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Baele, G.

Publications and source records attributed to Baele, G..

At least 19 recordsLinked to original sources
bioRxiv2026

Dairy cattle movement networks govern the spread and persistence of H5N1 B3.13 in the United States

Avian influenza virus H5N1 is predominantly found in wild and domestic birds. H5N1 genotype B3.13 was first detected in United States dairy cattle in March 2024 and spread through much of the country during 2024 and 2025, resulting in the largest known H5N1 outbreak in domestic mammals. By analysing viral genomic data jointly with historical cattle movement patterns, we find that live cattle movement is the predominant contributor to H5N1 spatial spread, rather than wild or domestic birds. Using phylogeographic methods we estimate that >40% of interstate viral transmissions occurred before B3.13's first detection and >70% occurred before interstate dairy cattle movement restrictions were implemented in April 2024. After this implementation, interstate transmission slowed but within-state spread persisted, driven by local cattle network density and movement volume. Our epidemic simulations demonstrate that the connectivity of counties within and around the Texas Panhandle - where the earliest cases were documented - predisposed the region to become an outbreak epicentre. Counterfactual simulations indicate that interstate restrictions needed to have begun 2-3 months earlier in order to limit the epizootic substantively, given the extent of cryptic spread before detection. Network-informed interventions outperformed geographically-defined containment, but response speed (rather than intervention strength) was the limiting factor in our epidemic models, underscoring the need for surveillance systems capable of detecting pathogen emergence before widespread dissemination.

evolutionary biology↗
bioRxiv2026

Phylodynamic analysis reveals asymmetric and accelerating global spread of dengue virus

Dengue virus (DENV) continues to expand globally, with increasing incidence in both endemic and non-endemic countries. Leveraging a large global dataset of DENV genomes starting from the 1940s, we reconstructed global and regional patterns of DENV spread using phylogeographic and phylodynamic methods. Our analyses reveal that the current global diversity of urban DENV originated in Asia in the first half of the 20th century. During the more than 100 years since, we find that the highest density of between-country spread occurs within Asia and the region continues to be a global source, seeding transmission in Africa and the Americas. Global spread is, however, not balanced. Even though there is more travel to South America, we show that DENV lineages primarily enter North America (mostly from South Asia) through a link defined by seasonal synchrony across the Northern Hemisphere. Along with a substantial increase in global travel starting ~2010, the rate of spread to the Americas is increasing, establishing new lineages associated with large recent outbreaks. DENV lineages do not frequently spread back to Asia from the Americas or Africa, and we posit this is due to higher genetic diversity in Asia, leading to stiffer competition for invading lineages. Within the Americas, the network of DENV dispersal initiates in North America, primarily the Caribbean, and then is maintained by major hubs in South America. Overall, we find that travel volume, rather than climate factors, is mostly responsible for long-term patterns of DENV spread globally and within the Americas. Our results highlight key nodes in the global dynamics of DENV lineages, and suggest spread will continue to accelerate in a more connected world.

genomics↗
bioRxiv2026

First detected incursions of avian influenza H5N1 clade 2.3.4.4b into mainland Australia from the Southern Ocean

High pathogenicity avian influenza H5N1 clade 2.3.4.4b has caused a panzootic of devastating impact to poultry and wildlife globally. The Australian continent and broader Oceania until recently remained the last major region without confirmed detections. Here we report the first H5N1 clade 2.3.4.4b detections from two live seabirds - a brown skua and a southern giant petrel - found on the south coast of Western Australia in June 2026. Virus genome sequencing showed that both viruses were most closely related to H5N1 viruses detected recently on sub-Antarctic islands in the Southern Indian Ocean. In time-calibrated phylogeographic analyses, both viruses sampled in Western Australia clustered with viruses from Heard Island, a sub-Antarctic external territory of Australia. Ancestral location reconstruction also identified Heard Island as the most probable source location, although unsampled intermediate locations cannot be excluded. The two Western Australian detections were estimated to be independent incursions from Heard Island, rather than local transmission on mainland Australia. There was no evidence of reassortment with endemic avian influenza viruses in Australia, and both virus sequences retained key avian-like genetic markers and lacked known substitutions for reduced antiviral susceptibility. These detections revealed a Southern Ocean pathway of recurrent H5N1 incursions into Australia, highlighting the risk of potential establishment on the mainland and the need for heightened surveillance and rapid, nationally-coordinated, virus genomic characterisation.

microbiology↗
bioRxiv2026

Mass mortality of southern elephant seals during multi-species outbreak of HPAI H5N1 on sub-Antarctic Heard Island

High pathogenicity avian influenza (HPAI) has spread across the sub-Antarctic, causing significant wildlife impacts. We report its first detection in an Australian external territory, Heard Island and McDonald Islands, which supports over one million breeding seabirds and seals. Drone and ground surveys (October 2025, January 2026), combined with viral genome analysis, confirmed infection with Influenza A H5N1 clade 2.3.4.4b at Heard Island. Drone surveys revealed mass mortality in southern elephant seals, with 8,573 pups (62%) recorded dead across Heard Island by the final surveys. Mortality increased at an average rate of 5.6% per day in a subset of harems, and the highest observed mortality in a harem was 97%. Based on the average (76%) mortality in the final surveys, total estimated pup mortality at Heard Island was 13,359 (from a total population of 17,364 pups), though this may be an underestimate as mortality was ongoing at this time. HPAI was detected in six of nine species tested and, we suspect, led to elevated mortality in king and gentoo penguins. Phylogenetic analysis indicates the virus was introduced from Crozet Islands, with an estimated arrival around August 2025. These data show the continued easterly spread of HPAI around the sub-Antarctic, with severe but heterogeneous impacts across taxa. Our results demonstrate the value of drones for large-scale monitoring, underscoring the need for continued and enhanced HPAI surveillance across the Southern Ocean.

ecology↗
bioRxiv2026

An optimized RNA polymerase II minigenome system for Nipah virus

Nipah virus is a highly lethal, zoonotic paramyxovirus that has caused recurring outbreaks in several South and Southeast Asian countries since its discovery in Malaysia in 1998. Symptoms of infection include severe respiratory and neurological disease, often resulting in death. As no approved vaccines or antivirals are currently available to reduce the burden of this virus, it is classified as a biosafety level 4 pathogen. There is an urgent need for systems that enable research in a lower biocontainment setting, especially since the World Health Organization declared Nipah virus a priority pathogen for pandemic concern. In the past, several minigenome systems have already been developed as safe alternatives to working with infectious virus; however, these systems remain relatively inefficient and lack robustness and reliability for further applications. Therefore, we developed novel optimized RNA polymerase II-driven minigenomes with nanoluciferase or enhanced green fluorescent protein reporter genes. Both systems outperform previously designed Nipah virus minigenomes, are easily operable, and can be implemented for antiviral compound screenings.

microbiology↗
bioRxiv2026

Reconciling fast Hepatitis B evolutionary rates with ancient co-divergence

Estimating evolutionary rates and divergence times for hepatitis B virus (HBV) has long been complicated by conflicting calibration approaches and extensive rate variation. To unlock the full potential of ancient and modern HBV genomic data, we develop a Bayesian mixed-effects molecular clock model that accounts for various sources of rate variation including time-dependent rate decay. Our analyses reveal a pronounced decline in evolutionary rates over time, reconciling HBV divergence estimates with human migration events across both deep and more recent timescales. We show that HBV spread into Europe through both Neolithic farming expansions and later steppe migrations, paralleling patterns proposed for Indo-European language origins. Phylogeographic reconstructions suggest that the Neolithic-associated lineage dispersed at approximately 1 km/year, consistent with archaeological estimates, while genotype D expanded during the Bronze Age at an almost threefold higher rate, plausibly driven by technological innovations underlying steppe expansions. Historical overlap between these lineages facilitated recombination, giving rise to genotype E, which has become a dominant HBV genotype in Africa. These findings demonstrate that ancient viral genomes, when analyzed with models capturing complex rate dynamics, provide a powerful lens on human prehistory and the processes shaping pathogen diversity.

evolutionary biology↗
bioRxiv2026

Evolutionary history of Jamestown Canyon virus disentangles complex multi-vector ecology

Jamestown Canyon virus (JCV) is a re-emerging mosquito-borne virus of increasing concern in North America. It has been historically understudied, leading to significant gaps in our understanding of its evolutionary history, ecological maintenance, and transmission dynamics. Here, we generated 658 whole-genome JCV sequences from the Northeast United States, including 84% (500/597) of all JCV-positive mosquitoes detected in Connecticut from 1997-2022. Then we applied phylodynamic methods to demonstrate how mosquito phenology and host interaction structure the persistence and spread of JCV. Our phylogenetic analyses estimate that JCV was introduced in the Northeast by at least the early 1700s and the primary introductions of lineages A and B into Connecticut occurred during the mid-1800s to mid-1900s. Further, we estimate that JCV evolves at a rate of [~]3 x 10-5 s/s/y, making it one of the slowest evolving known RNA viruses, because the virus spends [~]10 months per year in evolutionary stasis while over-wintering in mosquito eggs. To investigate ecological drivers of JCV spread in Connecticut, we paired discrete trait and continuous phylogeographic reconstructions with mosquito surveillance data. We estimate that JCV has a low diffusion rate of [~]30-60 km2/year, which is more similar to slow-moving tick-borne viruses than other mosquito-borne viruses. We found that univoltine Aedes mosquitoes were likely to maintain the virus across years through overwintering in eggs, accounting for its slow evolution and dispersal, while multivoltine mosquitoes contribute to periodic bursts of spatial diffusion and amplification within seasons. By characterizing seasonal dynamics of JCV, we demonstrate the utility of dense sequencing and phylodynamics to disentangle complex transmission cycles, offering a framework to rapidly advance our evolutionary and ecological knowledge of understudied viruses.

ecology↗
bioRxiv2025

Emergence of D1.1 reassortant H5N1 avian influenza viruses in North America

Since 2021, highly pathogenic avian influenza viruses (HPAIVs) belonging to H5N1 clade 2.3.4.4b have circulated widely in North American wild birds and repeatedly spilled over into mammals. In 2025, the first H5N1-associated deaths in humans were recorded in the Western hemisphere, raising questions about how the ongoing evolution of the virus in wild birds impacts spillover risk. Here, our analysis of 21,471 H5N1 genomes identified an evolutionary shift in mid-2024, driven by interhemispheric migration from Asia and reassortment with new antigens. The genotypes that dominated the early years of North Americas H5N1 epizootic traced their ancestry back to Europe, but Asia was the source of new "D1.1" genotype viruses that (a) spread faster, (b) have higher reassortment potential, (c) a broader host range, (d) repeatedly spill over to bovines, and (e) cause severe disease in humans, including non-farm workers.

evolutionary biology↗
bioRxiv2025

Evidence of latency reshapes our understanding of Ebola virus reservoir dynamics

The reservoir of Ebola virus (EBOV) has remained a mystery since the first recorded zoonotic epidemic in 1976. While recent outbreaks have revealed much about the epidemiological dynamics that sustain human-to-human transmission, the mechanisms by which the virus persists between epidemics are unknown. Observations of extreme EBOV evolutionary rate heterogeneity in humans suggest latency is an important, yet untested, factor in persistence between epidemics. We here explicitly model latency with a novel phylogenetic approach to characterise the natural history of EBOV and, by extension, its reservoir. We find evidence that EBOV undergoes extended periods (i.e., possibly decades) of quiescence in the reservoir. Accounting for these dynamics significantly changes our understanding of EBOVs evolutionary past and its geographic spread through Central Africa.

evolutionary biology↗
bioRxiv2025

Parallel algorithms for phylogenetic inference under a structured coalescent approximation

While advances in molecular epidemiology and computational modeling have enhanced our capacity to track pathogen evolution, the accurate reconstruction of spatiotemporal transmission dynamics remains essential for developing epidemic preparedness frame-works and implementing outbreak response measures. Structured coalescent models offer a phylogeographic framework by restricting lineage coalescence events to geographically proximate host populations. Although the Bayesian structured coalescent approximation (BASTA) provides a tractable approach, contemporary phylogeographic analyses involving dozens of geographic localities and hundreds to thousands of viral genomes substantially exceed the computational capacity of existing implementations. The BASTA likelihood scales cubically with deme count and quadratically with sequence count due to matrix exponentiation and pairwise coalescent probability calculations. Here, we introduce a comprehensive algorithmic restructuring of the structured coalescent likelihood that eliminates redundancies, optimizes memory access, and exposes parallelization opportunities. Our approach reorganizes computations along three dimensions: (i) independent calculation of deme-transition probability matrices across time intervals; (ii) simultaneous evaluation of partial likelihood vectors within temporal slices; and (iii) concurrent aggregation of coalescent probabilities. Algorithmic restructuring cuts average coalescent likelihood computation by 7-8 fold, and parallelization further boosts performance to 10-26 fold, enabling joint phylogeographic analyses of dengue virus across 10 South American countries and H5N1 avian influenza across 20 Eurasian regions to finish in a fraction of prior time. This computational efficiency also enables comparison between backward-in-time structured coalescent approximations and forward-in-time phylogeographic methods, revealing that the former provides appropriately conservative posterior estimates, particularly at intermediate phylogenetic depths. We integrate our implementation into the popular BEAST X and BEAGLE software packages, with an accompanying interface in BEAUti X to easily set up the analyses, providing researchers with an accessible and scalable tool for real-time phylogeographic surveillance of rapidly evolving pathogens.

bioinformatics↗
bioRxiv2025

Biological causes and impacts of rugged tree landscapes in phylodynamic inference

Phylodynamic analysis has been instrumental in elucidating epidemiological and evolutionary dynamics of pathogens. Bayesian phylodynamics integrates out phylogenetic uncertainty, which is typically substantial in phylodynamic datasets due to limited genetic diversity. Phylodynamic inference does not, however, scale with modern datasets, partly due to difficulties in traversing tree space. Here, we characterize tree space and landscape in phylodynamic inference and assess its impacts on analysis difficulty and key biological estimates. By running extensive Bayesian analyses of 15 classic large phylodynamic datasets and carefully analyzing the posterior samples, we find that the posterior tree landscape is diffuse yet rugged, leading to widespread tree sampling problems that usually stem from sequences in a small part of the tree. We develop clade-specific diagnostics to show that a few sequences--including putative recombinants and recurrent mutants--frequently drive the ruggedness and sampling problems, although existing data-quality tests show limited power to detect them. The sampling problems can significantly impact phylodynamic inferences or distort major biological conclusions; the impact is usually stronger on "local" estimates (e.g., introduction history) associated with particular clades than on "global" parameters (e.g., demographic trajectory) governed by general tree shape. We evaluate existing and newly-developed MCMC diagnostics, and offer strategies for optimizing phylodynamic analysis settings and mitigating sampling problem impacts. Our findings highlight the need and directions to develop efficient traversal over rugged tree landscapes, ultimately advancing scalable and reliable phylodynamics. Significance StatementBayesian phylodynamics is central to epidemiological studies, but exploring the vast and complex tree space is computationally challenging. Phylodynamic datasets comprise many highly similar sequences, sampled through time, creating a uniquely structured landscape of optimal trees. Here, we show that phylodynamic tree landscapes are often highly rugged, with multiple peaks separated by difficult-to-cross valleys. These features lead to widespread sampling problems which are often driven by a few sequences. These problems can significantly impact phylodynamic estimates, especially those associated with particular clades, distorting biological conclusions. We develop diagnostics to identify problematic sequences and provide solutions to mitigate their impacts. We offer strategies to optimize phylodynamic analysis workflows and to develop algorithms for navigating rugged landscapes, thereby advancing infectious disease investigation.

evolutionary biology↗
bioRxiv2025

Navigating sampling bias in discrete phylogeographic analysis: assessing the performance of an adjusted Bayes factor

Bayesian phylogeographic inference is widely used in molecular epidemiological studies to reconstruct the dispersal history of pathogens. Discrete phylogeographic analysis treats geographic locations as discrete traits and infers lineage transition events among them, and is typically followed by a Bayes factor (BF) test to assess the statistical support. In the standard BF (BFstd) test, the relative abundance of the involved trait states is not considered, which can be problematic in the case of unbalanced sampling. Existing methods to correct sampling bias in discrete phylogeographic analyses using continuous-time Markov chain (CTMC) model, often require additional epidemiological information to balance the sampling effort among locations. As such data is not necessarily available, alternative approaches that rely solely on available genomic data are needed. In this perspective, we assess the performance of a modification of the BFstd, the adjusted Bayes factor (BFadj), which incorporates information on the relative abundance of samples by location when inferring support for transition events and root location inference without requiring additional data. Using a simulation framework, we assess the statistical performance of BFstd and BFadj under varying levels of sampling bias, estimating their type I and type II error rates. Our results show that BFadj complements the BFstd by reducing type I errors at the cost increasing type II errors for inferred transition events, while improving type I and type II errors in root location inference. Our findings provide guidelines for implementing the complementary BFadj to detect and mitigate sampling bias in discrete phylogeographic inference using CTMC modelling.

evolutionary biology↗
bioRxiv2025

Comparative performance of novel viral landscape phylogeography approaches

The fast rate of evolution in RNA viruses implies that their evolutionary and ecological processes occur on the same time scale. Genome sequences of these pathogens can therefore contain information about the processes that govern their transmission and dispersal. In particular, landscape phylogeographic approaches use phylogeographic reconstructions to investigate the impact of environmental factors and variables on the spatial spread of viruses. Here, we extend and improve existing approaches and develop three novel landscape phylogeographic methods that can test the impact of continuous environmental factors on the diffusion velocity of viral lineages. In order to evaluate the different methods, we also implemented two simulation frameworks to test and compare their statistical performance. The results enable us to formulate clear guidelines for the use of three complementary landscape phylogeographic approaches that have sufficient statistical power and low rates of false positives. Our open-source methods are available to the scientific community and can be used to investigate the drivers of viral spread, with potential benefits for understanding virus epidemiology and designing tailored intervention strategies.

evolutionary biology↗
bioRxiv2025

Mass mortality events in the sub-Antarctic Indian Ocean caused by long-distance circumpolar spread of highly pathogenic avian influenza H5N1 clade 2.3.4.4b

Since 2020, the outbreak of highly pathogenic avian influenza (HPAI) virus clade 2.3.4.4b has turned into the largest documented panzootic to date, reaching the sub-Antarctic region and Antarctica via the tip of South America in 2023. Here, we describe its recent arrival into the Indian Ocean sub-Antarctic archipelagos of Crozet and Kerguelen, where we first detected the virus in October 2024 in dead southern elephant seals, king penguins, gentoo penguins, brown skuas and kelp gulls. While the panzootic is ongoing, it has already caused unprecedented and alarming mortalities of southern elephant seals. We collected brain swabs from various seal and bird carcasses, subsequently isolated the virus and obtained 25 novel HPAI H5N1 clade 2.3.4.4b sequences. Our phylogenetic and phylogeographic analyses show that there have been independent introductions of the virus to Crozet and Kerguelen, from the distant South Georgia Islands in the Southern Atlantic, and not from the more nearby coasts of South Africa. Our results point to a year-long gap in genomic surveillance in the south polar region, obscuring how HPAI H5N1 clade 2.3.4.4b is spreading in the sub-Antarctic and illustrating the difficulties in tracking pathogen dispersal in the region. Locally, our phylogenetic analyses show that the virus is transmitted between different species. Moreover, our serological analyses show that some southern elephant seal pups had mounted an anti-H5 antibody response. With the spread to Crozet and Kerguelen, HPAI H5N1 2.3.4.4b is moving ever closer to Australia and New Zealand, which currently remain free from infections with this strain, and represents a major threat to the sub-Antarctic wildlife. Our results provide key elements to enable stakeholders to anticipate the arrival and spread of the virus in remote areas of critical wildlife conservation concerns.

microbiology↗
bioRxiv2024

HIPSTR: highest independent posterior subtree reconstruction in TreeAnnotator X

In Bayesian phylogenetic and phylodynamic studies it is common to summarise the posterior distribution of trees with a time-calibrated consensus phylogeny. While the maximum clade credibility (MCC) tree is often used for this purpose, we here show that a novel consensus tree method - the highest independent posterior subtree reconstruction, or HIPSTR - contains consistently higher supported clades over MCC. We also provide faster computational routines for estimating both consensus trees in an updated version of TreeAnnotator X, an open-source software program that summarizes the information from a sample of trees and returns many helpful statistics such as individual clade credibilities contained in the consensus tree. HIPSTR and MCC reconstructions on two Ebola virus and two SARS-CoV-2 data sets show that HIPSTR yields consensus trees that consistently contain clades with higher support compared to MCC trees. The MCC trees regularly fail to include several clades with very high posterior probability ([≥] 0.95) as well as a large number of clades with moderate to high posterior probability ([≥] 0.50), whereas HIPSTR achieves near-perfect performance in this respect. HIPSTR also exhibits favorable computational performance over MCC in TreeAnnotator X. Comparison to the recently developed CCD0-MAP algorithm yielded mixed results, and requires more in-depth exploration in follow-up studies. TreeAnnotator X - which is part of the BEAST X (v10.5.0) software package - is available at https://github.com/beast-dev/beast-mcmc/releases.

bioinformatics↗
bioRxiv2023

Detecting episodic evolution through Bayesian inference of molecular clock models

Molecular evolutionary rate variation is a key aspect of the evolution of many organisms that can be modelled using molecular clock models. For example, fixed local clocks revealed the role of episodic evolution in the emergence of SARS-CoV-2 variants of concern. Like all statistical models, however, the reliability of such inferences is contingent on an assessment of statistical evidence. We present a novel Bayesian phylogenetic approach for detecting episodic evolution. It consists of computing Bayes factors, as the ratio of posterior and prior odds of evolutionary rate increases, effectively quantifying support for the effect size. We conducted an extensive simulation study to illustrate the power of this method and benchmarked it to formal model comparison of a range of molecular clock models using (log) marginal likelihood estimation, and to inference under a random local clock model. Quantifying support for the effect size has higher sensitivity than formal model testing and is straight-forward to compute, because it only needs samples from the posterior and prior distribution. However, formal model testing has the advantage of accommodating a wide range molecular clock models. We also assessed the ability of an automated approach, known as the random local clock, where branches under episodic evolution may be detected without their a priori definition. In an empirical analysis of a data set of SARS-CoV-2 genomes, we find very strong evidence for episodic evolution. Our results provide guidelines and practical methods for Bayesian detection of episodic evolution, as well as avenues for further research into this phenomenon.

bioinformatics↗
bioRxiv2022

Phylogeographic reconstruction of the emergence and spread of Powassan virus in the northeastern United States

Powassan virus is an emerging tick-borne virus of concern for public health, but very little is known about its transmission patterns and ecology. Here, we expanded the genomic dataset by sequencing 279 Powassan viruses isolated from Ixodes scapularis ticks from the northeastern United States. Our phylogeographic reconstructions revealed that Powassan virus lineage II was likely introduced or emerged from a relict population in the Northeast between 1940-1975. Sequences strongly clustered by sampling location, suggesting a highly focal geographical distribution. Our analyses further indicated that Powassan virus lineage II emerged in the northeastern U.S. mostly following a south to north pattern, with a weighted lineage dispersal velocity of ~3 km/year. Since the emergence in the Northeast, we found an overall increase in the effective population size of Powassan virus lineage II, but with growth stagnating during recent years. The cascading effect of population expansion of white-tailed deer and I. scapularis populations likely facilitated the emergence of Powassan virus in the northeastern U.S. Significance statementOur work provides important fundamental insights in the local transmission dynamics of an emerging tick-borne pathogen of public health concern. Without the availability of vaccines or specific treatments, prevention of Powassan virus infection is dependent on education and control. We identified that Powassan virus is maintained in highly localized transmission foci that have been maintained for several years, without introductions of new virus clades. This provides both opportunities for better education about high risk areas and effective targeted control in Powassan virus foci with a long lasting impact.

genomics↗