MASCOT: parameter and state inference under the marginal structured coalescent approximation

Deep Learning and Likelihood Approaches for Viral Phylogeography Converge on the Same Answers Whether the Inference Model Is Right or Wrong

Analysis of phylogenetic trees has become an essential tool in epidemiology. Likelihood-based methods fit models to phylogenies to draw inferences about the phylodynamics and history of viral transmission. However, these methods are often computationally expensive, which limits the complexity and realism of phylodynamic models and makes them ill-suited for informing policy decisions in real-time during rapidly developing outbreaks. Likelihood-free methods using deep learning are pushing the boundaries of inference beyond these constraints. In this paper, we extend, compare, and contrast a recently developed deep learning method for likelihood-free inference from trees. We trained multiple deep neural networks using phylogenies from simulated outbreaks that spread among 5 locations and found they achieve close to the same levels of accuracy as Bayesian inference under the true simulation model. We compared robustness to model misspecification of a trained neural network to that of a Bayesian method. We found that both models had comparable performance, converging on similar biases. We also implemented a method of uncertainty quantification called conformalized quantile regression that we demonstrate has similar patterns of sensitivity to model misspecification as Bayesian highest posterior density (HPD) and greatly overlap with HPDs, but have lower precision (more conservative). Finally, we trained and tested a neural network against phylogeographic data from a recent study of the SARS-Cov-2 pandemic in Europe and obtained similar estimates of region-specific epidemiological parameters and the location of the common ancestor in Europe. Along with being as accurate and robust as likelihood-based methods, our trained neural networks are on average over 3 orders of magnitude faster after training. Our results support the notion that neural networks can be trained with simulated data to accurately mimic the good and bad statistical properties of the likelihood functions of generative phylogenetic models.

How fast are viruses spreading in the wild?

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.

Underdetected dispersal and extensive local transmission drove the 2022 mpox epidemic

The World Health Organization declared mpox a public health emergency of international concern in July 2022. To investigate global mpox transmission and population-level changes associated with controlling spread, we built phylogeographic and phylodynamic models to analyze MPXV genomes from five global regions together with air traffic and epidemiological data. Our models reveal community transmission prior to detection, changes in case reporting throughout the epidemic, and a large degree of transmission heterogeneity. We find that viral introductions played a limited role in prolonging spread after initial dissemination, suggesting that travel bans would have had only a minor impact. We find that mpox transmission in North America began declining before more than 10% of high-risk individuals in the USA had vaccine-induced immunity. Our findings highlight the importance of broader routine specimen screening surveillance for emerging infectious diseases and of joint integration of genomic and epidemiological information for early outbreak control.

MASCOT-Skyline integrates population and migration dynamics to enhance phylogeographic reconstructions

The spread of infectious diseases is shaped by spatial and temporal aspects, such as host population structure or changes in the transmission rate or number of infected individuals over time. These spatiotemporal dynamics are imprinted in the genome of pathogens and can be recovered from those genomes using phylodynamics methods. However, phylodynamic methods typically quantify either the temporal or spatial transmission dynamics, which leads to unclear biases, as one can potentially not be inferred without the other. Here, we address this challenge by introducing a structured coalescent skyline approach, MASCOT-Skyline that allows us to jointly infer spatial and temporal transmission dynamics of infectious diseases using Markov chain Monte Carlo inference. To do so, we model the effective population size dynamics in different locations using a non-parametric function, allowing us to approximate a range of population size dynamics. We show, using a range of different viral outbreak datasets, potential issues with phylogeographic methods. We then use these viral datasets to motivate simulations of outbreaks that illuminate the nature of biases present in the different phylogeographic methods. We show that spatial and temporal dynamics should be modeled jointly even if one seeks to recover just one of the two. Further, we showcase conditions under which we can expect phylogeographic analyses to be biased, particularly different subsampling approaches, as well as provide recommendations of when we can expect them to perform well. We implemented MASCOT-Skyline as part of the open-source software package MASCOT for the Bayesian phylodynamics platform BEAST2.

Local-scale phylodynamics reveal differential community impact of SARS-CoV-2 in a metropolitan US county

SARS-CoV-2 transmission is largely driven by heterogeneous dynamics at a local scale, leaving local health departments to design interventions with limited information. We analyzed SARS-CoV-2 genomes sampled between February 2020 and March 2022 jointly with epidemiological and cell phone mobility data to investigate fine scale spatiotemporal SARS-CoV-2 transmission dynamics in King County, Washington, a diverse, metropolitan US county. We applied an approximate structured coalescent approach to model transmission within and between North King County and South King County alongside the rate of outside introductions into the county. Our phylodynamic analyses reveal that following stay-at-home orders, the epidemic trajectories of North and South King County began to diverge. We find that South King County consistently had more reported and estimated cases, COVID-19 hospitalizations, and longer persistence of local viral transmission when compared to North King County, where viral importations from outside drove a larger proportion of new cases. Using mobility and demographic data, we also find that South King County experienced a more modest and less sustained reduction in mobility following stay-at-home orders than North King County, while also bearing more socioeconomic inequities that might contribute to a disproportionate burden of SARS-CoV-2 transmission. Overall, our findings suggest a role for local-scale phylodynamics in understanding the heterogeneous transmission landscape.

Molecular evolution and phylogeographic analysis of wheat dwarf virus

Wheat dwarf virus (WDV) has caused considerable economic loss in the global production of grain crops. Knowledge of the evolutionary biology and population history of the pathogen remain poorly understood. We performed molecular evolution and worldwide phylodynamic analyses of the virus based on the genes in the protein-coding region of the entire viral genome. Our results showed that host-driven and geography-driven adaptation are major factors that affects the evolution of WDV. Bayesian phylogenetic analysis estimates that the average WDV substitution rate was 4.240 × 10-4 substitutions/site/year (95% credibility interval, 2.828 × 10-4-5.723 × 10-4), and the evolutionary rates of genes encoding proteins with virion-sense transcripts and genes encoding proteins with complementary-sense transcripts were different. The positively selected sites were detected in only two genes encoding proteins with complementary-sense, and WDV-barley are subject to stronger purifying selection than WDV-wheat. The time since the most recent common WDV ancestor was 1746 (95% credibility interval, 1517-1893) CE. Further analyses identified that the WDV-barley population and WDV-wheat population experienced dramatic expansion-decline episodes, and the expansion time of the WDV-barley population was earlier than that of the WDV-wheat population. Our phylogeographic analysis showed that the WDV population originating in Iran was subsequently introduced to Europe, and then spread from Eastern Europe to China.

Contemporary and historical human migration patterns shape hepatitis B virus diversity

Infection by hepatitis B virus (HBV) is responsible for approximately 296 million chronic cases of hepatitis B, and roughly 880,000 deaths annually. The global burden of HBV is distributed unevenly, largely owing to the heterogeneous geographic distribution of its subtypes, each of which demonstrates different severity and responsiveness to antiviral therapy. It is therefore crucial to the global public health response to HBV that the spatiotemporal spread of each genotype is well characterized. In this study, we describe a collection of 133 newly sequenced HBV strains from recent African immigrants upon their arrival in Belgium. We incorporate these sequences-all of which we determine to come from genotypes A, D, and E-into a large-scale phylogeographic study with genomes sampled across the globe. We focus on investigating the spatio-temporal processes shaping the evolutionary history of the three genotypes we observe. We incorporate several recently published ancient HBV genomes for genotypes A and D to aid our analysis. We show that different spatio-temporal processes underlie the A, D, and E genotypes with the former two having originated in southeastern Asia, after which they spread across the world. The HBV E genotype is estimated to have originated in Africa, after which it spread to Europe and the Americas. Our results highlight the use of phylogeographic reconstruction as a tool to understand the recent spatiotemporal dynamics of HBV, and highlight the importance of supporting vulnerable populations in accordance with the needs presented by specific HBV genotypes.

Approaches and challenges to inferring the geographical source of infectious disease outbreaks using genomic data

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.

Early underdetected dissemination across countries followed by extensive local transmission propelled the 2022 mpox epidemic

The World Health Organization declared mpox a public health emergency of international concern in July 2022. To investigate global mpox transmission and population-level changes associated with controlling spread, we built phylogeographic and phylodynamic models to analyze MPXV genomes from five global regions together with air traffic and epidemiological data. Our models reveal community transmission prior to detection, changes in case-reporting throughout the epidemic, and a large degree of transmission heterogeneity. We find that viral introductions played a limited role in prolonging spread after initial dissemination, suggesting that travel bans would have had only a minor impact. We find that mpox transmission in North America began declining before more than 10% of high-risk individuals in the USA had vaccine-induced immunity. Our findings highlight the importance of broader routine specimen screening surveillance for emerging infectious diseases and of joint integration of genomic and epidemiological information for early outbreak control.

Efficient Bayesian inference under the multispecies coalescent with migration

Analyses of genome sequence data have revealed pervasive interspecific gene flow and enriched our understanding of the role of gene flow in speciation and adaptation. Inference of gene flow using genomic data requires powerful statistical methods. Yet current likelihood-based methods involve heavy computation and are feasible for small datasets only. Here, we implement the multispecies-coalescent-with-migration model in the Bayesian program bpp, which can be used to test for gene flow and estimate migration rates, as well as species divergence times and population sizes. We develop Markov chain Monte Carlo algorithms for efficient sampling from the posterior, enabling the analysis of genome-scale datasets with thousands of loci. Implementation of both introgression and migration models in the same program allows us to test whether gene flow occurred continuously over time or in pulses. Analyses of genomic data from Anopheles mosquitoes demonstrate rich information in typical genomic datasets about the mode and rate of gene flow.

Phylodynamic approaches to studying avian influenza virus

Avian influenza viruses can cause severe disease in domestic and wild birds and are a pandemic threat. Phylodynamics is the study of how epidemiological, evolutionary, and immunological processes can interact to shape viral phylogenies. This review summarizes how phylodynamic methods have and could contribute to the study of avian influenza viruses. Specifically, we assess how phylodynamics can be used to examine viral spread within and between wild or domestic bird populations at various geographical scales, identify factors associated with virus dispersal, and determine the order and timing of virus lineage movement between geographic regions or poultry production systems. We discuss factors that can complicate the interpretation of phylodynamic results and identify how future methodological developments could contribute to improved control of the virus.

Population genomics of diarrheagenic Escherichia coli uncovers high connectivity between urban and rural communities in Ecuador

Human movement may be an important driver of transmission dynamics for enteric pathogens but has largely been underappreciated except for international 'travelers' diarrhea or cholera. Phylodynamic methods, which combine genomic and epidemiological data, are used to examine rates and dynamics of disease matching underlying evolutionary history and biogeographic distributions, but these methods often are not applied to enteric bacterial pathogens. We used phylodynamics to explore the phylogeographic and evolutionary patterns of diarrheagenic E. coli in northern Ecuador to investigate the role of human travel in the geographic distribution of strains across the country. Using whole genome sequences of diarrheagenic E. coli isolates, we built a core genome phylogeny, reconstructed discrete ancestral states across urban and rural sites, and estimated migration rates between E. coli populations. We found minimal structuring based on site locations, urban vs. rural locality, pathotype, or clinical status. Ancestral states of phylogenomic nodes and tips were inferred to have 51% urban ancestry and 49% rural ancestry. Lack of structuring by location or pathotype E. coli isolates imply highly connected communities and extensive sharing of genomic characteristics across isolates. Using an approximate structured coalescent model, we estimated rates of migration among circulating isolates were 6.7 times larger for urban towards rural populations compared to rural towards urban populations. This suggests increased inferred migration rates of diarrheagenic E. coli from urban populations towards rural populations. Our results indicate that investments in water and sanitation prevention in urban areas could limit the spread of enteric bacterial pathogens among rural populations.

A genomic assessment of the marine-speciation paradox within the toothed whale superfamily Delphinoidea

The impact of post-divergence gene flow in speciation has been documented across a range of taxa in recent years, and may have been especially widespread in highly mobile, wide-ranging marine species, such as cetaceans. Here, we studied individual genomes from nine species across the three families of the toothed whale superfamily Delphinoidea (Delphinidae, Phocoenidae and Monodontidae). To investigate the role of post-divergence gene flow in the speciation process, we used a multifaceted approach, including (i) phylogenomics, (ii) the distribution of shared derived alleles and (iii) demographic inference. We found the divergence of lineages within Delphinoidea did not follow a process of pure bifurcation, but was much more complex. Sliding-window phylogenomics reveal a high prevalence of discordant topologies within the superfamily, with further analyses indicating these discordances arose due to both incomplete lineage sorting and gene flow. D-statistics and f-branch analyses supported gene flow between members of Delphinoidea, with the vast majority of gene flow occurring as ancient interfamilial events. Demographic analyses provided evidence that introgressive gene flow has likely ceased between all species pairs tested, despite reports of contemporary interspecific hybrids. Our study provides the first steps towards resolving the large complexity of speciation within Delphinoidea; we reveal the prevalence of ancient interfamilial gene flow events prior to the diversification of each family, and suggest that contemporary hybridisation events may be disadvantageous, as hybrid individuals do not appear to contribute to the parental species' gene pools.

Global molecular evolution and phylogeographic analysis of barley yellow dwarf virus based on the cp and mp genes

Barley yellow dwarf virus (BYDV) has caused considerable losses in the global production of grain crops such as wheat, barley and maize. We investigated the phylodynamics of the virus by analysing 379 and 485 nucleotide sequences of the genes encoding the coat protein and movement protein, respectively. The maximum clade credibility tree indicated that BYDV-GAV and BYDV-MAV, BYDV-PAV and BYDV-PAS share the same evolutionary lineage, respectively. The diversification of BYDV arises from its adaptability to vector insects and geography. Bayesian phylogenetic analyses showed that the mean substitution rates of the coat and movement proteins of BYDV ranged from 8.327 × 10^- 4 (4.700 × 10^- 4-1.228 × 10^- 3) and 8.671 × 10^- 4 (6.143 × 10^- 4-1.130 × 10^- 3) substitutions/site/year, respectively. The time since the most recent common BYDV ancestor was 1434 (1040-1766) CE (Common Era). The Bayesian skyline plot (BSP) showed that the BYDV population experienced dramatic expansions approximately 8 years into the 21st century, followed by a dramatic decline in less than 15 years. Our phylogeographic analysis showed that the BYDV population originating in the United States was subsequently introduced to Europe, South America, Australia and Asia. The migration pathways of BYDV suggest that the global spread of BYDV is associated with human activities.

Mapping the Dynamics of Contemporary PRRSV-2 Evolution and Its Emergence and Spreading Hotspots in the U.S. Using Phylogeography

The repeated emergence of new genetic variants of PRRSV-2, the virus that causes porcine reproductive and respiratory syndrome (PRRS), reflects its rapid evolution and the failure of previous control efforts. Understanding spatiotemporal heterogeneity in variant emergence and spread is critical for future outbreak prevention. Here, we investigate how the pace of evolution varies across time and space, identify the origins of sub-lineage emergence, and map the patterns of the inter-regional spread of PRRSV-2 Lineage 1 (L1)-the current dominant lineage in the U.S. We performed comparative phylogeographic analyses on subsets of 19,395 viral ORF5 sequences collected across the U.S. and Canada between 1991 and 2021. The discrete trait analysis of multiple spatiotemporally stratified sampled sets (n = 500 each) was used to infer the ancestral geographic region and dispersion of each sub-lineage. The robustness of the results was compared to that of other modeling methods and subsampling strategies. Generally, the spatial spread and population dynamics varied across sub-lineages, time, and space. The Upper Midwest was a main spreading hotspot for multiple sub-lineages, e.g., L1C and L1F, though one of the most recent emergence events (L1A(2)) spread outwards from the east. An understanding of historical patterns of emergence and spread can be used to strategize disease control and the containment of emerging variants.

Spatial, temporal and evolutionary insight into seasonal epidemic Influenza A virus strains near the equatorial line: The case of Ecuador

To study the spatial and temporal patterns of Influenza A virus (IAV) is essential for an efficient control of the disease caused by IAV and efficient vaccination programs. However, spatiotemporal patterns of spread as well as genetic lineage circulation of IAV on a countrywide scale have not been clearly determined for many tropical regions of the world. In order to gain insight into these matters, the spatial and temporal patterns of IAV in six different geographic regions of Ecuador, from 2011 to 2021, were determined and the timing and magnitude of IAV outbreaks in these localities investigated. The results of these studies revealed that although Ecuador is a South American country situated in the Equator line, its IAV epidemiology resembles that of temperate Northern Hemisphere countries. Phylogenetic analysis of H1N1pdm09 and H3N2 IAV strains isolated in five different localities of Ecuador revealed that provinces in the south of this country have the largest effective population size by comparison with provinces in the north, suggesting that the southern provinces may be acting as a source of IAV. Co-circulation of different H1N1pdm09 and H3N2 genetic lineages was observed in different geographic regions of Ecuador.

Impact and mitigation of sampling bias to determine viral spread: Evaluating discrete phylogeography through CTMC modeling and structured coalescent model approximations

Bayesian phylogeographic inference is a powerful tool in molecular epidemiological studies, which enables reconstruction of the origin and subsequent geographic spread of pathogens. Such inference is, however, potentially affected by geographic sampling bias. Here, we investigated the impact of sampling bias on the spatiotemporal reconstruction of viral epidemics using Bayesian discrete phylogeographic models and explored different operational strategies to mitigate this impact. We considered the continuous-time Markov chain (CTMC) model and two structured coalescent approximations (Bayesian structured coalescent approximation [BASTA] and marginal approximation of the structured coalescent [MASCOT]). For each approach, we compared the estimated and simulated spatiotemporal histories in biased and unbiased conditions based on the simulated epidemics of rabies virus (RABV) in dogs in Morocco. While the reconstructed spatiotemporal histories were impacted by sampling bias for the three approaches, BASTA and MASCOT reconstructions were also biased when employing unbiased samples. Increasing the number of analyzed genomes led to more robust estimates at low sampling bias for the CTMC model. Alternative sampling strategies that maximize the spatiotemporal coverage greatly improved the inference at intermediate sampling bias for the CTMC model, and to a lesser extent, for BASTA and MASCOT. In contrast, allowing for time-varying population sizes in MASCOT resulted in robust inference. We further applied these approaches to two empirical datasets: a RABV dataset from the Philippines and a SARS-CoV-2 dataset describing its early spread across the world. In conclusion, sampling biases are ubiquitous in phylogeographic analyses but may be accommodated by increasing the sample size, balancing spatial and temporal composition in the samples, and informing structured coalescent models with reliable case count data.

The Evolution and Global Spatiotemporal Dynamics of Senecavirus A

Recurrent outbreaks of senecavirus A (SVA)-associated vesicular disease have led to a large number of infected pigs being culled and has caused considerable economic losses to the swine industry. Although SVA was discovered 2 decades ago, knowledge about the evolutionary and transmission histories of SVA remains unclear. Herein, we performed an integrated analysis of the recombination, phylogeny, selection, and spatiotemporal dynamics of SVA. Phylogenetic analysis demonstrated that SVA diverged into two main branches, clade I (pre-2007 strains) and clade II (post-2007 strains). Importantly, analysis of selective strength showed that clade II was evolving under relaxed selection compared with clade I. Positive selection analysis identified 27 positive selective sites, most of which are located on the outer surface of capsid protomer or on the important functional domains of nonstructure proteins. Bayesian phylodynamics suggested that the estimated time to the most recent common ancestor of SVA was around 1986, and the estimated substitution rate of SVA was 3.3522 × 10-3 nucleotide substitutions/site/year. Demographic history analysis revealed that the effective population size of SVA has experienced a gradually increasing trend with slight fluctuation until 2017 followed by a sharp decline. Notably, Bayesian phylogeographic analysis inferred that Brazil might be the source of SVA's global transmission since 2015. In summary, these data illustrated that the ongoing evolution of SVA drove the lineage-specific innovation and potentially phenotypically important variation. Our study sheds new light on the fundamental understanding of SVA evolution and spread history. IMPORTANCE Recurrent outbreaks and global epidemics of senecavirus A-associated vesicular disease have caused heavy economic losses and have threatened the development of the pig industry. However, the question of where the virus comes from has been one of the biggest puzzles due to the stealthy nature of the virus. Consequently, tracing the source, evolution, and transmission pattern of SVA is a very challenging task. Based on the most comprehensive analysis, we revealed the origin time, rapid evolution, epidemic dynamics, and selection of SVA. We observed two main genetic branches, clade I (pre-2007 strains) and clade II (post-2007 strains), and described the epidemiological patterns of SVA in different countries. We also first identified Brazil as the source of SVA's global transmission since 2015. Findings in this study provide important implications for the control and prevention of the virus.

Multiclonal human origin and global expansion of an endemic bacterial pathogen of livestock

Most new pathogens of humans and animals arise via switching events from distinct host species. However, our understanding of the evolutionary and ecological drivers of successful host adaptation, expansion, and dissemination are limited. Staphylococcus aureus is a major bacterial pathogen of humans and a leading cause of mastitis in dairy cows worldwide. Here we trace the evolutionary history of bovine S. aureus using a global dataset of 10,254 S. aureus genomes including 1,896 bovine isolates from 32 countries in 6 continents. We identified 7 major contemporary endemic clones of S. aureus causing bovine mastitis around the world and traced them back to 4 independent host-jump events from humans that occurred up to 2,500 y ago. Individual clones emerged and underwent clonal expansion from the mid-19th to late 20th century coinciding with the commercialization and industrialization of dairy farming, and older lineages have become globally distributed via established cattle trade links. Importantly, we identified lineage-dependent differences in the frequency of host transmission events between humans and cows in both directions revealing high risk clones threatening veterinary and human health. Finally, pangenome network analysis revealed that some bovine S. aureus lineages contained distinct sets of bovine-associated genes, consistent with multiple trajectories to host adaptation via gene acquisition. Taken together, we have dissected the evolutionary history of a major endemic pathogen of livestock providing a comprehensive temporal, geographic, and gene-level perspective of its remarkable success.

Bayesian Phylogeographic Inference Suggests Japan as the Center for the Origin and Dissemination of Rice Stripe Virus

Rice stripe virus (RSV) is one of the most important viral pathogens of rice in East Asia. The origin and dispersal of RSV remain poorly understood, but an emerging hypothesis suggests that: (i) RSV originates from Yunnan, a southwest province of China; and (ii) some places of eastern China have acted as a center for the international dissemination of RSV. This hypothesis, however, has never been tested rigorously. Using a data set comprising more than 200 time-stamped coat protein gene sequences of RSV from Japan, China and South Korea, we reconstructed the phylogeographic history of RSV with Bayesian phylogeographic inference. Unexpectedly, the results did not support the abovementioned hypothesis. Instead, they suggested that RSV originates from Japan and Japan has been the major center for the dissemination of RSV in the past decades. Based on these data and the temporal dynamics of RSV reported recently by another group, we proposed a new hypothesis to explain the origin and dispersal of RSV. This new hypothesis may be valuable for further studies aiming to clarify the epidemiology of RSV. It may also be useful in designing management strategies against this devastating virus.

Ecological and evolutionary dynamics of multi-strain RNA viruses

Potential interactions among co-circulating viral strains in host populations are often overlooked in the study of virus transmission. However, these interactions probably shape transmission dynamics by influencing host immune responses or altering the relative fitness among co-circulating strains. In this Review, we describe multi-strain dynamics from ecological and evolutionary perspectives, outline scales in which multi-strain dynamics occur and summarize important immunological, phylogenetic and mathematical modelling approaches used to quantify interactions among strains. We also discuss how host-pathogen interactions influence the co-circulation of pathogens. Finally, we highlight outstanding questions and knowledge gaps in the current theory and study of ecological and evolutionary dynamics of multi-strain viruses.

Phylodynamic and phylogeographic reconstruction of beak and feather disease virus epidemiology and its implications for the international exotic bird trade

Beak and feather disease virus (BFDV) infects domestic and wild psittacine species and is able to cause progressive beak, claw and feather malformation and necrosis. In addition to having an impact on the health and welfare of domesticated birds, BFDV represents a significant threat to wild endangered species. Understanding the epidemiology, dynamics, viral migration rate, interaction between wild and domestic animals and the effect of implemented control strategies is fundamental in controlling the spread of the disease. With this in mind, a phylodynamic and phylogeographic analysis has been performed on a database of more than 400 replication-associated protein (Rep) gene (ORF1) sequences downloaded from Genbank including some recently generated sequences from fifteen samples collected in Namibia. The results allowed us to reconstruct the variation of viral population size and demonstrated the effect of enforced international bans on these dynamics. A good correlation was found between viral migration rate and the intensity of animal trade between regions over time. A dominant flux of viral strains was observed from wild to domestic populations, highlighting the directionality of viral transmission and the risk associated with the capturing and trade of wild birds. Nevertheless, the flow of viruses from domestic to wild species was not negligible and should be considered as a threat to biodiversity. Therefore, considering the strong relationship demonstrated in this study between animal trade and BFDV viral fluxes more effort should be made to prevent contact opportunities between wild and domestic populations from different countries in order to control disease spread.

Phylogeography and Cryptic Species Structure of a Locally Adapted Parasite in New Zealand

Phylogeographic patterns of many taxa on New Zealand's South Island are characterised by disjunct distributions that have been attributed to Pleistocene climatic cycles and the formation of the Southern Alps. Pleistocene glaciation has also been implicated in shaping the contemporary genetic differentiation between populations of the aquatic snail Potamopyrgus antipodarum. We investigated whether similar phylogeographic patterns exist for the snail's locally adapted trematode parasite, Atriophallophorus winterbourni. We found evidence for a barrier to gene-flow in sympatry between cryptic, but ecologically divergent species. When focusing on the most common of these species, disjunct geographic distributions are found for mitochondrial lineages that diverged during the Pleistocene. The boundary between these distributions is found in the central part of the South Island and reinforced by low cross-alpine migration. Further support for a vicariant origin of the phylogeographic pattern was found when assessing nuclear multilocus SNP data. Nuclear and mitochondrial population differentiation was concordant in pattern, except for populations in a potential secondary contact zone. Additionally, we found larger than expected differentiation between nuclear- and mitochondrial-based empirical Bayes F_ST estimates (global F_ST : 0.02 versus 0.39 for nuclear and mitochondrial data, respectively). Population subdivision is theoretically expected to be stronger for mitochondrial genomes due to a smaller effective population size, but the strong difference here, together with mito-nuclear discordance in a putative contact zone, is potentially indicative of divergent gene flow of nuclear and mitochondrial genomes.

Epidemiological Inference From Pathogen Genomes: A Review of Phylodynamic Models and Applications

Phylodynamics requires an interdisciplinary understanding of phylogenetics, epidemiology, and statistical inference. It has also experienced more intense application than ever before amid the SARS-CoV-2 pandemic. In light of this, we present a review of phylodynamic models beginning with foundational models and assumptions. Our target audience is public health researchers, epidemiologists, and biologists seeking a working knowledge of the links between epidemiology, evolutionary models, and resulting epidemiological inference. We discuss the assumptions linking evolutionary models of pathogen population size to epidemiological models of the infected population size. We then describe statistical inference for phylodynamic models and list how output parameters can be rearranged for epidemiological interpretation. We go on to cover more sophisticated models and finish by highlighting future directions.

Phylogeographical Analyses of a Relict Fern of Palaeotropical Flora (Vandenboschia speciosa): Distribution and Diversity Model in Relation to the Geological and Climate Events of the Late Miocene and Early Pliocene

Fern phylogeographic studies have mostly focused on the influence of the Pleistocene climate on fern distributions and the prevalence of long-distance dispersal. The effect of pre-Pleistocene events on the distributions of fern species is largely unexplored. Here, we elucidate a hypothetical scenario for the evolutionary history of Vandenboschia speciosa, hypothesised to be of Tertiary palaeotropical flora with a peculiar perennial gametophyte. We sequenced 40 populations across the species range in one plastid region and two variants of the nuclear gapCp gene and conducted time-calibrated phylogenetic, phylogeographical, and species distribution modelling analyses. Vandenboschia speciosa is an allopolyploid and had a Tertiary origin. Late Miocene aridification possibly caused the long persistence in independent refugia on the Eurosiberian Atlantic and Mediterranean coasts, with the independent evolution of gene pools resulting in two evolutionary units. The Cantabrian Cornice, a major refugium, could also be a secondary contact zone during Quaternary glacial cycles. Central European populations resulted from multiple post-glacial, long-distance dispersals. Vandenboschia speciosa reached Macaronesia during the Pliocene–Pleistocene, with a phylogeographical link between the Canary Islands, Madeira, and southern Iberia, and between the Azores and northwestern Europe. Our results support the idea that the geological and climate events of the Late Miocene/Early Pliocene shifted Tertiary fern distribution patterns in Europe.

Global invasion history of the emerging plant pathogen Phytophthora multivora

Background

global trade in living plants and plant material has significantly increased the geographic distribution of many plant pathogens. As a consequence, several pathogens have been first found and described in their introduced range where they may cause severe damage on naïve host species. Knowing the center of origin and the pathways of spread of a pathogen is of importance for several reasons, including identifying natural enemies and reducing further spread. Several Phytophthora species are well-known invasive pathogens of natural ecosystems, including Phytophthora multivora. Following the description of P. multivora from dying native vegetation in Australia in 2009, the species was subsequently found to be common in South Africa where it does not cause any remarkable disease. There are now reports of P. multivora from many other countries worldwide, but not as a commonly encountered species in natural environments.

Results

a global collection of 335 isolates from North America, Europe, Africa, Australia, the Canary Islands, and New Zealand was used to unravel the worldwide invasion history of P. multivora, using 10 microsatellite markers for all isolates and sequence data from five loci from 94 representative isolates. Our population genetic analysis revealed an extremely low heterozygosity, significant non-random association of loci and substantial genotypic diversity suggesting the spread of P. multivora readily by both asexual and sexual propagules. The P. multivora populations in South Africa, Australia, and New Zealand show the most complex genetic structure, are well established and evolutionary older than those in Europe, North America and the Canary Islands.

Conclusions

according to the conducted analyses, the world invasion of P. multivora most likely commenced from South Africa, which can be considered the center of origin of the species. The pathogen was then introduced to Australia, which acted as bridgehead population for Europe and North America. Our study highlights a complex global invasion pattern of P. multivora, including both direct introductions from the native population and secondary spread/introductions from bridgehead populations.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08363-5.

Phylogeography and Re-Evaluation of Evolutionary Rate of Powassan Virus Using Complete Genome Data

Simple Summary

The evolution of human pathogenic viruses is one of the pressing problems of modern biology and directly relevant to public health. Many important aspects of virus evolution (e.g., evolutionary rate, population size, and migration history) are ‘hidden’ from the naked eye of a researcher. Modern bioinformatics methods make it possible to evaluate and visualize such evolutionary particularities of viruses. In this paper, we reconstructed the migration history and estimated the evolutionary rate of one of the most dangerous neuroinvasive and neurotropic tick-borne flaviviruses—Powassan virus (POWV)—distributed in North America and the Far East of Russia. Using the dates obtained, we hypothesized that the divergence of the most recent common ancestor of POWV into two independent genetic lineages most likely occurred because of the melting of glaciers that began at 11.72 Kya in the Holocene due to the climate warming-caused flooding of the isthmus between Eurasia and North America.

Abstract

In this paper, we revealed the genetic structure and migration history of the Powassan virus (POWV) reconstructed based on 25 complete genomes available in NCBI and ViPR databases (accessed in June 2021). The usage of this data set allowed us to perform a more precise assessment of the evolutionary rate of this virus. In addition, we proposed a simple Bayesian technique for the evaluation and visualization of ‘temporal signal dynamics’ along the phylogenetic tree. We showed that the evolutionary rate value of POWV is 3.3 × 10⁻⁵ nucleotide substitution per site per year (95% HPD, 2.0 × 10⁻⁵–4.7 × 10⁻⁵), which is lower than values reported in the previous studies. Divergence of the most recent common ancestor (MRCA) of POWV into two independent genetic lineages most likely occurred in the period between 2600 and 6030 years ago. We assume that the divergence of the virus lineages happened due to the melting of glaciers about 12,000 years ago, which led to the disappearance of the Bering Land Bridge between Eurasia and North America (the modern Alaskan territory) and spatial division of the viral areal into two parts. Genomic data provide evidence of the virus migrations between two continents. The mean migration rate detected from the Far East of Russia to North America was one event per 1750 years. The migration to the opposite direction occurred approximately once per 475 years.

Integrating animal movements with phylogeography to model the spread of PRRSV in the USA

Viral sequence data coupled with phylodynamic models have become instrumental in investigating the outbreaks of human and animal diseases, and the incorporation of the hypothesized drivers of pathogen spread can enhance the interpretation from phylodynamic inference. Integrating animal movement data with phylodynamics allows us to quantify the extent to which the spatial diffusion of a pathogen is influenced by animal movements and contrast the relative importance of different types of movements in shaping pathogen distribution. We combine animal movement, spatial, and environmental data in a Bayesian phylodynamic framework to explain the spatial diffusion and evolutionary trends of a rapidly spreading sub-lineage (denoted L1A) of porcine reproductive and respiratory syndrome virus (PRRSV) Type 2 from 2014 to 2017. PRRSV is the most important endemic pathogen affecting pigs in the USA, and this particular virulent sub-lineage emerged in 2014 and continues to be the dominant lineage in the US swine industry to date. Data included 984 open reading frame 5 (ORF5) PRRSV L1A sequences obtained from two production systems in a swine-dense production region (∼85,000 mi²) in the USA between 2014 and 2017. The study area was divided into sectors for which model covariates were summarized, and animal movement data between each sector were summarized by age class (wean: 3–4 weeks; feeder: 8–25 weeks; breeding: ≥21 weeks). We implemented a discrete-space phylogeographic generalized linear model using Bayesian evolutionary analysis by sampling trees (BEAST) to infer factors associated with variability in between-sector diffusion rates of PRRSV L1A. We found that between-sector spread was enhanced by the movement of feeder pigs, spatial adjacency of sectors, and farm density in the destination sector. The PRRSV L1A strain was introduced in the study area in early 2013, and genetic diversity and effective population size peaked in 2015 before fluctuating seasonally (peaking during the summer months). Our study underscores the importance of animal movements and shows, for the first time, that the movement of feeder pigs (8–25 weeks old) shaped the spatial patterns of PRRSV spread much more strongly than the movements of other age classes of pigs. The inclusion of movement data into phylodynamic models as done in this analysis may enhance our ability to identify crucial pathways of disease spread that can be targeted to mitigate the spatial spread of infectious human and animal pathogens.

Using host genetics to infer the global spread and evolutionary history of HCV subtype 3a

Studies have shown that hepatitis C virus subtype 3a (HCV-3a) is likely to have been circulating in South Asia before its global spread. However, the time and route of this dissemination remain unclear. For the first time, we generated host and virus genome-wide data for more than 500 patients infected with HCV-3a from the UK, North America, Australia, and New Zealand. We used the host genomic data to infer the ancestry of the patients and used this information to investigate the epidemic history of HCV-3a. We observed that viruses from hosts of South Asian ancestry clustered together near the root of the tree, irrespective of the sampling country, and that they were more diverse than viruses from other host ancestries. We hypothesized that South Asian hosts are more likely to have been infected in South Asia and used the inferred host ancestries to distinguish between the location where the infection was acquired and where the sample was taken. Next, we inferred that three independent transmission events resulted in the spread of the virus from South Asia to the UK, North America, and Oceania. This initial spread happened during or soon after the end of World War II. This was subsequently followed by many independent transmissions between the UK, North America, and Oceania. Using both host and virus genomic information can be highly informative in studying the virus epidemic history, especially in the context of chronic infections where migration histories need to be accounted for.

Phylodynamics reveals the role of human travel and contact tracing in controlling the first wave of COVID-19 in four island nations

New Zealand, Australia, Iceland, and Taiwan all saw success in controlling their first waves of Coronavirus Disease 2019 (COVID-19). As islands, they make excellent case studies for exploring the effects of international travel and human movement on the spread of COVID-19. We employed a range of robust phylodynamic methods and genome subsampling strategies to infer the epidemiological history of Severe acute respiratory syndrome coronavirus 2 in these four countries. We compared these results to transmission clusters identified by the New Zealand Ministry of Health by contact tracing strategies. We estimated the effective reproduction number of COVID-19 as 1-1.4 during early stages of the pandemic and show that it declined below 1 as human movement was restricted. We also showed that this disease was introduced many times into each country and that introductions slowed down markedly following the reduction of international travel in mid-March 2020. Finally, we confirmed that New Zealand transmission clusters identified via standard health surveillance strategies largely agree with those defined by genomic data. We have demonstrated how the use of genomic data and computational biology methods can assist health officials in characterising the epidemiology of viral epidemics and for contact tracing.

Repeated introductions and intensive community transmission fueled a mumps virus outbreak in Washington State

In 2016/2017, Washington State experienced a mumps outbreak despite high childhood vaccination rates, with cases more frequently detected among school-aged children and members of the Marshallese community. We sequenced 166 mumps virus genomes collected in Washington and other US states, and traced mumps introductions and transmission within Washington. We uncover that mumps was introduced into Washington approximately 13 times, primarily from Arkansas, sparking multiple co-circulating transmission chains. Although age and vaccination status may have impacted transmission, our data set could not quantify their precise effects. Instead, the outbreak in Washington was overwhelmingly sustained by transmission within the Marshallese community. Our findings underscore the utility of genomic data to clarify epidemiologic factors driving transmission and pinpoint contact networks as critical for mumps transmission. These results imply that contact structures and historic disparities may leave populations at increased risk for respiratory virus disease even when a vaccine is effective and widely used.

Local adaptation in populations of Mycobacterium tuberculosis endemic to the Indian Ocean Rim

Background: Lineage 1 (L1) and 3 (L3) are two lineages of the Mycobacterium tuberculosis complex (MTBC) causing tuberculosis (TB) in humans. L1 and L3 are prevalent around the rim of the Indian Ocean, the region that accounts for most of the world's new TB cases. Despite their relevance for this region, L1 and L3 remain understudied. Methods: We analyzed 2,938 L1 and 2,030 L3 whole genome sequences originating from 69 countries. We reconstructed the evolutionary history of these two lineages and identified genes under positive selection. Results: We found a strongly asymmetric pattern of migration from South Asia toward neighboring regions, highlighting the historical role of South Asia in the dispersion of L1 and L3. Moreover, we found that several genes were under positive selection, including genes involved in virulence and resistance to antibiotics. For L1 we identified signatures of local adaptation at the esxH locus, a gene coding for a secreted effector that targets the human endosomal sorting complex, and is included in several vaccine candidates. Conclusions: Our study highlights the importance of genetic diversity in the MTBC, and sheds new light on two of the most important MTBC lineages affecting humans.

Genomic epidemiology of coxsackievirus A16 in mainland of China, 2000-18

Hand, foot, and mouth disease (HFMD), which is a frequently reported and concerning disease worldwide, is a severe burden on societies globally, especially in the countries of East and Southeast Asia. Coxsackievirus A16 (CV-A16) is one of the most important causes of HFMD and a severe threat to human health, especially in children under 5 years of age. To investigate the epidemiological characteristics, spread dynamics, recombinant forms (RFs), and other features of CV-A16, we leveraged the continuous surveillance data of CV-A16-related HFMD cases collected over an 18-year period. With the advent of the EV-A71 vaccine since 2016, which targeted the EV-A71-related HFMD cases, EV-A71-related HFMD cases decreased dramatically, whereas the CV-A16-related HFMD cases showed an upward trend from 2017 to October 2019. The CV-A16 strains observed in this study were genetically related and widely distributed in the mainland of China. Our results show that three clusters (B1a-B1c) existed in the mainland of China and that the cluster of B1b dominates the diffusion of CV-A16 in China. We found that eastern China played a decisive role in seeding the diffusion of CV-A16 in China, with a more complex and variant transmission trend. Although EV-A71 vaccine was launched in China in 2016, it did not affect the genetic diversity of CV-A16, and its genetic diversity did not decline, which confirmed the epidemiological surveillance trend of CV-A16. Two discontinuous clusters (2000-13 and 2014-18) were observed in the full-length genome and arranged along the time gradient, which revealed the reason why the relative genetic diversity of CV-A16 increased and experienced more complex fluctuation model after 2014. In addition, the switch from RFs B (RF-B) and RF-C co-circulation to RF-D contributes to the prevalence of B1b cluster in China after 2008. The correlation between genotype and RFs partially explained the current prevalence of B1b. This study provides unprecedented full-length genomic sequences of CV-A16 in China, with a wider geographic distribution and a long-term time scale. The study presents valuable information about CV-A16, aimed at developing effective control strategies, as well as a call for a more robust surveillance system, especially in the Asia-Pacific region.

Adaptive Metropolis-coupled MCMC for BEAST 2

With ever more complex models used to study evolutionary patterns, approaches that facilitate efficient inference under such models are needed. Metropolis-coupled Markov chain Monte Carlo (MCMC) has long been used to speed up phylogenetic analyses and to make use of multi-core CPUs. Metropolis-coupled MCMC essentially runs multiple MCMC chains in parallel. All chains are heated except for one cold chain that explores the posterior probability space like a regular MCMC chain. This heating allows chains to make bigger jumps in phylogenetic state space. The heated chains can then be used to propose new states for other chains, including the cold chain. One of the practical challenges using this approach, is to find optimal temperatures of the heated chains to efficiently explore state spaces. We here provide an adaptive Metropolis-coupled MCMC scheme to Bayesian phylogenetics, where the temperature difference between heated chains is automatically tuned to achieve a target acceptance probability of states being exchanged between individual chains. We first show the validity of this approach by comparing inferences of adaptive Metropolis-coupled MCMC to MCMC on several datasets. We then explore where Metropolis-coupled MCMC provides benefits over MCMC. We implemented this adaptive Metropolis-coupled MCMC approach as an open source package licenced under GPL 3.0 to the Bayesian phylogenetics software BEAST 2, available from https://github.com/nicfel/CoupledMCMC.

Genesis and spread of multiple reassortants during the 2016/2017 H5 avian influenza epidemic in Eurasia

In 2016/2017, highly pathogenic avian influenza (HPAI) virus of the subtype H5 spilled over into wild birds and caused the largest known HPAI epidemic in Europe, affecting poultry and wild birds. During its spread, the virus frequently exchanged genetic material (reassortment) with cocirculating low-pathogenic avian influenza viruses. To determine where and when these reassortments occurred, we analyzed Eurasian avian influenza viruses and identified a large set of H5 HPAI reassortants. We found that new genetic material likely came from wild birds across their migratory range and from domestic ducks not only in China, but also in central Europe. This knowledge is important to understand how the virus could adapt to wild birds and become established in wild bird populations.

Highly pathogenic avian influenza (HPAI) viruses of the H5 A/goose/Guangdong/1/96 lineage can cause severe disease in poultry and wild birds, and occasionally in humans. In recent years, H5 HPAI viruses of this lineage infecting poultry in Asia have spilled over into wild birds and spread via bird migration to countries in Europe, Africa, and North America. In 2016/2017, this spillover resulted in the largest HPAI epidemic on record in Europe and was associated with an unusually high frequency of reassortments between H5 HPAI viruses and cocirculating low-pathogenic avian influenza viruses. Here, we show that the seven main H5 reassortant viruses had various combinations of gene segments 1, 2, 3, 5, and 6. Using detailed time-resolved phylogenetic analysis, most of these gene segments likely originated from wild birds and at dates and locations that corresponded to their hosts’ migratory cycles. However, some gene segments in two reassortant viruses likely originated from domestic anseriforms, either in spring 2016 in east China or in autumn 2016 in central Europe. Our results demonstrate that, in addition to domestic anseriforms in Asia, both migratory wild birds and domestic anseriforms in Europe are relevant sources of gene segments for recent reassortant H5 HPAI viruses. The ease with which these H5 HPAI viruses reassort, in combination with repeated spillovers of H5 HPAI viruses into wild birds, increases the risk of emergence of a reassortant virus that persists in wild bird populations yet remains highly pathogenic for poultry.

Cross-Species Transmission of Deltacoronavirus and the Origin of Porcine Deltacoronavirus

Deltacoronavirus is the last identified Coronaviridae subfamily genus. Differing from other coronavirus (CoV) genera, which mainly infect birds or mammals, deltacoronaviruses (δ‐CoVs) reportedly infect both animal types. Recent studies show that a novel δ‐CoV, porcine deltacoronavirus (PDCoV), can also infect calves and chickens with the potential to infect humans, raising the possibility of cross‐species transmission of δ‐CoVs. Here, we explored the deep phylogenetic history and cross‐species transmission of δ‐CoVs. Virus–host cophylogenetic analyses showed that δ‐CoVs have undergone frequent host switches in birds, and sparrows may serve as the unappreciated hubs for avian to mammal transmission. Our molecular clock analyses show that PDCoV possibly originated in Southeast Asia in the 1990s and that the PDCoV cluster shares a common ancestor with Sparrow‐CoV of around 1,810. Our findings contribute valuable insights into the diversification, evolution, and interspecies transmission of δ‐CoVs and the origin of PDCoV, providing a model for exploring the relationships of δ‐CoVs in birds and mammals.

Animal Disease Surveillance in the 21st Century: Applications and Robustness of Phylodynamic Methods in Recent U.S. Human-Like H3 Swine Influenza Outbreaks

Emerging and endemic animal viral diseases continue to impose substantial impacts on animal and human health. Most current and past molecular surveillance studies of animal diseases investigated spatio-temporal and evolutionary dynamics of the viruses in a disjointed analytical framework, ignoring many uncertainties and made joint conclusions from both analytical approaches. Phylodynamic methods offer a uniquely integrated platform capable of inferring complex epidemiological and evolutionary processes from the phylogeny of viruses in populations using a single Bayesian statistical framework. In this study, we reviewed and outlined basic concepts and aspects of phylodynamic methods and attempted to summarize essential components of the methodology in one analytical pipeline to facilitate the proper use of the methods by animal health researchers. Also, we challenged the robustness of the posterior evolutionary parameters, inferred by the commonly used phylodynamic models, using hemagglutinin (HA) and polymerase basic 2 (PB2) segments of the currently circulating human-like H3 swine influenza (SI) viruses isolated in the United States and multiple priors. Subsequently, we compared similarities and differences between the posterior parameters inferred from sequence data using multiple phylodynamic models. Our suggested phylodynamic approach attempts to reduce the impact of its inherent limitations to offer less biased and biologically plausible inferences about the pathogen evolutionary characteristics to properly guide intervention activities. We also pinpointed requirements and challenges for integrating phylodynamic methods in routine animal disease surveillance activities.

A brief history of bird flu

In 1918, a strain of influenza A virus caused a human pandemic resulting in the deaths of 50 million people. A century later, with the advent of sequencing technology and corresponding phylogenetic methods, we know much more about the origins, evolution and epidemiology of influenza epidemics. Here we review the history of avian influenza viruses through the lens of their genetic makeup: from their relationship to human pandemic viruses, starting with the 1918 H1N1 strain, through to the highly pathogenic epidemics in birds and zoonoses up to 2018. We describe the genesis of novel influenza A virus strains by reassortment and evolution in wild and domestic bird populations, as well as the role of wild bird migration in their long-range spread. The emergence of highly pathogenic avian influenza viruses, and the zoonotic incursions of avian H5 and H7 viruses into humans over the last couple of decades are also described. The threat of a new avian influenza virus causing a human pandemic is still present today, although control in domestic avian populations can minimize the risk to human health.

This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’. This issue is linked with the subsequent theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’.

Inferring time-dependent migration and coalescence patterns from genetic sequence and predictor data in structured populations

Population dynamics can be inferred from genetic sequence data by using phylodynamic methods. These methods typically quantify the dynamics in unstructured populations or assume migration rates and effective population sizes to be constant through time in structured populations. When considering rates to vary through time in structured populations, the number of parameters to infer increases rapidly and the available data might not be sufficient to inform these. Additionally, it is often of interest to know what predicts these parameters rather than knowing the parameters themselves. Here, we introduce a method to  infer the predictors for time-varying migration rates and effective population sizes by using a generalized linear model (GLM) approach under the marginal approximation of the structured coalescent. Using simulations, we show that our approach is able to reliably infer the model parameters and its predictors from phylogenetic trees. Furthermore, when simulating trees under the structured coalescent, we show that our new approach outperforms the discrete trait GLM model. We then apply our framework to a previously described Ebola virus dataset, where we infer the parameters and its predictors from genome sequences while accounting for phylogenetic uncertainty. We infer weekly cases to be the strongest predictor for effective population size and geographic distance the strongest predictor for migration. This approach is implemented as part of the BEAST2 package MASCOT, which allows us to jointly infer population dynamics, i.e. the parameters and predictors, within structured populations, the phylogenetic tree, and evolutionary parameters.

Bayesian phylodynamics of avian influenza A virus H9N2 in Asia with time-dependent predictors of migration

Model-based phylodynamic approaches recently employed generalized linear models (GLMs) to uncover potential predictors of viral spread. Very recently some of these models have allowed both the predictors and their coefficients to be time-dependent. However, these studies mainly focused on predictors that are assumed to be constant through time. Here we inferred the phylodynamics of avian influenza A virus H9N2 isolated in 12 Asian countries and regions under both discrete trait analysis (DTA) and structured coalescent (MASCOT) approaches. Using MASCOT we applied a new time-dependent GLM to uncover the underlying factors behind H9N2 spread. We curated a rich set of time-series predictors including annual international live poultry trade and national poultry production figures. This time-dependent phylodynamic prediction model was compared to commonly employed time-independent alternatives. Additionally the time-dependent MASCOT model allowed for the estimation of viral effective sub-population sizes and their changes through time, and these effective population dynamics within each country were predicted by a GLM. International annual poultry trade is a strongly supported predictor of virus migration rates. There was also strong support for geographic proximity as a predictor of migration rate in all GLMs investigated. In time-dependent MASCOT models, national poultry production was also identified as a predictor of virus genetic diversity through time and this signal was obvious in mainland China. Our application of a recently introduced time-dependent GLM predictors integrated rich time-series data in Bayesian phylodynamic prediction. We demonstrated the contribution of poultry trade and geographic proximity (potentially unheralded wild bird movements) to avian influenza spread in Asia. To gain a better understanding of the drivers of H9N2 spread, we suggest increased surveillance of the H9N2 virus in countries that are currently under-sampled as well as in wild bird populations in the most affected countries.

Investigation of the validity of two Bayesian ancestral state reconstruction models for estimating Salmonella transmission during outbreaks

Ancestral state reconstruction models use genetic data to characterize a group of organisms’ common ancestor. These models have been applied to salmonellosis outbreaks to estimate the number of transmissions between different animal species that share similar geographical locations, with animal host as the state. However, as far as we are aware, no studies have validated these models for outbreak analysis. In this study, salmonellosis outbreaks were simulated using a stochastic Susceptible-Infected-Recovered model, and the host population and transmission parameters of these simulated outbreaks were estimated using Bayesian ancestral state reconstruction models (discrete trait analysis (DTA) and structured coalescent (SC)). These models were unable to accurately estimate the number of transmissions between the host populations or the amount of time spent in each host population. The DTA model was inaccurate because it assumed the number of isolates sampled from each host population was proportional to the number of individuals infected within each host population. The SC model was inaccurate possibly because it assumed that each host population's effective population size was constant over the course of the simulated outbreaks. This study highlights the need for phylodynamic models that can take into consideration factors that influence the characteristics and behavior of outbreaks, e.g. changing effective population sizes, variation in infectious periods, intra-population transmissions, and disproportionate sampling of infected individuals.

Phylodynamic Model Adequacy Using Posterior Predictive Simulations

Rapidly evolving pathogens, such as viruses and bacteria, accumulate genetic change at a similar timescale over which their epidemiological processes occur, such that, it is possible to make inferences about their infectious spread using phylogenetic time-trees. For this purpose it is necessary to choose a phylodynamic model. However, the resulting inferences are contingent on whether the model adequately describes key features of the data. Model adequacy methods allow formal rejection of a model if it cannot generate the main features of the data. We present TreeModelAdequacy, a package for the popular BEAST2 software that allows assessing the adequacy of phylodynamic models. We illustrate its utility by analyzing phylogenetic trees from two viral outbreaks of Ebola and H1N1 influenza. The main features of the Ebola data were adequately described by the coalescent exponential-growth model, whereas the H1N1 influenza data were best described by the birth–death susceptible-infected-recovered model.

BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis

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.

Phylogenies from dynamic networks

The relationship between the underlying contact network over which a pathogen spreads and the pathogen phylogenetic trees that are obtained presents an opportunity to use sequence data to learn about contact networks that are difficult to study empirically. However, this relationship is not explicitly known and is usually studied in simulations, often with the simplifying assumption that the contact network is static in time, though human contact networks are dynamic. We simulate pathogen phylogenetic trees on dynamic Erdős-Renyi random networks and on two dynamic networks with skewed degree distribution, of which one is additionally clustered. We use tree shape features to explore how adding dynamics changes the relationships between the overall network structure and phylogenies. Our tree features include the number of small substructures (cherries, pitchforks) in the trees, measures of tree imbalance (Sackin index, Colless index), features derived from network science (diameter, closeness), as well as features using the internal branch lengths from the tip to the root. Using principal component analysis we find that the network dynamics influence the shapes of phylogenies, as does the network type. We also compare dynamic and time-integrated static networks. We find, in particular, that static network models like the widely used Barabasi-Albert model can be poor approximations for dynamic networks. We explore the effects of mis-specifying the network on the performance of classifiers trained identify the transmission rate (using supervised learning methods). We find that both mis-specification of the underlying network and its parameters (mean degree, turnover rate) have a strong adverse effect on the ability to estimate the transmission parameter. We illustrate these results by classifying HIV trees with a classifier that we trained on simulated trees from different networks, infection rates and turnover rates. Our results point to the importance of correctly estimating and modelling contact networks with dynamics when using phylodynamic tools to estimate epidemiological parameters.

Bayesian phylodynamic inference with complex models

Population genetic modeling can enhance Bayesian phylogenetic inference by providing a realistic prior on the distribution of branch lengths and times of common ancestry. The parameters of a population genetic model may also have intrinsic importance, and simultaneous estimation of a phylogeny and model parameters has enabled phylodynamic inference of population growth rates, reproduction numbers, and effective population size through time. Phylodynamic inference based on pathogen genetic sequence data has emerged as useful supplement to epidemic surveillance, however commonly-used mechanistic models that are typically fitted to non-genetic surveillance data are rarely fitted to pathogen genetic data due to a dearth of software tools, and the theory required to conduct such inference has been developed only recently. We present a framework for coalescent-based phylogenetic and phylodynamic inference which enables highly-flexible modeling of demographic and epidemiological processes. This approach builds upon previous structured coalescent approaches and includes enhancements for computational speed, accuracy, and stability. A flexible markup language is described for translating parametric demographic or epidemiological models into a structured coalescent model enabling simultaneous estimation of demographic or epidemiological parameters and time-scaled phylogenies. We demonstrate the utility of these approaches by fitting compartmental epidemiological models to Ebola virus and Influenza A virus sequence data, demonstrating how important features of these epidemics, such as the reproduction number and epidemic curves, can be gleaned from genetic data. These approaches are provided as an open-source package PhyDyn for the BEAST2 phylogenetics platform.