Tracking virus outbreaks in the twenty-first century

Novel human coronavirus in an infant patient with pneumonia, Republic of Korea

Coronaviruses (CoVs) pose a significant threat to public health, causing a wide spectrum of clinical manifestations and outcomes. Beyond precipitating global outbreaks, Human CoVs (HCoVs) are frequently found among patients with respiratory infections. To date, limited attention has been directed towards alphacoronaviruses due to their low prevalence and fatality rates. Nasal swab and serum samples were collected from a paediatric patient, and an epidemiological survey was conducted. Retrospective surveillance investigated the molecular prevalence of CoV in 880 rodents collected in the Republic of Korea (ROK) from 2018 to 2022. Next-generation sequencing (NGS) and phylogenetic analyses characterized the novel HCoV and closely related CoVs harboured by Apodemus spp. On 15 December 2022, a 103-day-old infant was admitted with fever, cough, sputum production, and rhinorrhea, diagnosed with human parainfluenza virus 1 (HPIV-1) and rhinovirus co-infection. Elevated AST/ALT levels indicated transient liver dysfunction on the fourth day of hospitalization. Metagenomic NGS (mNGS) identified a novel HCoV in nasal swab and serum samples. Retrospective rodent surveillance and phylogenetic analyses showed the novel HCoV was closely related to alphacoronaviruses carried by Apodemus spp. in the ROK and China. This case highlights the potential of mNGS to identify emerging pathogens and raises awareness of possible extra-respiratory manifestations, such as transient liver dysfunction, associated with novel HCoVs. While the liver injury in this case may be attributable to the novel HCoV, further research is necessary to elucidate its clinical significance, epidemiological prevalence, and zoonotic origins.

Anterior nasal swabs compared to nasopharyngeal swabs for detection of respiratory viruses in children

Respiratory viral testing often uses invasive nasopharyngeal (NP) swabs, which can be painful and require trained personnel. Anterior nasal swabs (NS) are less invasive and can be self-collected. The sensitivity of NS compared to NP specimens for detecting multiple respiratory viruses in children are not well described. Hospitalized children in Kansas City, MO, from January 2023 to February 2024, who had NP specimens obtained for standard of care multiplex respiratory viral testing in the previous 72 h, were enrolled. NS specimens were collected and tested alongside salvaged NP specimens for adenovirus, seasonal coronaviruses, human metapneumovirus, respiratory syncytial virus, influenza, rhinovirus/enterovirus, SARS-CoV-2, and parainfluenza viruses using multiplex molecular testing. Concordance, sensitivity, and specificity of NS compared to NP specimens were assessed. A total of 147 paired NP/NS specimens were analyzed. Overall, 114 (77.6 %) NP/NS pairs were concordant, including 86 (58.5 %) virus-positive and 28 (19.1 %) virus-negative pairs. NS sensitivity was 84.3 % compared to NP, increasing to 95.7 % when collected within 24 h of NP specimens. Sensitivity for seasonal coronavirus was poor (36.4 %), but was over 75 % for other viruses, and 100 % for adenovirus, influenza, parainfluenza, RSV, and SARS-CoV-2 within 24 h of NP specimens. Virus cycle threshold counts were similar among paired specimens. NS specimens showed good concordance with NP specimens and high sensitivity for most viruses, except seasonal coronavirus. NS testing may enable respiratory virus monitoring outside medical settings.

Accelerating Cleavage Activity of CRISPR-Cas13 System on a Microfluidic Chip for Rapid Detection of RNA

It is extremely advantageous to detect nucleic acid levels in the early phases of disease management; such early detection facilitates timely treatment, and it can prevent altogether certain cancers and infectious diseases. A simple, rapid, and versatile detection platform without enzymatic amplification for both short and long sequences would be highly desirable in this regard. Our study addresses this need by introducing IMACC, an ICP-based Microfluidic Accelerator Combined with CRISPR, for amplification-free nucleic acid detection. It exploits electrokinetically induced ion concentration polarization (ICP) to concentrate target nucleic acids and CRISPR reagents near the depletion zone boundary within a microfluidic channel. This localized accumulation accelerates the CRISPR-guided promiscuous cleavage of reporter molecules while enhancing their fluorescence signals simultaneously. Simultaneous accumulation of RNA and ribonucleoproteins (RNP) in confined spaces was validated experimentally and numerically, showing overlapping regions. IMACC enabled detection of miRNA-21 (22 bp) down to 10 pM within 2 min of ICP. IMACC ensured CRISPR specificity (single mismatch (N = 1) sensitivity) during ICP, as shown by off-target and mismatch sequence experiments. IMACC was applied to long RNA samples (i.e., SARS-CoV-2), but it statistically remained challenging at this point due to nonlinear intensity trends with copy numbers and large deviations. IMACC enabled rapid detection of short RNAs such as microRNAs using only basic CRISPR reagents in a single microfluidic channel, eliminating the need for extra enzymes or buffer sets, streamlining workflow and reducing turnaround time. IMACC has the potential to advance CRISPR diagnostics and holds promise for improved detection and future prescreening applications.

Characterizing spatial epidemiology in a heterogeneous transmission landscape using the spatial transmission count statistic

BACKGROUND

Viral genomes contain records of geographic movements and cross-scale transmission dynamics. However, the impact of regional heterogeneity, particularly among rural and urban centers, on viral spread and epidemic trajectory has been less explored due to limited data availability. Intensive and widespread efforts to collect and sequence SARS-CoV-2 viral samples have enabled the development of comparative genomic approaches to reconstruct spatial transmission history and understand viral transmission across different scales.

METHODS

We proposed the spatial transmission count statistic that efficiently summarizes the geographic transmission patterns imprinted in viral phylogenies. Guided by a time-scaled tree with ancestral trait states, we identified spatial transmission linkages and categorized them as imports, local transmissions, and exports. These linkages were then summarized to represent the epidemic profile of the focal area.

RESULTS

Here, we demonstrate the utility of this approach for near real-time outbreak analysis using over 12,000 full genomes and linked epidemiological data to investigate the spread of SARS-CoV-2 in Texas. Our findings indicate that (1) highly populated urban centers were the main sources of the epidemic in Texas; (2) outbreaks in urban centers were connected to the global epidemic; and (3) outbreaks in urban centers were locally maintained, while epidemics in rural areas were driven by repeated introductions.

CONCLUSIONS

In this study, we introduce the Source Sink Score, which determines whether a localized outbreak serves as a source or sink for other regions, and the Local Import Score, which assesses whether the outbreak has transitioned to local transmission rather than being maintained by continued introductions. These epidemiological statistics provide actionable insights for developing public health interventions tailored to the needs of affected areas.

Detection of patient metadata in published articles for genomic epidemiology using machine learning and large language models

Objective

Patient metadata exist in published articles, but are often disconnected from genome sequences in databases, limiting their utility for genomic epidemiology. The objective of this study was to develop and evaluate natural language processing methods to facilitate the large-scale detection of patient metadata associated with reports of genome sequencing in published articles, drawing on the case of SARS-CoV-2.

Methods

We applied filters to select a sample of 245 PubMed articles (50,918 sentences) in LitCovid for manual annotation of sentences that reported generating SARS-CoV-2 sequences. We trained, deployed, and validated a BERT-based classifier, and selected a sample of 150 predicted articles (22,147 sentences) for manual annotation of sentences that reported patient metadata associated with the sequences. In addition to training BERT-based classifiers, we experimented with a generative AI approach, prompting the Llama-3-70B LLM using zero-shot, role-based, few-shot, chain-of-thought, and reasoning-eliciting prompting.

Results

BERT-based models that were pre-trained on corpora in biomedical or, more specifically, COVID-19 domains outperformed those that were pre-trained on corpora in general domains for detecting reports of patient metadata associated with SARS-CoV-2 sequences, achieving the best performance with a classifier based on a BiomedBERT-Large-Abstract model (F1-score = 0.776). While the best performance of our generative AI approach was achieved using role-based, few-shot, and chain-of-thought prompting (F1-score = 0.558), it was nonetheless outperformed by all of our machine learning-based classifiers.

Conclusion

Our methods were applied to more than 350,000 published articles and can be used to advance the utility and efficiency of genomic epidemiology for public health responses to virus outbreaks.

Patient-Related Metadata Reported in Sequencing Studies of SARS-CoV-2: Protocol for a Scoping Review and Bibliometric Analysis

BACKGROUND

There has been an unprecedented effort to sequence the SARS-CoV-2 virus and examine its molecular evolution. This has been facilitated by the availability of publicly accessible databases, such as the GISAID (Global Initiative on Sharing All Influenza Data) and GenBank, which collectively hold millions of SARS-CoV-2 sequence records. Genomic epidemiology, however, seeks to go beyond phylogenetic (the study of evolutionary relationships among biological entities) analysis by linking genetic information to patient characteristics and disease outcomes, enabling a comprehensive understanding of transmission dynamics and disease impact. While these repositories include fields reflecting patient-related metadata for a given sequence, the inclusion of these demographic and clinical details is scarce. The current understanding of patient-related metadata in published sequencing studies and its quality remains unexplored.

OBJECTIVE

Our review aims to quantitatively assess the extent and quality of patient-reported metadata in papers reporting original whole genome sequencing of the SARS-CoV-2 virus and analyze publication patterns using bibliometric analysis. Finally, we will evaluate the efficacy and reliability of a machine learning classifier in accurately identifying relevant papers for inclusion in the scoping review.

METHODS

The National Institutes of Health's LitCovid collection will be used for the automated classification of papers reporting having deposited SARS-CoV-2 sequences in public repositories, while an independent search will be conducted in MEDLINE and PubMed Central for validation. Data extraction will be conducted using Covidence (Veritas Health Innovation Ltd). The extracted data will be synthesized and summarized to quantify the availability of patient metadata in the published literature of SARS-CoV-2 sequencing studies. For the bibliometric analysis, relevant data points, such as author affiliations, citation metrics, author keywords, and Medical Subject Headings terms will be extracted.

RESULTS

This study is expected to be completed in early 2025. Our classification model has been developed and we have classified publications in LitCovid published through February 2023. As of September 2024, papers through August 2024 are being prepared for processing. Screening is underway for validated papers from the classifier. Direct literature searches and screening of the results began in October 2024. We will summarize and narratively describe our findings using tables, graphs, and charts where applicable.

CONCLUSIONS

This scoping review will report findings on the extent and types of patient-related metadata reported in genomic viral sequencing studies of SARS-CoV-2, identify gaps in the reporting of patient metadata, and make recommendations for improving the quality and consistency of reporting in this area. The bibliometric analysis will uncover trends and patterns in the reporting of patient-related metadata, including differences in reporting based on study types or geographic regions. The insights gained from this study may help improve the quality and consistency of reporting patient metadata, enhancing the utility of sequence metadata and facilitating future research on infectious diseases.

TRIAL REGISTRATION

OSF Registries osf.io/wrh95; https://doi.org/10.17605/OSF.IO/WRH95.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/58567.

Natural and Designed Cyclic Peptides as Potential Antiviral Drugs to Combat Future Coronavirus Outbreaks

The COVID-19 pandemic has underscored the need for effective and affordable antiviral drugs. Anthropogenic activities have increased interactions among humans, animals, and wildlife, contributing to the emergence of new and re-emerging viral diseases. RNA viruses pose significant challenges due to their rapid mutation rates, high transmissibility, and ability to adapt to host immune responses and antiviral treatments. The World Health Organization has identified several diseases (COVID-19, Ebola, Marburg, Zika, and others), all caused by RNA viruses, designated as being of priority concern as potential causes of future pandemics. Despite advances in antiviral treatments, many viruses lack specific therapeutic options, and more importantly, there is a paucity of broad-spectrum antiviral drugs. Additionally, the high costs of current treatments such as Remdesivir and Paxlovid highlight the need for more affordable antiviral drugs. Cyclic peptides from natural sources or designed through molecular modeling have shown promise as antiviral drugs with stability, low toxicity, high target specificity, and low antiviral resistance properties. This review emphasizes the urgent need to develop specific and broad-spectrum antiviral drugs and highlights cyclic peptides as a sustainable solution to combat future pandemics. Further research into these compounds could provide a new weapon to combat RNA viruses and address the gaps in current antiviral drug development.

Fine-scale patterns of SARS-CoV-2 spread from identical pathogen sequences

Pathogen genomics can provide insights into underlying infectious disease transmission patterns1,2, but new methods are needed to handle modern large-scale pathogen genome datasets and realize this full potential3-5. In particular, genetically proximal viruses should be highly informative about transmission events as genetic proximity indicates epidemiological linkage. Here we use pairs of identical sequences to characterize fine-scale transmission patterns using 114,298 SARS-CoV-2 genomes collected through Washington State (USA) genomic sentinel surveillance with associated age and residence location information between March 2021 and December 2022. This corresponds to 59,660 sequences with another identical sequence in the dataset. We find that the location of pairs of identical sequences is highly consistent with expectations from mobility and social contact data. Outliers in the relationship between genetic and mobility data can be explained by SARS-CoV-2 transmission between postcodes with male prisons, consistent with transmission between prison facilities. We find that transmission patterns between age groups vary across spatial scales. Finally, we use the timing of sequence collection to understand the age groups driving transmission. Overall, this study improves our ability to use large pathogen genome datasets to understand the determinants of infectious disease spread.

Unveiling the Dynamics of SARS-CoV-2 Gamma and Delta Waves in Paraná, Brazil - Delta Displacing a Persistent Gamma Through Alternative Routes of Dispersal

The Gamma and Delta variants of concern (VOCs) of SARS-CoV-2 drove the second and third wave in Brazil and significantly intensified the number of cases and deaths. In this study, we investigate the timeline and origins of the Gamma and Delta variants using a spatiotemporal analysis based on 1508 genomes collected between March and September 2021 from health administrative regions in Paraná state, Brazil. Our findings indicate that community transmission of Gamma-P.1 began in late 2020, with substantial contributions from the Northeast and North regions. In contrast, our analysis of the Delta-AY.101 genomes underscored the crucial role of Paraná in national-level transmission dynamics beginning in late March 2021. At a local level, the movement estimates inferred from the monophyletic clades showed that the Curitiba health region was the primary source for Gamma-P.1, with a substantial contribution from Londrina. This health-region also emerged as an important hub for Delta-AY.101. Our phylogeographical GLM analysis demonstrates that air travel fluxes and population size at the origin of locations were the strongest predictors of shaping SARS-CoV-2 dispersal dynamics within Paraná. In addition, viral load analysis suggests that Gamma-P.1 and Delta-AY.101 may have maintained a similarly high transmissibility potential throughout the evaluated months, providing insights into the prolonged co-circulation dynamics. Our study underscores the relevance of understanding SARS-CoV-2 introductions and regional circulation contributions at the country level to enhance public health preparedness and strengthen local surveillance programs.

Bayesian Inference of Pathogen Phylogeography using the Structured Coalescent Model

Over the past decade, pathogen genome sequencing has become well established as a powerful approach to study infectious disease epidemiology. In particular, when multiple genomes are available from several geographical locations, comparing them is informative about the relative size of the local pathogen populations as well as past migration rates and events between locations. The structured coalescent model has a long history of being used as the underlying process for such phylogeographic analysis. However, the computational cost of using this model does not scale well to the large number of genomes frequently analysed in pathogen genomic epidemiology studies. Several approximations of the structured coalescent model have been proposed, but their effects are difficult to predict. Here we show how the exact structured coalescent model can be used to analyse a precomputed dated phylogeny, in order to perform Bayesian inference on the past migration history, the effective population sizes in each location, and the directed migration rates from any location to another. We describe an efficient reversible jump Markov Chain Monte Carlo scheme which is implemented in a new R package StructCoalescent. We use simulations to demonstrate the scalability and correctness of our method and to compare it with existing software. We also applied our new method to several state-of-the-art datasets on the population structure of real pathogens to showcase the relevance of our method to current data scales and research questions.

Drug repositioning as a promising approach for the eradication of emerging and re-emerging viral agents

The global impact of emerging and re-emerging viral agents during epidemics and pandemics leads to serious health and economic burdens. Among the major emerging or re-emerging viruses include SARS-CoV-2, Ebola virus (EBOV), Monkeypox virus (Mpox), Hepatitis viruses, Zika virus, Avian flu, Influenza virus, Chikungunya virus (CHIKV), Dengue fever virus (DENV), West Nile virus, Rhabdovirus, Sandfly fever virus, Crimean-Congo hemorrhagic fever (CCHF) virus, and Rift Valley fever virus (RVFV). A comprehensive literature search was performed to identify existing studies, clinical trials, and reviews that discuss drug repositioning strategies for the treatment of emerging and re-emerging viral infections using databases, such as PubMed, Scholar Google, Scopus, and Web of Science. By utilizing drug repositioning, pharmaceutical companies can take advantage of a cost-effective, accelerated, and effective strategy, which in turn leads to the discovery of innovative treatment options for patients. In light of antiviral drug resistance and the high costs of developing novel antivirals, drug repositioning holds great promise for more rapid substitution of approved drugs. Main repositioned drugs have included chloroquine, ivermectin, dexamethasone, Baricitinib, tocilizumab, Mab114 (Ebanga™), ZMapp (pharming), Artesunate, imiquimod, saquinavir, capmatinib, naldemedine, Trametinib, statins, celecoxib, naproxen, metformin, ruxolitinib, nitazoxanide, gemcitabine, Dorzolamide, Midodrine, Diltiazem, zinc acetate, suramin, 5-fluorouracil, quinine, minocycline, trifluoperazine, paracetamol, berbamine, Nifedipine, and chlorpromazine. This succinct review will delve into the topic of repositioned drugs that have been utilized to combat emerging and re-emerging viral pathogens.

Evolutionary and epidemic dynamics of COVID-19 in Germany exemplified by three Bayesian phylodynamic case studies

The importance of genomic surveillance strategies for pathogens has been particularly evident during the coronavirus disease 2019 (COVID-19) pandemic, as genomic data from the causative agent, severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), have guided public health decisions worldwide. Bayesian phylodynamic inference, integrating epidemiology and evolutionary biology, has become an essential tool in genomic epidemiological surveillance. It enables the estimation of epidemiological parameters, such as the reproductive number, from pathogen sequence data alone. Despite the phylodynamic approach being widely adopted, the abundance of phylodynamic models often makes it challenging to select the appropriate model for specific research questions. This article illustrates the application of phylodynamic birth-death-sampling models in public health using genomic data, with a focus on SARS-CoV-2. Targeting researchers less familiar with phylodynamics, it introduces a comprehensive workflow, including the conceptualisation of a research study and detailed steps for data preprocessing and postprocessing. In addition, we demonstrate the versatility of birth-death-sampling models through three case studies from Germany, utilising the BEAST2 software and its model implementations. Each case study addresses a distinct research question relevant not only to SARS-CoV-2 but also to other pathogens: Case study 1 finds traces of a superspreading event at the start of an early outbreak, exemplifying how simple models for genomic data can provide information that would otherwise only be accessible through extensive contact tracing. Case study 2 compares transmission dynamics in a nosocomial outbreak to community transmission, highlighting distinct dynamics through integrative analysis. Case study 3 investigates whether local transmission patterns align with national trends, demonstrating how phylodynamic models can disentangle complex population substructure with little additional information. For each case study, we emphasise critical points where model assumptions and data properties may misalign and outline appropriate validation assessments. Overall, we aim to provide researchers with examples on using birth-death-sampling models in genomic epidemiology, balancing theoretical and practical aspects.

Equine Influenza: Epidemiology, Pathogenesis, and Strategies for Prevention and Control

Equine influenza (EI) is a highly contagious respiratory disease caused by the equine influenza virus (EIV), posing a significant threat to equine populations worldwide. EIV exhibits considerable antigenic variability due to its segmented genome, complicating long-term disease control efforts. Although infections are rarely fatal, EIV's high transmissibility results in widespread outbreaks, leading to substantial morbidity and considerable economic impacts on veterinary care, quarantine, and equestrian activities. The H3N8 subtype has undergone significant antigenic evolution, resulting in the emergence of distinct lineages, including Eurasian and American, with the Florida sublineage being particularly prevalent. Continuous genetic surveillance and regular updates to vaccine formulations are necessary to address antigenic drift and maintain vaccination efficacy. Additionally, rare cross-species transmissions have raised concerns regarding the zoonotic potential of EIV. This review provides a comprehensive overview of the epidemiology, pathogenesis, and prevention of EI, emphasizing vaccination strategies and addressing the socio-economic consequences of the disease in regions where the equine industry is vital.

Multiple introductions and sustained local transmission of Monkeypox virus in Southern Brazil between 2022-2023

Mpox is a zoonotic viral disease caused by the Monkeypox virus (MPXV). Human cases have been mainly restricted to the African continent until the worldwide multi-country outbreak unfolded in 2022. We reconstructed epidemiological links of 53 MPXV infections using genomic epidemiology in Rio Grande do Sul State, southern Brazil, during 2022 and 2023. We detected five well-supported clades, three representing local transmission chains that were mostly restricted to the 2022 virus spread, one supported year-long maintenance encompassing samples from 2022 and 2023, and one new importation from Europe in 2023. Our results provide new insights into the geographic extent of community transmission and its association with viral diversity during the more pronounced 2022 mpox upsurge and during the following lower incidence phase. These findings highlight the power of continued genomic surveillance to uncover hidden transmission chains to understand viral dynamics and inform public health responses. The detection of sustained transmission in the state is important to guide targeted control measures to curtail further community and international transmission and highlight the need for maintaining genomic surveillance efforts.

Co-occurrence of SARS-CoV-2 variants in rivers and sewage in India and Brazil

The genomic monitoring of SARS-CoV-2 variants of concern (VOCs) in riverine and sewage water has been widely used as an epidemiological tool worldwide. But its utility for epidemiological assessments still needs to be evaluated in some areas. Our study encompassed thirteen Brazilian rivers spanning a vast urban expanse across the states of Rio de Janeiro, São Paulo, and Paraná. The sampled rivers in Rio de Janeiro are heavily contaminated with sewage. Meanwhile, the Indian samples were all wastewater before joining the water bodies from urban regions (Andhra Pradesh and Telangana). The viral copies were quantified using quantitative polymerase chain reaction (qPCR) in all examined samples (N = 91). The abundance of viral particles varied from 567 to 85,700,000 copies/ml. Subsequently, Illumina CovidSeq was applied to identify the major variants. In Brazil, while a single SARS-CoV-2 VOC was identified for just a few samples (6/50, 12 %), most samples harbored multiple VOCs (44/50, 88 %). In India only one probed sample had a single variant identified. Gamma (2021) and Omicron (2021 and 2022) were the most abundant variants. Delta and Omicron genetic material were detected in Rio de Janeiro city rivers before Brazil's first cases of these variants. Several negative samples in the Real-Time RT-PCR (qPCR) turned out to have SARS-CoV-2 sequences suggesting CovidSeq was more sensitive than RT-PCR for virus detection in environmental samples. Sewage surveillance holds promise for early detection of emerging variants driving pandemic waves, exemplified by the Delta and Omicron variants, potentially offering a preemptive advantage over clinical sample reports.

iACVP-MR: Accurate Identification of Anti-coronavirus Peptide based on Multiple Features Information and Recurrent Neural Network

BACKGROUND

Over the years, viruses have caused human illness and threatened human health. Therefore, it is pressing to develop anti-coronavirus infection drugs with clear function, low cost, and high safety. Anti-coronavirus peptide (ACVP) is a key therapeutic agent against coronavirus. Traditional methods for finding ACVP need a great deal of money and man power. Hence, it is a significant task to establish intelligent computational tools to able rapid, efficient and accurate identification of ACVP.

METHODS

In this paper, we construct an excellent model named iACVP-MR to identify ACVP based on multiple features and recurrent neural networks. Multiple features are extracted by using reduced amino acid component and dipeptide component, compositions of k-spaced amino acid pairs, BLOSUM62 encoder according to the N5C5 sequence, as well as second-order moving average approach based on 16 physicochemical properties. Then, two recurrent neural networks named long-short term memory (LSTM) and bidirectional gated recurrent unit (BiGRU) combined attention mechanism are used for feature fusion and classification, respectively.

RESULTS

The accuracies of ENNAVIA-C and ENNAVIA-D datasets under the 10-fold cross-validation are 99.15% and 98.92%, respectively, and other evaluation indexes have also obtained satisfactory results. The experimental results show that our model is superior to other existing models.

CONCLUSION

The iACVP-MR model can be viewed as a powerful and intelligent tool for the accurate identification of ACVP. The datasets and source codes for iACVP-MR are freely downloaded at https://github.com/yunyunliang88/iACVP-MR.

Concepts and Methods for Predicting Viral Evolution

The seasonal human influenza virus undergoes rapid evolution, leading to significant changes in circulating viral strains from year to year. These changes are typically driven by adaptive mutations, particularly in the antigenic epitopes, the regions of the viral surface protein hemagglutinin targeted by human antibodies. Here, we describe a consistent set of methods for data-driven predictive analysis of viral evolution. Our pipeline integrates four types of data: (1) sequence data of viral isolates collected on a worldwide scale, (2) epidemiological data on incidences, (3) antigenic characterization of circulating viruses, and (4) intrinsic viral phenotypes. From the combined analysis of these data, we obtain estimates of relative fitness for circulating strains and predictions of clade frequencies for periods of up to 1 year. Furthermore, we obtain comparative estimates of protection against future viral populations for candidate vaccine strains, providing a basis for pre-emptive vaccine strain selection. Continuously updated predictions obtained from the prediction pipeline for influenza and SARS-CoV-2 are available at https://previr.app .

Synthesis and Antiviral Evaluation of 5-(4-Aryl-1,3-butadiyn-1-yl)-uridines and Their Phosphoramidate Pronucleotides

The emergence of RNA viruses driven by global population growth and international trade highlights the urgent need for effective antiviral agents that can inhibit viral replication. Nucleoside analogs, which mimic natural nucleotides, have shown promise in targeting RNA-dependent RNA polymerases (RdRps). Starting from protected 5-iodouridine, we report the synthesis of hitherto unknown C5-substituted-(1,3-diyne)-uridines nucleosides and their phosphoramidate prodrugs. The modifications at C5 include 4-(trifluoromethyl)benzene (a), 4-pentyl-benzene (b), 3,5-dimethoxy-benzene (c), 4-(trifluoromethoxy)benzene (d), 3-aniline (e), 4-pyridine (f), 3-thiophene (g), C6H13 (h), 2-pyrimidine (i), cyclopropyl (j), and phenyl (k) groups. These compounds were synthesized using Sonogashira palladium-catalyzed reactions and nickel-copper-catalyzed C-H activation between various alkynes, yielding between 25% and 67%. The antiviral activities of obtained compounds were measured through HTS against RNA viruses including influenza H1N1 and H3N2, human respiratory syncytial virus (RSV), SARS-CoV-2, Zika, hepatitis C virus (HCV), Hepatitis E virus (HEV), as well as against coronavirus (HCoV-229E). Unfortunately, none of them showed promising antiviral activity, with less than 85% inhibition observed in the cell viability screening of infected cells.

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.

A meta-analysis highlights the idiosyncratic nature of tradeoffs in laboratory models of virus evolution

Different theoretical frameworks have been invoked to guide the study of virus evolution. Three of the more prominent ones are (i) the evolution of virulence, (ii) life history theory, and (iii) the generalism-specialism dichotomy. All involve purported tradeoffs between traits that define the evolvability and constraint of virus-associated phenotypes. However, as popular as these frameworks are, there is a surprising paucity of direct laboratory tests of the frameworks that support their utility as broadly applicable theoretical pillars that can guide our understanding of disease evolution. In this study, we conduct a meta-analysis of direct experimental evidence for these three frameworks across several widely studied virus-host systems: plant viruses, fungal viruses, animal viruses, and bacteriophages. We extracted 60 datasets from 28 studies and found a range of relationships between traits in different analysis categories (e.g., frameworks, virus-host systems). Our work demonstrates that direct evidence for relationships between traits is highly idiosyncratic and specific to the host-virus system and theoretical framework. Consequently, scientists researching viral pathogens from different taxonomic groups might reconsider their allegiance to these canons as the basis for expectation, explanation, or prediction. Future efforts could benefit from consistent definitions, and from developing frameworks that are compatible with the evidence and apply to particular biological and ecological contexts.

How fast are viruses spreading in the wild?

Genomic data collected from viral outbreaks can be exploited to reconstruct the dispersal history of viral lineages in a two-dimensional space using continuous phylogeographic inference. These spatially explicit reconstructions can subsequently be used to estimate dispersal metrics that can be informative of the dispersal dynamics and the capacity to spread among hosts. Heterogeneous sampling efforts of genomic sequences can however impact the accuracy of phylogeographic dispersal metrics. While the impact of spatial sampling bias on the outcomes of continuous phylogeographic inference has previously been explored, the impact of sampling intensity (i.e., sampling size) when aiming to characterise dispersal patterns through continuous phylogeographic reconstructions has not yet been thoroughly evaluated. In our study, we use simulations to evaluate the robustness of 3 dispersal metrics - a lineage dispersal velocity, a diffusion coefficient, and an isolation-by-distance (IBD) signal metric - to the sampling intensity. Our results reveal that both the diffusion coefficient and IBD signal metrics appear to be the most robust to the number of samples considered for the phylogeographic reconstruction. We then use these 2 dispersal metrics to compare the dispersal pattern and capacity of various viruses spreading in animal populations. Our comparative analysis reveals a broad range of IBD patterns and diffusion coefficients mostly reflecting the dispersal capacity of the main infected host species but also, in some cases, the likely signature of rapid and/or long-distance dispersal events driven by human-mediated movements through animal trade. Overall, our study provides key recommendations for the use of lineage dispersal metrics to consider in future studies and illustrates their application to compare the spread of viruses in various settings.

Advancing West Nile virus monitoring through whole genome sequencing: Insights from a One Health genomic surveillance study in Romagna (Italy)

In the last 6 years, Italy accounted for 36 % of the total autochthonous European West Nile virus (WNV) cases reported to ECDC. Since 2001, the country put in place a multi-species national surveillance plan. The plan was enhanced in 2020 by adopting a fully integrated "One Health" approach, including human, wild bird, equine, and mosquito surveillance for the early detection of WNV. In this context, the systematic acquisition of whole viral genetic information from human patients and animals is fundamental to obtain an in-depth knowledge on the patterns of virus evolution and transmission and to gain insights on the role virus genetics in morbidity and mortality, The purpose of this pilot study was thus to design a One-Health surveillance framework based on the genomic surveillance of WNV circulating at the vector-human-animal interface, in the endemic territory of Romagna (North-Eastern Italy) during the 2023 transmission season. Whole genome sequencing (WGS) analyses confirmed the circulation of WNV lineage 2 showing high nucleotide and amino acid identity of 99.82 % and 99.92 % respectively among viral sequences from human patients, vectors and birds. All the sequences clustered with other Italian strains in the Central and Southern European clade with robust bootstrap support and BLASTn identity exceeding 99.7 %. The highest nucleotide identity was observed with sequences from Emilia-Romagna and Veneto regions (Italy), confirming a local virus circulation and overwintering of WNV lineage 2 with a confined virus spread and no (or limited) external introduction of viral strains. Our results, support the adoption of a One Health approach to WNV surveillance, based on WGS and integrating the clinical diagnosis, epidemiology, and genomic characterisation, to create a suitable operational process for the characterisation of autochthonous and imported Arboviruses circulating in Romagna to effectively integrate the already established surveillance plan.

Concepts and methods for predicting viral evolution

The seasonal human influenza virus undergoes rapid evolution, leading to significant changes in circulating viral strains from year to year. These changes are typically driven by adaptive mutations, particularly in the antigenic epitopes, the regions of the viral surface protein haemagglutinin targeted by human antibodies. Here we describe a consistent set of methods for data-driven predictive analysis of viral evolution. Our pipeline integrates four types of data: (1) sequence data of viral isolates collected on a worldwide scale, (2) epidemiological data on incidences, (3) antigenic characterization of circulating viruses, and (4) intrinsic viral phenotypes. From the combined analysis of these data, we obtain estimates of relative fitness for circulating strains and predictions of clade frequencies for periods of up to one year. Furthermore, we obtain comparative estimates of protection against future viral populations for candidate vaccine strains, providing a basis for pre-emptive vaccine strain selection. Continuously updated predictions obtained from the prediction pipeline for influenza and SARS-CoV-2 are available on the website previr.app.

Genomic Surveillance of SARS-CoV-2 in Taiwan: A Perspective on Evolutionary Data Interpretation and Sequencing Issues

This review presents a comprehensive perspective on the genomic surveillance of SARS-CoV-2 in Taiwan, with a focus on next-generation sequencing and phylogenetic interpretation. This article aimed to explore how Taiwan has utilized genomic sequencing technologies and surveillance to monitor and mitigate the spread of COVID-19. We examined databases and sources of genomic sequences and highlighted the role of data science methodologies in the explanation and analyses of evolutionary data. This review addressed the challenges and limitations inherent in genomic surveillance, such as concerns regarding data quality and the necessity for interdisciplinary expertise for accurate data interpretation. Special attention was given to the unique challenges faced by Taiwan, including its high population density and major transit destination for international travelers. We underscored the far-reaching implications of genomic surveillance data for public health policy, particularly in influencing decisions regarding travel restrictions, vaccine administration, and public health decision-making. Studies were examined to demonstrate the effectiveness of using genomic data to implement public health measures. Future research should prioritize the integration of methodologies and technologies in evolutionary data science, particularly focusing on phylodynamic analytics. This integration is crucial to enhance the precision and applicability of genomic data. Overall, we have provided an overview of the significance of genomic surveillance in tracking SARS-CoV-2 variants globally and the pivotal role of data science methodologies in interpreting these data for effective public health interventions.

Long-term co-circulation of multiple arboviruses in southeast Australia revealed by xeno-monitoring and viral whole-genome sequencing

Arbovirus surveillance of wild-caught mosquitoes is an affordable and sensitive means of monitoring virus transmission dynamics at various spatial-temporal scales, and emergence and re-emergence during epidemic and interepidemic periods. A variety of molecular diagnostics for arbovirus screening of mosquitoes (known as xeno-monitoring) are available, but most provide limited information about virus diversity. Polymerase chain reaction (PCR)-based screening coupled with RNA sequencing is an increasingly affordable and sensitive pipeline for integrating complete viral genome sequencing into surveillance programs. This enables large-scale, high-throughput arbovirus screening from diverse samples. We collected mosquitoes in CO2-baited light traps from five urban parks in Brisbane from March 2021 to May 2022. Mosquito pools of ≤200 specimens were screened for alphaviruses and flaviviruses using virus genus-specific primers and reverse transcription quantitative PCR (qRT-PCR). A subset of virus-positive samples was then processed using a mosquito-specific ribosomal RNA depletion method and then sequenced on the Illumina NextSeq. Overall, 54,670 mosquitoes representing 26 species were screened in 382 pools. Thirty detections of arboviruses were made in 28 pools. Twenty of these positive pools were further characterized using RNA sequencing generating 18 full-length genomes. These full-length sequences belonged to four medically relevant arboviruses: Barmah Forest, Ross River, Sindbis-like, and Stratford viruses. Phylogenetic and evolutionary analyses revealed the evolutionary progression of arbovirus lineages over the last 100 years, demonstrating that different epidemiological, immunological, and evolutionary processes may actively shape the evolution of Australian arboviruses. These results underscore the need for more genomic surveillance data to explore the complex evolutionary pressures acting on arboviruses. Overall, our findings highlight the effectiveness of our methodology, which can be applied broadly to enhance arbovirus surveillance in various ecological contexts and improve understanding of transmission dynamics.

Dimensionality reduction distills complex evolutionary relationships in seasonal influenza and SARS-CoV-2

Public health researchers and practitioners commonly infer phylogenies from viral genome sequences to understand transmission dynamics and identify clusters of genetically-related samples. However, viruses that reassort or recombine violate phylogenetic assumptions and require more sophisticated methods. Even when phylogenies are appropriate, they can be unnecessary or difficult to interpret without specialty knowledge. For example, pairwise distances between sequences can be enough to identify clusters of related samples or assign new samples to existing phylogenetic clusters. In this work, we tested whether dimensionality reduction methods could capture known genetic groups within two human pathogenic viruses that cause substantial human morbidity and mortality and frequently reassort or recombine, respectively: seasonal influenza A/H3N2 and SARS-CoV-2. We applied principal component analysis, multidimensional scaling (MDS), t-distributed stochastic neighbor embedding (t-SNE), and uniform manifold approximation and projection to sequences with well-defined phylogenetic clades and either reassortment (H3N2) or recombination (SARS-CoV-2). For each low-dimensional embedding of sequences, we calculated the correlation between pairwise genetic and Euclidean distances in the embedding and applied a hierarchical clustering method to identify clusters in the embedding. We measured the accuracy of clusters compared to previously defined phylogenetic clades, reassortment clusters, or recombinant lineages. We found that MDS embeddings accurately represented pairwise genetic distances including the intermediate placement of recombinant SARS-CoV-2 lineages between parental lineages. Clusters from t-SNE embeddings accurately recapitulated known phylogenetic clades, H3N2 reassortment groups, and SARS-CoV-2 recombinant lineages. We show that simple statistical methods without a biological model can accurately represent known genetic relationships for relevant human pathogenic viruses. Our open source implementation of these methods for analysis of viral genome sequences can be easily applied when phylogenetic methods are either unnecessary or inappropriate.

CovTransformer: A transformer model for SARS-CoV-2 lineage frequency forecasting

With hundreds of SARS-CoV-2 lineages circulating in the global population, there is an ongoing need for predicting and forecasting lineage frequencies and thus identifying rapidly expanding lineages. Accurate prediction would allow for more focused experimental efforts to understand pathogenicity of future dominating lineages and characterize the extent of their immune escape. Here, we first show that the inherent noise and biases in lineage frequency data make a commonly-used regression-based approach unreliable. To address this weakness, we constructed a machine learning model for SARS-CoV-2 lineage frequency forecasting, called CovTransformer, based on the transformer architecture. We designed our model to navigate challenges such as a limited amount of data with high levels of noise and bias. We first trained and tested the model using data from the UK and the USA, and then tested the generalization ability of the model to many other countries and US states. Remarkably, the trained model makes accurate predictions two months into the future with high levels of accuracy both globally (in 31 countries with high levels of sequencing effort) and at the US-state level. Our model performed substantially better than a widely used forecasting tool, the multinomial regression model implemented in Nextstrain, demonstrating its utility in SARS-CoV-2 monitoring. Assuming a newly emerged lineage is identified and assigned, our test using retrospective data shows that our model is able to identify the dominating lineages 7 weeks in advance on average before they became dominant. Overall, our work demonstrates that transformer models represent a promising approach for SARS-CoV-2 forecasting and pandemic monitoring.

Benzocarbazoledinones as SARS-CoV-2 Replication Inhibitors: Synthesis, Cell-Based Studies, Enzyme Inhibition, Molecular Modeling, and Pharmacokinetics Insights

Endemic and pandemic viruses represent significant public health challenges, leading to substantial morbidity and mortality over time. The COVID-19 pandemic has underscored the urgent need for the development and discovery of new, potent antiviral agents. In this study, we present the synthesis and anti-SARS-CoV-2 activity of a series of benzocarbazoledinones, assessed using cell-based screening assays. Our results indicate that four compounds (4a, 4b, 4d, and 4i) exhibit EC50 values below 4 μM without cytotoxic effects in Calu-3 cells. Mechanistic investigations focused on the inhibition of the SARS-CoV-2 main protease (Mpro) and papain-like protease (PLpro) have used enzymatic assays. Notably, compounds 4a and 4b showed Mpro inhibition activity with IC50 values of 0.11 ± 0.05 and 0.37 ± 0.05 µM, respectively. Furthermore, in silico molecular docking, physicochemical, and pharmacokinetic studies were conducted to validate the mechanism and assess bioavailability. Compound 4a was selected for preliminary drug-likeness analysis and in vivo pharmacokinetics investigations, which yielded promising results and corroborated the in vitro and in silico findings, reinforcing its potential as an anti-SARS-CoV-2 lead compound.

Epidemiological Surveillance and Genomic Characterization of Soochong Virus From Apodemus Species Using Multiplex PCR-Based Next-Generation Sequencing, Republic of Korea

Orthohantavirus hantanense causes hemorrhagic fever with renal syndrome in Eurasia, posing a substantial public health threat. Although the Hantaan virus is the primary etiological agent in the Republic of Korea (ROK), evidence suggests the potential zoonotic transmission of the Amur virus (AMRV), closely related to the Soochong virus (SOOV), to humans in China and Russia. This study examined 31 Apodemus spp. captured from six regions in Gangwon Province, ROK, between 2015 and 2018. Of these, 5/31 (16.1%) tested positive for anti-SOOV immunoglobulin G and SOOV RNA, with 3/6 (50%) in Hongcheon-gun and 2/5 (40%) in Pyeongchang-gun. Utilizing a multiplex polymerase chain reaction-based next-generation sequencing approach, we achieved complete genomic sequencing of SOOV from rodent lung tissues, with coverage rates of 90.3%-98.2% for the S segment, 92.3%-98.1% for the M segment, and 88.1%-93.0% for the L segment. Five novel whole-genome sequences of SOOV were obtained from rodents in Hongcheon-gun and Pyeongchang-gun, representing the first documented SOOV in Pyeongchang-gun. The evolutionary rate analysis of SOOV tripartite genomes demonstrated lower divergence in the S segment. Phylogenetic analysis revealed a well-supported divergence of the SOOV and AMRV lineages across the ROK, China, and Russia, with incongruences suggesting differential segment evolution. Co-divergence analysis indicated the inter-species transmission of SOOV Aa18-104 from Apodemus agrarius in Pyeongchang-gun. The high zoonotic potential of all SOOV strains underscores the need for extensive monitoring and surveillance. This report provides crucial insights for the development of effective control strategies against hantaviral outbreaks in the ROK.

Accessible viral metagenomics for public health and clinical domains with Jovian

The integration of next-generation sequencing into clinical diagnostics and surveillance initiatives is impeded by the lack of data analysis pipelines that align with privacy legislation and laboratory certification protocols. To address these challenges, we developed Jovian, an open-source, virus-focused, metagenomic analysis workflow for Illumina data. Jovian generates scaffolds enriched with pertinent annotations, including taxonomic classification, combined with metrics needed for quality assessment (coverage depth, average GC content, localization of open reading frames, minority single nucleotide polymorphisms), and incorporates host and disease metadata. Interactive web-based reports with an audit trail are generated. Jovian was employed on four systems, hosted by three institutes, utilizing grid-computers, a high-performance compute singular server, and a Windows10 laptop. All systems yielded identical results with matching MD5sums. Comparison with a commercial online reference tool using viral gastroenteritis samples confirmed the identification of the same pathogens. Jovian provides comparable results to a commercially available online reference tool and generates identical results at different institutes with different IT architectures, proving it is portable and reproducible. Jovian addresses bottlenecks in the deployment of metagenomics within public health and clinical laboratories and has the potential to enhance the breadth of surveillance and testing programs, thereby fostering more effective public health interventions.

Spatio-temporal distribution of rhinovirus types in Kenya: a retrospective analysis, 2014

The epidemiology and circulation patterns of various rhinovirus types within populations remains under-explored. We generated 803 VP4/VP2 gene sequences from rhinovirus-positive samples collected from acute respiratory illness (ARI) patients, including both in-patient and outpatient cases, between 1st January and 31st December 2014 from eleven surveillance sites across Kenya and used phylogenetics to characterise virus introductions and spread. RVs were detected throughout the year, with the highest detection rates observed from January to March and June to July. We detected a total of 114 of the 169 currently classified types. Our analysis revealed numerous virus introductions into Kenya characterized by local expansion and extinction, and extensive spatial mixing of types within the country due to the widespread transmission of the virus after an introduction. This work demonstrates that in a single year, the circulation of rhinovirus in Kenya was characterized by substantial genetic diversity, multiple introductions, and extensive geographical spread.

Genomic identification of Oryctes rhinoceros nudivirus isolates, a biocontrol agent for coconut rhinoceros beetle

The coconut rhinoceros beetle (Oryctes rhinoceros, CRB) is a serious pest of coconut and oil palms. It is native to South and Southeast Asia and was inadvertently introduced to Samoa in 1909. It has invaded many other Pacific countries throughout the last century. Oryctes rhinoceros nudivirus (OrNV), a natural pathogen of CRB in its native range, was successfully introduced as a classical biocontrol agent and has effectively suppressed invasive CRB populations for decades. However, resurgence of CRB has been recorded, with new invasions detected in several Pacific Island Countries and Territories. Additionally, new populations of CRB are emerging in some invaded areas that have a degree of resistance to the virus isolates commonly released for CRB biocontrol. Here, we designed a fast and reliable tool for distinguishing between different OrNV isolates that can help with the selection process to identify effective isolates for management of new CRB invasions. A comparison of 13 gene/gene region sequences within the OrNV genome of 16 OrNV isolates from native and invaded ranges allowed us to identify unique Single Nucleotide Polymorphisms (SNPs). With these SNPs, we developed an assay using multiplex PCR-amplicon-based nanopore sequencing to distinguish between OrNV isolates. We found that as few as four gene fragments were sufficient to identify 15 out of 20 OrNV isolates. This method can be used as a tool to monitor the establishment and distribution of OrNV isolates selected for release as biocontrol agents in CRB-infected areas.

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.

One Health Ethics and the Ethics of Zoonoses: A Silent Call for Global Action

This paper presents a critical review of key issues related to the emergence of new networks for the spread of zoonotic diseases amid the mass extinction of species. Zoonotic and infectious diseases account for approximately 70% of new and existing diseases affecting humans and animals. The initial section argues that the term "zoonoses" should not be confined to single-cause events within veterinary medicine. Instead, zoonoses should be viewed as complex, systemic phenomena shaped by interrelated factors, including environmental, sociocultural, and economic elements, influenced by anthropogenic climate change. The second section presents bioethical principles and potential strategies for those engaged in zoonotic disease prevention. The third section uses the slaughter of animals in disaster settings as a case study to illustrate the need for further clarification of normative and interspecies justice conflicts in One Health ethics. This section concludes with an outlook on "zoonoethics". Section four develops the analysis of the interlinked elements that trigger zoonoses and examines antimicrobial resistance (AMR) from an ethical and political standpoint, concluding with policy recommendations for addressing AMR. Section five offers a critical reflection, integrating contributions from zoonoethics, human ecology, and the ecotheological turn. Finally, section six concludes with a call to action and policy recommendations for an inclusive, intercultural, and gender-sensitive One Health approach.

A micro-disc-based multiplex method for monitoring emerging SARS-CoV-2 variants using the molecular diagnostic tool Intelli-OVI

BACKGROUND

Highly transmissible viruses including SARS-CoV-2 frequently accumulate novel mutations that are detected via high-throughput sequencing. However, there is a need to develop an alternative rapid and non-expensive approach. Here we developed a novel multiplex DNA detection method Intelli-OVI for analysing existing and novel mutations of SARS-CoV-2.

METHODS

We have developed Intelli-OVI that includes the micro-disc-based method IntelliPlex and computational algorithms of objective variant identification (OVI). More than 250 SARS-CoV-2 positive samples including wastewater ones were analysed to verify the efficiency of the method.

RESULTS

IntelliPlex uses micro-discs printed with a unique pictorial pattern as a labelling conjugate for DNA probes, and OVI allows simultaneous identification of several variants using multidimensional data obtained by the IntelliPlex method. Importantly, de novo mutations can be identified by decreased signals, which indicates that there is an emergence of de novo variant virus as well as prompts the need to design additional primers and probes. We have upgraded probe panel according to the emergence of new variants and demonstrated that Intelli-OVI efficiently identified more than 20 different SARS-CoV-2 variants by using 35 different probes simultaneously.

CONCLUSIONS

Intelli-OVI can be upgraded to keep up with rapidly evolving viruses as we showed in this study using SARS-CoV-2 as an example and may be suitable for other viruses but would need to be validated.

Zoonotic infections by avian influenza virus: changing global epidemiology, investigation, and control

Avian influenza virus continues to pose zoonotic, epizootic, and pandemic threats worldwide, as exemplified by the 2020-23 epizootics of re-emerging H5 genotype avian influenza viruses among birds and mammals and the fatal jump to humans of emerging A(H3N8) in early 2023. Future influenza pandemic threats are driven by extensive mutations and reassortments of avian influenza viruses rooted in frequent interspecies transmission and genetic mixing and underscore the urgent need for more effective actions. We examine the changing global epidemiology of human infections caused by avian influenza viruses over the past decade, including dramatic increases in both the number of reported infections in humans and the spectrum of avian influenza virus subtypes that have jumped to humans. We also discuss the use of advanced surveillance, diagnostic technologies, and state-of-the-art analysis methods for tracking emerging avian influenza viruses. We outline an avian influenza virus-specific application of the One Health approach, integrating enhanced surveillance, tightened biosecurity, targeted vaccination, timely precautions, and timely clinical management, and fostering global collaboration to control the threats of avian influenza viruses.

Understanding the Transmission Dynamics of the Chikungunya Virus in Africa

The Chikungunya virus (CHIKV) poses a significant global public health concern, especially in Africa. Since its first isolation in Tanzania in 1953, CHIKV has caused recurrent outbreaks, challenging healthcare systems in low-resource settings. Recent outbreaks in Africa highlight the dynamic nature of CHIKV transmission and the challenges of underreporting and underdiagnosis. Here, we review the literature and analyse publicly available cases, outbreaks, and genomic data, providing insights into the epidemiology, genetic diversity, and transmission dynamics of CHIKV in Africa. Our analyses reveal the circulation of geographically distinct CHIKV genotypes, with certain regions experiencing a disproportionate burden of disease. Phylogenetic analysis of sporadic outbreaks in West Africa suggests repeated emergence of the virus through enzootic spillover, which is markedly different from inferred transmission dynamics in East Africa, where the virus is often introduced from Asian outbreaks, including the recent reintroduction of the Indian Ocean lineage from the Indian subcontinent to East Africa. Furthermore, there is limited evidence of viral movement between these two regions. Understanding the history and transmission dynamics of outbreaks is crucial for effective public health planning. Despite advances in surveillance and research, diagnostic and surveillance challenges persist. This review and secondary analysis highlight the importance of ongoing surveillance, research, and collaboration to mitigate the burden of CHIKV in Africa and improve public health outcomes.

Origins, diversity, and adaptive evolution of DWV in the honey bees of the Azores: the impact of the invasive mite Varroa destructor

Deformed wing virus (DWV) is a honey bee virus, whose emergence from relative obscurity is driven by the recent host-switch, adaptation, and global dispersal of the ectoparasitic mite Varroa destructor (a highly efficient vector of DWV) to reproduction on honey bees (Apis mellifera). Our study examines how varroa affects the continuing evolution of DWV, using the Azores archipelago, where varroa is present on only three out of the eight Islands, as a natural experimental system for comparing different evolutionary conditions and trajectories. We combined qPCR of 494 honey bee colonies sampled across the archipelago with amplicon deep sequencing to reveal how the DWV genetic landscape is altered by varroa. Two of the varroa-free Islands were also free of DWV, while a further two Islands were intriguingly dominated by the rare DWV-C major variant. The other four Islands, including the three varroa-infested Islands, were dominated by the common DWV-A and DWV-B variants. The varroa-infested Islands had, as expected, an elevated DWV prevalence relative to the uninfested Islands, but not elevated DWV loads, due the relatively high prevalence and loads of DWV-C on the varroa-free Islands. This establishes the Azores as a stable refuge for DWV-C and provides the most convincing evidence to date that at least some major strains of DWV may be capable of not just surviving, but actually thriving in honey bees in the absence of varroa-mediated transmission. We did not detect any change in DWV genetic diversity associated with island varroa status but did find a positive association of DWV diversity with virus load, irrespective of island varroa status.

Global contribution of statistical control charts to epidemiology monitoring: A 23-year analysis with optimized EWMA real-life application on COVID-19

Control charts help epidemiologists and healthcare professionals monitor disease incidence and prevalence in real time, preventing outbreaks and health emergencies. However, there remains a notable gap in the comprehensive exploration and application of these techniques, particularly in the context of monitoring and managing disease outbreaks. This study analyses and categorizes worldwide control chart applications from 2000 to 2023 in outbreak monitoring in over 20 countries, focusing on corona-virus (COVID-19), and chooses optimal control charts for monitoring US COVID-19 death waves from February 2020 to December 2023. The systematic literature review analyzes available 35 articles, categorizing data by year, variable, country, study type, and chart design. A selected optimal chart is applied to monitor COVID-19 death patterns and waves in the USA. Control chart adoption in epidemiology monitoring increased during the COVID-19 pandemic, with annual patterns showing a rise in 2021 to 2023 (18%, 36%, 41%). Important variables from 2000 to 2019 include influenza counts, Salmonella cases, and infection rates, while COVID-19 studies focus more on cases, infection rates, symptoms, and deaths. Among 22 countries, the USA (29%) is the top applier of control charts. The monitoring of USA COVID-19 deaths reveals 8 waves with varying severity  >  >  >  >  >  >  > . The associated with the JN.1 variant, highlights ongoing challenges. This study emphasizes the significance of control charts in outbreak monitoring for early disease diagnosis and intervention. Control charts help healthcare workers manage epidemics using data-driven methods, improving public health. COVID-19 mortality analysis emphasizes their importance, encouraging worldwide use.

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.

Viral genome sequencing methods: benefits and pitfalls of current approaches

Whole genome sequencing of viruses provides high-resolution molecular insights, enhancing our understanding of viral genome function and phylogeny. Beyond fundamental research, viral sequencing is increasingly vital for pathogen surveillance, epidemiology, and clinical applications. As sequencing methods rapidly evolve, the diversity of viral genomics applications and catalogued genomes continues to expand. Advances in long-read, single molecule, real-time sequencing methodologies present opportunities to sequence contiguous, haplotype resolved viral genomes in a range of research and applied settings. Here we present an overview of nucleic acid sequencing methods and their applications in studying viral genomes. We emphasise the advantages of different viral sequencing approaches, with a particular focus on the benefits of third-generation sequencing technologies in elucidating viral evolution, transmission networks, and pathogenesis.

Phylodynamics unveils invading and diffusing patterns of dengue virus serotype-1 in Guangdong, China from 1990 to 2019 under a global genotyping framework

BACKGROUND

The strong invasiveness and rapid expansion of dengue virus (DENV) pose a great challenge to global public health. However, dengue epidemic patterns and mechanisms at a genetic scale, particularly in term of cross-border transmissions, remain poorly understood. Importation is considered as the primary driver of dengue outbreaks in China, and since 1990 a frequent occurrence of large outbreaks has been triggered by the imported cases and subsequently spread to the western and northern parts of China. Therefore, this study aims to systematically reveal the invasion and diffusion patterns of DENV-1 in Guangdong, China from 1990 to 2019.

METHODS

These analyses were performed on 179 newly assembled genomes from indigenous dengue cases in Guangdong, China and 5152 E gene complete sequences recorded in Chinese mainland. The genetic population structure and epidemic patterns of DENV-1 circulating in Chinese mainland were characterized by phylogenetics, phylogeography, phylodynamics based on DENV-1 E-gene-based globally unified genotyping framework.

RESULTS

Multiple serotypes of DENV were co-circulating in Chinese mainland, particularly in Guangdong and Yunnan provinces. A total of 189 transmission clusters in 38 clades belonging to 22 subgenotypes of genotype I, IV and V of DENV-1 were identified, with 7 Clades of Concern (COCs) responsible for the large outbreaks since 1990. The epidemic periodicity was inferred from the data to be approximately 3 years. Dengue transmission events mainly occurred from Great Mekong Subregion-China (GMS-China), Southeast Asia (SEA), South Asia Subcontinent (SASC), and Oceania (OCE) to coastal and land border cities respectively in southeastern and southwestern China. Specially, Guangzhou was found to be the most dominant receipting hub, where DENV-1 diffused to other cities within the province and even other parts of the country. Genome phylogeny combined with epidemiological investigation demonstrated a clear local consecutive transmission process of a 5C1 transmission cluster (5C1-CN4) of DENV-1 in Guangzhou from 2013 to 2015, while the two provinces of Guangdong and Yunnan played key roles in ongoing transition of dengue epidemic patterns. In contextualizing within Invasion Biology theories, we have proposed a derived three-stage model encompassing the stages of invasion, colonization, and dissemination, which is supposed to enhance our understanding of dengue spreading patterns.

CONCLUSIONS

This study demonstrates the invasion and diffusion process of DENV-1 in Chinese mainland within a global genotyping framework, characterizing the genetic diversities of viral populations, multiple sources of importation, and periodic dynamics of the epidemic. These findings highlight the potential ongoing transition trends from epidemic to endemic status offering a valuable insight into early warning, prevention and control of rapid spreading of dengue both in China and worldwide.

Genomic epidemiology of West Nile virus in Europe

West Nile virus is one of the most widespread mosquito-borne zoonotic viruses, with unique transmission dynamics in various parts of the world. Genomic surveillance has provided important insights in the global patterns of West Nile virus emergence and spread. In Europe, multiple West Nile virus lineages have been isolated, with lineage 1a and 2 being the main lineages responsible for human infections. In contrast to North America, where a single introduction of lineage 1a resulted in the virus establishing itself in a new continent, at least 13 introductions of lineages 1a and 2 have occurred into Europe, which is likely a vast underestimation of the true number of introductions. Historically, lineage 1a was the main lineage circulating in Europe, but since the emergence of lineage 2 in the early 2000s, the latter has become the predominant lineage. This shift in West Nile virus lineage prevalence has been broadly linked to the expansion of the virus into northerly temperate regions, where autochthonous cases in animals and humans have been reported in Germany and The Netherlands. Here, we discuss how genomic analysis has increased our understanding of the epidemiology of West Nile virus in Europe, and we present a global Nextstrain build consisting of publicly available West Nile virus genomes (https://nextstrain.org/community/grubaughlab/WNV-Global). Our results elucidate recent insights in West Nile virus lineage dynamics in Europe, and discuss how expanded programs can fill current genomic surveillance gaps.

Alternative genome sequencing approaches of SARS-CoV-2 using Ion AmpliSeq Technology

A thorough understanding of SARS-CoV-2 genetic features is compulsory to track the ongoing pandemic across multiple geographical locations of the world. Thermo Fisher Scientific USA has developed the Ion AmpliSeq SARS-CoV-2 Research Panel for the targeted sequencing of SARS-CoV-2 complete genome with high coverage and lower error rate. In this study an alternative approach of complete genome sequencing has been validated using different commercial sequencing kits to sequence the SARS-CoV-2. Amplification of cDNA with the SARS-CoV-2 primer pool was performed separately using two different master mixes: 2X environmental master mix (EM) and Platinum™ PCR SuperMix High Fidelity master mix (PM) instead of 5X Ion AmpliSeq™ HiFi Mix whereas NEBNext® Fast DNA Library Prep Set for Ion Torrent™ kit was used as an alternative to Ion AmpliSeq Library Kit Plus for other reagents. This study demonstrated a successful procedure to sequence the SARS-CoV-2 whole genome with average ∼2351 depth and 98.1% of total the reads aligned against the reference sequence (SARS-CoV-2, isolate Wuhan-Hu-1, complete genome). Although genome coverage varied, complete genomes were retrieved for both reagent sets with a reduced cost. This study proposed an alternative approach of high throughput sequencing using Ion torrent technology for the sequencing of SARS-CoV-2 in developing countries where sequencing facilities are low. This blended sequencing technique also offers a low cost protocol in developing countries like Bangladesh.

Development and application of a whole genome amplicon sequencing method for infectious salmon anemia virus (ISAV)

Infectious salmon anemia (ISA) is an infectious disease primarily affecting farmed Atlantic salmon, Salmo salar, which is caused by the ISA virus (ISAV). ISAV belongs to the Orthomyxoviridae family. The disease is a serious condition resulting in reduced fish welfare and high mortality. In this study, we designed an amplicon-based sequencing protocol for whole genome sequencing of ISAV. The method consists of 80 ISAV-specific primers that cover 92% of the virus genome and was designed to be used on an Illumina MiSeq platform. The sequencing accuracy was investigated by comparing sequences with previously published Sanger sequences. The sequences obtained were nearly identical to those obtained by Sanger sequencing, thus demonstrating that sequences produced by this amplicon sequencing protocol had an acceptable accuracy. The amplicon-based sequencing method was used to obtain the whole genome sequence of 12 different ISAV isolates from a small local epidemic in the northern part of Norway. Analysis of the whole genome sequences revealed that segment reassortment took place between some of the isolates and could identify which segments that had been reassorted.

How fast are viruses spreading in the wild?

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

Efficient SARS-CoV-2 variant detection and monitoring with Spike Screen next-generation sequencing

The emergence and rapid spread of SARS-CoV-2 prompted the global community to identify innovative approaches to diagnose infection and sequence the viral genome because at several points in the pandemic positive case numbers exceeded the laboratory capacity to characterize sufficient samples to adequately respond to the spread of emerging variants. From week 10, 2020, to week 13, 2023, Slovenian routine complete genome sequencing (CGS) surveillance network yielded 41 537 complete genomes and revealed a typical molecular epidemiology with early lineages gradually being replaced by Alpha, Delta, and finally Omicron. We developed a targeted next-generation sequencing based variant surveillance strategy dubbed Spike Screen through sample pooling and selective SARS-CoV-2 spike gene amplification in conjunction with CGS of individual cases to increase throughput and cost-effectiveness. Spike Screen identifies variant of concern (VOC) and variant of interest (VOI) signature mutations, analyses their frequencies in sample pools, and calculates the number of VOCs/VOIs at the population level. The strategy was successfully applied for detection of specific VOC/VOI mutations prior to their confirmation by CGS. Spike Screen complemented CGS efforts with an additional 22 897 samples sequenced in two time periods: between week 42, 2020, and week 24, 2021, and between week 37, 2021, and week 2, 2022. The results showed that Spike Screen can be applied to monitor VOC/VOI mutations among large volumes of samples in settings with limited sequencing capacity through reliable and rapid detection of novel variants at the population level and can serve as a basis for public health policy planning.

How much should we sequence? An analysis of the Swiss SARS-CoV-2 surveillance effort

During the SARS-CoV-2 pandemic, many countries directed substantial resources toward genomic surveillance to detect and track viral variants. There is a debate over how much sequencing effort is necessary in national surveillance programs for SARS-CoV-2 and future pandemic threats. We aimed to investigate the effect of reduced sequencing on surveillance outcomes in a large genomic data set from Switzerland, comprising more than 143k sequences. We employed a uniform downsampling strategy using 100 iterations each to investigate the effects of fewer available sequences on the surveillance outcomes: (i) first detection of variants of concern (VOCs), (ii) speed of introduction of VOCs, (iii) diversity of lineages, (iv) first cluster detection of VOCs, (v) density of active clusters, and (vi) geographic spread of clusters. The impact of downsampling on VOC detection is disparate for the three VOC lineages, but many outcomes including introduction and cluster detection could be recapitulated even with only 35% of the original sequencing effort. The effect on the observed speed of introduction and first detection of clusters was more sensitive to reduced sequencing effort for some VOCs, in particular Omicron and Delta, respectively. A genomic surveillance program needs a balance between societal benefits and costs. While the overall national dynamics of the pandemic could be recapitulated by a reduced sequencing effort, the effect is strongly lineage-dependent-something that is unknown at the time of sequencing-and comes at the cost of accuracy, in particular for tracking the emergence of potential VOCs.IMPORTANCESwitzerland had one of the most comprehensive genomic surveillance systems during the COVID-19 pandemic. Such programs need to strike a balance between societal benefits and program costs. Our study aims to answer the question: How would surveillance outcomes have changed had we sequenced less? We find that some outcomes but also certain viral lineages are more affected than others by sequencing less. However, sequencing to around a third of the original effort still captured many important outcomes for the variants of concern such as their first detection but affected more strongly other measures like the detection of first transmission clusters for some lineages. Our work highlights the importance of setting predefined targets for a national genomic surveillance program based on which sequencing effort should be determined. Additionally, the use of a centralized surveillance platform facilitates aggregating data on a national level for rapid public health responses as well as post-analyses.

Building a pathway to One Health surveillance and response in Asian countries

To detect and respond to emerging diseases more effectively, an integrated surveillance strategy needs to be applied to both human and animal health. Current programs in Asian countries operate separately for the two sectors and are principally concerned with detection of events that represent a short-term disease threat. It is not realistic to either invest only in efforts to detect emerging diseases, or to rely solely on event-based surveillance. A comprehensive strategy is needed, concurrently investigating and managing endemic zoonoses, studying evolving diseases which change their character and importance due to influences such as demographic and climatic change, and enhancing understanding of factors which are likely to influence the emergence of new pathogens. This requires utilisation of additional investigation tools that have become available in recent years but are not yet being used to full effect. As yet there is no fully formed blueprint that can be applied in Asian countries. Hence a three-step pathway is proposed to move towards the goal of comprehensive One Health disease surveillance and response.