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PubMed2026

Spatiotemporal patterns of Rift Valley fever virus in Africa: a retrospective genomic epidemiology and phylodynamic modelling study.

BACKGROUND: Rift Valley fever virus (RVFV) is a mosquito-borne zoonotic pathogen causing outbreaks in humans and ruminants across Africa and the Arabian Peninsula. Originally restricted to the Great Rift Valley, RVFV has expanded geographically, prompting its classification by WHO as a pathogen of pandemic potential. We investigated the evolutionary and spatial dynamics of RVFV across Africa. METHODS: We used genomic data generated at the International Livestock Research Institute Nairobi genomic laboratory (BioProject PRJNA1106221) and combined with publicly available datasets retrieved from the National Center for Biotechnology (NCBI) GenBank nucleotide database. In retrieving RVFV genome sequences from the NCBI GenBank, we applied the search terms "Rift Valley fever virus segment L AND 6404[SLEN]", "Rift Valley fever virus segment M AND 3885[SLEN]", and "Rift Valley fever virus segment S AND 1520:1690[SLEN]" for L (Large), M (Medium), and S (Small) segments, respectively. For sequences without additional spatiotemporal information, we searched PubMed to extract the associated sequence metadata. We performed molecular clock analysis, phylogenetic inference, phylodynamic modelling (continuous phylogeographic reconstruction), and landscape phylogeography on the three RVFV genome segments (L, M, and S). We aimed to assess evolutionary rates, dispersal patterns, and environmental drivers. Focus was placed on lineage C, the most widely distributed variant. FINDINGS: The global dataset used in this study consisted of large (n=236), medium (n=237), and small (n=247), which were further filtered to exclude potential reassortants and vaccine strains. Genome sequences retrieved from NCBI GenBank database comprised large (n=180), medium (n=184), and small (n=202). The genome sequences from retrospective human and livestock isolates comprised large (n=56), medium (n=53), and small (n=45) collected in Burundi (2018), Kenya (2007, 2018, 2019, 2021, and 2022), and Rwanda (2018 and 2022). Our dataset revealed that RVFV exhibited low overall genetic diversity. Lineage C, however, showed evidence of active evolution, with substitution rates ranging from 3·58 × 10-4 to 9·76 × 10-4 substitutions per site per year. This lineage probably originated in Zimbabwe in the mid-1970s and has since expanded across eastern and southern Africa. Phylogeographic reconstructions revealed rapid spread, with diffusion coefficients exceeding 50 000 km2 per year. INTERPRETATION: Lineage C appears capable of establishing endemic transmission in new regions, with ongoing diversification observed during interepidemic periods. These observations reinforce the value of continuous genomic surveillance, particularly during cryptic transmission phases when adaptive mutations might emerge. Although further evidence is needed, observed trends in climate variability and land-use change point to the potential benefit of targeted surveillance in settings that could be at increased risk, including urban centres and wetlands. FUNDING: This work was supported by the German Federal Ministry for Economic Cooperation and Development, the Rockefeller Foundation, and the Africa Centres for Disease Control and Prevention.

Rift Valley fever virus↗
PubMed2025

How does date-rounding affect phylodynamic inference for public health?

Phylodynamic analyses infer epidemiological parameters from pathogen genome sequences for enhanced genomic surveillance in public health. Pathogen genome sequences and their associated sampling dates are the essential data in every analysis. However, sampling dates are usually associated with hospitalisation or testing and can sometimes be used to identify individual patients, posing a threat to patient confidentiality. To lower this risk, sampling dates are often given with reduced date-resolution to the month or year, which can potentially bias inference. Here, we introduce a practical guideline on when date-rounding biases the inference of epidemiologically important parameters across a diverse range of empirical and simulated datasets. We show that the direction of bias varies for different parameters, datasets, and tree priors, while compounding with lower date-resolution and higher substitution rates. We also find that bias decreases for datasets with longer sampling intervals, implying that our guideline is most applicable to emerging datasets. We conclude by discussing future solutions that prioritise patient confidentiality and propose a method for safer sharing of sampling dates that translates them them uniformly by a random number.

Humans↗
PubMed2025

Multi-scale phylodynamic modelling of rapid punctuated pathogen evolution.

Computational multi-scale pandemic modelling remains a major and timely challenge. Here we identify specific requirements for a new class of models simulating pandemics across three scales: (1) pathogen evolution, often punctuated by the rapid emergence of new variants, (2) human interactions within a heterogeneous population, and (3) public health responses which constrain individual actions to control the disease transmission. We then present a pandemic modelling framework satisfying these requirements and capable of simulating feedback loops between dynamics unfolding at these different scales. The developed framework comprises a stochastic agent-based model of pandemic spread, coupled with a phylodynamic model that incorporates within-host pathogen evolution. It is validated with a case study, modelling the punctuated evolution of SARS-CoV-2, based on global and contemporary genomic surveillance data, which captures a large heterogeneous population. We demonstrate that the model replicates the essential features of the COVID-19 pandemic and virus evolution, while retaining computational tractability and scalability.

SARS-CoV-2↗
PubMed2026

Molecular characteristics, phylodynamics, and evolutionary changes of avian infectious bronchitis virus detected from chickens in Yunnan Province, 2021-2024.

Avian infectious bronchitis virus (IBV) is endemic in poultry flocks worldwide, posing a significant threat to the global poultry industry. Frequent mixing of free-range local chickens with introduced chickens in Yunnan Province, China, facilitates the transmission, recombination, and mutation of avian IBV, thereby complicating disease prevention and control. In this study, we aimed to investigate the presence of IBV in poultry populations in Yunnan Province. Samples were collected from live poultry markets (LPMs) and breeding farms, comprising 725 randomly sampled cloacal/fecal swabs and 55 tissue samples. IBV-positive samples were confirmed via polymerase chain reaction (PCR), with an overall positivity rate of 0.89% (7/780) for all tested samples. The positivity rate was 0.35% (2/564) in Kunming, 3.7% (2/54) in Zhaotong, 20% (1/5) in Yuxi, and 12.5% (2/16) in Baoshan, while no IBV was detected in samples from Lanping, Xichou, or Ninglang. Six IBV strains, including five GI-19 strains and one GVI-1 strain, were successfully isolated. Phylogenetic analysis further showed that the Yunnan GI-19 strains predominantly clustered with strains originating from Sichuan Province. Sequencing of the S1 gene revealed several amino acids substitutions per isolate in hypervariable regions HVR1-HVR3. Notably, a valine (V) and glycine (G) insertion between amino acid positions 88 and 89 was identified exclusively in isolate F210, a feature rarely reported in IBV. Protein-protein docking analysis indicated that the unique 88-89 insertion in isolate F210 S1 may alter its binding interactions with the host receptor ANPEP. Whole-genome comparison revealed that isolate YX3 shared 97.05% nucleotide identity with strain CK/CH/GX/YL17/2017 from Guangxi, whereas isolates Q47, F13, and F210 shared 96.40%-97.27% identity with strain CK/Henan/H1036/2021 from Henan. Recombination analysis detected obvious recombination events in isolates F13, F210, Q47, and YX3, with GI-22 strains serving as the major parental donors. These genetic characteristics, recombination patterns, and structural insights demonstrate the complex evolutionary dynamics of circulating IBV strains in Yunnan. Continuous molecular epidemiological surveillance combined with functional protein analysis is essential to monitor emerging variants and formulating targeted, effective disease control strategies.

Avian infectious bronchitis virus↗
PubMed2025

Genome-scale evolution and phylodynamics of swine influenza A viruses in China: a genomic epidemiology study.

BACKGROUND: Pigs are recognised as crucial intermediate hosts for the emergence of influenza viruses of pandemic potential. As the largest pork-producing nation, China hosts a complex ecosystem of swine influenza viruses (SIVs). We aimed to investigate the evolutionary processes, spatiotemporal dynamics, and biological characteristics of SIVs in China. METHODS: From Jan 15, 2016, to Dec 22, 2020, we collected nasal swabs from pigs at eight abattoirs and 16 swine farms in the Guangdong, Henan, and Shandong provinces of China, as part of SIV surveillance. SIVs were detected with RT-PCR. Positive samples underwent viral isolation and genome sequencing. We analysed evolution and spatiotemporal dynamics using the whole genomes of isolated SIVs, as well as genome sequences of SIV isolates from human infections worldwide retrieved from the Global Initiative on Sharing All Influenza Data and GenBank Flu databases up to April 28, 2024. Viral sequences without a sample collection area or date were excluded from the analysis. Viral receptor-binding properties and in-vitro replication of strains isolated in this study were evaluated with a solid-phase binding assay and various cell lines, including Madin-Darby canine kidney cells, porcine alveolar macrophages, primary porcine trachea epithelial cells, human bronchial epithelioid, and human lung adenocarcinoma epithelial (A549) cells. Viral replication and transmission studies were conducted in 33 guinea pigs and 13 pigs. Additionally, we collected serum samples from pig farm workers and members of the general public recruited by the Third Affiliated Hospital of Sun Yat-sen University between Feb 28 and May 11, 2023, to detect specific antibodies against Eurasian avian-like A(H1) and human-like A(H3N2) SIVs using the haemagglutination inhibition assay. FINDINGS: 23 (1·3%) of 1818 nasal swabs collected in abattoirs had SIVs; 22 (0·9%) of 2375 swabs from swine farms had SIVs. Further viral isolation yielded 39 strains of SIV. We identified 534 A(H1N1), 69 A(H1N2), and 92 A(H3N2) SIVs, representing 20 genotypes within the Eurasian avian-like lineage, 14 within the classical swine A(H1) lineage, and 16 within the human-like A(H3N2) lineage. The introduction of the A(H1N1)pdm/09 virus significantly influenced the internal gene pool of SIVs, enhancing genotypic diversity in China. Notably, the Eurasian avian-like A(H1), classical swine A(H1), and human-like A(H3N2) lineages showed human-mediated spread over long distances between provinces, with the Eurasian avian-like A(H1) lineage showing the most prevalent spread pathways. Eurasian avian-like A(H1) SIVs showed a preference for binding to sialic acid α-2,6 glycan receptors, predominantly found in humans, resulting in an increased production of progeny viruses in human airway epithelial cells, as well as effective transmission and infectivity among guinea pigs and pigs. Among 54 eligible serum samples collected from pig farm workers (24 from slaughterhouses and 30 from swine farms), 23 (43%) were seropositive for Eurasian avian-like A(H1) SIVs and 46 (85%) for human-like A(H3N2) SIVs. Among 100 eligible samples from members of the general public, 14 (14%) were seropositive for Eurasian avian-like A(H1) SIVs and 85 (85%) for human-like A(H3N2) SIVs. INTERPRETATION: This study elucidates the evolutionary processes and spatiotemporal patterns of SIVs, highlighting potential risks to public health. These findings are crucial for informing public health interventions that aim to prevent future SIV epidemics in China and other countries worldwide. FUNDING: Scientific Innovation Strategy-Construction of High-Level Academy of Agriculture Science-Distinguished Scholar (R2020PY-JC001).

Animals↗
PubMed2025

ScITree: Scalable Bayesian inference of transmission tree from epidemiological and genomic data.

Phylodynamic models capture joint epidemiological-evolutionary dynamics during an outbreak, providing a powerful tool to enhance understanding and management of disease transmission. Existing phylodynamic approaches, however, mostly rely on various non-mechanistic or semi-mechanistic approximations of the underlying epidemiological-evolutionary process. Previous work by Lau and colleagues has shown that full Bayesian mechanistic models, without relying on these approximations, can enable highly accurate joint inference of the epidemiological-evolutionary dynamics including the unobserved transmission tree. However, the Lau method faces major computational bottlenecks. As the volume of genomic data collected during outbreaks continues to grow, it is crucial to develop scalable yet accurate phylodynamic methods. Here we propose a new Bayesian phylodynamic model, overcoming the major scalability issue in the previous method and enabling a readily deployable, yet accurate, phylodynamic modeling framework. Specifically, we develop a scalable spatio-temporal phylodynamic framework for inferring the transmission tree (ScITree) and other key epidemiological parameters considering the infinite sites assumption in modeling mutation on the sequence level, in contrast to the Lau method in which mutation was modeled explicitly on the nucleotide level. Our approach features full Bayesian implementation utilizing an exact likelihood to mechanistically integrate epidemiological and evolutionary processes. We develop a computationally-efficient data-augmentation Markov Chain Monte Carlo algorithm, inferring key model parameters and unobserved dynamics including the transmission tree. We assess performance of our method using multiple simulated outbreak datasets. Our results indicate that our method can achieve high inference accuracy, comparable to the performance of the Lau method. Additionally, our method scales significantly more efficiently for large outbreaks, with computing time increasing linearly with outbreak size, compared to the exponential scaling of the Lau method. We also demonstrate our method's utility by applying our validated modeling framework to a dataset describing a foot-and-mouth disease outbreak in the UK. Our results show that our method is able to generate estimates of the transmission dynamics consistent with those from the prior method, further demonstrating the robustness of our new approach. In summary, our method provides a computationally-efficient, highly scalable, accurate modeling framework for inferring the joint spatio-temporal dynamics of epidemiological and evolutionary processes, facilitating timely and effective outbreak responses in space and time. Our method is implemented in our R package ScITree.

Bayes Theorem↗
PubMed2025

Accounting for contact tracing in epidemiological birth-death models.

Phylodynamics bridges the gap between classical epidemiology and pathogen genome sequence data by estimating epidemiological parameters from time-scaled pathogen phylogenetic trees. The models used in phylodynamics typically assume that the sampling procedure is independent between infected individuals. However, this assumption does not hold for many epidemics, in particular for such sexually transmitted infections as HIV-1, for which contact tracing schemes are included in health policies of many countries. We extended phylodynamic multi-type birth-death (MTBD) models with contact tracing (CT), and developed a simulator to generate trees under MTBD and MTBD-CT models. We proposed a non-parametric test for detecting contact tracing in pathogen phylogenetic trees. Its application to simulated data showed that it is both highly specific and sensitive. For the simplest representative of the MTBD-CT family, the BD-CT(1) model, where only the last contact can be notified, we solved the differential equations and proposed a closed form solution for the likelihood function. We implemented a maximum-likelihood program, which estimates the BD-CT(1) model parameters and their confidence intervals from phylogenetic trees. It performed accurate parameter inference on BD and BD-CT(1) simulated data, and detected contact tracing in HIV-1 B epidemics in Zurich and the UK. Importantly, we showed that not accounting for contact tracing when it is present, leads to bias in parameter estimation with the BD model (overestimation of the becoming-non-infectious rate). This bias is also present, but greatly reduced, when the BD-CT(1) model is used on data where multiple contacts can be notified. Our CT test, MTBD-CT tree simulator and BD-CT(1) parameter estimator are freely available at GitHub (evolbioinfo/treesimulator and evolbioinfo/bdct).

Contact Tracing↗
PubMed2026

Evolutionary history of Jamestown Canyon virus reveals complex multi-vector ecology.

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

Animals↗
PubMed2026

Unraveling the epidemiological and dispersal dynamics of the 2024-2025 chikungunya virus epidemic on Réunion Island.

Réunion Island experienced a massive chikungunya virus epidemic in 2024-2025, with >54,000 confirmed cases. This is the second major chikungunya epidemic on the island, following the first one that peaked 20 years ago. It has been asserted that this new outbreak finds its origin in a single introduction event into the island, offering an opportunity to exploit viral genomic data to understand the epidemiological and dispersal dynamics of the introduced transmission chain. We sequenced >3,000 viral genomes collected during the epidemic. Harnessing this genomic dataset, we used several phylogeographic and phylodynamic approaches to unravel the paths taken by the transmission chain and the external factors that might have impacted its dispersal and epidemiological dynamics on the island. Our analyses highlight a dispersal pattern in line with a gravity-model dynamic with viral transition events being more frequent from and toward more populated areas. Our analyses reveal that the transmission chain was overall spatially intermixed, with frequent exchanges among residential areas. In addition, we show that the temporal dynamic and intensity of the epidemic were associated with climatic variables, namely temperature and precipitation. Our results also show that in theory, the population immunity-resulting from this epidemic and the previous one (2005-2006)-could be sufficient to explain on its own the decrease in the transmission rate that led to the end of the epidemic. While a short-term resurgence cannot be excluded, the risk of a large-scale circulation of the virus in the human population appears therefore relatively limited in the upcoming seasons.

Reunion↗
PubMed2025

Global diversity and evolution of Salmonella enterica serovar Panama: a genomic epidemiology study.

BACKGROUND: Non-typhoidal Salmonella is a globally important bacterial pathogen, typically associated with foodborne gastrointestinal infection. Some non-typhoidal Salmonella serovars can also colonise typically sterile sites in people to cause invasive non-typhoidal Salmonella disease. Salmonella enterica serovar Panama is responsible for a substantial number of cases of human bloodstream infection, but despite its global dissemination, numerous outbreaks, and a reported association with invasive non-typhoidal Salmonella disease, S enterica serovar Panama (S Panama) is understudied. We aimed to describe the genomic epidemiology and evolutionary history of S Panama to provide a vital baseline of understanding for this globally important serovar. METHODS: In this genomic epidemiology study, we analysed S Panama genomes derived from historical collections, national surveillance datasets, and publicly available epidemiological and whole-genome sequencing data which span the years 1931-2019. Maximum likelihood and Bayesian phylodynamic approaches were used to investigate population structure and evolutionary history and to infer geotemporal dissemination. A combination of different bioinformatic approaches with short-read and long-read data were used to characterise geographical and clade-specific trends in antimicrobial resistance (AMR) and genetic markers for invasiveness. FINDINGS: We analysed 836 S Panama genomes, of which 559 (67%) were sequenced as part of this study. The collection represents all inhabited continents and includes isolates collected between 1931 and 2019. We identified the presence of four geographically linked S Panama clades (C1 [ie, the Latin America and the Caribbean clade; n=338], C2 [ie, the European clade; n=124], C3 [ie, the Martinique clade; n=131], and C4 [ie, the Asia and Oceania clade; n=104]) and regional trends in AMR profiles. Most isolates (715 [86%] of 836) were pan-susceptible to antibiotics and belonged to clades circulating in Latin America and the Caribbean (64%, n=458). Most antibiotic-resistant isolates in our collection (113 [93%] of 121) fell within clades C4 (ie, the Asia and Oceania clade) and C2 (ie, the European clade), the latter of which had the highest invasiveness index values based on the conservation of 196 extraintestinal predictor genes. INTERPRETATION: This first large-scale phylogenetic analysis of S Panama has revealed important information about the population structure, AMR, global ecology, and genetic markers of invasiveness of the identified genomic subtypes. Our findings provide an important baseline for understanding S Panama infection. The presence of multidrug-resistant clades with elevated invasiveness index values should be monitored through ongoing surveillance, as such clades could pose an increased public health risk. FUNDING: UK Research and Innovation Global Challenges Research Fund and Biotechnology and Biological Sciences Research Council, UK Medical Research Council, Wellcome Trust, John Lennon Memorial Scholarship, Institut Pasteur, Santé publique France, Fondation Le Roch-Les Mousquetaires, Investissement d'Avenir Programme, and Australian National Health and Medical Research Council.

Humans↗
PubMed2026

CholeraSeq: a comprehensive genomic pipeline for cholera surveillance and near real-time outbreak investigation.

SUMMARY: Next Generation Sequencing is widely deployed in cholera-endemic regions, yet an end-to-end reproducible pipeline that unifies read QC, filtering, reference mapping, variant calling/annotation, recombination screening, and extraction of parsimony informative sites/variant codons, phylogenetic inference for downstream phylodynamic and epidemiological analyses have been lacking, slowing outbreak investigation and public health response. CholeraSeq is a high-throughput genomics pipeline for cholera genomic surveillance. It ingests consensus genomes, short read sequence data, draft assemblies, and scales seamlessly from local to cloud environments. To accelerate epidemiological context placement of new outbreak strains, we provide a curated ready-to-use core genome alignment compiled from public data, enabling flexible, fast, integration of new samples for outbreak investigations. AVAILABILITY AND IMPLEMENTATION: CholeraSeq is freely available on the GitHub platform https://github.com/CERI-KRISP/CholeraSeq. CholeraSeq is implemented in Nextflow with a modular design building upon the nf-core community standards.

Cholera↗
PubMed2025

Distinct Evolutionary Signatures of Human Parainfluenza Viruses 2 and 4 Reveal Host Antagonism Divergence and Phylogenetic Discordance.

Human parainfluenza virus 2 (HPIV-2) and human parainfluenza virus 4 (HPIV-4) are significant but underappreciated respiratory pathogens, particularly among high-risk populations including children, the elderly, and immunocompromised individuals. In this study, we sequenced 101 HPIV-2 and HPIV-4 genomes from respiratory samples collected in western Washington State and performed comprehensive evolutionary analyses using both new and publicly available sequences. Phylogenetic and phylodynamic analyses revealed that both HPIV-2 and HPIV-4 evolve at significantly faster rates compared to the mumps virus, a reference human orthorubulavirus. Notably, while HPIV-2 demonstrated the highest evolutionary rates in the surface glycoprotein HN, consistent with humoral immune-driven selection, the innate immune antagonist V/P gene evolved fastest in HPIV-4. We identified a hypervariable region within the HPIV-4V/P protein (residues 35 to 75), which structural modeling placed in a loop overlapping a known interferon antagonism domain in other paramyxovirus V proteins, though HPIV-4 is functionally incompetent in this activity. Expanded phylogenetic analysis across the Paramyxoviridae family uncovered a striking evolutionary discordance: while the HN glycoprotein and L polymerase of HPIV-4 and its 2 closest bat-derived viruses clustered within the Orthorubulavirus genus, their nucleoprotein (N), phosphoprotein (P), matrix (M), and fusion (F) proteins formed a distinct lineage outside the Rubulavirinae subfamily. Together, these findings highlight the distinct evolutionary trajectories of HPIV-2 and HPIV-4, raise hypotheses around complex Paramyxoviridae zoonotic events including recombination-like patterns, and demonstrate limitations of current L protein-based taxonomic classification schemes.

Humans↗
PubMed2025

Temporal reconstruction of a Salmonella Enteritidis ST11 outbreak in New Zealand.

Outbreaks caused by Salmonella Enteritidis are commonly linked to eggs and poultry meat internationally, but this serovar had never been detected in Aotearoa New Zealand (NZ) poultry prior to 2021. Locally designated genomic cluster Salmonella Enteritidis_2019_C_01, was implicated in a 2019 outbreak associated with a restaurant in Auckland. Four Enteritidis_2019_C_01 sub-clusters have since been identified, two retrospectively, in the Auckland region. Authorities initiated a formal outbreak investigation after genomically indistinguishable S. Enteritidis was isolated from the NZ poultry production environment. This study analysed 231 S. Enteritidis genomes obtained from the outbreak using Bayesian phylodynamic tools to gain insight into the outbreak's dynamics and origin. We used Bayesian integrated coalescent epoch plots to estimate the change of the Enteritidis ST11 population size over time and marginal structured coalescent approximation to estimate transmission between poultry producers. We investigated human and poultry isolates to elucidate the time and location of the most recent common ancestor of the outbreak and transmission pathways. The median most recent common ancestor was estimated to be February 2019. We found evidence of amplification and spread of strain Enteritidis_2019_C_01 within the poultry industry, as well as transmission events throughout the production chain. The intervention by the public health and food safety authorities coincided with a drop in the effective population size of the S. Enteritidis ST11 as well as notified human cases. This information is crucial for understanding and preventing the transmission of S. Enteritidis in NZ poultry to ensure poultry meat and eggs are safe for consumption.

Salmonella enteritidis↗
PubMed2025

Phylogenetic diversity and molecular evolution of Hantaan virus harbored by Apodemus chejuensis on Jeju Island, Republic of Korea, 2022-2023.

BACKGROUND: Hantaan virus (HTNV), hosted by Apodemus spp., is a well-recognized causative agent of hemorrhagic fever with renal syndrome (HFRS) and poses a crucial global public health concern. Based on the current evidence, HTNV carried by A. chejuensis is proposed as the likely etiological agent of HFRS on Jeju Island, Republic of Korea (ROK). METHODOLOGY/PRINCIPAL FINDINGS: In this study, 50 small mammals were collected from five locations in Seogwipo-si and Jeju-si on Jeju Island, ROK, during 2022-2023. Serological and molecular analyses revealed HTNV prevalence rates of 34% (16/47) and 27.7% (13/47), respectively. Using a multiplex polymerase chain reaction-based nanopore sequencing approach, nine complete HTNV genomes were sequenced from the lung tissues of A. chejuensis, representing the first comprehensive genomic characterization of HTNV from Seogwipo-si (Hogeun-dong) and Jeju-si (Sangdae-ri). Phylodynamic analyses suggest evolutionary divergence and phylogeographic diversity, with four unique amino acid substitutions identified in HTNV genomes from Seogwipo-si. CONCLUSION/SIGNIFICANCE: This study provides important insights into the genomic surveillance, genetic diversity, and evolutionary dynamics of orthohantaviruses, which are essential for guiding effective public health strategies to control and prevent future HFRS outbreaks in the ROK.

Animals↗
PubMed2025

Evolution of SARS-CoV-2 in white-tailed deer in Pennsylvania 2021-2024.

SARS-CoV-2 continues to transmit and evolve in humans and animals. White-tailed deer (Odocoileus virginianus) have been previously identified as a zoonotic reservoir for SARS-CoV-2 with high rates of infection and probable spillback into humans. Here we report sampling 1,127 white-tailed deer (WTD) in Pennsylvania, and a genomic analysis of viral dynamics spanning 1,017 days between April 2021 and January 2024. To assess viral load and genotypes, RNA was isolated from retropharyngeal lymph nodes and analyzed using RT-qPCR and viral whole genome sequencing. Samples showed a 14.64% positivity rate by RT-qPCR. Analysis showed no association of SARS-CoV-2 prevalence with age, sex, or diagnosis with Chronic Wasting Disease. From the 165 SARS-CoV-2 positive WTD, we recovered 25 whole genome sequences and an additional 17 spike-targeted amplicon sequences. The viral variants identified included 17 Alpha, 11 Delta, and 14 Omicron. Alpha largely stopped circulating in humans around September 2021, but persisted in WTD as recently as March of 2023. Phylodynamic analysis of pooled genomic data from Pennsylvania documents at least 12 SARS-CoV-2 spillovers from humans into WTD, including a recent series of Omicron spillovers. Prevalence was higher in WTD in regions with crop coverage rather than forest, suggesting an association with proximity to humans. Analysis of seasonality showed increased prevalence in winter and spring. Multiple examples of recurrent mutations were identified associated with transmissions, suggesting WTD-specific evolutionary pressures. These data document ongoing infections in white-tailed deer, probable onward transmission in deer, and a remarkable rate of new spillovers from humans.

Animals↗
PubMed2026

Genomic epidemiology of coxsackievirus A24 variant during the 2024 acute hemorrhagic conjunctivitis outbreak in Coastal Kenya.

Several African countries experienced a surge in acute hemorrhagic conjunctivitis (AHC) cases in 2024. Investigations in Kenya, Mayotte (an Indian Ocean island) and Tanzania identified coxsackievirus A24 variant (CVA24v) as the causative agent. To date, however, limited genomic data exist to elucidate the sources, epidemiology, and evolution of CVA24v in Africa. We generated 245 CVA24v genomes from samples collected between January and September 2024 in coastal Kenya, representing the largest outbreak CVA24v genomic data set available globally. Phylogenetic analysis showed that these viruses belonged to genotype IV, falling into two major clusters that differed by 52 nucleotide and five amino acid changes, and with an inter-species recombination event involving another enterovirus in the 3Dpol gene. Notably, the Kenyan sequences clustered closely with contemporaneous Africa (2024) sequences, specifically Mayotte and Malawi, reflecting a regionally connected CVA24v outbreak, but were distinct from those sampled previously in Asia in 2023, with phylodynamic analysis revealing that the Most Recent Common Ancestor of Kenyan sequences existed between June and October 2023. In summary, this study provides the first detailed genomic analysis of CVA24v from Africa to inform future surveillance and control strategies.

Journal Article↗
PubMed2026

Hidden Burden of a Measles Outbreak Revealed by Genomic and Transmission Models.

Declining childhood vaccination rates have fueled a resurgence of measles in the United States. Surveillance systems may not accurately measure the true extent of outbreaks. As of May 2026, the largest ongoing measles outbreak in the United States originated along the Utah-Arizona border in a community with high vaccine exemption rates and limited engagement with healthcare systems, leading to incomplete testing and reporting. To quantify the true outbreak size, we used two independent approaches with complementary data sources: a phylodynamic analysis and an agent-based model. Both methods found significant underreporting, estimating the true outbreak size to be 3.1- to 4.8-fold larger than reported, with confirmed cases representing only 20.96%-32.5% total infections. These findings suggest that substantial underreporting of measles occurs, especially in tight knit communities. The use of complementary analytical approaches to evaluate completeness of reporting can reveal the extent of measles transmission and aid control efforts.

Journal Article↗
PubMed2025

Phylogenetic Methods Meet Deep Learning.

Deep learning (DL) has been widely used in various scientific fields, but its integration into phylogenetics has been slower, primarily due to the complex nature of phylogenetic data. The studies that apply DL to sequencing data often limit analyses to four-taxon trees. Many of these studies serve as "proof of principle" and perform similarly to traditional phylogeny reconstruction methods. New ways of using training data, such as encoding with compact bijective ladderized vectors or transformers, enable the handling of much larger trees and genomic data sets. This short perspective focuses on the application of DL in phylogenetics, introducing prevalent DL architectures. We highlight potential problems in the field by discussing the risks of using simulation-based training data and emphasize the importance of reproducibility and robustness in computational estimates. Finally, we explore promising research areas, including the combination of phylogenetics and population genetics in DL, the analysis of neighbor dependencies, and the potential to significantly reduce computational cost compared to traditional methods. This perspective illustrates the potential of DL in complementing traditional phylogeny reconstruction methods and aiding the advancement of phylogenetic analysis, especially in performing computationally demanding tasks such as model selection or estimating branch support values.

Humans↗