Evolutionary Dynamics and Computational Modeling of Viral Mutation Rates

Introduction

Viral mutation rates represent a fundamental parameter governing pathogen evolution, host adaptation, and emergence of novel variants in veterinary medicine. The error-prone nature of viral RNA-dependent RNA polymerases, combined with host-mediated selective pressures, generates extensive genetic diversity within infected populations [1]. This diversity underpins the capacity of viruses to evade host immune responses, alter tissue tropism, and acquire resistance to antiviral compounds [2, 3]. Understanding the evolutionary dynamics of viral mutation rates requires integration of biophysical principles, population genetics, and computational modeling approaches [4, 5].

The mutation rate of a virus is not a fixed property but varies across genomic regions, is influenced by sequence context, RNA secondary structure, and replication kinetics [1]. For RNA viruses, mutation rates typically range from 10^-4 to 10^-6 substitutions per nucleotide per replication cycle, several orders of magnitude higher than those observed in DNA viruses or host genomes [1, 6]. This elevated mutation rate generates a dynamic population structure often described as a quasispecies, a swarm of closely related but genetically distinct variants that collectively determine viral fitness [5, 6].

Biophysical Basis of Viral Mutation Rates

Polymerase Fidelity and Proofreading Mechanisms

The intrinsic error rate of viral polymerases is determined by the biochemical properties of the nucleotide incorporation active site. RNA-dependent RNA polymerases lack the 3'-5' exoribonuclease proofreading activity present in many DNA polymerases, resulting in higher misincorporation frequencies [7, 1]. However, some viral families, including coronaviruses, possess a proofreading exoribonuclease (nsp14-ExoN) that substantially increases replication fidelity [7]. Inhibition of this proofreading activity has been explored as an antiviral strategy, as reduced fidelity can lead to error catastrophe and viral extinction [7].

The mutation rate of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been shown to be highly variable between genomic sites, with local sequence context and RNA structure exerting significant influence [1]. Regions with stable secondary structures may exhibit altered mutation rates due to steric constraints on polymerase translocation or differential exposure to host RNA editing enzymes [1]. These site-specific rate variations have important implications for predicting the emergence of variants of concern and for designing conserved diagnostic targets [8, 9].

Host-Mediated Mutagenesis and Editing

Host cellular factors contribute substantially to viral mutation rates through enzymatic modification of viral nucleic acids. Adenosine deaminases acting on RNA (ADARs) catalyze the deamination of adenosine to inosine, which is read as guanosine during replication, leading to A-to-G hypermutation [1]. Similarly, apolipoprotein B mRNA editing enzyme catalytic polypeptide-like (APOBEC) proteins deaminate cytosine to uracil, generating C-to-U transitions [1]. These host restriction factors create characteristic mutational signatures that can be identified through computational analysis of viral genome sequences [8, 9].

The interplay between host editing enzymes and viral countermeasures shapes the evolutionary trajectory of viral populations. Viruses that replicate in tissues with high ADAR or APOBEC expression, such as the central nervous system, may exhibit distinct mutational profiles compared to those replicating in other anatomical sites [10]. For example, intra-host variation of Nipah virus during the 1998-1999 Malaysian outbreak revealed CNS-associated genetic diversity patterns consistent with tissue-specific selective pressures [10].

Quasispecies Dynamics and Population Genetics

Theoretical Framework

The quasispecies concept, originally developed from theoretical studies of RNA replicators, describes viral populations as dynamic distributions of mutants centered around one or more master sequences [6]. The equilibrium distribution of variants is determined by the mutation rate, the fitness landscape, and the population size [4, 6]. In large viral populations, such as those found in acute infections, multiple beneficial mutations can arise simultaneously, leading to clonal interference and complex evolutionary dynamics [5, 6].

Mathematical models of quasispecies evolution typically employ deterministic differential equations or stochastic simulations to capture the balance between mutation and selection [4, 5]. The basic model describes the change in frequency of each variant i as a function of its replication rate (fitness) and the probability of mutation from other variants to i. For a population of n variants, the system is described by:

dx_i/dt = (f_i * Q_ii - phi) * x_i + sum_{j != i} (f_j * Q_ji * x_j)

where x_i is the frequency of variant i, f_i is its fitness, Q_ji is the probability of mutation from variant j to i, and phi is the average fitness of the population [4, 6]. This formulation captures the continuous generation of genetic diversity and the competitive dynamics that drive variant frequency changes.

Fitness Landscapes and Epistasis

The fitness landscape describes the mapping between genotype and reproductive success. For viral populations, this landscape is rugged, with multiple peaks and valleys corresponding to combinations of mutations that confer high or low fitness [5, 11]. Epistatic interactions, where the effect of one mutation depends on the genetic background, are common in viral genomes and can constrain evolutionary trajectories [12, 13].

Compensatory mutations that restore fitness losses caused by deleterious mutations are frequently observed in viral evolution [13]. For example, the S375F mutation in the SARS-CoV-2 spike protein compensates for the fitness cost of S371F, maintaining infectivity of Omicron variants [13]. Computational models that incorporate epistasis are essential for predicting the likelihood of specific evolutionary pathways and for identifying mutations that may facilitate host range expansion [12, 13].

Computational Modeling Approaches

Phylogenetic Reconstruction and Molecular Clock Models

Phylogenetic methods estimate the evolutionary relationships among viral sequences and provide a framework for inferring mutation rates and divergence times [14, 15, 16]. Molecular clock models relate genetic divergence to time, allowing estimation of substitution rates and the timing of key evolutionary events [14, 15]. The relaxed molecular clock, which allows rate variation among lineages, is often more appropriate for viral datasets where substitution rates may change over time or across host species [14, 16].

Bayesian phylogenetic methods implemented in programs such as BEAST and MrBayes incorporate prior distributions on model parameters and use Markov chain Monte Carlo sampling to estimate posterior distributions of trees, divergence times, and substitution rates [14, 15, 16]. These approaches have been applied to trace the evolutionary and spatial dynamics of viral outbreaks, including chikungunya virus in Paraguay [14], hepatitis A virus in Japan [15], and West Nile virus lineage 2 in the Russian Federation [16].

Phylodynamic Models

Phylodynamics integrates phylogenetic inference with epidemiological modeling to understand how pathogen evolution shapes and is shaped by population dynamics [14, 17, 18]. Birth-death skyline models and coalescent-based approaches allow estimation of effective population size changes, reproduction numbers (R_e), and transmission rates from genetic data [18]. These models can identify periods of rapid viral spread and estimate the overdispersion in secondary cases from the size of identical sequence clusters [18].

For veterinary pathogens, phylodynamic analyses have revealed the evolutionary dynamics and molecular adaptation of Rift Valley fever virus across human and non-human outbreaks in Africa [17]. Similarly, phylogeographic approaches have traced the spatial spread of West Nile virus lineage 2 across the Russian Federation, identifying key migration corridors and reservoir populations [16].

Machine Learning and Graph-Based Approaches

Recent advances in machine learning have provided powerful tools for analyzing viral evolutionary dynamics [5, 9, 19, 20]. Graph representation learning methods, which represent viral sequences as nodes in a graph with edges encoding genetic distances or evolutionary relationships, can capture complex patterns of fitness variation [5]. These approaches have been used to characterize heterogeneity in SARS-CoV-2 fitness dynamics and to identify variants with enhanced transmissibility [5].

Large language models trained on viral sequence data can generate novel sequences with desired properties and predict the impact of mutations on protein function [19]. The SARITA model, for example, generates S1 subunit sequences of the SARS-CoV-2 spike protein and has been used to explore sequence space for variants with altered receptor binding properties [19]. Machine learning approaches have also been developed to identify key residues involved in protein-protein interactions, enabling prediction of mutations that may affect host range or antibody neutralization [20].

Predicting Mutation Trends from Historical Data

Time-series analysis of viral sequence data can reveal temporal patterns in mutation frequencies and enable prediction of future evolutionary trends [9]. Models that incorporate historical mutation frequency data, selective pressures, and epidemiological parameters can forecast the emergence of dominant variants [9, 21]. These predictive models are valuable for veterinary surveillance programs, allowing proactive development of diagnostic assays and vaccines [9].

Stochastic perturbation models that incorporate random fluctuations in epidemiological parameters provide a more realistic representation of viral evolution in natural populations [21]. These models account for the inherent randomness in mutation events, transmission bottlenecks, and host immune responses, generating probabilistic forecasts of variant dynamics [21].

Mapping Mutations onto Three-Dimensional Structures

Structural Bioinformatics Approaches

Understanding the functional impact of viral mutations requires mapping sequence changes onto three-dimensional protein structures [8, 12, 22, 23, 24, 25, 26]. Computational structural biology methods, including homology modeling, molecular dynamics simulations, and docking studies, allow prediction of how mutations alter protein stability, receptor binding affinity, and antibody recognition [12, 22, 27, 28].

The spike glycoprotein of coronaviruses has been extensively studied using structural approaches [8, 12, 22, 29, 28]. Mutations in the receptor binding domain can be mapped onto the three-dimensional structure to identify residues that directly contact the host receptor or are recognized by neutralizing antibodies [8, 22]. The D614G mutation in SARS-CoV-2 spike, for example, was shown to reshape allosteric networks and alter the conformational dynamics of the spike trimer, enhancing infectivity [12].

Identification of Hypervariable and Conserved Regions

Structural mapping of mutations across viral genomes reveals regions of high variability (hypervariable regions) and regions that are evolutionarily constrained (conserved regions) [8, 23]. Hypervariable regions typically correspond to surface-exposed loops that are under strong immune selection pressure, while conserved regions often include active sites, receptor binding pockets, and structural cores essential for protein function [8, 23].

For veterinary pathogens, structural analysis of the VP2 protein from infectious bursal disease virus has identified hypervariable loops that determine antigenic diversity and conserved structural elements required for capsid assembly [23]. Similarly, analysis of the Nipah virus fusion protein has identified conserved epitopes that represent promising targets for therapeutic nanobody development [25].

Molecular Dynamics Simulations

Molecular dynamics simulations provide atomic-level insights into the effects of mutations on protein dynamics and function [12, 27, 29, 28, 30, 26]. These simulations model the physical movements of atoms and molecules over time, revealing how mutations alter conformational equilibria, protein stability, and intermolecular interactions [12, 29, 28].

Enhanced sampling methods, such as metadynamics and replica exchange molecular dynamics, can capture rare conformational events that are critical for understanding mutation effects [29]. These approaches have revealed metastable conformations driving antibody escape mediated by the K417N mutation in SARS-CoV-2 spike [29]. For veterinary applications, molecular dynamics simulations have been used to study the binding kinetics of protease inhibitors in the context of drug resistance mutations [30] and to investigate how single mutations in the Ebola virus matrix protein VP40 enhance plasma membrane localization [26].

Protein-Protein Interaction Networks

Comprehensive atomic-scale three-dimensional viral-host protein interactomes enable dissection of key mechanisms underlying viral pathogenesis [24]. These interactome maps integrate structural data from X-ray crystallography, cryo-electron microscopy, and computational predictions to create detailed models of viral-host protein complexes [24]. By mapping mutations onto these interactomes, researchers can identify mutations that disrupt or enhance specific protein-protein interactions, providing mechanistic explanations for changes in host range, tissue tropism, and virulence [24].

The following table summarizes key computational methods for analyzing viral mutation rates and their structural impacts:

Workflow for Computational Analysis of Viral Mutation Rates

The following Mermaid diagram illustrates a typical computational workflow for analyzing viral mutation rates and their evolutionary implications:

flowchart TD
    A[Viral Sequence Data Collection], > B[Quality Control and Alignment]
    B, > C[Mutation Rate Estimation]
    C, > D[Phylogenetic Reconstruction]
    D, > E[Phylodynamic Analysis]
    E, > F[Selection Pressure Detection]
    F, > G[Structural Mapping of Mutations]
    G, > H[Molecular Dynamics Simulations]
    H, > I[Functional Impact Prediction]
    I, > J[Veterinary Diagnostic and Surveillance Applications]
    
    C, > K[Quasispecies Analysis]
    K, > L[Intra-host Variant Characterization]
    L, > M[Tissue-Specific Mutation Patterns]
    M, > G
    
    F, > N[Identification of Conserved Regions]
    N, > O[Diagnostic Target Design]
    O, > J
    
    F, > P[Identification of Hypervariable Regions]
    P, > Q[Vaccine Target Assessment]
    Q, > J

This workflow begins with sequence data collection from clinical or environmental samples, followed by quality control and multiple sequence alignment [14, 15]. Mutation rates are estimated using maximum likelihood or Bayesian methods, and phylogenetic trees are reconstructed to infer evolutionary relationships [14, 15, 16]. Phylodynamic analyses integrate epidemiological data to understand transmission dynamics [14, 17, 18]. Selection pressure detection identifies sites under positive or negative selection, which are then mapped onto three-dimensional protein structures [8, 22, 23]. Molecular dynamics simulations provide mechanistic insights into mutation effects [12, 27, 29, 28, 30, 26]. The results inform veterinary diagnostic target design, vaccine development, and surveillance strategies.

Applications in Veterinary Virology

Host Range Evolution and Zoonotic Risk Assessment

Computational modeling of viral mutation rates is essential for assessing zoonotic potential and host range evolution [17, 31]. Mutations that alter receptor binding specificity, enhance replication in new host species, or evade host immune responses can be identified through comparative genomic analyses and structural modeling [17, 31]. For example, in silico assessment of feline coronavirus FCoV-23 has evaluated the risk of possible human transmission by analyzing mutations in the spike protein that may affect human ACE2 receptor binding [31].

Phylodynamic analyses of Rift Valley fever virus across human and non-human outbreaks in Africa have revealed patterns of cross-species transmission and identified viral lineages with enhanced capacity for spillover [17]. These analyses inform veterinary surveillance programs and guide the development of control strategies for livestock populations [17].

Antiviral Resistance Monitoring

The evolution of antiviral resistance is a major concern in veterinary medicine [7, 3, 30]. Computational models can predict the likelihood of resistance emergence under different treatment regimens and identify mutations that confer resistance [7, 3]. For chikungunya virus, evolution of antiviral resistance has been shown to capture transient interdomain functional interactions between envelope glycoproteins, revealing structural constraints on resistance pathways [3].

Molecular dynamics simulations of protease-inhibitor complexes can predict how resistance mutations alter binding kinetics and drug efficacy [30]. These predictions guide the selection of antiviral compounds with higher genetic barriers to resistance and inform treatment protocols for veterinary patients [7, 30].

Vaccine Design and Efficacy Prediction

Understanding viral mutation rates and evolutionary dynamics is critical for vaccine design [32, 23, 6]. Vaccines targeting conserved epitopes are less likely to be rendered ineffective by viral evolution, while vaccines targeting hypervariable regions may require frequent updates [32, 23]. Computational structural analysis can identify conserved epitopes that are under strong functional constraint and therefore less likely to mutate [23, 25].

For coxsackievirus A6, evolutionary dynamics analysis has informed antiviral strategy development by identifying conserved targets and predicting the emergence of antigenic variants [32]. Similarly, structural modeling of the infectious bursal disease virus VP2 protein has guided epitope prediction for vaccine development [23].

Limitations and Future Directions

Despite significant advances, computational modeling of viral mutation rates faces several challenges. The accuracy of mutation rate estimates depends on the quality and representativeness of sequence data, which may be biased by sampling protocols and sequencing technologies [1]. Models must account for the complex interplay between mutation, selection, genetic drift, and population structure, which requires sophisticated statistical frameworks [4, 5, 6].

Future directions include the integration of multi-omics data (transcriptomics, proteomics, metabolomics) with evolutionary models to provide a more comprehensive understanding of viral fitness [2]. Advances in artificial intelligence, particularly deep learning and generative models, promise to improve prediction of mutation effects and enable real-time monitoring of viral evolution [5, 9, 19, 20]. The development of user-friendly computational platforms that integrate these methods will facilitate their adoption in veterinary diagnostic laboratories and research institutions [4].

Conclusion

Evolutionary dynamics and computational modeling of viral mutation rates represent a critical intersection of virology, biophysics, and bioinformatics. The error-prone replication of RNA viruses generates extensive genetic diversity that drives adaptation, host range expansion, and immune evasion. Mathematical models of quasispecies dynamics, phylogenetic reconstruction, and phylodynamic analysis provide frameworks for understanding and predicting viral evolution. Structural bioinformatics approaches, including molecular dynamics simulations and protein-protein interaction mapping, enable mechanistic interpretation of mutation effects. These computational methods have direct applications in veterinary medicine, including zoonotic risk assessment, antiviral resistance monitoring, and vaccine design. Continued development of these approaches will enhance our ability to respond to emerging viral threats in animal populations.

References

[1] Haddox HK, Angehrn G, Sesta L et al. The mutation rate of SARS-CoV-2 is highly variable between sites and is influenced by sequence context, genomic region, and RNA structure. Nucleic Acids Res. 2025. https://pubmed.ncbi.nlm.nih.gov/40503682/

[2] Huang Y, Ou Z, Xue X et al. Experimental and computational approaches to adaptive viral evolution: Linking molecular variation to phenotypic outcomes. J Microbiol Methods. 2026. https://pubmed.ncbi.nlm.nih.gov/41429194/

[3] Battini L, Thannickal SA, Tejerina Cibello M et al. Evolution of antiviral resistance captures a transient interdomain functional interaction between chikungunya virus envelope glycoproteins. mBio. 2025. https://pubmed.ncbi.nlm.nih.gov/41159720/

[4] Das B, Heath LS. ViraFit: Tunable Fitness Model for Viral Evolution Within a Contact Network. J Comput Biol. 2026. https://pubmed.ncbi.nlm.nih.gov/42117686/

[5] Wang Z, Zhou Z, Wang J et al. Characterization of the heterogeneity in SARS-CoV-2 fitness dynamics via graph representation learning. PLoS Comput Biol. 2026. https://pubmed.ncbi.nlm.nih.gov/41525306/

[6] Rouzine IM. Evolutionary Mechanisms of the Emergence of the Variants of Concern of SARS-CoV-2. Viruses. 2025. https://pubmed.ncbi.nlm.nih.gov/40006952/

[7] Easton V, McPhillie MJ, Santos IA et al. Identification and characterization of candidate inhibitors of the SARS-CoV-2 nsp14 3'-5' exoribonuclease. J Gen Virol. 2025. https://pubmed.ncbi.nlm.nih.gov/41417636/

[8] Hasan M, Chen S, Jia M et al. Deciphering the temporal and spatial mutation dynamics of the SARS-CoV-2 spike glycoprotein. Phys Chem Chem Phys. 2026. https://pubmed.ncbi.nlm.nih.gov/42300480/

[9] Zhou X, Yan Y, Hu K et al. Predicting the trend of SARS-CoV-2 mutation frequencies using historical data. Bioinformatics. 2025. https://pubmed.ncbi.nlm.nih.gov/40973044/

[10] Cheong HC, Tan FH, Ong HM et al. Genomic conservation and CNS-associated intra-host variation of Nipah virus during the 1998-1999 Malaysian outbreak. Int J Infect Dis. 2026. https://pubmed.ncbi.nlm.nih.gov/42320843/

[11] Longley H, Fraser C, Wymant C et al. Attenuation of HIV severity by slightly deleterious mutations can explain the long-term trajectory of virulence evolution. PLoS Comput Biol. 2025. https://pubmed.ncbi.nlm.nih.gov/41325373/

[12] Kearns FL, Bogetti AT, Calvó-Tusell C et al. D614G reshapes allosteric networks and opening mechanisms of SARS-CoV-2 spikes. Proc Natl Acad Sci U S A. 2026. https://pubmed.ncbi.nlm.nih.gov/42101997/

[13] Liu S, Liu P, Lu Q et al. The Compensatory Effect of S375F on S371F Is Vital for Maintaining the Infectivity of SARS-CoV-2 Omicron Variants. J Med Virol. 2025. https://pubmed.ncbi.nlm.nih.gov/40062404/

[14] Cardozo F, Morel R, Márquez S et al. Tracing the evolutionary and spatial dynamics of the 2022-2023 chikungunya outbreak in Paraguay and its regional spread across the Southern Cone. IJID Reg. 2026. https://pubmed.ncbi.nlm.nih.gov/42317503/

[15] Shih YC, Lin MY, Hsiao BC et al. Phylodynamics and Molecular Evolution of Hepatitis A Virus During the Years 1957-2021 in Japan. J Med Virol. 2026. https://pubmed.ncbi.nlm.nih.gov/42304976/

[16] Antonov AS, Shpak IM, Guseva AN et al. Evolutionary dynamics and phylogeographic analysis of West Nile virus lineage 2 in the Russian Federation. Virology. 2026. https://pubmed.ncbi.nlm.nih.gov/42289137/

[17] Omara IE, Juma J, Tshiabuila D et al. Evolutionary dynamics and molecular adaptation of Rift Valley fever virus across human and non-human outbreaks in Africa. BMC Genomics. 2026. https://pubmed.ncbi.nlm.nih.gov/42289653/

[18] Hodcroft EB, Wohlfender MS, Neher RA et al. Estimating Re and overdispersion in secondary cases from the size of identical sequence clusters of SARS-CoV-2. PLoS Comput Biol. 2025. https://pubmed.ncbi.nlm.nih.gov/40233303/

[19] Rancati S, Nicora G, Bergomi L et al. SARITA: a large language model for generating the S1 subunit of the SARS-CoV-2 spike protein. Brief Bioinform. 2025. https://pubmed.ncbi.nlm.nih.gov/40755284/

[20] Quitté L, Leclercq M, Prunier J et al. A Machine Learning Approach to Identify Key Residues Involved in Protein-Protein Interactions Exemplified with SARS-CoV-2 Variants. Int J Mol Sci. 2024. https://pubmed.ncbi.nlm.nih.gov/38928241/

[21] S P Rajasekar, R Ramesh, Sabbar Y. Based on epidemiological parameter data, probe into a stochastically perturbed dominant variant of the COVID-19 pandemic model. Gene. 2024. https://pubmed.ncbi.nlm.nih.gov/38823655/

[22] Usama M, Azeem M, Mustafa G. Computational prediction of binding affinity and structural impact of three Pakistani SARS-CoV-2 spike RBD variants on human ACE2 interaction. PLoS One. 2026. https://pubmed.ncbi.nlm.nih.gov/41920812/

[23] Mansour AY, Omar AR, Bejo MH et al. In silico analysis of VP2 protein from infectious bursal disease virus isolate UPM1432/2019: structural dynamics and epitope prediction. Antonie Van Leeuwenhoek. 2025. https://pubmed.ncbi.nlm.nih.gov/41348167/

[24] Li L, Roy PG, Liu Y et al. Comprehensive Atomic-Scale 3D Viral-Host Protein Interactomes Enable Dissection of Key Mechanisms and Evolutionary Processes Underlying Viral Pathogenesis. bioRxiv. 2025. https://pubmed.ncbi.nlm.nih.gov/40236211/

[25] Odchimar NMO, Dulay ANG, Orosco FL. Molecular modelling and optimization of a high-affinity nanobody targeting the nipah virus fusion protein through in silico site-directed mutagenesis. Comput Biol Chem. 2025. https://pubmed.ncbi.nlm.nih.gov/39848170/

[26] Cioffi MD, Sharma T, Motsa BB et al. Ebola Virus Matrix Protein VP40 Single Mutations G198R and G201R Significantly Enhance Plasma Membrane Localization. J Phys Chem B. 2024. https://pubmed.ncbi.nlm.nih.gov/39326870/

[27] Kumar P, Chen L, Chen RY et al. AI-Guided Binding Mechanisms and Molecular Dynamics for MERS-CoV. Int J Mol Sci. 2026. https://pubmed.ncbi.nlm.nih.gov/41752125/

[28] Huan X, Li M, Gao H. Molecular dynamics to explore the neutralizing efficacy and mechanisms of SARS-CoV-2 antibodies against single-point mutations. Int J Biol Macromol. 2025. https://pubmed.ncbi.nlm.nih.gov/40553863/

[29] Pan X, Tadokoro T, Onodera T et al. Enhanced Sampling Reveals Metastable Conformations Driving K417N-Mediated Class I Antibody Escape. J Chem Inf Model. 2025. https://pubmed.ncbi.nlm.nih.gov/41117347/

[30] Eche S, Kumar A, Sonela N et al. Binding kinetics of highly mutated HIV-1 subtype C protease inhibition by Lopinavir and Darunavir in the face of altered conformational dynamics. J Biomol Struct Dyn. 2026. https://pubmed.ncbi.nlm.nih.gov/39697065/

[31] Ozketen AC, Kazan HH, Özverel CS et al. In Silico Assessment for Risk of Possible Human Transmission of FCoV-23. Transbound Emerg Dis. 2024. https://pubmed.ncbi.nlm.nih.gov/40303145/

[32] Zhou X, He F, Lin Z et al. Coxsackievirus A6 on the rise: epidemiology, pathogenicity, evolutionary dynamics, and antiviral strategy. Clin Microbiol Rev. 2026. https://pubmed.ncbi.nlm.nih.gov/42312839/

[33] Tam PT, Vu MH, Hoa-Tran TN et al. Molecular epidemiology of DS-1-like G1P[8] rotavirus strains in Vietnam, 2012-2016: Evolutionary dynamics of an unusual rotavirus reassortant. Arch Virol. 2026. https://pubmed.ncbi.nlm.nih.gov/42310243/

[34] Kumosani TA, Abbas AT, Basheer B et al. Investigating Pb2 CAP-binding domain inhibitors from marine bacteria for targeting the influenza A H5N1. PLoS One. 2025. https://pubmed.ncbi.nlm.nih.gov/39874356/

[35] M L SP, Kumari S, Martinek TA