Virus Evolution

Cell tropism, or the preference of a virus for particular cell types, has major implications for viral transmission, pathogenesis, and evolution. An increase in viral fitness -- increased within-host replication, also leading to increased transmission between hosts -- can result from a virus changing its cell tropism. This is illustrated in the context of influenza, where adaptation to infect cells expressing 2-6 linked sialic acid receptors enhances human-to-human transmissibility. Target cell populations differ not only in abundance but also in intrinsic properties such as susceptibility, viral production, and interferon responses, rendering the relationship between tropism and viral fitness multi-faceted and complex. Understanding how different cell tropisms quantitatively change fitness remains an important open question in virology and quantitative biology. Here, we present a within-host mathematical model that incorporates distinct target cell types differing in key properties, and examine how cell tropism affects viral fitness, as measured by metrics such as peak viral load, infection duration, or total virus produced. Our analysis reveals that tradeoffs may arise when cell types differ by multiple characteristics. We further demonstrate that model parameters describing heterogeneity between cell types can be more accurately inferred when cell type proportions are measured alongside viral load. Our findings provide a framework for assessing the links between viral evolution, cell tropism, and within-host fitness, and motivate the design of experiments to collect quantitative data on between-cell heterogeneity.

BA.3.2, a variant of SARS-CoV-2 containing [~]40 mutations in its spike protein compared to its nearest ancestor, has spread globally since its first detection in South Africa in November 2024. Here, we report antigenic characterization of BA.3.2 viruses in three naive animal models, and visualize its antigenic phenotype in the context of SARS-CoV-2 evolution using antigenic cartography. We find that: (1) BA.3.2 is substantially antigenically divergent from existing SARS-CoV-2 variants; (2) infection with BA.3.2 in hamster and mouse animal models produces sera with lower homologous titer than infection with other variants. Both of these results may have implications for the selection of vaccine antigens.

Hydrangea ringspot virus (HdRSV) is an emerging plant virus infecting ornamental hydrangea species worldwide, yet its genomic diversity and host associations remain poorly understood. To expand the available genomic resources and assess HdRSV variability, we screened 210 publicly available Hydrangea spp. transcriptomes from diverse tissues, complemented with four newly generated H. macrophylla transcriptomes from Colombia. Viral genomes were assembled from infected samples and analyzed to infer phylogenetic relationships, lineage distribution, and relative viral RNA abundance. Two well-supported phylogenetic lineages (HdRSV-L1 and HdRSV-L2) were recovered from both full-genome and replicase coding sequence (CDS) analyses. HdRSV was detected across all host tissues examined, with the highest median viral loads in roots, followed by stems and leaves. H. macrophylla harbored both viral lineages, while H. serrata was exclusively infected by HdRSV-L1. Cultivar-level analysis revealed marked differences in viral abundance, with lineages showing distinct tissue preferences but no co-infection patterns, except in the Bailer cultivar. Comparative analysis of the replicase CDS identified a single lineage-defining nonsynonymous mutation (C1578T; Thr[->]Ile), fixed in 90% of HdRSV-L2 genomes, corresponding to a polar-to-nonpolar amino acid change potentially associated with structural adaptation. Together, these findings provide the most comprehensive overview to date of HdRSV genomic diversity, host and tissue distribution, and molecular variation, offering new insights into the evolution and epidemiology of this understudied plant virus.

Deep mutational scans across receptor-binding domains (RBDs) of diverging SARS-CoV-2 variants reveal ongoing changes to the effects of mutations, a phenomenon known as epistasis. Careful accounting for these altered mutational effects is important in viral surveillance and forecasting, and more broadly, for understanding the impacts of epistasis on real-world viral evolutionary trajectories. Using a yeast-display RBD deep mutational scanning (DMS) platform, we measure the impacts of virtually all single amino acid mutations and single-residue deletions in the Omicron KP.3.1.1 and LP.8.1 RBDs on folded RBD expression and binding affinity for the human ACE2 receptor. Our comprehensive maps reveal patterns of evolutionary accessibility and constraint at single-residue resolution and when compared to prior datasets, highlight sites whose amino acid preferences continue to change across viral variants. Notably, sites 455, 456, and 493 - which have exhibited repeated substitutions and epistatic dependencies across Omicron subvariants going back to BA.1 - again demonstrate altered patterns of mutational accessibility and constraint. Therefore, it appears that these hotspots of repeated RBD evolution have not yet converged on fixed amino acid solutions, but instead remain sites of ongoing epistatic reconfiguration. We compare our measurements of direct RBD:ACE2 affinity with recently published measurements of mutation impacts on ACE2 binding in the full quaternary spike context, which also integrates the effects of spike conformational dynamics; our analysis uncovers mutations like H505W that could favor adoption of the down/closed RBD conformation as a viral strategy for future antigenic evolution.

Fitness landscapes offer a compact representation of adaptation, yet are rarely inferred from multi-environment data. We present a Bayesian approach to infer a multi-host phenotypic fitness landscape from cross-inoculation assays by linking successful infection probabilities to Fishers geometrical model under strong selection and weak mutation. The model estimates (i) the distance matrix among host-specific phenotypic optima, (ii) host-specific permissiveness through the widths of fitness peaks on target hosts, and (iii) host-specific differences in the efficiency with which phenotypic suitability translates into successful infection. We apply the approach to an experimental evolution dataset for endive necrotic mosaic virus evolved on five Asteraceae hosts and challenged in a full cross-inoculation design. The inferred landscape can be visualized as a phenotypic map of the host community, revealing pronounced heterogeneity in host permissiveness and a geometry broadly concordant with host phylogeny. By grounding assay-derived distances in an explicit mechanistic model, the approach provides a parsimonious representation of multi-host constraints that can be used to discuss establishment barriers and potential springboard hosts in heterogeneous communities. More broadly, it offers a general method for inferring effective fitness landscapes from sparse multi-environment data.

The Capripoxviruses (CaPV) comprise three species: goatpox virus (GTPV), sheeppox virus (SPPV) and lumpy skin disease virus (LSDV). They are large double-stranded DNA viruses with highly conserved core genomes and variable terminal regions. Previous studies have described variation in CaPV gene content, their broader population structure and the contribution of non-coding and structural variation remains opaque. This study investigated the genomic diversity and evolutionary history of GTPV and SPPV using an integrative framework combining phylogenetics, pangenome variation graphs (PVGs), and gene-specific analyses. We found marked differences in population structure between the two viruses. GTPV comprised three deeply divergent and genetically stable lineages with limited evidence of recent gene flow, whereas SPPV had weaker clade separation consistent with an ancestral bottleneck followed by recent population expansion. PVG-based analyses indicated that GTPV has a comparatively closed pangenome, while SPPV remains open, particularly at the genome termini. Structural and haplotypic variation was concentrated at the inverted terminal repeats (ITRs), which moderate host immunity and specificity. In several lineages, extended putative ORFs spanning adjacent terminal genes were observed, indicating recurrent structural plasticity at the genome ends. Patterns of gene-specific conservation and divergence highlighted loci under strong constraint and lineage-specific structural changes that may contribute to host specificity. Together, these results demonstrate how graph-based genome models complement gene-based analyses in resolving poxvirus genome evolution and provide a resource for improved comparative and population genomic studies of large DNA viruses. SignificanceCapripoxviruses are economically important livestock pathogens, yet the genomic mechanisms underlying their diversification and host specificity remain poorly resolved. By applying pangenome variation graphs alongside phylogenetic and gene-level analyses, this study reveals fundamental differences in how goatpox and sheeppox viruses have evolved. Goatpox virus had a deeper, more stable lineage structure, whereas sheeppox virus was more recent and diverse. Importantly, structural variation at the inverted terminal repeats emerged as a major driver of genomic diversity, including lineage-specific haplotypes and variable gene structures. These findings demonstrated the value of graph-based genome representations for resolving complex variation in large DNA viruses and provides a framework for improving genomic surveillance, comparative analyses, and future investigations into host range, virulence and tropism.

Influenza viruss rapid evolution is shaped by both neutral mutation and selection. Phylogenetics can be used to study these processes, but this approach has typically only been applied to a few thousand influenza genome sequences at once. Here, we built phylogenetic trees with >100,000 influenza sequences, and then used these trees to estimate neutral rates of mutations to the viruss genome. Neutral rates varied by up to ~100-fold among the 12 nucleotide mutation types (A[->]C,A[->]G, etc.). These rates were highly correlated among influenza, SARS-CoV-2, and HIV, though more nuanced context-dependent patterns showed marked differences between influenza and SARS-CoV-2. We also estimated fitness effects of mutations by comparing the number of times a mutation was observed to occur along the branches of a tree to the number of times we expect it to have occurred under neutrality. We estimated effects for ~33,000 nonsynonymous and ~8,000 synonymous mutations spanning all influenza proteins. This compendium of estimated effects helps map the relationship between sequence and fitness in a natural setting, including regions where synonymous mutations are under functional constraint, and for proteins with limited experimentally measured effects. We built interactive heatmaps of the estimated fitness effects to help readers explore these data (see https://matsen.group/flu-mut-rates). Altogether, this work places influenzas mutation rates in a broader cross-viral context and deepens our understanding of how mutation and selection shape influenza evolution in nature at a site-specific level.

A virus with a circular single-stranded DNA genome, HthCRESSV1, was discovered in an oomycete, Halophytophthora thermoambigua, isolated from brackish waters off the Algarve coast in southern Portugal. Phylogenetic analyses place this virus outside of all currently classified families within the phylum Cressdnaviricota, suggesting it represents a distinct lineage. Cellular fractionation, mitochondrial marker co-enrichment, and Southern blot analyses indicate that HthCRESSV1 is associated with mitochondria and replicates episomally. The virus is stably transmitted through zoospores and, notably, infected host isolates are associated with reduced growth and changes in temperature-dependent performance. Homologous sequences corresponding to both the replication-associated protein and a membrane-associated hypothetical protein were identified in the mitochondrial genomes of several oomycete species, suggesting recurrent endogenization of HthCRESSV1-like viruses in oomycete mitochondria. Together, these findings expand the known diversity of oomycete-associated DNA viruses, identify mitochondria as an unexpected niche for DNA virus replication, and highlight marine environments as underexplored reservoirs of DNA viruses in stramenopiles.

Poxviruses constitute a threat to human health. Since 2022, two public health emergencies of international concern due to global spread of mpox viruses (MPXVs) were declared. The emergence of the novel MPXV subclade Ib has placed the global health community on alert as sustained human-to-human and travel-related transmission is prevalent in Africa and 30 non-African countries. Metagenomic and outbreak surveillance data often generates complete as well as partial assemblies of genomes which then require efficient taxonomic classification. Traditional viral genome classifiers rely on poorly scalable alignment methods creating computational bottlenecks in taxonomic classifications. Here, we present CladePredictor- MPXV: an alignment-free AI-based classifier of complete and partial MPXV genomes. Our classification framework consists of an ensemble of XGBoost and CNNs to classify between subclades Ia, Ib and IIb. CladePredictor-MPXV was trained with 3,866 MPXV genomes. XGBoost models were trained with 3-mers which are representative of the global feature space of complete MPXV genomes. CNNs were trained with short-range, position-independent sequence patterns to assign clades to partial genomes with a minimum size of 1000 nucleotides. Our XGBoost instance attained a weighted average accuracy of 90.2% while our CNN instance attained a weighted average accuracy of 95% in classifying clade (I vs II) and subclade (Ia vs Ib) from complete (>= 188,000 nucleotides) and partial MPXV genomes on a phylogenetically distinct validation set. CladePredictor-MPXV is freely available at https://clade-predictor.microbiologyandimmunology.dal.ca and provides a fast and efficient framework for the assignment of clades to MPXV subclade Ia, Ib, and IIb complete and partial genomes.

Understanding how viral communities vary across co-occurring hosts and environments is essential for assessing species-specific viral risks under changing land use and climate. This is particularly relevant for managing introduced bees, which face persistent viral threats themselves, as well as transmitting plant viruses. Here, we compare RNA viromes of the long-established honeybee (Apis mellifera, introduced to Tasmania in 1831) and the more recent invader, the bumblebee (Bombus terrestris, invasive since 1992), across 14 Tasmanian sites - an island still free of the viral vector, Varroa destructor. Using a metatranscriptomic approach on total RNA from whole bees, we identified insect- and plant-associated viruses and inferred phylogenetic patterns of insect viral sharing, divergence, and potential cross-species transmission. We also assessed spatial and environmental drivers of viral composition, diversity, and richness. Geographic longitude, precipitation, temperature, and pasture percentage influenced the total, insect-, and plant-associated viromes of B. terrestris. In contrast, for A. mellifera, only precipitation and temperature were associated with insect and plant viral alpha diversity and community composition. Phylogenetic analyses revealed that Black Queen Cell virus in A. mellifera from Tasmania has diverged from mainland Australian sequences, and two distinct sub-strains of Lake Sinai virus 1 were shared by both bee species. Lake Sinai virus 3 showed evidence of interspecies transmission between A. mellifera and B. terrestris. Notably, this study provides the first detection of Moku virus in Australian bees and globally in bumblebees, suggesting potential interspecies transmission among social Hymenoptera. Overall, our findings demonstrate local viral diversification and reveal that B. terrestris viromes are more strongly shaped by environmental factors than those of A. mellifera, underscoring the importance of monitoring invasive pollinators as reservoirs and vectors of viral emergence.

Peste des petits ruminants (PPR) is a highly contagious viral disease of small ruminants caused by the peste des petits ruminants virus (PPRV), which is classified into four distinct genetic lineages (I-IV). A critical concern in the recent epidemiological history of PPRV is the rapid and widespread expansion of lineage IV (LIV) across West Africa over the past decade. This dominance suggests a potential adaptive advantage of circulating LIV strains in the regions current epidemiological context. In this study, we obtain the genome sequence of 26 new PPRV samples, including historical (pre-2000) and many recent African LIV isolates, offering the first opportunity to investigate the evolutionary history of LIV in Africa and identify genetic events potentially associated with its recent spread. Phylogenomic analyses implemented on a dataset of 167 curated PPRV genome sequences reveal that the most ancestral LIV group comprises strains circulating in Sub-Saharan Africa (designated clade LIVssa), providing robust evidence for an African origin of lineage IV. Our results further indicate that PPRV strains linked to the recent West African expansion of LIV belong to a specific LIVssa subgroup, termed NigB. We identified multiple signatures of selection pressure within the LIVssa sublineage, particularly in the NigB cluster. Several amino acid substitutions unique to LIVssa or NigB were detected, some of which may impact protein function and warrant prioritised investigation. Additional genomic data are required to confirm the association between the NigB group and the ongoing spread of LIV in West Africa. The evolutionary adaptations observed in LIVssa - potentially enhancing transmission efficiency, host range or pathogenicity - could undermine current disease control strategies in regions where PPR poses significant threats to food security and local economies. Author SummaryPeste des petits ruminants virus (PPRV) infects sheep and goats across Africa, Middle East, Asia and Europe, causing disease with major impact on global economy and food security. One genetic lineage of PPRV, called lineage IV (LIV), is at the origin of most recent expansion of the distribution of the disease, including replacement of other lineages in areas of African where PPRV is historically present. Here, we generated genome sequences from PPRV LIV isolates from different dates and places to study the evolution of this genetic lineage and explore whether its recent spread can be associated with the appearance of new mutations in the virus genome. Our results provide evidence that the PPRV LIV originated in Sub-Saharan Africa and identify mutations present only virus isolates currently spready in new regions of Africa. Further research should investigate the impact of these mutations on protein functions and capacity of transmission of PPRV.

The emergence in 2025/26 of the influenza A/H3N2 K substrain (H3N2/K) was the cause of significant public health concern. This genetically divergent virus was assessed to have a strongly decreased reactivity to contemporary vaccine strains. Respectively prolonged and early influenza seasons in the Southern and Northern Hemispheres contributed to concerns about vaccine efficacy. Here we retrospectively assessed the genetic and antigenic properties of this virus, combining epidemiological surveillance data, computational antigenic analysis, and serological data using samples from a well-stratified UK cohort. In contrast to initial indications, we found that despite the genetic distinctiveness of H3N2/K the virus had undergone limited antigenic change, suggesting that its emergence was instead the result of selection for non-antigenic properties. We confirmed previous results showing that contemporary vaccines produced an enhanced neutralising response to H3N2/K but, in a stratified serological analysis, showed that responses to the J and K substrains were age-dependent, largely driven by patterns of vaccination. Our results have implications for antigenic surveillance and for public communication strategies in future influenza seasons.

Lamprey reddening syndrome (LRS) is an emerging disease affecting pouched lamprey (Geotria australis; kanakana/piharau), a culturally and ecologically significant species in Aotearoa New Zealand. Characterised by skin haemorrhaging and elevated mortality, the aetiology of LRS has remained unresolved despite previous investigations. We used a metatranscriptomic approach to characterise viral communities in 28 lamprey from New Zealand and Tasmania, Australia, comparing diseased and presumably healthy individuals. This analysis revealed eight fish-infecting RNA viruses, seven of which were novel, including two highly divergent coronaviruses. One of these coronaviruses possessed a bi-segmented genome structure, and three lamprey were co-infected with both coronaviruses. While these coronaviruses were detected in both healthy and diseased individuals, lamprey with reddening exhibited markedly higher viral abundance, driven by elevated RNA transcripts of both viruses. This pattern suggests that increased coronavirus replication in diseased individuals may be influenced by host stress to environmental factors or co-infection with other pathogens, rather than acting as a sole causative agent of disease. Beyond identifying candidate viral associations, this study expands the known virosphere of an ancient vertebrate lineage and demonstrates the utility of genomics-informed diagnostics for investigating disease in threatened wildlife.

The antigenic evolution of human seasonal influenza viruses is primarily driven by single amino acid substitutions immediately adjacent to the receptor binding site in the hemagglutinin (HA) protein. The ability to predict these substitutions would allow vaccine strains to be selected with an understanding of likely future antigenic variation. Here, we estimate the effect of HA substitutions on viral fitness using measurements of convergent evolution in a large phylogeny. We show that the substitutions which have historically caused major antigenic changes in H3N2 influenza viruses were nearly always one of few substitutions near the HA receptor binding site estimated to be under positive selection in sequences collected before the antigenic transition, based on convergent acquisition of the substitution in multiple independent lineages. Furthermore, this signal predates the establishment of the major clade containing the antigenic substitution by more than one year, so is highly informative for prospective prediction.

Since 2020, high pathogenicity avian influenza H5Nx viruses of clade 2.3.4.4b have become enzootic in Europe, causing recurrent epidemic waves characterized by extensive reassortment events. Here, we describe the emergence of a single high-fitness genotype (EA-2024-DI) that has driven two consecutive waves, evolving into distinct sub-lineages. While its circulation is ongoing, during the 2025-2026 wave it caused an unprecedented number of cases in wild birds. Using phylodynamic analyses of a large dataset of genomic sequences, we compared the spatial diffusion and host transmission pattern of the EA-2024-DI sub-lineages across the three most recent epidemic waves (2023-2024, 2024-2025 and 2025-2026). We show that the genotype has persisted over time and has spread primarily through wild Anseriformes, but with a marked change in the transmission patterns between the different waves and a shift in the epicenter from Eastern to Central Europe, the latter having emerged as an important hub for virus diffusion throughout Europe. Our results reveal a recent increase in the frequency of viruses from wild and domestic mammals carrying mutations enhancing virus replication in mammalian hosts, highlighting the importance of proactive monitoring of this group of hosts to better understand its role in the virus ecology and evolution.

In most paramyxoviruses, RNA editing in the P gene enables expression of the V protein. Human parainfluenza virus type 1 (HPIV-1) differs from most paramyxoviruses in that it lacks RNA editing and does not produce a functional V protein, although its genome retains sequences corresponding to the ancestral V reading frame. Here, we analyzed all HPIV-1 genome sequences available in the NCBI GenBank database to assess the evolutionary state of this V protein-specific region. Using Sendai virus (SeV) as a closely related reference with an identical P gene length, we defined a pseudo-V reading frame by virtually inserting a single nucleotide at the conserved RNA editing site. In this pseudo-V frame, HPIV-1 showed a marked excess of stop codons within the 253-amino-acid region corresponding to the post-editing sequence, far exceeding expectations under random codon usage. This pattern was not observed in other viral genes analyzed under the same definition, nor in SeV, nor was it reproduced by in silico evolutionary simulations under constraints preserving the primary open reading frame. These results are consistent with a virus-specific evolutionary trajectory following the loss of RNA editing, rather than with generic coding constraints acting on overlapping reading frames.

We used 2492 whole genome sequences from Laos to investigate the molecular epidemiology of SARS-CoV-2 from 2021 through 2024, covering the major waves of COVID-19 disease in Laos including time periods of travel restrictions and after relaxation of travel across international borders. We identify successive waves of COVID-19 caused by shifts in the dominant lineage, beginning with the Alpha variant in April 2021 and continuing through the Delta and Omicron variants. We quantify a shift from a small number of viral introductions responsible for widespread transmission in early waves to a larger number of introductions for each variant after travel restrictions were lifted, and identify potential routes of introduction into the country. Our study underscores the importance of genomic surveillance to public health responses to characterize viral transmission dynamics during pandemics.

Prior to 2024, highly pathogenic avian influenza H5N1 clade 2.3.4.4b viruses circulated predominantly in wild birds and poultry. In 2024 and 2025, 2.3.4.4b genotypes B3.13 and D1.1 were detected in United States dairy cattle. Using whole-genome and segment-specific phylodynamic inference, we estimate that B3.13 and D1.1 spilled over from wild birds into dairy cattle in late 2023 and late 2024, respectively. Spillover occurred shortly after the formation of the reassortant genotypes and was followed by months of cryptic transmission prior to detection. We found that both B3.13 and D1.1 evolved at higher rates in cattle relative to birds, primarily due to relaxed purifying selection. Site-specific analyses identified genomic sites under positive selection in cattle relative to birds, indicating adaptation and likely contributing to improved viral fitness after spillover. Intensified genomic surveillance in dairy cattle is essential as population immunity introduces additional selection pressures, with ever-changing risk for human emergence.

The circulation of Influenza A viruses (IAVs) in wildlife and livestock presents a significant public health threat due to their zoonotic potential and rapid genomic diversification. Accurate classification of viral subtypes and characterization of within-host diversity are crucial for risk assessment and vaccine development. Although metagenomic sequencing facilitates early detection, prevalent memory-efficient k-mer-based pipelines often discard critical linkage information. This loss of information can result in missed or imprecise pathogen identification, potentially delaying clinical and public health responses. We introduce PREMISE (Pathogen Resolution via Expectation Maximization In Sequencing Experiments), a probabilistic, alignment-based framework implemented in RUST for high-resolution viral genome identification. By integrating advanced string data structures for efficient alignment with a quality-score-aware Expectation-Maximization algorithm, PREMISE accurately identifies source strains, estimates relative abundances, and performs precise read assignments. This framework provides superior source estimation with statistical confidence, enabling the identification of mixed infections, recombination, and IAV-reassortment directly from raw data. Validated against simulated and empirical datasets, PREMISE outperforms state-of-the-art k-mer methods. Ultimately, this framework represents a significant advancement in viral identification, providing a foundation for novel approaches that can automatically flag reassorted viruses or recombination events in the future, thereby improving the detection of emerging pathogens with zoonotic potential. Availabilityhttps://github.com/sriram98v/premise under a MIT license. Contactsriramv@iastate.edu

AO_SCPLOWBSTRACTC_SCPLOWSerological assays remain the standard experimental approach for estimating the cumulative incidence of a pathogen and monitoring population immunity. The predominant approach for analysing serum titration data from virus neutralisation assays uses a nearly century-old interpolation-based method which neglects inherent imperfections in the assay and produces estimates with no measure of uncertainty. We introduce a two-part Bayesian modelling framework to estimate the underlying antibody concentrations in the raw serum samples taken from serosurveyed individuals, to improve the interpretation of serological data over age. First, we develop a mechanistic Bayesian model for serum antibody titration data that estimates latent antibody concentrations while accounting for assay variability and quantifying uncertainty. Second, we propagate this uncertainty into an age-structured serocatalytic model by integrating over posterior draws of individual antibody concentrations, allowing joint inference on latent serostate membership, force of infection, and serological waning rate. We use this framework to explore the dynamics of infection and immunity for three enterovirus serotypes: enteroviruses A71 (EV-A71) and D68 (EV-D68) and coxsackievirus A6 (CVA6). These serotypes are leading causes of outbreaks of severe respiratory illness and hand, foot, and mouth disease. Applying these approaches to three cross-sectional serosurveys, we estimated consistently higher and more persistent antibody concentrations throughout life for EV-D68 compared to EV-A71 and CVA6. Our analysis suggests that the proportion of recently infected individuals (i.e. individuals with high estimated antibody concentration levels given their age) peaks around 25% by age 7 years for both EV-A71 and CVA6 before gradually declining with age. In contrast, for EV-D68 the inferred proportion of the population in the infected state exceeds 50% by age 9 years and continues to grow with age. We also estimate that EV-D68 antibody concentration levels are higher than those of the other two serotypes, with the force of infection estimated to be highest in early childhood and declining more gradually with age than for EV-A71 and CVA6. These estimates are different to previous estimates found in the literature. Our inferential framework uncovers the wide-ranging variation in antibody levels that are often obscured by conventional endpoint titre estimation methods. We demonstrate that our framework can infer infection rates without relying on predetermined seropositivity cut-offs and without making explicit assumptions of virus-specific infection mechanisms. Author summarySerological tests measure antibody levels in blood to show how widely a virus has spread and how well populations are protected. Titre-based tests dilute blood samples in steps, mix these dilutions with virus, and add the mixture to living cells; the titre is the highest dilution where antibodies still protect cells from infection. Traditional analyses overlook test imperfections. We present a new two-part Bayesian framework to estimate antibody levels and track age-related exposure to infection. First, we estimate underlying antibody concentrations while accounting for uncertainty, then use these estimates in another model to infer age-specific transmission of three common viruses - EV-A71, EV-D68, and CVA6. Our results show that EV-D68 infections may be more common, especially in children, compared to the other viruses. This new approach provides a clearer picture of the dynamics of seroconversion, without relying on arbitrary thresholds, helping to improve public health monitoring and responses.