Virus genomes reveal factors that spread and sustained the Ebola epidemic

Bayesian mixture models for phylogenetic source attribution from consensus sequences and time since infection estimates

In stopping the spread of infectious diseases, pathogen genomic data can be used to reconstruct transmission events and characterize population-level sources of infection. Most approaches for identifying transmission pairs do not account for the time passing since the divergence of pathogen variants in individuals, which is problematic in viruses with high within-host evolutionary rates. This prompted us to consider possible transmission pairs in terms of phylogenetic data and additional estimates of time since infection derived from clinical biomarkers. We develop Bayesian mixture models with an evolutionary clock as a signal component and additional mixed effects or covariate random functions describing the mixing weights to classify potential pairs into likely and unlikely transmission pairs. We demonstrate that although sources cannot be identified at the individual level with certainty, even with the additional data on time elapsed, inferences into the population-level sources of transmission are possible, and more accurate than using only phylogenetic data without time since infection estimates. We apply the proposed approach to estimate age-specific sources of HIV infection in Amsterdam tranamission networks among men who have sex with men between 2010 and 2021. This study demonstrates that infection time estimates provide informative data to characterize transmission sources, and shows how phylogenetic source attribution can then be done with multi-dimensional mixture models.

Multiple cell types support productive infection and dynamic translocation of infectious Ebola virus to the surface of human skin

Ebola virus (EBOV) causes severe human disease. During late infection, EBOV virions are on the skin's surface; however, the permissive skin cell types and the route of virus translocation to the epidermal surface are unknown. We describe a human skin explant model and demonstrate that EBOV infection of human skin via basal media increases in a time-dependent and dose-dependent manner. In the dermis, cells of myeloid, endothelial, and fibroblast origin were EBOV antigen-positive whereas keratinocytes harbored virus in the epidermis. Infectious virus was detected on the apical epidermal surface within 3 days, indicating that virus propagates and traffics through the explants. Purified human fibroblasts and keratinocytes supported EBOV infection ex vivo and both cell types required the phosphatidylserine receptor, AXL, and the endosomal protein, NPC1, for virus entry. This platform identified susceptible cell types and demonstrated dynamic trafficking of EBOV virions. These findings may explain person-to-person transmission via skin contact.

SARS-CoV-2 introductions to the island of Ireland: a phylogenetic and geospatiotemporal study of infection dynamics

BACKGROUND

Ireland's COVID-19 response combined extensive SARS-CoV-2 testing to estimate incidence, with whole genome sequencing (WGS) for genome surveillance. As an island with two political jurisdictions-Northern Ireland (NI) and Republic of Ireland (RoI)-and access to detailed passenger travel data, Ireland provides a unique setting to study virus introductions and evaluate public health measures. Using a substantial Irish genomic dataset alongside global data from GISAID, this study aimed to trace the introduction and spread of SARS-CoV-2 across the island.

METHODS

We recursively searched for 29,518 SARS-CoV-2 genome sequences collected in Ireland from March 2020 to June 2022 within the global SARS-CoV-2 phylogenetic tree and identified clusters based on shared last common non-Irish ancestors. A maximum parsimony approach was used to assign a likely country of origin to each cluster. The geographic locations and collection dates of the samples in each introduction cluster were used to map the spread of the virus across Ireland. Downsampling was used to model the impact of varying levels of sequencing and normalisation for population permitted comparison between jurisdictions.

RESULTS

Six periods spanning the early introductions and the emergence of Alpha, Delta, and Omicron variants were studied in detail. Among 4439 SARS-CoV-2 introductions to Ireland, 2535 originated in England, with additional cases largely from the rest of Great Britain, United States of America, and Northwestern Europe. Introduction clusters ranged in size from a single to thousands of cases. Introductions were concentrated in the densely populated Dublin and Belfast areas, with many clusters spreading islandwide. Genetic phylogeny was able to effectively trace localised transmission patterns. Introduction rates were similar in NI and RoI for most variants, except for Delta, which was more frequently introduced to NI.

CONCLUSIONS

Tracking individual introduction events enables detailed modelling of virus spread patterns and clearer assessment of the effectiveness of control measures. Stricter travel restrictions in RoI likely reduced Delta introductions but not infection rates, which were similar across jurisdictions. Local and global sequencing levels influence the information available from phylogenomic analyses and we describe an approach to assess the ability of a chosen WGS level to detect virus introductions.

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.

The Bayesian Phylogenetic Bootstrap and its Application to Short Trees and Branches

Felsenstein's bootstrap is the most commonly used method to measure branch support in phylogenetics. Current sequencing technologies can result in massive sampling of taxa (e.g. SARS-CoV-2). In this case, the sequences are very similar, the trees are short, and the branches correspond to a small number of mutations (possibly 0). Nevertheless, these trees contain a strong signal, with unresolved parts but a low rate of false branches. With such data, Felsenstein's bootstrap is not satisfactory. Due to the frequentist nature of bootstrap sampling, the expected support of a branch corresponding to a single mutation is ∼63%, even though it is highly likely to be correct. Here, we propose a Bayesian version of the phylogenetic bootstrap in which sites are assigned uninformative prior probabilities. The branch support can then be interpreted as a posterior probability. We do not view the alignment as a small subsample of a large sample of sites, but rather as containing all available information (e.g. as with complete viral genomes, which are becoming routine). We give formulas for expected supports under the assumption of perfect phylogeny, in both the frequentist and Bayesian frameworks, where a branch corresponding to a single mutation now has an expected support of ∼90%. Simulations show that these theoretical results are robust to realistic data. Analyses on low-homoplasy viral and nonviral datasets show that Bayesian bootstrap support is easier to interpret, with high supports for branches very likely to be correct. As homoplasy increases, the two supports become closer and strongly correlated.

From emergence to endemicity of highly pathogenic H5 avian influenza viruses in Taiwan

A/goose/Guangdong/1/96-like (GsGd) highly pathogenic avian influenza (HPAI) H5 viruses cause severe outbreaks in poultry when introduced. Since emergence in 1996, control measures in most countries have suppressed local GsGd transmission following introductions, making persistent transmission in domestic birds rare. However, geographical expansion of clade 2.3.4.4 sublineages has raised concern about establishment of endemic circulation, while mechanistic drivers leading to endemicity remain unknown. We reconstructed the evolutionary history of GsGd sublineage, clade 2.3.4.4c, in Taiwan using a time-heterogeneous rate phylogeographic model. During Taiwan's initial epidemic wave (January 2015 - August 2016), we inferred that localised outbreaks had multiple origins from rapid spread between counties/cities nationwide. Subsequently, outbreaks predominantly originated from a single county, Yunlin, where persistent transmission harbours the trunk viruses of the sublineage. Endemic hotspots determined by phylogeographic reconstruction largely predicted the locations of re-emerging outbreaks in Yunlin. The transition to endemicity involved a shift to chicken-dominant circulation, following the initial bidirectional spread between chicken and domestic waterfowl. Our results suggest that following their emergence in Taiwan, source-sink dynamics from a single county have maintained GsGd endemicity up until 2023, pointing to where control efforts should be targeted to eliminate the disease.

Hong S, Gao J, Matsen IV FA, Rambaut A, Lemey P, Suchard MA, Dudas G, Baele G. On the importance of assessing topological convergence in Bayesian phylogenetic inference

Modern phylogenetics research is often performed within a Bayesian framework, using sampling algorithms such as Markov chain Monte Carlo (MCMC) to approximate the posterior distribution. These algorithms require careful evaluation of the quality of the generated samples. Within the field of phylogenetics, one frequently adopted diagnostic approach is to evaluate the effective sample size and to investigate trace graphs of the sampled parameters. A major limitation of these approaches is that they are developed for continuous parameters and therefore incompatible with a crucial parameter in these inferences: the tree topology. Several recent advancements have aimed at extending these diagnostics to topological space. In this reflection paper, we present two case studies-one on Ebola virus and one on HIV-illustrating how these topological diagnostics can contain information not found in standard diagnostics, and how decisions regarding which of these diagnostics to compute can impact inferences regarding MCMC convergence and mixing. Our results show the importance of running multiple replicate analyses and of carefully assessing topological convergence using the output of these replicate analyses. To this end, we illustrate different ways of assessing and visualizing the topological convergence of these replicates. Given the major importance of detecting convergence and mixing issues in Bayesian phylogenetic analyses, the lack of a unified approach to this problem warrants further action, especially now that additional tools are becoming available to researchers.

Untangling lineage introductions, persistence and transmission drivers of HP-PRRSV sublineage 8.7

Despite a rapid expansion of Porcine reproductive and respiratory syndrome virus (PRRSV) sublineage 8.7 over recent years, very little is known about the patterns of virus evolution, dispersal, and the factors influencing this dispersal. Relying on a national PRRSV surveillance project established over 20 years ago, we expand the available genomic data of sublineage 8.7 from China. We perform independent interlineage and intralineage recombination analyses for the entire study period, which showed a heterogeneous recombination pattern. A series of Bayesian phylogeographic analyses uncover the role of Guangdong as an important infection hub within Asia. The spatial spread of PRRSV is highly linked with a composite of human activities and the heterogeneous provincial distribution of the swine industry, largely propelled by the smaller-scale Chinese rural farming systems in the past years. We sequence all four available modified live vaccines (MLVs) and perform genomic analyses with publicly available data, of which our results suggest a key "leaky" period spanning 2011-2017 with two concurrent amino acid mutations in ORF1a 957 and ORF2 250. Overall, our study provides an in-depth overview of the evolution, transmission dynamics, and potential leaky status of HP-PRRS MLVs, providing critical insights into new MLV development.

Epi-Clock: A sensitive platform to help understand pathogenic disease outbreaks and facilitate the response to future outbreaks of concern

To predict potential epidemic outbreaks, we tested our strategy, Epi-Clock, which applies the novel ZHU algorithm to different SARS-CoV-2 datasets before outbreaks to search for significant mutational accumulation patterns correlated with outbreak events. Surprisingly, some inter-species genetic distances in Coronaviridae may represent intermediate states of different species or subspecies in the evolutionary history of Coronaviridae. The insertions and deletions in whole-genome sequences between different hosts were separately associated with important roles in host transmission and shifts in Coronaviridae. Furthermore, we believe that non-nucleosomal DNA may play a dominant role in the divergence of different lineages of SARS-CoV-2 in different regions of the world owing to the lack of nucleosome protection. We suggest that strong selective variation among different lineages of SARS-CoV-2 is required to produce strong codon usage bias, which appears in B.1.640.2 and B.1.617.2 (Delta). Notably, we found that an increasing number of other types of substitutions, such as those resulting from the hitchhiking effect, accumulated, especially in the pre-breakout phase, although some of the previous substitutions were replaced by other dominant genotypes. From most validations, we could accurately predict the potential pre-phase of outbreaks with a median interval of 5 days.

Harnessing computational tools of the digital era for enhanced infection control

This paper explores the potential of artificial intelligence, machine learning, and big data analytics in revolutionizing infection control. It addresses the challenges and innovative approaches in combating infectious diseases and antimicrobial resistance, emphasizing the critical role of interdisciplinary collaboration, ethical data practices, and integration of advanced computational tools in modern healthcare.

Random-Effects Substitution Models for Phylogenetics via Scalable Gradient Approximations

Phylogenetic and discrete-trait evolutionary inference depend heavily on an appropriate characterization of the underlying character substitution process. In this paper, we present random-effects substitution models that extend common continuous-time Markov chain models into a richer class of processes capable of capturing a wider variety of substitution dynamics. As these random-effects substitution models often require many more parameters than their usual counterparts, inference can be both statistically and computationally challenging. Thus, we also propose an efficient approach to compute an approximation to the gradient of the data likelihood with respect to all unknown substitution model parameters. We demonstrate that this approximate gradient enables scaling of sampling-based inference, namely Bayesian inference via Hamiltonian Monte Carlo, under random-effects substitution models across large trees and state-spaces. Applied to a dataset of 583 SARS-CoV-2 sequences, an HKY model with random-effects shows strong signals of nonreversibility in the substitution process, and posterior predictive model checks clearly show that it is a more adequate model than a reversible model. When analyzing the pattern of phylogeographic spread of 1441 influenza A virus (H3N2) sequences between 14 regions, a random-effects phylogeographic substitution model infers that air travel volume adequately predicts almost all dispersal rates. A random-effects state-dependent substitution model reveals no evidence for an effect of arboreality on the swimming mode in the tree frog subfamily Hylinae. Simulations reveal that random-effects substitution models can accommodate both negligible and radical departures from the underlying base substitution model. We show that our gradient-based inference approach is over an order of magnitude more time efficient than conventional approaches.

On the importance of assessing topological convergence in Bayesian phylogenetic inference

Modern phylogenetics research is often performed within a Bayesian framework, using sampling algorithms such as Markov chain Monte Carlo (MCMC) to approximate the posterior distribution. These algorithms require careful evaluation of the quality of the generated samples. Within the field of phylogenetics, one frequently adopted diagnostic approach is to evaluate the effective sample size (ESS) and to investigate trace graphs of the sampled parameters. A major limitation of these approaches is that they are developed for continuous parameters and therefore incompatible with a crucial parameter in these inferences: the tree topology. Several recent advancements have aimed at extending these diagnostics to topological space. In this reflection paper, we present two case studies - one on Ebola virus and one on HIV - illustrating how these topological diagnostics can contain information not found in standard diagnostics, and how decisions regarding which of these diagnostics to compute can impact inferences regarding MCMC convergence and mixing. Our results show the importance of running multiple replicate analyses and of carefully assessing topological convergence using the output of these replicate analyses. To this end, we illustrate different ways of assessing and visualizing the topological convergence of these replicates. Given the major importance of detecting convergence and mixing issues in Bayesian phylogenetic analyses, the lack of a unified approach to this problem warrants further action, especially now that additional tools are becoming available to researchers.

Integrative genomics would strengthen AMR understanding through ONE health approach

Emergence of drug-induced antimicrobial resistance (AMR) forms a crippling health and economic crisis worldwide, causing high mortality from otherwise treatable diseases and infections. Next Generation Sequencing (NGS) has significantly augmented detection of culture independent microbes, potential AMR in pathogens and elucidation of mechanisms underlying it. Here, we review recent findings of AMR evolution in pathogens aided by integrated genomic investigation strategies inclusive of bacteria, virus, fungi and AMR alleles. While AMR monitoring is dominated by data from hospital-related infections, we review genomic surveillance of both biotic and abiotic components involved in global AMR emergence and persistence. Identification of pathogen-intrinsic as well as environmental and/or host factors through robust genomics/bioinformatics, along with monitoring of type and frequency of antibiotic usage will greatly facilitate prediction of regional and global patterns of AMR evolution. Genomics-enabled AMR prediction and surveillance will be crucial - in shaping health and economic policies within the One Health framework to combat this global concern.

Comparing Phylogeographies to Reveal Incompatible Geographical Histories within Genomes

Modern phylogeography aims at reconstructing the geographic movement of organisms based on their genomic sequences and spatial information. Phylogeographic approaches are often applied to pathogen sequences and therefore tend to neglect the possibility of recombination, which decouples the evolutionary and geographic histories of different parts of the genome. Genomic regions of recombining or reassorting pathogens often originate and evolve at different times and locations, which characterize their unique spatial histories. Measuring the extent of these differences requires new methods to compare geographic information on phylogenetic trees reconstructed from different parts of the genome. Here we develop for the first time a set of measures of phylogeographic incompatibility, aimed at detecting differences between geographical histories in terms of distances between phylogeographies. We study the effect of varying demography and recombination on phylogeographic incompatibilities using coalescent simulations. We further apply these measures to the evolutionary history of human and livestock pathogens, either reassorting or recombining, such as the Victoria and Yamagata lineages of influenza B and the O/Ind-2001 foot-and-mouth disease virus strain. Our results reveal diverse geographical paths of migration that characterize the origins and evolutionary histories of different viral genes and genomic segments. These incompatibility measures can be applied to any phylogeography, and more generally to any phylogeny where each tip has been assigned either a continuous or discrete "trait" independent of the sequence. We illustrate this flexibility with an analysis of the interplay between the phylogeography and phylolinguistics of Uralic-speaking human populations, hinting at patrilinear language transmission.

SARS-CoV-2 Genomic Epidemiology Dashboards: A Review of Functionality and Technological Frameworks for the Public Health Response

During the coronavirus disease 2019 (COVID-19) pandemic, the number and types of dashboards produced increased to convey complex information using digestible visualizations. The pandemic saw a notable increase in genomic surveillance data, which genomic epidemiology dashboards presented in an easily interpretable manner. These dashboards have the potential to increase the transparency between the scientists producing pathogen genomic data and policymakers, public health stakeholders, and the public. This scoping review discusses the data presented, functional and visual features, and the computational architecture of six publicly available SARS-CoV-2 genomic epidemiology dashboards. We found three main types of genomic epidemiology dashboards: phylogenetic, genomic surveillance, and mutational. We found that data were sourced from different databases, such as GISAID, GenBank, and specific country databases, and these dashboards were produced for specific geographic locations. The key performance indicators and visualization used were specific to the type of genomic epidemiology dashboard. The computational architecture of the dashboards was created according to the needs of the end user. The genomic surveillance of pathogens is set to become a more common tool used to track ongoing and future outbreaks, and genomic epidemiology dashboards are powerful and adaptable resources that can be used in the public health response.

Genomic epidemiology uncovers the timing and origin of the emergence of mpox in humans

Five years before the 2022-2023 global mpox outbreak Nigeria reported its first cases in nearly 40 years, with the ongoing epidemic since driven by sustained human-to-human transmission. However, limited genomic data has left questions about the timing and origin of the mpox virus' (MPXV) emergence. Here we generated 112 MPXV genomes from Nigeria from 2021-2023. We identify the closest zoonotic outgroup to the human epidemic in southern Nigeria, and estimate that the lineage transmitting from human-to-human emerged around July 2014, circulating cryptically until detected in September 2017. The epidemic originated in Southern Nigeria, particularly Rivers State, which also acted as a persistent and dominant source of viral dissemination to other states. We show that APOBEC3 activity increased MPXV's evolutionary rate twenty-fold during human-to-human transmission. We also show how Delphy, a tool for near-real-time Bayesian phylogenetics, can aid rapid outbreak analytics. Our study sheds light on MPXV's establishment in West Africa before the 2022-2023 global outbreak and highlights the need for improved pathogen surveillance and response.

Genomic Diversity and Geographic Distribution of Newcastle Disease Virus Genotypes in Africa: Implications for Diagnosis, Vaccination, and Regional Collaboration

The emergence of new virulent genotypes and the continued genetic drift of Newcastle disease virus (NDV) implies that distinct genotypes of NDV are simultaneously evolving in different geographic locations across the globe, including throughout Africa, where NDV is an important veterinary pathogen. Expanding the genomic diversity of NDV increases the possibility of diagnostic and vaccine failures. In this review, we systematically analyzed the genetic diversity of NDV genotypes in Africa using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Information published between 1999 and 2022 were used to obtain the genetic background of different genotypes of NDV and their geographic distributions in Africa. The following genotypes were reported in Africa: I, II, III, IV, V, VI, VII, VIII, XI, XIII, XIV, XVII, XVIII, XX, and XXI. A new putative genotype has been detected in the Democratic Republic of the Congo. However, of 54 African countries, only 26 countries regularly report information on NDV outbreaks, suggesting that this number may be vastly underestimated. With eight different genotypes, Nigeria is the country with the greatest genotypic diversity of NDV among African countries. Genotype VII is the most prevalent group of NDV in Africa, which was reported in 15 countries. A phylogeographic analysis of NDV sequences revealed transboundary transmission of the virus in Eastern Africa, Western and Central Africa, and in Southern Africa. A regional and continental collaboration is recommended for improved NDV risk management in Africa.

Viral epidemic preparedness: a perspective from five clinical microbiology laboratories in Europe

BACKGROUND

Pandemic preparedness is critical to respond effectively to existing and emerging/new viral pathogens. Important lessons have been learned during the last pandemic at various levels. This revision discusses some of the major challenges and potential ways to address them in the likely event of future pandemics.

OBJECTIVES

To identify critical points of readiness that may help us accelerate the response to future pandemics from a clinical microbiology laboratory perspective with a focus on viral diagnostics and genomic sequencing. The potential areas of improvement identified are discussed from the sample collection to information reporting.

SOURCES

Microbiologists and researchers from five countries reflect on challenges encountered during the COVID-19 pandemic, review published literature on prior and current pandemics, and suggest potential solutions in preparation for future outbreaks.

CONTENT

Major challenges identified in the pre-analytic and post-analytic phases from sample collection to result reporting are discussed. From the perspective of clinical microbiology laboratories, the preparedness for a new pandemic should focus on zoonotic viruses. Laboratory readiness for scalability is critical and should include elements related to material procurement, training personnel, specific funding programmes, and regulatory issues to rapidly implement "in-house" tests. Laboratories across various countries should establish (or re-use) operational networks to communicate to respond effectively, ensuring the presence of agile circuits with full traceability of samples.

IMPLICATIONS

Laboratory preparedness is paramount to respond effectively to emerging and re-emerging viral infections and to limit the clinical and societal impact of new potential pandemics. Agile and fully traceable methods for sample collection to report are the cornerstone of a successful response. Expert group communication and early involvement of information technology personnel are critical for preparedness. A specific budget for pandemic preparedness should be ring-fenced and added to the national health budgets.

Advances in rapid point-of-care virus testing

Outbreaks of viral diseases seriously jeopardize people's health and cause huge economic losses. At the same time, virology provides a new perspective for biology, molecular biology and cancer research, and it is important to study the discovered viruses with potential applications. Therefore, the development of immediate and rapid viral detection methods for the prevention and treatment of viral diseases as well as the study of viruses has attracted extensive attention from scientists. With the continuous progress of science and technology, especially in the field of bioanalysis, a series of new detection techniques have been applied to the on-site rapid detection of viruses, which has become a powerful approach for human beings to fight against viruses. In this paper, the latest research progress of rapid point-of-care detection of viral nucleic acids, antigens and antibodies is presented. In addition, the advantages and disadvantages of these technologies are discussed from the perspective of practical application requirements. Finally, the problems and challenges faced by rapid viral detection methods and their development prospects are discussed.

Evaluating the performance of Plasmodium falciparum genetic metrics for inferring National Malaria Control Programme reported incidence in Senegal

BACKGROUND

Genetic surveillance of the Plasmodium falciparum parasite shows great promise for helping National Malaria Control Programmes (NMCPs) assess parasite transmission. Genetic metrics such as the frequency of polygenomic (multiple strain) infections, genetic clones, and the complexity of infection (COI, number of strains per infection) are correlated with transmission intensity. However, despite these correlations, it is unclear whether genetic metrics alone are sufficient to estimate clinical incidence.

METHODS

This study examined parasites from 3147 clinical infections sampled between the years 2012-2020 through passive case detection (PCD) across 16 clinic sites spread throughout Senegal. Samples were genotyped with a 24 single nucleotide polymorphism (SNP) molecular barcode that detects parasite strains, distinguishes polygenomic (multiple strain) from monogenomic (single strain) infections, and identifies clonal infections. To determine whether genetic signals can predict incidence, a series of Poisson generalized linear mixed-effects models were constructed to predict the incidence level at each clinical site from a set of genetic metrics designed to measure parasite clonality, superinfection, and co-transmission rates.

RESULTS

Model-predicted incidence was compared with the reported standard incidence data determined by the NMCP for each clinic and found that parasite genetic metrics generally correlated with reported incidence, with departures from expected values at very low annual incidence (< 10/1000/annual [‰]).

CONCLUSIONS

When transmission is greater than 10 cases per 1000 annual parasite incidence (annual incidence > 10‰), parasite genetics can be used to accurately infer incidence and is consistent with superinfection-based hypotheses of malaria transmission. When transmission was < 10‰, many of the correlations between parasite genetics and incidence were reversed, which may reflect the disproportionate impact of importation and focal transmission on parasite genetics when local transmission levels are low.

Scalable gradients enable Hamiltonian Monte Carlo sampling for phylodynamic inference under episodic birth-death-sampling models

Birth-death models play a key role in phylodynamic analysis for their interpretation in terms of key epidemiological parameters. In particular, models with piecewise-constant rates varying at different epochs in time, to which we refer as episodic birth-death-sampling (EBDS) models, are valuable for their reflection of changing transmission dynamics over time. A challenge, however, that persists with current time-varying model inference procedures is their lack of computational efficiency. This limitation hinders the full utilization of these models in large-scale phylodynamic analyses, especially when dealing with high-dimensional parameter vectors that exhibit strong correlations. We present here a linear-time algorithm to compute the gradient of the birth-death model sampling density with respect to all time-varying parameters, and we implement this algorithm within a gradient-based Hamiltonian Monte Carlo (HMC) sampler to alleviate the computational burden of conducting inference under a wide variety of structures of, as well as priors for, EBDS processes. We assess this approach using three different real world data examples, including the HIV epidemic in Odesa, Ukraine, seasonal influenza A/H3N2 virus dynamics in New York state, America, and Ebola outbreak in West Africa. HMC sampling exhibits a substantial efficiency boost, delivering a 10- to 200-fold increase in minimum effective sample size per unit-time, in comparison to a Metropolis-Hastings-based approach. Additionally, we show the robustness of our implementation in both allowing for flexible prior choices and in modeling the transmission dynamics of various pathogens by accurately capturing the changing trend of viral effective reproductive number.

Virus sequencing performance during the SARS-CoV-2 pandemic: a retrospective analysis of data from multiple rounds of external quality assessment in Austria

Introduction: A notable feature of the 2019 coronavirus disease (COVID-19) pandemic was the widespread use of whole genome sequencing (WGS) to monitor severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. Countries around the world relied on sequencing and other forms of variant detection to perform contact tracing and monitor changes in the virus genome, in the hopes that epidemic waves caused by variants would be detected and managed earlier. As sequencing was encouraged and rewarded by the government in Austria, but represented a new technicque for many laboratories, we designed an external quality assessment (EQA) scheme to monitor the accuracy of WGS and assist laboratories in validating their methods. Methods: We implemented SARS-CoV-2 WGS EQAs in Austria and report the results from 7 participants over 5 rounds from February 2021 until June 2023. The participants received sample material, sequenced genomes with routine methods, and provided the sequences as well as information about mutations and lineages. Participants were evaluated on the completeness and accuracy of the submitted sequence and the ability to analyze and interpret sequencing data. Results: The results indicate that performance was excellent with few exceptions, and these exceptions showed improvement over time. We extend our findings to infer that most publicly available sequences are accurate within ≤1 nucleotide, somewhat randomly distributed through the genome. Conclusion: WGS continues to be used for SARS-CoV-2 surveillance, and will likely be instrumental in future outbreak scenarios. We identified hurdles in building next-generation sequencing capacity in diagnostic laboratories. EQAs will help individual laboratories maintain high quality next-generation sequencing output, and strengthen variant monitoring and molecular epidemiology efforts.

Many-core algorithms for high-dimensional gradients on phylogenetic trees

MOTIVATION

Advancements in high-throughput genomic sequencing are delivering genomic pathogen data at an unprecedented rate, positioning statistical phylogenetics as a critical tool to monitor infectious diseases globally. This rapid growth spurs the need for efficient inference techniques, such as Hamiltonian Monte Carlo (HMC) in a Bayesian framework, to estimate parameters of these phylogenetic models where the dimensions of the parameters increase with the number of sequences N. HMC requires repeated calculation of the gradient of the data log-likelihood with respect to (wrt) all branch-length-specific (BLS) parameters that traditionally takes O(N2) operations using the standard pruning algorithm. A recent study proposes an approach to calculate this gradient in O(N), enabling researchers to take advantage of gradient-based samplers such as HMC. The CPU implementation of this approach makes the calculation of the gradient computationally tractable for nucleotide-based models but falls short in performance for larger state-space size models, such as Markov-modulated and codon models. Here, we describe novel massively parallel algorithms to calculate the gradient of the log-likelihood wrt all BLS parameters that take advantage of graphics processing units (GPUs) and result in many fold higher speedups over previous CPU implementations.

RESULTS

We benchmark these GPU algorithms on three computing systems using three evolutionary inference examples exploring complete genomes from 997 dengue viruses, 62 carnivore mitochondria and 49 yeasts, and observe a >128-fold speedup over the CPU implementation for codon-based models and >8-fold speedup for nucleotide-based models. As a practical demonstration, we also estimate the timing of the first introduction of West Nile virus into the continental Unites States under a codon model with a relaxed molecular clock from 104 full viral genomes, an inference task previously intractable.

AVAILABILITY AND IMPLEMENTATION

We provide an implementation of our GPU algorithms in BEAGLE v4.0.0 (https://github.com/beagle-dev/beagle-lib), an open-source library for statistical phylogenetics that enables parallel calculations on multi-core CPUs and GPUs. We employ a BEAGLE-implementation using the Bayesian phylogenetics framework BEAST (https://github.com/beast-dev/beast-mcmc).

SARS-CoV-2 lineage assignments using phylogenetic placement/UShER are superior to pangoLEARN machine-learning method

With the rapid spread and evolution of SARS-CoV-2, the ability to monitor its transmission and distinguish among viral lineages is critical for pandemic response efforts. The most commonly used software for the lineage assignment of newly isolated SARS-CoV-2 genomes is pangolin, which offers two methods of assignment, pangoLEARN and pUShER. PangoLEARN rapidly assigns lineages using a machine-learning algorithm, while pUShER performs a phylogenetic placement to identify the lineage corresponding to a newly sequenced genome. In a preliminary study, we observed that pangoLEARN (decision tree model), while substantially faster than pUShER, offered less consistency across different versions of pangolin v3. Here, we expand upon this analysis to include v3 and v4 of pangolin, which moved the default algorithm for lineage assignment from pangoLEARN in v3 to pUShER in v4, and perform a thorough analysis confirming that pUShER is not only more stable across versions but also more accurate. Our findings suggest that future lineage assignment algorithms for various pathogens should consider the value of phylogenetic placement.

Fast and Accurate Maximum-Likelihood Estimation of Multi-Type Birth-Death Epidemiological Models from Phylogenetic Trees

Multi-type birth-death (MTBD) models are phylodynamic analogies of compartmental models in classical epidemiology. They serve to infer such epidemiological parameters as the average number of secondary infections Re and the infectious time from a phylogenetic tree (a genealogy of pathogen sequences). The representatives of this model family focus on various aspects of pathogen epidemics. For instance, the birth-death exposed-infectious (BDEI) model describes the transmission of pathogens featuring an incubation period (when there is a delay between the moment of infection and becoming infectious, as for Ebola and SARS-CoV-2), and permits its estimation along with other parameters. With constantly growing sequencing data, MTBD models should be extremely useful for unravelling information on pathogen epidemics. However, existing implementations of these models in a phylodynamic framework have not yet caught up with the sequencing speed. Computing time and numerical instability issues limit their applicability to medium data sets (≤ 500 samples), while the accuracy of estimations should increase with more data. We propose a new highly parallelizable formulation of ordinary differential equations for MTBD models. We also extend them to forests to represent situations when a (sub-)epidemic started from several cases (e.g., multiple introductions to a country). We implemented it for the BDEI model in a maximum likelihood framework using a combination of numerical analysis methods for efficient equation resolution. Our implementation estimates epidemiological parameter values and their confidence intervals in two minutes on a phylogenetic tree of 10,000 samples. Comparison to the existing implementations on simulated data shows that it is not only much faster but also more accurate. An application of our tool to the 2014 Ebola epidemic in Sierra-Leone is also convincing, with very fast calculation and precise estimates. As MTBD models are closely related to Cladogenetic State Speciation and Extinction (ClaSSE)-like models, our findings could also be easily transferred to the macroevolution domain.

Scalable gradients enable Hamiltonian Monte Carlo sampling for phylodynamic inference under episodic birth-death-sampling models

Birth-death models play a key role in phylodynamic analysis for their interpretation in terms of key epidemiological parameters. In particular, models with piecewise-constant rates varying at different epochs in time, to which we refer as episodic birth-death-sampling (EBDS) models, are valuable for their reflection of changing transmission dynamics over time. A challenge, however, that persists with current time-varying model inference procedures is their lack of computational efficiency. This limitation hinders the full utilization of these models in large-scale phylodynamic analyses, especially when dealing with high-dimensional parameter vectors that exhibit strong correlations. We present here a linear-time algorithm to compute the gradient of the birth-death model sampling density with respect to all time-varying parameters, and we implement this algorithm within a gradient-based Hamiltonian Monte Carlo (HMC) sampler to alleviate the computational burden of conducting inference under a wide variety of structures of, as well as priors for, EBDS processes. We assess this approach using three different real world data examples, including the HIV epidemic in Odesa, Ukraine, seasonal influenza A/H3N2 virus dynamics in New York state, America, and Ebola outbreak in West Africa. HMC sampling exhibits a substantial efficiency boost, delivering a 10- to 200-fold increase in minimum effective sample size per unit-time, in comparison to a Metropolis-Hastings-based approach. Additionally, we show the robustness of our implementation in both allowing for flexible prior choices and in modeling the transmission dynamics of various pathogens by accurately capturing the changing trend of viral effective reproductive number.

Emerging Infectious Diseases Are Virulent Viruses-Are We Prepared? An Overview

The recent pandemic caused by SARS-CoV-2 affected the global population, resulting in a significant loss of lives and global economic deterioration. COVID-19 highlighted the importance of public awareness and science-based decision making, and exposed global vulnerabilities in preparedness and response systems. Emerging and re-emerging viral outbreaks are becoming more frequent due to increased international travel and global warming. These viral outbreaks impose serious public health threats and have transformed national strategies for pandemic preparedness with global economic consequences. At the molecular level, viral mutations and variations are constantly thwarting vaccine efficacy, as well as diagnostic, therapeutic, and prevention strategies. Here, we discuss viral infectious diseases that were epidemic and pandemic, currently available treatments, and surveillance measures, along with their limitations.

The episodic resurgence of highly pathogenic avian influenza H5 virus

Highly pathogenic avian influenza (HPAI) H5N1 activity has intensified globally since 2021, increasingly causing mass mortality in wild birds and poultry and incidental infections in mammals1-3. However, the ecological and virological properties that underscore future mitigation strategies still remain unclear. Using epidemiological, spatial and genomic approaches, we demonstrate changes in the origins of resurgent HPAI H5 and reveal significant shifts in virus ecology and evolution. Outbreak data show key resurgent events in 2016-2017 and 2020-2021, contributing to the emergence and panzootic spread of H5N1 in 2021-2022. Genomic analysis reveals that the 2016-2017 epizootics originated in Asia, where HPAI H5 reservoirs are endemic. In 2020-2021, 2.3.4.4b H5N8 viruses emerged in African poultry, featuring mutations altering HA structure and receptor binding. In 2021-2022, a new H5N1 virus evolved through reassortment in wild birds in Europe, undergoing further reassortment with low-pathogenic avian influenza in wild and domestic birds during global dissemination. These results highlight a shift in the HPAI H5 epicentre beyond Asia and indicate that increasing persistence of HPAI H5 in wild birds is facilitating geographic and host range expansion, accelerating dispersion velocity and increasing reassortment potential. As earlier outbreaks of H5N1 and H5N8 were caused by more stable genomic constellations, these recent changes reflect adaptation across the domestic-bird-wild-bird interface. Elimination strategies in domestic birds therefore remain a high priority to limit future epizootics.

Genomic epidemiology offers high resolution estimates of serial intervals for COVID-19

Serial intervals - the time between symptom onset in infector and infectee - are a fundamental quantity in infectious disease control. However, their estimation requires knowledge of individuals' exposures, typically obtained through resource-intensive contact tracing efforts. We introduce an alternate framework using virus sequences to inform who infected whom and thereby estimate serial intervals. We apply our technique to SARS-CoV-2 sequences from case clusters in the first two COVID-19 waves in Victoria, Australia. We find that our approach offers high resolution, cluster-specific serial interval estimates that are comparable with those obtained from contact data, despite requiring no knowledge of who infected whom and relying on incompletely-sampled data. Compared to a published serial interval, cluster-specific serial intervals can vary estimates of the effective reproduction number by a factor of 2-3. We find that serial interval estimates in settings such as schools and meat processing/packing plants are shorter than those in healthcare facilities.

A topology-marginal composite likelihood via a generalized phylogenetic pruning algorithm

Bayesian phylogenetics is a computationally challenging inferential problem. Classical methods are based on random-walk Markov chain Monte Carlo (MCMC), where random proposals are made on the tree parameter and the continuous parameters simultaneously. Variational phylogenetics is a promising alternative to MCMC, in which one fits an approximating distribution to the unnormalized phylogenetic posterior. Previous work fit this variational approximation using stochastic gradient descent, which is the canonical way of fitting general variational approximations. However, phylogenetic trees are special structures, giving opportunities for efficient computation. In this paper we describe a new algorithm that directly generalizes the Felsenstein pruning algorithm (a.k.a. sum-product algorithm) to compute a composite-like likelihood by marginalizing out ancestral states and subtrees simultaneously. We show the utility of this algorithm by rapidly making point estimates for branch lengths of a multi-tree phylogenetic model. These estimates accord with a long MCMC run and with estimates obtained using a variational method, but are much faster to obtain. Thus, although generalized pruning does not lead to a variational algorithm as such, we believe that it will form a useful starting point for variational inference.

Investigating and combatting the key drivers of viral zoonoses in Africa: an analysis of eight epidemics

Investigating the interplay of factors that result in a viral zoonotic outbreak is difficult, though it is increasingly important. As anthropogenic influences shift the delicate balance of ecosystems, new zoonoses emerge in humans. Sub-Saharan Africa is a notable hotspot for zoonotic disease due to abundant competent mammalian reservoir hosts. Furthermore, poverty, corruption, and an overreliance on natural resources play considerable roles in depleting biological resources, exacerbating the population's susceptibility. Unsurprisingly, viral zoonoses have emerged in Africa, including HIV/AIDS, Ebola, Avian influenza, Lassa fever, Zika, and Monkeypox. These diseases are among the principal causes of death in endemic areas. Though typically distinct in their manifestations, viral zoonoses are connected by underlying, definitive factors. This review summarises vital findings on viral zoonoses in Africa using nine notable case studies as a benchmark for future studies. We discuss the importance of ecological recuperation and protection as a central strategy to control zoonotic diseases. Emphasis was made on moderating key drivers of zoonotic diseases to forestall future pandemics. This is in conjunction with attempts to redirect efforts from reactive to pre-emptive through a multidisciplinary "one health" approach.

Genomic epidemiology of dengue in Shantou, China, 2019

Objectives

Dengue has been endemic in Southeast Asian countries for decades. There are few reports tracing the dynamics of dengue in real time. In this study, we generated hundreds of pathogen genomes to understand the genomic epidemiology of an outbreak in a hyper-endemic area of dengue.

Methods

We leveraged whole-genome short-read sequencing (PE150) to generate genomes of the dengue virus and investigated the genomic epidemiology of a dengue virus transmission in a mesoscale outbreak in Shantou, China, in 2019.

Results

The outbreak was sustained from July to December 2019. The total accumulated number of laboratory-confirmed cases was 944. No gender bias or fatalities were recorded. Cambodia and Singapore were the main sources of imported dengue cases (74.07%, n = 20). A total of 284 dengue virus strains were isolated, including 259 DENV-1, 24 DENV-2, and 1 DENV-3 isolates. We generated the entire genome of 252 DENV isolates (229 DENV-1, 22 DENV-2, and 1 DENV-3), which represented 26.7% of the total cases. Combined epidemiological and phylogenetic analyses indicated multiple independent introductions. The internal transmission evaluations and transmission network reconstruction supported the inference of phylodynamic analysis, with high Bayes factor support in BSSVS analysis. Two expansion founders and transmission chains were detected in CCH and LG of Shantou.

Conclusions

We observed the instant effects of genomic epidemiology in monitoring the dynamics of DENV and highlighted its prospects for real-time tracing of outbreaks of other novel agents in the future.

Toward a global virus genomic surveillance network

The COVID-19 pandemic galvanized the field of virus genomic surveillance, demonstrating its utility for public health. Now, we must harness the momentum that led to increased infrastructure, training, and political will to build a sustainable global genomic surveillance network for other epidemic and endemic viruses. We suggest a generalizable modular sequencing framework wherein users can easily switch between virus targets to maximize cost-effectiveness and maintain readiness for new threats. We also highlight challenges associated with genomic surveillance and when global inequalities persist. We propose solutions to mitigate some of these issues, including training and multilateral partnerships. Exploring alternatives to clinical sequencing can also reduce the cost of surveillance programs. Finally, we discuss how establishing genomic surveillance would aid control programs and potentially provide a warning system for outbreaks, using a global respiratory virus (RSV), an arbovirus (dengue virus), and a regional zoonotic virus (Lassa virus) as examples.

Ecogeographic Drivers of the Spatial Spread of Highly Pathogenic Avian Influenza Outbreaks in Europe and the United States, 2016-Early 2022

H5Nx highly pathogenic avian influenza (HPAI) viruses of clade 2.3.4.4 have caused outbreaks in Europe among wild and domestic birds since 2016 and were introduced to North America via wild migratory birds in December 2021. We examined the spatiotemporal extent of HPAI viruses across continents and characterized ecological and environmental predictors of virus spread between geographic regions by constructing a Bayesian phylodynamic generalized linear model (phylodynamic-GLM). The findings demonstrate localized epidemics of H5Nx throughout Europe in the first several years of the epizootic, followed by a singular branching point where H5N1 viruses were introduced to North America, likely via stopover locations throughout the North Atlantic. Once in the United States (US), H5Nx viruses spread at a greater rate between US-based regions as compared to prior spread in Europe. We established that geographic proximity is a predictor of virus spread between regions, implying that intercontinental transport across the Atlantic Ocean is relatively rare. An increase in mean ambient temperature over time was predictive of reduced H5Nx virus spread, which may reflect the effect of climate change on declines in host species abundance, decreased persistence of the virus in the environment, or changes in migratory patterns due to ecological alterations. Our data provide new knowledge about the spread and directionality of H5Nx virus dispersal in Europe and the US during an actively evolving intercontinental outbreak, including predictors of virus movement between regions, which will contribute to surveillance and mitigation strategies as the outbreak unfolds, and in future instances of uncontained avian spread of HPAI viruses.

Outbreak.info genomic reports: scalable and dynamic surveillance of SARS-CoV-2 variants and mutations

In response to the emergence of SARS-CoV-2 variants of concern, the global scientific community, through unprecedented effort, has sequenced and shared over 11 million genomes through GISAID, as of May 2022. This extraordinarily high sampling rate provides a unique opportunity to track the evolution of the virus in near real-time. Here, we present outbreak.info , a platform that currently tracks over 40 million combinations of Pango lineages and individual mutations, across over 7,000 locations, to provide insights for researchers, public health officials and the general public. We describe the interpretable visualizations available in our web application, the pipelines that enable the scalable ingestion of heterogeneous sources of SARS-CoV-2 variant data and the server infrastructure that enables widespread data dissemination via a high-performance API that can be accessed using an R package. We show how outbreak.info can be used for genomic surveillance and as a hypothesis-generation tool to understand the ongoing pandemic at varying geographic and temporal scales.

Linked Mutations in the Ebola Virus Polymerase Are Associated with Organ Specific Phenotypes

Ebola virus (EBOV) causes a severe infection called Ebola virus disease (EVD). The pathogenesis of EBOV infection is complex, and outcome has been associated with a variety of immunological and cellular factors. Disease can result from several mechanisms, including direct organ and endothelial cell damage as a result of viral replication. During the2013 to 2016 Western Africa EBOV outbreak, several mutants emerged, with changes in the genes of nucleoprotein (NP), glycoprotein (GP), and the large (L) protein. Reverse genetic analysis has been used to investigate whether these mutations played any role in pathogenesis with mixed results depending on the experimental system used. Previous studies investigated the impact of three single nonsynonymous mutations (GP-A82V, NP-R111C, and L-D759G) on the fatality rate of mouse and ferret models and suggested that the L-D759G mutation decreased the virulence of EBOV. In this study, the effect of these three mutations was further evaluated by deep sequencing to determine viral population genetics and the host response in longitudinal samples of blood, liver, kidney, spleen, and lung tissues taken from the previous ferret model. The data indicated that the mutations were maintained in the different tissues, but the frequency of minor genomic mutations were different. In addition, compared to wild-type virus, the recombinant mutants had different within host effects, where the D759G (and accompanying Q986H) substitution in the L protein resulted in an upregulation of the immune response in the kidney, liver, spleen, and lungs. Together these studies provide insights into the biology of EBOV mutants both between and within hosts. IMPORTANCE Ebola virus infection can have dramatic effects on the human body which manifest in Ebola virus disease. The outcome of infection is either survival or death and in the former group with the potential of longer-term health consequences and persistent infection. Disease severity is undoubtedly associated with the host response, often with overt inflammatory responses correlated with poorer outcomes. The scale of the2013 to 2016 Western African Ebola virus outbreak revealed new aspects of viral biology. This included the emergence of mutants with potentially altered virulence. Biobanked tissue from ferret models of EBOV infected with different mutants that emerged in the Western Africa outbreak was used to investigate the effect of EBOV genomic variation in different tissues. Overall, the work provided insights into the population genetics of EBOV and showed that different organs in an animal model can respond differently to variants of EBOV.

Gaps in mobility data and implications for modelling epidemic spread: A scoping review and simulation study

Reliable estimates of human mobility are important for understanding the spatial spread of infectious diseases and the effective targeting of control measures. However, when modelling infectious disease dynamics, data on human mobility at an appropriate temporal or spatial resolution are not always available, leading to the common use of model-derived mobility proxies. In this study we reviewed the different data sources and mobility models that have been used to characterise human movement in Africa. We then conducted a simulation study to better understand the implications of using human mobility proxies when predicting the spatial spread and dynamics of infectious diseases. We found major gaps in the availability of empirical measures of human mobility in Africa, leading to mobility proxies being used in place of data. Empirical data on subnational mobility were only available for 17/54 countries, and in most instances, these data characterised long-term movement patterns, which were unsuitable for modelling the spread of pathogens with short generation times (time between infection of a case and their infector). Results from our simulation study demonstrated that using mobility proxies can have a substantial impact on the predicted epidemic dynamics, with complex and non-intuitive biases. In particular, the predicted times and order of epidemic invasion, and the time of epidemic peak in different locations can be underestimated or overestimated, depending on the types of proxies used and the country of interest. Our work underscores the need for regularly updated empirical measures of population movement within and between countries to aid the prevention and control of infectious disease outbreaks. At the same time, there is a need to establish an evidence base to help understand which types of mobility data are most appropriate for describing the spread of emerging infectious diseases in different settings.

Minimal Antigenic Evolution after a Decade of Norovirus GII.4 Sydney_2012 Circulation in Humans

Norovirus is a major human pathogen that can cause severe gastroenteritis in vulnerable populations. The extensive viral diversity presented by human noroviruses constitutes a major roadblock for the development of effective vaccines. In addition to the large number of genotypes, antigenically distinct variants of GII.4 noroviruses have chronologically emerged over the last 3 decades. The last variant to emerge, Sydney_2012, has been circulating at high incidence worldwide for over a decade. We analyzed 1449 capsid sequences from GII.4 Sydney_2012 viruses to determine genetic changes indicative of antigenic diversification. Phylogenetic analyses show that Sydney_2012 viruses scattered within the tree topology with no single cluster dominating during a given year or geographical location. Fourteen residues presented high variability, 7 of which mapped to 4 antigenic sites. Notably, ~52% of viruses presented mutations at 2 or more antigenic sites. Mutational patterns showed that residues 297 and 372, which map to antigenic site A, changed over time. Virus-like particles (VLPs) developed from wild-type Sydney_2012 viruses and engineered to display all mutations detected at antigenic sites were tested against polyclonal sera and monoclonal antibodies raised against Sydney_2012 and Farmington_Hills_2002 VLPs. Minimal changes in reactivity were detected with polyclonal sera and only 4 MAbs lost binding, with all mapping to antigenic site A. Notably, reversion of residues from Sydney_2012 reconstituted epitopes from ancestral GII.4 variants. Overall, this study demonstrates that, despite circulating for over a decade, Sydney_2012 viruses present minimal antigenic diversification and provides novel insights on the diversification of GII.4 noroviruses that could inform vaccine design. IMPORTANCE GII.4 noroviruses are the major cause of acute gastroenteritis in all age groups. This predominance has been attributed to the continued emergence of phylogenetically discrete variants that escape immune responses to previous infections. The last GII.4 variant to emerge, Sydney_2012, has been circulating at high incidence for over a decade, raising the question of whether this variant is undergoing antigenic diversification without presenting a major distinction at the phylogenetic level. Sequence analyses that include >1400 capsid sequences from GII.4 Sydney_2012 showed changes in 4 out of the 6 major antigenic sites. Notably, while changes were detected in one of the most immunodominant sites over time, these resulted in minimal changes in the antigenic profile of these viruses. This study provides new insights on the mechanism governing the antigenic diversification of GII.4 norovirus that could help in the development of cross-protective vaccines to human noroviruses.

Comparing the transmission potential from sequence and surveillance data of 2009 North American influenza pandemic waves

Technological advancements in phylodynamic modeling coupled with the accessibility of real-time pathogen genetic data are increasingly important for understanding the infectious disease transmission dynamics. In this study, we compare the transmission potentials of North American influenza A(H1N1)pdm09 derived from sequence data to that derived from surveillance data. The impact of the choice of tree-priors, informative epidemiological priors, and evolutionary parameters on the transmission potential estimation is evaluated. North American Influenza A(H1N1)pdm09 hemagglutinin (HA) gene sequences are analyzed using the coalescent and birth-death tree prior models to estimate the basic reproduction number (R ₀ ). Epidemiological priors gathered from published literature are used to simulate the birth-death skyline models. Path-sampling marginal likelihood estimation is conducted to assess model fit. A bibliographic search to gather surveillance-based R ₀ values were consistently lower (mean ≤ 1.2) when estimated by coalescent models than by the birth-death models with informative priors on the duration of infectiousness (mean ≥ 1.3 to ≤2.88 days). The user-defined informative priors for use in the birth-death model shift the directionality of epidemiological and evolutionary parameters compared to non-informative estimates. While there was no certain impact of clock rate and tree height on the R ₀ estimation, an opposite relationship was observed between coalescent and birth-death tree priors. There was no significant difference (p = 0.46) between the birth-death model and surveillance R ₀ estimates. This study concludes that tree-prior methodological differences may have a substantial impact on the transmission potential estimation as well as the evolutionary parameters. The study also reports a consensus between the sequence-based R ₀ estimation and surveillance-based R ₀ estimates. Altogether, these outcomes shed light on the potential role of phylodynamic modeling to augment existing surveillance and epidemiological activities to better assess and respond to emerging infectious diseases.

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.

Distinguishing imported cases from locally acquired cases within a geographically limited genomic sample of an infectious disease

MOTIVATION

The ability to distinguish imported cases from locally acquired cases has important consequences for the selection of public health control strategies. Genomic data can be useful for this, for example, using a phylogeographic analysis in which genomic data from multiple locations are compared to determine likely migration events between locations. However, these methods typically require good samples of genomes from all locations, which is rarely available.

RESULTS

Here, we propose an alternative approach that only uses genomic data from a location of interest. By comparing each new case with previous cases from the same location, we are able to detect imported cases, as they have a different genealogical distribution than that of locally acquired cases. We show that, when variations in the size of the local population are accounted for, our method has good sensitivity and excellent specificity for the detection of imports. We applied our method to data simulated under the structured coalescent model and demonstrate relatively good performance even when the local population has the same size as the external population. Finally, we applied our method to several recent genomic datasets from both bacterial and viral pathogens, and show that it can, in a matter of seconds or minutes, deliver important insights on the number of imports to a geographically limited sample of a pathogen population.

AVAILABILITY AND IMPLEMENTATION

The R package DetectImports is freely available from https://github.com/xavierdidelot/DetectImports.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

GEViTRec: Data Reconnaissance Through Recommendation Using a Domain-Specific Visualization Prevalence Design Space

Genomic Epidemiology (genEpi) is a branch of public health that uses many different data types including tabular, network, genomic, and geographic, to identify and contain outbreaks of deadly diseases. Due to the volume and variety of data, it is challenging for genEpi domain experts to conduct data reconnaissance; that is, have an overview of the data they have and make assessments toward its quality, completeness, and suitability. We present an algorithm for data reconnaissance through automatic visualization recommendation, GEViTRec. Our approach handles a broad variety of dataset types and automatically generates visually coherent combinations of charts, in contrast to existing systems that primarily focus on singleton visual encodings of tabular datasets. We automatically detect linkages across multiple input datasets by analyzing non-numeric attribute fields, creating a data source graph within which we analyze and rank paths. For each high-ranking path, we specify chart combinations with positional and color alignments between shared fields, using a gradual binding approach to transform initial partial specifications of singleton charts to complete specifications that are aligned and oriented consistently. A novel aspect of our approach is its combination of domain-agnostic elements with domain-specific information that is captured through a domain-specific visualization prevalence design space. Our implementation is applied to both synthetic data and real Ebola outbreak data. We compare GEViTRec's output to what previous visualization recommendation systems would generate, and to manually crafted visualizations used by practitioners. We conducted formative evaluations with ten genEpi experts to assess the relevance and interpretability of our results. Code, Data, and Study Materials Availability: https://github.com/amcrisan/GEVitRec.

Advancing disease genomics beyond COVID-19 and reducing health disparities: what does the future hold for Africa?

The COVID-19 pandemic has ushered in high-throughput sequencing technology as an essential public health tool. Scaling up and operationalizing genomics in Africa is crucial as enhanced capacity for genome sequencing could address key health problems relevant to African populations. High-quality genomics research can be leveraged to improve diagnosis, understand the aetiology of unexplained illnesses, improve surveillance of infectious diseases and inform efficient control and therapeutic methods of known, rare and emerging infectious diseases. Achieving these within Africa requires strong commitment from stakeholders. A roadmap is needed to guide training of scientists, infrastructural development, research funding, international collaboration as well as promote public–private partnerships. Although the COVID-19 pandemic has significantly boosted genomics capacity in Africa, the continent still lags other regions. Here, we highlighted key initiatives in genomics research and efforts to address health challenges facing the diverse and fast-growing populations on the continent. We explore the scalability of genomic tools and techniques to tackle a broader range of infectious diseases in Africa, a continent that desperately requires a boost from genomic science.

Scalable Bayesian phylogenetics

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

Data integration in Bayesian phylogenetics

Researchers studying the evolution of viral pathogens and other organisms increasingly encounter and use large and complex data sets from multiple different sources. Statistical research in Bayesian phylogenetics has risen to this challenge. Researchers use phylogenetics not only to reconstruct the evolutionary history of a group of organisms, but also to understand the processes that guide its evolution and spread through space and time. To this end, it is now the norm to integrate numerous sources of data. For example, epidemiologists studying the spread of a virus through a region incorporate data including genetic sequences (e.g. DNA), time, location (both continuous and discrete) and environmental covariates (e.g. social connectivity between regions) into a coherent statistical model. Evolutionary biologists routinely do the same with genetic sequences, location, time, fossil and modern phenotypes, and ecological covariates. These complex, hierarchical models readily accommodate both discrete and continuous data and have enormous combined discrete/continuous parameter spaces including, at a minimum, phylogenetic tree topologies and branch lengths. The increased size and complexity of these statistical models have spurred advances in computational methods to make them tractable. We discuss both the modeling and computational advances below, as well as unsolved problems and areas of active research.