel/statistics & numerical data ; United States/epidemiology
Language	English
Publishing date	2022-10-05
Publishing country	United States
Document type	Journal Article
ISSN	2691-1299
ISSN (online)	2691-1299
DOI	10.1002/cpz1.98
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Article ; Online: Shrinkage-based Random Local Clocks with Scalable Inference.

Fisher, Alexander A / Ji, Xiang / Nishimura, Akihiko / Baele, Guy / Lemey, Philippe / Suchard, Marc A

Molecular biology and evolution

2023 Volume 40, Issue 11

Abstract: Molecular clock models undergird modern methods of divergence-time estimation. Local clock models propose that the rate of molecular evolution is constant within phylogenetic subtrees. Current local clock inference procedures exhibit one or more ... ...

Abstract	Molecular clock models undergird modern methods of divergence-time estimation. Local clock models propose that the rate of molecular evolution is constant within phylogenetic subtrees. Current local clock inference procedures exhibit one or more weaknesses, namely they achieve limited scalability to trees with large numbers of taxa, impose model misspecification, or require a priori knowledge of the existence and location of clocks. To overcome these challenges, we present an autocorrelated, Bayesian model of heritable clock rate evolution that leverages heavy-tailed priors with mean zero to shrink increments of change between branch-specific clocks. We further develop an efficient Hamiltonian Monte Carlo sampler that exploits closed form gradient computations to scale our model to large trees. Inference under our shrinkage clock exhibits a speed-up compared to the popular random local clock when estimating branch-specific clock rates on a variety of simulated datasets. This speed-up increases with the size of the problem. We further show our shrinkage clock recovers known local clocks within a rodent and mammalian phylogeny. Finally, in a problem that once appeared computationally impractical, we investigate the heritable clock structure of various surface glycoproteins of influenza A virus in the absence of prior knowledge about clock placement. We implement our shrinkage clock and make it publicly available in the BEAST software package.
MeSH term(s)	Animals ; Phylogeny ; Bayes Theorem ; Evolution, Molecular ; Mammals ; Time Factors ; Models, Genetic
Language	English
Publishing date	2023-11-11
Publishing country	United States
Document type	Journal Article
ZDB-ID	998579-7
ISSN	1537-1719 ; 0737-4038
ISSN (online)	1537-1719
ISSN	0737-4038
DOI	10.1093/molbev/msad242
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Article ; Online: Many-core algorithms for high-dimensional gradients on phylogenetic trees.

Gangavarapu, Karthik / Ji, Xiang / Baele, Guy / Fourment, Mathieu / Lemey, Philippe / Matsen, Frederick A / Suchard, Marc A

Bioinformatics (Oxford, England)

2024 Volume 40, Issue 2

Abstract: Motivation: Advancements in high-throughput genomic sequencing are delivering genomic pathogen data at an unprecedented rate, positioning statistical phylogenetics as a critical tool to monitor infectious diseases globally. This rapid growth spurs the ... ...

Abstract	Motivation: Advancements in high-throughput genomic sequencing are delivering genomic pathogen data at an unprecedented rate, positioning statistical phylogenetics as a critical tool to monitor infectious diseases globally. This rapid growth spurs the need for efficient inference techniques, such as Hamiltonian Monte Carlo (HMC) in a Bayesian framework, to estimate parameters of these phylogenetic models where the dimensions of the parameters increase with the number of sequences N. HMC requires repeated calculation of the gradient of the data log-likelihood with respect to (wrt) all branch-length-specific (BLS) parameters that traditionally takes O(N2) operations using the standard pruning algorithm. A recent study proposes an approach to calculate this gradient in O(N), enabling researchers to take advantage of gradient-based samplers such as HMC. The CPU implementation of this approach makes the calculation of the gradient computationally tractable for nucleotide-based models but falls short in performance for larger state-space size models, such as Markov-modulated and codon models. Here, we describe novel massively parallel algorithms to calculate the gradient of the log-likelihood wrt all BLS parameters that take advantage of graphics processing units (GPUs) and result in many fold higher speedups over previous CPU implementations. Results: We benchmark these GPU algorithms on three computing systems using three evolutionary inference examples exploring complete genomes from 997 dengue viruses, 62 carnivore mitochondria and 49 yeasts, and observe a >128-fold speedup over the CPU implementation for codon-based models and >8-fold speedup for nucleotide-based models. As a practical demonstration, we also estimate the timing of the first introduction of West Nile virus into the continental Unites States under a codon model with a relaxed molecular clock from 104 full viral genomes, an inference task previously intractable. Availability and implementation: We provide an implementation of our GPU algorithms in BEAGLE v4.0.0 (https://github.com/beagle-dev/beagle-lib), an open-source library for statistical phylogenetics that enables parallel calculations on multi-core CPUs and GPUs. We employ a BEAGLE-implementation using the Bayesian phylogenetics framework BEAST (https://github.com/beast-dev/beast-mcmc).
MeSH term(s)	Phylogeny ; Software ; Bayes Theorem ; Algorithms ; Codon ; Nucleotides
Chemical Substances	Codon ; Nucleotides
Language	English
Publishing date	2024-01-19
Publishing country	England
Document type	Journal Article
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btae030
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Article: Integrating dynamical modeling and phylogeographic inference to characterize global influenza circulation.

Parino, Francesco / Gustani-Buss, Emanuele / Bedford, Trevor / Suchard, Marc A / Trovão, Nídia Sequeira / Rambaut, Andrew / Colizza, Vittoria / Poletto, Chiara / Lemey, Philippe

medRxiv : the preprint server for health sciences

2024

Abstract: Global seasonal influenza circulation involves a complex interplay between local (seasonality, demography, host immunity) and global factors (international mobility) shaping recurrent epidemic patterns. No studies so far have reconciled the two spatial ... ...

Abstract	Global seasonal influenza circulation involves a complex interplay between local (seasonality, demography, host immunity) and global factors (international mobility) shaping recurrent epidemic patterns. No studies so far have reconciled the two spatial levels, evaluating the coupling between national epidemics, considering heterogeneous coverage of epidemiological and virological data, integrating different data sources. We propose a novel combined approach based on a dynamical model of global influenza spread (GLEAM), integrating high-resolution demographic and mobility data, and a generalized linear model of phylogeographic diffusion that accounts for time-varying migration rates. Seasonal migration fluxes across global macro-regions simulated with GLEAM are tested as phylogeographic predictors to provide model validation and calibration based on genetic data. Seasonal fluxes obtained with a specific transmissibility peak time and recurrent travel outperformed the raw air-transportation predictor, previously considered as optimal indicator of global influenza migration. Influenza A subtypes supported autumn-winter reproductive number as high as 2.25 and an average immunity duration of 2 years. Similar dynamics were preferred by influenza B lineages, with a lower autumn-winter reproductive number. Comparing simulated epidemic profiles against FluNet data offered comparatively limited resolution power. The multiscale approach enables model selection yielding a novel computational framework for describing global influenza dynamics at different scales - local transmission and national epidemics vs. international coupling through mobility and imported cases. Our findings have important implications to improve preparedness against seasonal influenza epidemics. The approach can be generalized to other epidemic contexts, such as emerging disease outbreaks to improve the flexibility and predictive power of modeling.
Language	English
Publishing date	2024-03-15
Publishing country	United States
Document type	Preprint
DOI	10.1101/2024.03.14.24303719
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Article ; Online: Comparative evolution of influenza A virus H1 and H3 head and stalk domains across host species.

Trovão, Nidia S / Khan, Sairah M / Lemey, Philippe / Nelson, Martha I / Cherry, Joshua L

mBio

2023 Volume 15, Issue 1, Page(s) e0264923

Abstract: Importance: For decades, researchers have studied the rapid evolution of influenza A viruses for vaccine design and as a useful model system for the study of host/parasite evolution. By performing an exhaustive analysis of hemagglutinin protein (HA) ... ...

Abstract	Importance: For decades, researchers have studied the rapid evolution of influenza A viruses for vaccine design and as a useful model system for the study of host/parasite evolution. By performing an exhaustive analysis of hemagglutinin protein (HA) sequences from 49 lineages independently evolving in birds, swine, canines, equines, and humans over the last century, our work uncovers surprising features of HA evolution. In particular, the canine H3 stalk, unlike human H3 and H1 stalk domains, is not evolving slowly, suggesting that evolution in the stalk domain is not universally constrained across all host species. Therefore, a broader multi-host perspective on HA evolution may be useful during the evaluation and design of stalk-targeted vaccine candidates.
MeSH term(s)	Animals ; Dogs ; Humans ; Swine ; Horses ; Influenza A virus/genetics ; Hemagglutinin Glycoproteins, Influenza Virus ; Hemagglutinins ; Host Specificity ; Vaccines ; Influenza, Human ; Influenza Vaccines ; Antibodies, Viral ; Orthomyxoviridae Infections
Chemical Substances	Hemagglutinin Glycoproteins, Influenza Virus ; Hemagglutinins ; Vaccines ; Influenza Vaccines ; Antibodies, Viral
Language	English
Publishing date	2023-12-11
Publishing country	United States
Document type	Journal Article
ZDB-ID	2557172-2
ISSN	2150-7511 ; 2161-2129
ISSN (online)	2150-7511
ISSN	2161-2129
DOI	10.1128/mbio.02649-23
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Article ; Online: Drivers and impact of the early silent invasion of SARS-CoV-2 Alpha.

Faucher, Benjamin / Sabbatini, Chiara E / Czuppon, Peter / Kraemer, Moritz U G / Lemey, Philippe / Colizza, Vittoria / Blanquart, François / Boëlle, Pierre-Yves / Poletto, Chiara

Nature communications

2024 Volume 15, Issue 1, Page(s) 2152

Abstract: SARS-CoV-2 variants of concern (VOCs) circulated cryptically before being identified as a threat, delaying interventions. Here we studied the drivers of such silent spread and its epidemic impact to inform future response planning. We focused on Alpha ... ...

Abstract	SARS-CoV-2 variants of concern (VOCs) circulated cryptically before being identified as a threat, delaying interventions. Here we studied the drivers of such silent spread and its epidemic impact to inform future response planning. We focused on Alpha spread out of the UK. We integrated spatio-temporal records of international mobility, local epidemic growth and genomic surveillance into a Bayesian framework to reconstruct the first three months after Alpha emergence. We found that silent circulation lasted from days to months and decreased with the logarithm of sequencing coverage. Social restrictions in some countries likely delayed the establishment of local transmission, mitigating the negative consequences of late detection. Revisiting the initial spread of Alpha supports local mitigation at the destination in case of emerging events.
MeSH term(s)	Humans ; Bayes Theorem ; COVID-19/epidemiology ; SARS-CoV-2/genetics ; Epidemics
Language	English
Publishing date	2024-03-09
Publishing country	England
Document type	Journal Article
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-024-46345-1
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Article: How fast are viruses spreading in the wild?

Dellicour, Simon / Bastide, Paul / Rocu, Pauline / Fargette, Denis / Hardy, Olivier J / Suchard, Marc A / Guindon, Stéphane / Lemey, Philippe

bioRxiv : the preprint server for biology

2024

Abstract: Genomic data collected from viral outbreaks can be exploited to reconstruct the dispersal history of viral lineages in a two-dimensional space using continuous phylogeographic inference. These spatially explicit reconstructions can subsequently be used ... ...

Abstract	Genomic data collected from viral outbreaks can be exploited to reconstruct the dispersal history of viral lineages in a two-dimensional space using continuous phylogeographic inference. These spatially explicit reconstructions can subsequently be used to estimate dispersal metrics allowing to unveil the dispersal dynamics and evaluate the capacity to spread among hosts. Heterogeneous sampling intensity of genomic sequences can however impact the accuracy of dispersal insights gained through phylogeographic inference. In our study, we implement a simulation framework to evaluate the robustness of three dispersal metrics - a lineage dispersal velocity, a diffusion coefficient, and an isolation-by-distance signal metric - to the sampling effort. Our results reveal that both the diffusion coefficient and isolation-by-distance signal metrics appear to be robust to the number of samples considered for the phylogeographic reconstruction. We then use these two dispersal metrics to compare the dispersal pattern and capacity of various viruses spreading in animal populations. Our comparative analysis reveals a broad range of isolation-by-distance patterns and diffusion coefficients mostly reflecting the dispersal capacity of the main infected host species but also, in some cases, the likely signature of rapid and/or long-distance dispersal events driven by human-mediated movements through animal trade. Overall, our study provides key recommendations for the lineage dispersal metrics to consider in future studies and illustrates their application to compare the spread of viruses in various settings.
Language	English
Publishing date	2024-04-11
Publishing country	United States
Document type	Preprint
DOI	10.1101/2024.04.10.588821
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Article: Accommodating sampling location uncertainty in continuous phylogeography.

Dellicour, Simon / Lemey, Philippe / Suchard, Marc A / Gilbert, Marius / Baele, Guy

Virus evolution

2022 Volume 8, Issue 1, Page(s) veac041

Abstract: Phylogeographic inference of the dispersal history of viral lineages offers key opportunities to tackle epidemiological questions about the spread of fast-evolving pathogens across human, animal and plant populations. In continuous space, i.e. when ... ...

Abstract	Phylogeographic inference of the dispersal history of viral lineages offers key opportunities to tackle epidemiological questions about the spread of fast-evolving pathogens across human, animal and plant populations. In continuous space, i.e. when locations are specified by longitude and latitude, these reconstructions are however often limited by the availability or accessibility of precise sampling locations required for such spatially explicit analyses. We here review the different approaches that can be considered when genomic sequences are associated with a geographic area of sampling instead of precise coordinates. In particular, we describe and compare the approaches to define homogeneous and heterogeneous prior ranges of sampling coordinates.
Language	English
Publishing date	2022-05-18
Publishing country	England
Document type	Journal Article ; Review
ZDB-ID	2818949-8
ISSN	2057-1577
ISSN	2057-1577
DOI	10.1093/ve/veac041
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