Unifying the epidemiological and evolutionary dynamics of pathogens

Collateral effects of pandemic control

The response to COVID-19 altered global dispersal of influenza viruses.

Rapid SARS-CoV-2 surveillance using clinical, pooled, or wastewater sequence as a sensor for population change

The COVID-19 pandemic has highlighted the critical role of genomic surveillance for guiding policy and control. Timeliness is key, but sequence alignment and phylogeny slow most surveillance techniques. Millions of SARS-CoV-2 genomes have been assembled. Phylogenetic methods are ill equipped to handle this sheer scale. We introduce a pangenomic measure that examines the information diversity of a k-mer library drawn from a country's complete set of clinical, pooled, or wastewater sequence. Quantifying diversity is central to ecology. Hill numbers, or the effective number of species in a sample, provide a simple metric for comparing species diversity across environments. The more diverse the sample, the higher the Hill number. We adopt this ecological approach and consider each k-mer an individual and each genome a transect in the pangenome of the species. Structured in this way, Hill numbers summarize the temporal trajectory of pandemic variants, collapsing each day's assemblies into genome equivalents. For pooled or wastewater sequence, we instead compare days using survey sequence divorced from individual infections. Across data from the UK, USA, and South Africa, we trace the ascendance of new variants of concern as they emerge in local populations well before these variants are named and added to phylogenetic databases. Using data from San Diego wastewater, we monitor these same population changes from raw, unassembled sequence. This history of emerging variants senses all available data as it is sequenced, intimating variant sweeps to dominance or declines to extinction at the leading edge of the COVID-19 pandemic.

A conceptual health state diagram for modelling the transmission of a (re)emerging infectious respiratory disease in a human population

Mathematical modelling of (re)emerging infectious respiratory diseases among humans poses multiple challenges for modellers, which can arise as a result of limited data and surveillance, uncertainty in the natural history of the disease, as well as public health and individual responses to outbreaks. Here, we propose a COVID-19-inspired health state diagram (HSD) to serve as a foundational framework for conceptualising the modelling process for (re)emerging respiratory diseases, and public health responses, in the early stages of their emergence. The HSD aims to serve as a starting point for reflection on the structure and parameterisation of a transmission model to assess the impact of the (re)emerging disease and the capacity of public health interventions to control transmission. We also explore the adaptability of the HSD to different (re)emerging diseases using the characteristics of three respiratory diseases of historical public health importance. We outline key questions to contemplate when applying and adapting this HSD to (re)emerging infectious diseases and provide reflections on adapting the framework for public health-related interventions.

Phylodynamic Structure in the Botswana HIV Epidemic

Background

Studying viral sequences can provide insights into the structure of host contact networks through which the virus is transmitted. Uncovering the population structure of the HIV-1 epidemic in Botswana will help optimise public health interventions and may identify hidden sub-epidemics. We sought to determine the phylodynamic structure of the Botswana HIV-1 epidemic from viral sequence genetic data.

Methods

The Botswana Combination Prevention Project (BCPP) randomly sampled 20% of households in 30 villages in Botswana between 2013-2018 and tested for HIV-1. Extensive demographic data were collected from all participants and next-generation full-genome HIV-1 sequences were generated from HIV-1 positive participants (n = 4,164), 78% of whom were on antiretroviral treatment (ART). We inferred the stage of infection (< or > 1 year) among HIV-1 cases based on nucleotide diversity and clinical data using a previously trained machine learning model. We then reconstructed time-resolved gag and pol phylogenies from sequences, other Botswana cohorts and publicly available sequences that were genetically close to those from Botswana. We statistically explored phylogenies for partitions with diverging patterns of coalescence, indicating sub-epidemics, and estimated viral effective population size through time, a measure of viral incidence, for each partition. Finally, we compared the demographic makeup, clinical and geographic characteristics across partitions using χ2, ANOVA tests and Tukey analysis.

Results

We identified three partitions of time-resolved gag and pol phylogenies, revealing divergent patterns of coalescence and HIV-1 transmission. In both gag and pol phylogenies, partitions with persistent growth and transmission were characterised by lower treatment coverage and more recent infections when compared to other partitions. The Southern and South East regions of Botswana were over-represented in the fast-growing partitions.

Conclusion

Our findings suggest that transmission is slowing in segments of the population that have high ART coverage. However, recent infections are over-represented in ongoing sub-epidemics. The phylodynamic structure suggests that there are districts with higher growth and prioritising these in the deployment of public health interventions might curb new infections. Nonetheless the high mobility of Botswana residents should be taken into consideration in implementing effective interventions to combat HIV-1.

Estimating pathogen spread using structured coalescent and birth-death models: A quantitative comparison

Elucidating disease spread between subpopulations is crucial in guiding effective disease control efforts. Genomic epidemiology and phylodynamics have emerged as key principles to estimate such spread from pathogen phylogenies derived from molecular data. Two well-established structured phylodynamic methodologies - based on the coalescent and the birth-death model - are frequently employed to estimate viral spread between populations. Nonetheless, these methodologies operate under distinct assumptions whose impact on the accuracy of migration rate inference is yet to be thoroughly investigated. In this manuscript, we present a simulation study, contrasting the inferential outcomes of the structured coalescent model with constant population size and the multitype birth-death model with a constant rate. We explore this comparison across a range of migration rates in endemic diseases and epidemic outbreaks. The results of the epidemic outbreak analysis revealed that the birth-death model exhibits a superior ability to retrieve accurate migration rates compared to the coalescent model, regardless of the actual migration rate. Thus, to estimate accurate migration rates, the population dynamics have to be accounted for. On the other hand, for the endemic disease scenario, our investigation demonstrates that both models produce comparable coverage and accuracy of the migration rates, with the coalescent model generating more precise estimates. Regardless of the specific scenario, both models similarly estimated the source location of the disease. This research offers tangible modelling advice for infectious disease analysts, suggesting the use of either model for endemic diseases. For epidemic outbreaks, or scenarios with varying population size, structured phylodynamic models relying on the Kingman coalescent with constant population size should be avoided as they can lead to inaccurate estimates of the migration rate. Instead, coalescent models accounting for varying population size or birth-death models should be favoured. Importantly, our study emphasises the value of directly capturing exponential growth dynamics which could be a useful enhancement for structured coalescent models.

Beyond publishing primary research papers

Microbiologists, like scientists in any other biomedical field, are too engrossed in writing research papers. Aided by both expanding research programs and shrinking resources, this will continue for the foreseeable time. In this editorial, I discuss a compelling need for all microbiologists to dedicate some time to writing non-research publications such as minireviews, perspectives, commentary, opinion/hypothesis, and other non-research article types. I also list the benefits to the field, of review articles and how they can have the potential to change the field. I have provided a handful of classic examples of reviews that clearly changed the field in a remarkable way as well as a number of reviews that clarified the field and facilitated future research.

Adaptive human behaviour modulates the impact of immune life history and vaccination on long-term epidemic dynamics

The multiple immunity responses exhibited in the population and co-circulating variants documented during pandemics show a high potential to generate diverse long-term epidemiological scenarios. Transmission variability, immune uncertainties and human behaviour are crucial features for the predictability and implementation of effective mitigation strategies. Nonetheless, the effects of individual health incentives on disease dynamics are not well understood. We use a behavioural-immuno-epidemiological model to study the joint evolution of human behaviour and epidemic dynamics for different immunity scenarios. Our results reveal a trade-off between the individuals' immunity levels and the behavioural responses produced. We find that adaptive human behaviour can avoid dynamical resonance by avoiding large outbreaks, producing subsequent uniform outbreaks. Our forward-looking behaviour model shows an optimal planning horizon that minimizes the epidemic burden by balancing the individual risk-benefit trade-off. We find that adaptive human behaviour can compensate for differential immunity levels, equalizing the epidemic dynamics for scenarios with diverse underlying immunity landscapes. Our model can adequately capture complex empirical behavioural dynamics observed during pandemics. We tested our model for different US states during the COVID-19 pandemic. Finally, we explored extensions of our modelling framework that incorporate the effects of lockdowns, the emergence of a novel variant, prosocial attitudes and pandemic fatigue.

Bioinformatics Goes Viral: I. Databases, Phylogenetics and Phylodynamics Tools for Boosting Virus Research

Computer-aided analysis of proteins or nucleic acids seems like a matter of course nowadays; however, the history of Bioinformatics and Computational Biology is quite recent. The advent of high-throughput sequencing has led to the production of "big data", which has also affected the field of virology. The collaboration between the communities of bioinformaticians and virologists already started a few decades ago and it was strongly enhanced by the recent SARS-CoV-2 pandemics. In this article, which is the first in a series on how bioinformatics can enhance virus research, we show that highly useful information is retrievable from selected general and dedicated databases. Indeed, an enormous amount of information-both in terms of nucleotide/protein sequences and their annotation-is deposited in the general databases of international organisations participating in the International Nucleotide Sequence Database Collaboration (INSDC). However, more and more virus-specific databases have been established and are progressively enriched with the contents and features reported in this article. Since viruses are intracellular obligate parasites, a special focus is given to host-pathogen protein-protein interaction databases. Finally, we illustrate several phylogenetic and phylodynamic tools, combining information on algorithms and features with practical information on how to use them and case studies that validate their usefulness. Databases and tools for functional inference will be covered in the next article of this series: Bioinformatics goes viral: II. Sequence-based and structure-based functional analyses for boosting virus research.

Torque Teno Sus Virus 1: A Potential Surrogate Pathogen to Study Pig-Transmitted Transboundary Animal Diseases

Understanding the epidemiology and transmission dynamics of transboundary animal diseases (TADs) among wild pigs (Sus scrofa) will aid in preventing the introduction or containment of TADs among wild populations. Given the challenges associated with studying TADs in free-ranging populations, a surrogate pathogen system may predict how pathogens may circulate and be maintained within wild free-ranging swine populations, how they may spill over into domestic populations, and how management actions may impact transmission. We assessed the suitability of Torque teno sus virus 1 (TTSuV1) to serve as a surrogate pathogen for molecular epidemiological studies in wild pigs by investigating the prevalence, persistence, correlation with host health status and genetic variability at two study areas: Archbold's Buck Island Ranch in Florida and Savannah River Site in South Carolina. We then conducted a molecular epidemiological case study within Archbold's Buck Island Ranch site to determine how analysis of this pathogen could inform transmission dynamics of a directly transmitted virus. Prevalence was high in both study areas (40%, n = 190), and phylogenetic analyses revealed high levels of genetic variability within and between study areas. Our case study showed that pairwise host relatedness and geographic distance were highly correlated to pairwise viral genetic similarity. Molecular epidemiological analyses revealed a distinct pattern of direct transmission from pig to pig occurring within and between family groups. Our results suggest that TTSuV1 is highly suitable for molecular epidemiological analyses and will be useful for future studies of transmission dynamics in wild free-ranging pigs.

High-resolution epidemiological landscape from ~290,000 SARS-CoV-2 genomes from Denmark

Vast amounts of pathogen genomic, demographic and spatial data are transforming our understanding of SARS-CoV-2 emergence and spread. We examined the drivers of molecular evolution and spread of 291,791 SARS-CoV-2 genomes from Denmark in 2021. With a sequencing rate consistently exceeding 60%, and up to 80% of PCR-positive samples between March and November, the viral genome set is broadly whole-epidemic representative. We identify a consistent rise in viral diversity over time, with notable spikes upon the importation of novel variants (e.g., Delta and Omicron). By linking genomic data with rich individual-level demographic data from national registers, we find that individuals aged  < 15 and  > 75 years had a lower contribution to molecular change (i.e., branch lengths) compared to other age groups, but similar molecular evolutionary rates, suggesting a lower likelihood of introducing novel variants. Similarly, we find greater molecular change among vaccinated individuals, suggestive of immune evasion. We also observe evidence of transmission in rural areas to follow predictable diffusion processes. Conversely, urban areas are expectedly more complex due to their high mobility, emphasising the role of population structure in driving virus spread. Our analyses highlight the added value of integrating genomic data with detailed demographic and spatial information, particularly in the absence of structured infection surveys.

Waning immunity drives respiratory virus evolution and reinfection

Reinfections with respiratory viruses such as influenza viruses and coronaviruses are thought to be driven by ongoing antigenic immune escape in the viral population. However, this does not explain why antigenic variation is frequently observed in these viruses relative to viruses such as measles that undergo systemic replication. Here, we suggest that the rapid rate of waning immunity in the respiratory tract is the key driver of antigenic evolution in respiratory viruses. Waning immunity results in hosts with immunity levels that protect against homologous reinfection but are insufficient to protect against infection with a heterologous, antigenically different strain. As such, when partially immune hosts are present at a high enough density, an immune escape variant can invade the viral population even though that variant cannot infect fully immune hosts. Invasion can occur even when the variant's immune escape mutation incurs a fitness cost, and we expect the expanding mutant population will evolve compensatory mutations that mitigate this cost. Thus the mutant lineage should replace the wild-type, and as immunity to it builds, the process will repeat. Our model provides a new explanation for the pattern of successive emergence and replacement of antigenic variants that has been observed in many respiratory viruses. We discuss our model relative to others for understanding the drivers of antigenic evolution in these and other respiratory viruses.

Impact of waning immunity against SARS-CoV-2 severity exacerbated by vaccine hesitancy

The SARS-CoV-2 pandemic has generated a considerable number of infections and associated morbidity and mortality across the world. Recovery from these infections, combined with the onset of large-scale vaccination, have led to rapidly-changing population-level immunological landscapes. In turn, these complexities have highlighted a number of important unknowns related to the breadth and strength of immunity following recovery or vaccination. Using simple mathematical models, we investigate the medium-term impacts of waning immunity against severe disease on immuno-epidemiological dynamics. We find that uncertainties in the duration of severity-blocking immunity (imparted by either infection or vaccination) can lead to a large range of medium-term population-level outcomes (i.e. infection characteristics and immune landscapes). Furthermore, we show that epidemiological dynamics are sensitive to the strength and duration of underlying host immune responses; this implies that determining infection levels from hospitalizations requires accurate estimates of these immune parameters. More durable vaccines both reduce these uncertainties and alleviate the burden of SARS-CoV-2 in pessimistic outcomes. However, heterogeneity in vaccine uptake drastically changes immune landscapes toward larger fractions of individuals with waned severity-blocking immunity. In particular, if hesitancy is substantial, more robust vaccines have almost no effects on population-level immuno-epidemiology, even if vaccination rates are compensatorily high among vaccine-adopters. This pessimistic scenario for vaccination heterogeneity arises because those few individuals that are vaccine-adopters are so readily re-vaccinated that the duration of vaccinal immunity has no appreciable consequences on their immune status. Furthermore, we find that this effect is heightened if vaccine-hesitants have increased transmissibility (e.g. due to riskier behavior). Overall, our results illustrate the necessity to characterize both transmission-blocking and severity-blocking immune time scales. Our findings also underline the importance of developing robust next-generation vaccines with equitable mass vaccine deployment.

A macroevolutionary analysis of European Late Upper Palaeolithic stone tool shape using a Bayesian phylodynamic framework

Phylogenetic models are commonly used in palaeobiology to study the patterns and processes of organismal evolution. In the human sciences, phylogenetic methods have been deployed for reconstructing ancestor-descendant relationships using linguistic and material culture data. Within evolutionary archaeology specifically, phylogenetic analyses based on maximum parsimony and discrete traits dominate, which sets limitations for the downstream role cultural phylogenies, once derived, can play in more elaborate analytical pipelines. Recent methodological advances in Bayesian phylogenetics, however, now allow us to infer evolutionary dynamics using continuous characters. Capitalizing on these developments, we here present an exploratory analysis of cultural macroevolution of projectile point shape evolution in the European Final Palaeolithic and earliest Mesolithic (approx. 15 000-11 000 BP) using a Bayesian phylodynamic approach and the fossilized birth-death process model. This model-based approach leaps far beyond the application of parsimony, in that it not only produces a tree, but also divergence times, and diversification rates while incorporating uncertainties. This allows us to compare rates to the pronounced climatic changes that occurred during our time frame. While common in cultural evolutionary analyses of language, the extension of Bayesian phylodynamic models to archaeology arguably represents a major methodological breakthrough.

Measuring changes in Plasmodium falciparum census population size in response to sequential malaria control interventions

Here we introduce a new endpoint ″census population size″ to evaluate the epidemiology and control of Plasmodium falciparum infections, where the parasite, rather than the infected human host, is the unit of measurement. To calculate census population size, we rely on a definition of parasite variation known as multiplicity of infection (MOI var ), based on the hyper-diversity of the var multigene family. We present a Bayesian approach to estimate MOI var from sequencing and counting the number of unique DBLα tags (or DBLα types) of var genes, and derive from it census population size by summation of MOI var in the human population. We track changes in this parasite population size and structure through sequential malaria interventions by indoor residual spraying (IRS) and seasonal malaria chemoprevention (SMC) from 2012 to 2017 in an area of high-seasonal malaria transmission in northern Ghana. Following IRS, which reduced transmission intensity by > 90% and decreased parasite prevalence by ~40-50%, significant reductions in var diversity, MOI var , and population size were observed in ~2,000 humans across all ages. These changes, consistent with the loss of diverse parasite genomes, were short lived and 32-months after IRS was discontinued and SMC was introduced, var diversity and population size rebounded in all age groups except for the younger children (1-5 years) targeted by SMC. Despite major perturbations from IRS and SMC interventions, the parasite population remained very large and retained the var population genetic characteristics of a high-transmission system (high var diversity; low var repertoire similarity) demonstrating the resilience of P. falciparum to short-term interventions in high-burden countries of sub-Saharan Africa.

Interrogating Genomes and Geography to Unravel Multiyear Vesicular Stomatitis Epizootics

We conducted an integrative analysis to elucidate the spatial epidemiological patterns of the Vesicular Stomatitis New Jersey virus (VSNJV) during the 2014-15 epizootic cycle in the United States (US). Using georeferenced VSNJV genomics data, confirmed vesicular stomatitis (VS) disease cases from surveillance, and a suite of environmental factors, our study assessed environmental and phylogenetic similarity to compare VS cases reported in 2014 and 2015. Despite uncertainties from incomplete virus sampling and cross-scale spatial processes, patterns suggested multiple independent re-invasion events concurrent with potential viral overwintering between sequential seasons. Our findings pointed to a geographically defined southern virus pool at the US-Mexico interface as the source of VSNJV invasions and overwintering sites. Phylodynamic analysis demonstrated an increase in virus diversity before a rise in case numbers and a pronounced reduction in virus diversity during the winter season, indicative of a genetic bottleneck and a significant narrowing of virus variation between the summer outbreak seasons. Environment-vector interactions underscored the central role of meta-population dynamics in driving disease spread. These insights emphasize the necessity for location- and time-specific management practices, including rapid response, movement restrictions, vector control, and other targeted interventions.

Persistent SARS-CoV-2 infection: significance and implications

SARS-CoV-2 causes persistent infections in a subset of individuals, which is a major clinical and public health problem that should be prioritised for further investigation for several reasons. First, persistent SARS-CoV-2 infection often goes unrecognised, and therefore might affect a substantial number of people, particularly immunocompromised individuals. Second, the formation of tissue reservoirs (including in non-respiratory tissues) might underlie the pathophysiology of the persistent SARS-CoV-2 infection and require new strategies for diagnosis and treatment. Finally, persistent SARS-CoV-2 replication, particularly in the setting of suboptimal immune responses, is a possible source of new, divergent virus variants that escape pre-existing immunity on the individual and population levels. Defining optimal diagnostic and treatment strategies for patients with persistent virus replication and monitoring viral evolution are therefore urgent medical and public health priorities.

A speed limit on serial strain replacement from original antigenic sin

Many pathogens evolve to escape immunity, yet it remains difficult to predict whether immune pressure will lead to diversification, serial replacement of one variant by another, or more complex patterns. Pathogen strain dynamics are mediated by cross-protective immunity, whereby exposure to one strain partially protects against infection by antigenically diverged strains. There is growing evidence that this protection is influenced by early exposures, a phenomenon referred to as original antigenic sin (OAS) or imprinting. In this paper, we derive constraints on the emergence of the pattern of successive strain replacements demonstrated by influenza, SARS-CoV-2, seasonal coronaviruses, and other pathogens. We find that OAS implies that the limited diversity found with successive strain replacement can only be maintained if [Formula: see text] is less than a threshold set by the characteristic antigenic distances for cross-protection and for the creation of new immune memory. This bound implies a "speed limit" on the evolution of new strains and a minimum variance of the distribution of infecting strains in antigenic space at any time. To carry out this analysis, we develop a theoretical model of pathogen evolution in antigenic space that implements OAS by decoupling the antigenic distances required for protection from infection and strain-specific memory creation. Our results demonstrate that OAS can play an integral role in the emergence of strain structure from host immune dynamics, preventing highly transmissible pathogens from maintaining serial strain replacement without diversification.

The molecular epidemiology of a dengue virus outbreak in Taiwan: population wide versus infrapopulation mutation analysis

Dengue virus (DENV) causes approximately 390 million dengue infections worldwide every year. There were 22,777 reported DENV infections in Tainan, Taiwan in 2015. In this study, we sequenced the C-prM-E genes from 45 DENV 2015 strains, and phylogenetic analysis based on C-prM-E genes revealed that all strains were classified as DENV serotype 2 Cosmopolitan genotype. Sequence analysis comparing different DENV-2 genotypes and Cosmopolitan DENV-2 sequences prior to 2015 showed a clade replacement event in the DENV-2 Cosmopolitan genotype. Additionally, a major substitution C-A314G (K73R) was found in the capsid region which may have contributed to the clade replacement event. Reverse genetics virus rgC-A314G (K73R) showed slower replication in BHK-21 and C6/36 cells compared to wildtype virus, as well as a decrease in NS1 production in BHK-21-infected cells. After a series of passaging, the C-A314G (K73R) mutation reverted to wildtype and was thus considered to be unstable. Next generation sequencing (NGS) of three sera collected from a single DENV2-infected patient at 1-, 2-, and 5-days post-admission was employed to examine the genetic diversity over-time and mutations that may work in conjunction with C-A314G (K73R). Results showed that the number of haplotypes decreased with time in the DENV-infected patient. On the fifth day after admission, two new haplotypes emerged, and a single non-synonymous NS4A-L115I mutation was identified. Therefore, we have identified a persistent mutation C-A314G (K73R) in all of the DENV-2 isolates, and during the course of an infection, a single new non-synonymous mutation in the NS4A region appears in the virus population within a single host. The C-A314G (K73R) thus may have played a role in the DENV-2 2015 outbreak while the NS4A-L115I may be advantageous during DENV infection within the host.

Antigenic distance between primary and secondary dengue infections correlates with disease risk

Many pathogens continuously change their protein structure in response to immune-driven selection, resulting in weakened protection even in previously exposed individuals. In addition, for some pathogens, such as dengue virus, poorly targeted immunity is associated with increased risk of severe disease through a mechanism known as antibody-dependent enhancement. However, it remains unclear whether the antigenic distances between an individual's first infection and subsequent exposures dictate disease risk, explaining the observed large-scale differences in dengue hospitalizations across years. Here, we develop a framework that combines detailed antigenic and genetic characterization of viruses with details on hospitalized cases from 21 years of dengue surveillance in Bangkok, Thailand, to identify the role of the antigenic profile of circulating viruses in determining disease risk. We found that the risk of hospitalization depended on both the specific order of infecting serotypes and the antigenic distance between an individual's primary and secondary infections, with risk maximized at intermediate antigenic distances. These findings suggest that immune imprinting helps determine dengue disease risk and provide a pathway to monitor the changing risk profile of populations and to quantifying risk profiles of candidate vaccines.

Estimating the reproduction number and transmission heterogeneity from the size distribution of clusters of identical pathogen sequences

Quantifying transmission intensity and heterogeneity is crucial to ascertain the threat posed by infectious diseases and inform the design of interventions. Methods that jointly estimate the reproduction number R and the dispersion parameter k have however mainly remained limited to the analysis of epidemiological clusters or contact tracing data, whose collection often proves difficult. Here, we show that clusters of identical sequences are imprinted by the pathogen offspring distribution, and we derive an analytical formula for the distribution of the size of these clusters. We develop and evaluate an inference framework to jointly estimate the reproduction number and the dispersion parameter from the size distribution of clusters of identical sequences. We then illustrate its application across a range of epidemiological situations. Finally, we develop a hypothesis testing framework relying on clusters of identical sequences to determine whether a given pathogen genetic subpopulation is associated with increased or reduced transmissibility. Our work provides tools to estimate the reproduction number and transmission heterogeneity from pathogen sequences without building a phylogenetic tree, thus making it easily scalable to large pathogen genome datasets.

DeepDynaForecast: Phylogenetic-informed graph deep learning for epidemic transmission dynamic prediction

In the midst of an outbreak or sustained epidemic, reliable prediction of transmission risks and patterns of spread is critical to inform public health programs. Projections of transmission growth or decline among specific risk groups can aid in optimizing interventions, particularly when resources are limited. Phylogenetic trees have been widely used in the detection of transmission chains and high-risk populations. Moreover, tree topology and the incorporation of population parameters (phylodynamics) can be useful in reconstructing the evolutionary dynamics of an epidemic across space and time among individuals. We now demonstrate the utility of phylodynamic trees for transmission modeling and forecasting, developing a phylogeny-based deep learning system, referred to as DeepDynaForecast. Our approach leverages a primal-dual graph learning structure with shortcut multi-layer aggregation, which is suited for the early identification and prediction of transmission dynamics in emerging high-risk groups. We demonstrate the accuracy of DeepDynaForecast using simulated outbreak data and the utility of the learned model using empirical, large-scale data from the human immunodeficiency virus epidemic in Florida between 2012 and 2020. Our framework is available as open-source software (MIT license) at github.com/lab-smile/DeepDynaForcast.

How immune dynamics shape multi-season epidemics: a continuous-discrete model in one dimensional antigenic space

We extend a previously published model for the dynamics of a single strain of an influenza-like infection. The model incorporates a waning acquired immunity to infection and punctuated antigenic drift of the virus, employing a set of coupled integral equations within a season and a discrete map between seasons. The long term behaviour of the model is demonstrated by examples where immunity to infection depends on the time since a host was last infected, and where immunity depends on the number of times that a host has been infected. The first scenario leads to complicated dynamics in some regions of parameter space, and to regions of parameter space with more than one attractor. The second scenario leads to a stable fixed point, corresponding to an identical epidemic each season. We also examine the model with both paradigms in combination, almost always but not exclusively observing a stable fixed point or periodic solution. Adding stochastic perturbations to the between season map fails to destroy the model's qualitative dynamics. Our results suggest that if the level of host immunity depends on the elapsed time since the last infection then the epidemiological dynamics may be unpredictable.

Predicting Vaccine Effectiveness for Hospitalization and Symptomatic Disease for Novel SARS-CoV-2 Variants Using Neutralizing Antibody Titers

The emergence of new virus variants, including the Omicron variant (B.1.1.529) of SARS-CoV-2, can lead to reduced vaccine effectiveness (VE) and the need for new vaccines or vaccine doses if the extent of immune evasion is severe. Neutralizing antibody titers have been shown to be a correlate of protection for SARS-CoV-2 and other pathogens, and could be used to quickly estimate vaccine effectiveness for new variants. However, no model currently exists to provide precise VE estimates for a new variant against severe disease for SARS-CoV-2 using robust datasets from several populations. We developed predictive models for VE against COVID-19 symptomatic disease and hospitalization across a 54-fold range of mean neutralizing antibody titers. For two mRNA vaccines (mRNA-1273, BNT162b2), models fit without Omicron data predicted that infection with the BA.1 Omicron variant increased the risk of hospitalization 2.8-4.4-fold and increased the risk of symptomatic disease 1.7-4.2-fold compared to the Delta variant. Out-of-sample validation showed that model predictions were accurate; all predictions were within 10% of observed VE estimates and fell within the model prediction intervals. Predictive models using neutralizing antibody titers can provide rapid VE estimates, which can inform vaccine booster timing, vaccine design, and vaccine selection for new virus variants.

VIPERA: Viral Intra-Patient Evolution Reporting and Analysis

Viral mutations within patients nurture the adaptive potential of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during chronic infections, which are a potential source of variants of concern. However, there is no integrated framework for the evolutionary analysis of intra-patient SARS-CoV-2 serial samples. Herein, we describe Viral Intra-Patient Evolution Reporting and Analysis (VIPERA), a new software that integrates the evaluation of the intra-patient ancestry of SARS-CoV-2 sequences with the analysis of evolutionary trajectories of serial sequences from the same viral infection. We have validated it using positive and negative control datasets and have successfully applied it to a new case, which revealed population dynamics and evidence of adaptive evolution. VIPERA is available under a free software license at https://github.com/PathoGenOmics-Lab/VIPERA.

Population genomic evidence of structured and connected Plasmodium vivax populations under host selection in Latin America

Pathogen genomic epidemiology has the potential to provide a deep understanding of population dynamics, facilitating strategic planning of interventions, monitoring their impact, and enabling timely responses, and thereby supporting control and elimination efforts of parasitic tropical diseases. Plasmodium vivax, responsible for most malaria cases outside Africa, shows high genetic diversity at the population level, driven by factors like sub-patent infections, a hidden reservoir of hypnozoites, and early transmission to mosquitoes. While Latin America has made significant progress in controlling Plasmodium falciparum, it faces challenges with residual P. vivax. To characterize genetic diversity and population structure and dynamics, we have analyzed the largest collection of P. vivax genomes to date, including 1474 high-quality genomes from 31 countries across Asia, Africa, Oceania, and America. While P. vivax shows high genetic diversity globally, Latin American isolates form a distinctive population, which is further divided into sub-populations and occasional clonal pockets. Genetic diversity within the continent was associated with the intensity of transmission. Population differentiation exists between Central America and the North Coast of South America, vs. the Amazon Basin, with significant gene flow within the Amazon Basin, but limited connectivity between the Northwest Coast and the Amazon Basin. Shared genomic regions in these parasite populations indicate adaptive evolution, particularly in genes related to DNA replication, RNA processing, invasion, and motility - crucial for the parasite's survival in diverse environments. Understanding these population-level adaptations is crucial for effective control efforts, offering insights into potential mechanisms behind drug resistance, immune evasion, and transmission dynamics.

Multimodal Surveillance Model for Enterovirus D68 Respiratory Disease and Acute Flaccid Myelitis among Children in Colorado, USA, 2022

Surveillance for emerging pathogens is critical for developing early warning systems to guide preparedness efforts for future outbreaks of associated disease. To better define the epidemiology and burden of associated respiratory disease and acute flaccid myelitis (AFM), as well as to provide actionable data for public health interventions, we developed a multimodal surveillance program in Colorado, USA, for enterovirus D68 (EV-D68). Timely local, state, and national public health outreach was possible because prospective syndromic surveillance for AFM and asthma-like respiratory illness, prospective clinical laboratory surveillance for EV-D68 among children hospitalized with respiratory illness, and retrospective wastewater surveillance led to early detection of the 2022 outbreak of EV-D68 among Colorado children. The lessons learned from developing the individual layers of this multimodal surveillance program and how they complemented and informed the other layers of surveillance for EV-D68 and AFM could be applied to other emerging pathogens and their associated diseases.

Waning immunity can drive repeated waves of infections

In infectious disease models, it is known that mechanisms such as births, seasonality in transmission and pathogen evolution can generate oscillations in infection numbers. We show how waning immunity is also a mechanism that is sufficient on its own to enable sustained oscillations. When previously infected or vaccinated individuals lose full protective immunity, they become partially susceptible to reinfections. This partial immunity subsequently wanes over time, making individuals more susceptible to reinfections and potentially more infectious if infected. Losses of full and partial immunity lead to a surge in infections, which is the precursor of oscillations. We present a discrete-time Susceptible-Infectious-Immune-Waned-Infectious (SIRWY) model that features the waning of fully immune individuals (as a distribution of time at which individuals lose fully immunity) and the gradual loss of partial immunity (as increases in susceptibility and potential infectiousness over time). A special case of SIRWY is the discrete-time SIRS model with geometric distributions for waning and recovery. Its continuous-time analogue is the classic SIRS with exponential distributions, which does not produce sustained oscillations for any choice of parameters. We show that the discrete-time version can produce sustained oscillations and that the oscillatory regime disappears as discrete-time tends to continuous-time. A different special case of SIRWY is one with fixed times for waning and recovery. We show that this simpler model can also produce sustained oscillations. In conclusion, under certain feature and parameter choices relating to how exactly immunity wanes, fluctuations in infection numbers can be sustained without the need for any additional mechanisms.

Inference of Infectious Disease Transmission through a Relaxed Bottleneck Using Multiple Genomes Per Host

In recent times, pathogen genome sequencing has become increasingly used to investigate infectious disease outbreaks. When genomic data is sampled densely enough amongst infected individuals, it can help resolve who infected whom. However, transmission analysis cannot rely solely on a phylogeny of the genomes but must account for the within-host evolution of the pathogen, which blurs the relationship between phylogenetic and transmission trees. When only a single genome is sampled for each host, the uncertainty about who infected whom can be quite high. Consequently, transmission analysis based on multiple genomes of the same pathogen per host has a clear potential for delivering more precise results, even though it is more laborious to achieve. Here, we present a new methodology that can use any number of genomes sampled from a set of individuals to reconstruct their transmission network. Furthermore, we remove the need for the assumption of a complete transmission bottleneck. We use simulated data to show that our method becomes more accurate as more genomes per host are provided, and that it can infer key infectious disease parameters such as the size of the transmission bottleneck, within-host growth rate, basic reproduction number, and sampling fraction. We demonstrate the usefulness of our method in applications to real datasets from an outbreak of Pseudomonas aeruginosa amongst cystic fibrosis patients and a nosocomial outbreak of Klebsiella pneumoniae.

B cell phylogenetics in the single cell era

The widespread availability of single-cell RNA sequencing (scRNA-seq) has led to the development of new methods for understanding immune responses. Single-cell transcriptome data can now be paired with B cell receptor (BCR) sequences. However, RNA from BCRs cannot be analyzed like most other genes because BCRs are genetically diverse within individuals. In humans, BCRs are shaped through recombination followed by mutation and selection for antigen binding. As these processes co-occur with cell division, B cells can be studied using phylogenetic trees representing the mutations within a clone. B cell trees can link experimental timepoints, tissues, or cellular subtypes. Here, we review the current state and potential of how B cell phylogenetics can be combined with single-cell data to understand immune responses.

Population Genomics Reveals an Emerging Lineage of Xanthomonas perforans on Pepper

Xanthomonas perforans-the dominant causal agent of bacterial leaf spot of tomato-is an emerging pathogen of pepper, indicative of a potential host expansion across the southeastern United States. However, studies of the genetic diversity and evolution of X. perforans from pepper remain limited. In this study, the whole-genome sequences of 35 X. perforans strains isolated from pepper from four fields and two transplant facilities across southwest Florida between 2019 and 2021 were used to compare genomic divergence, evolution, and variation in type III secreted effectors. Phylogenetic analysis based on core genes revealed that all 35 X. perforans strains formed one genetic cluster with pepper and tomato strains from Alabama and Turkey and were closely related to strains isolated from tomato in Indiana, Mexico, and Louisiana. The in planta population growth of tomato strains isolated from Indiana, Mexico, Louisiana, and Turkey in pepper leaf mesophyll was on par with pepper X. perforans and X. euvesicatoria strains. Molecular clock analysis of the 35 Florida strains dated their emergence to approximately 2017. While strains varied in copper tolerance, all sequenced strains harbored the avrHah1 transcription activation-like effector located on a conjugative plasmid, not previously reported in Florida. Our findings suggest that there is a geographically distributed lineage of X. perforans strains on tomato that has the genetic background to cause disease on pepper. Moreover, this study clarifies potential adaptive variants of X. perforans on pepper that could help forecast the emergence of such strains and enable immediate or preemptive intervention.

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

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

Leaping through Tree Space: Continuous Phylogenetic Inference for Rooted and Unrooted Trees

Phylogenetics is now fundamental in life sciences, providing insights into the earliest branches of life and the origins and spread of epidemics. However, finding suitable phylogenies from the vast space of possible trees remains challenging. To address this problem, for the first time, we perform both tree exploration and inference in a continuous space where the computation of gradients is possible. This continuous relaxation allows for major leaps across tree space in both rooted and unrooted trees, and is less susceptible to convergence to local minima. Our approach outperforms the current best methods for inference on unrooted trees and, in simulation, accurately infers the tree and root in ultrametric cases. The approach is effective in cases of empirical data with negligible amounts of data, which we demonstrate on the phylogeny of jawed vertebrates. Indeed, only a few genes with an ultrametric signal were generally sufficient for resolving the major lineages of vertebrates. Optimization is possible via automatic differentiation and our method presents an effective way forward for exploring the most difficult, data-deficient phylogenetic questions.

Core genome multilocus sequence typing of Clostridioides difficile to investigate transmission in the hospital setting

PURPOSE

Traditional epidemiological investigations of healthcare-associated Clostridioides difficile infection (HA-CDI) are often insufficient. This study aimed to evaluate a procedure that includes secondary isolation and genomic typing of single toxigenic colonies using core genome multilocus sequence typing (cgMLST) for the investigation of C. difficile transmission.

METHODS

We analyzed retrospectively all toxigenic C. difficile-positive stool samples stored at the Lausanne University Hospital over 6 consecutive months. All isolates were initially typed and classified using a modified double-locus sequence typing (DLST) method. Genome comparison of isolates with the same DLST and clustering were subsequently performed using cgMLST. The electronic administrative records of patients with CDI were investigated for spatiotemporal epidemiological links supporting hospital transmission. A comparative descriptive analysis between genomic and epidemiological data was then performed.

RESULTS

From January to June 2021, 86 C. difficile isolates were recovered from thawed samples of 71 patients. Thirteen different DLST types were shared by > 1 patient, and 13 were observed in single patients. A genomic cluster was defined as a set of isolates from different patients with ≤ 3 locus differences, determined by cgMLST. Seven genomic clusters were identified, among which plausible epidemiological links were identified in only 4/7 clusters.

CONCLUSION

Among clusters determined by cgMLST analysis, roughly 40% included unexplained HA-CDI acquisitions, which may be explained by unidentified epidemiological links, asymptomatic colonization, and/or shared common community reservoirs. The use of DLST, followed by whole genome sequencing analysis, is a promising and cost-effective stepwise approach for the investigation of CDI transmission in the hospital setting.

Emergence of a new designated clade 16 with significant antigenic drift in hemagglutinin gene of H9N2 subtype avian influenza virus in eastern China

H9N2 avian influenza viruses (AIVs) pose an increasing threat to the poultry industry worldwide and have pandemic potential. Vaccination has been principal prevention strategy to control H9N2 in China since 1998, but vaccine effectiveness is persistently challenged by the emergence of the genetic and/or antigenic variants. Here, we analysed the genetic and antigenic characteristics of H9N2 viruses in China, including 70 HA sequences of H9N2 isolates from poultry, 7358 from online databases during 2010-2020, and 15 from the early reference strains. Bayesian analyses based on hemagglutinin (HA) gene revealed that a new designated clade16 emerged in April 2012, and was prevalent and co-circulated with clade 15 since 2013 in China. Clade 16 viruses exhibited decreased cross-reactivity with those from clade 15. Antigenic Cartography analyses showed represent strains were classified into three antigenic groups named as Group1, Group2 and Group3, and most of the strains in Group 3 (15/17, 88.2%) were from Clade 16 while most of the strains in Group2 (26/29, 89.7%) were from Clade 15. The mean distance between Group 3 and Group 2 was 4.079 (95%CI 3.605-4.554), revealing that major switches to antigenic properties were observed over the emergence of clade 16. Genetic analysis indicated that 11 coevolving amino acid substitutions primarily at antigenic sites were associated with the antigenic differences between clade 15 and clade 16. These data highlight complexities of the genetic evolution and provide a framework for the genetic basis and antigenic characterization of emerging clade 16 of H9N2 subtype avian influenza virus.

Reconstructing relative transmission rates in Bayesian phylodynamics: Two-fold transmission advantage of Omicron in Berlin, Germany during December 2021

Phylodynamic methods have lately played a key role in understanding the spread of infectious diseases. During the coronavirus disease (COVID-19) pandemic, large scale genomic surveillance has further increased the potential of dynamic inference from viral genomes. With the continual emergence of novel severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) variants, explicitly allowing transmission rate differences between simultaneously circulating variants in phylodynamic inference is crucial. In this study, we present and empirically validate an extension to the BEAST2 package birth-death skyline model (BDSKY), BDSKY[Formula: see text], which introduces a scaling factor for the transmission rate between independent, jointly inferred trees. In an extensive simulation study, we show that BDSKY[Formula: see text] robustly infers the relative transmission rates under different epidemic scenarios. Using publicly available genome data of SARS-CoV-2, we apply BDSKY[Formula: see text] to quantify the transmission advantage of the Omicron over the Delta variant in Berlin, Germany. We find the overall transmission rate of Omicron to be scaled by a factor of two with pronounced variation between the individual clusters of each variant. These results quantify the transmission advantage of Omicron over the previously circulating Delta variant, in a crucial period of pre-established non-pharmaceutical interventions. By inferring variant- as well as cluster-specific transmission rate scaling factors, we show the differences in transmission dynamics for each variant. This highlights the importance of incorporating lineage-specific transmission differences in phylodynamic inference.

Gut immune responses and evolution of the gut microbiome-a hypothesis

The gut microbiome is an assemblage of microbes that have profound effects on their hosts. The composition of the microbiome is affected by bottom-up, among-taxa interactions and by top-down, host effects, which includes the host immune response. While the high-level composition of the microbiome is generally stable over time, component strains and genotypes will constantly be evolving, with both bottom-up and top-down effects acting as selection pressures, driving microbial evolution. Secretory IgA is a major feature of the gut's adaptive immune response, and a substantial proportion of gut bacteria are coated with IgA, though the effect of this on bacteria is unclear. Here we hypothesize that IgA binding to gut bacteria is a selection pressure that will drive the evolution of IgA-bound bacteria, so that they will have a different evolutionary trajectory than those bacteria not bound by IgA. We know very little about the microbiome of wild animals and even less about their gut immune responses, but it must be a priority to investigate this hypothesis to understand if and how host immune responses contribute to microbiome evolution.

Population immunity predicts evolutionary trajectories of SARS-CoV-2

The large-scale evolution of the SARS-CoV-2 virus has been marked by rapid turnover of genetic clades. New variants show intrinsic changes, notably increased transmissibility, and antigenic changes that reduce cross-immunity induced by previous infections or vaccinations. How this functional variation shapes global evolution has remained unclear. Here, we establish a predictive fitness model for SARS-CoV-2 that integrates antigenic and intrinsic selection. The model is informed by tracking of time-resolved sequence data, epidemiological records, and cross-neutralization data of viral variants. Our inference shows that immune pressure, including contributions of vaccinations and previous infections, has become the dominant force driving the recent evolution of SARS-CoV-2. The fitness model can serve continued surveillance in two ways. First, it successfully predicts the short-term evolution of circulating strains and flags emerging variants likely to displace the previously predominant variant. Second, it predicts likely antigenic profiles of successful escape variants prior to their emergence.

Nuclear genetic diversity of head lice sheds light on human dispersal around the world

The human louse, Pediculus humanus, is an obligate blood-sucking ectoparasite that has coevolved with humans for millennia. Given the intimate relationship between this parasite and the human host, the study of human lice has the potential to shed light on aspects of human evolution that are difficult to interpret using other biological evidence. In this study, we analyzed the genetic variation in 274 human lice from 25 geographic sites around the world by using nuclear microsatellite loci and female-inherited mitochondrial DNA sequences. Nuclear genetic diversity analysis revealed the presence of two distinct genetic clusters I and II, which are subdivided into subclusters: Ia-Ib and IIa-IIb, respectively. Among these samples, we observed the presence of the two most common louse mitochondrial haplogroups: A and B that were found in both nuclear Clusters I and II. Evidence of nuclear admixture was uncommon (12%) and was predominate in the New World potentially mirroring the history of colonization in the Americas. These findings were supported by novel DIYABC simulations that were built using both host and parasite data to define parameters and models suggesting that admixture between cI and cII was very recent. This pattern could also be the result of a reproductive barrier between these two nuclear genetic clusters. In addition to providing new evolutionary knowledge about this human parasite, our study could guide the development of new analyses in other host-parasite systems.

Phylogenetic identification of influenza virus candidates for seasonal vaccines

The seasonal influenza (flu) vaccine is designed to protect against those influenza viruses predicted to circulate during the upcoming flu season, but identifying which viruses are likely to circulate is challenging. We use features from phylogenetic trees reconstructed from hemagglutinin (HA) and neuraminidase (NA) sequences, together with a support vector machine, to predict future circulation. We obtain accuracies of 0.75 to 0.89 (AUC 0.83 to 0.91) over 2016-2020. We explore ways to select potential candidates for a seasonal vaccine and find that the machine learning model has a moderate ability to select strains that are close to future populations. However, consensus sequences among the most recent 3 years also do well at this task. We identify similar candidate strains to those proposed by the World Health Organization, suggesting that this approach can help inform vaccine strain selection.

Open questions in the social lives of viruses

Social interactions among viruses occur whenever multiple viral genomes infect the same cells, hosts, or populations of hosts. Viral social interactions range from cooperation to conflict, occur throughout the viral world, and affect every stage of the viral lifecycle. The ubiquity of these social interactions means that they can determine the population dynamics, evolutionary trajectory, and clinical progression of viral infections. At the same time, social interactions in viruses raise new questions for evolutionary theory, providing opportunities to test and extend existing frameworks within social evolution. Many opportunities exist at this interface: Insights into the evolution of viral social interactions have immediate implications for our understanding of the fundamental biology and clinical manifestation of viral diseases. However, these opportunities are currently limited because evolutionary biologists only rarely study social evolution in viruses. Here, we bridge this gap by (1) summarizing the ways in which viruses can interact socially, including consequences for social evolution and evolvability; (2) outlining some open questions raised by viruses that could challenge concepts within social evolution theory; and (3) providing some illustrative examples, data sources, and conceptual questions, for studying the natural history of social viruses.

Dynamics in a behavioral-epidemiological model for individual adherence to a nonpharmaceutical intervention

The SARS-CoV-2 pandemic has highlighted the importance of behavioral drivers in epidemic dynamics. With the relaxation of mandated nonpharmaceutical interventions (NPIs) formerly in place to decrease transmission, such as mask-wearing or social distancing, adherence to an NPI is now the result of individual decision-making. To study these coupled dynamics, we embed a game-theoretic model for individual NPI adherence within an epidemiological model. When the disease is endemic, we find that our model has multiple (but none concurrently stable) equilibria: one each with zero, complete, or partial NPI adherence. Surprisingly, for the equilibrium with partial NPI adherence, the number of infections is independent of the transmission rate. Therefore, in that regime, a change in the rate of pathogen transmission, e.g., due to another (mandated) NPI or a new variant, has no effect on endemic infection levels. On the other hand, we show that vaccination successfully decreases endemic infection levels, and, unexpectedly, also reduces the number of susceptibles at equilibrium when there is partial adherence. From a game-theoretic perspective, we find that highly effective NPIs lead at most to partial adherence. As this effectiveness decreases, partially effective NPIs initially lead to increases in population-level adherence, especially if the risk is high enough. However, a completely ineffective NPI results in no adherence. Furthermore, we identify parameter regions where the individual incentives may not align with those of society as a whole. Overall, our findings illustrate complexities that can arise due to behavioral-epidemiological feedback and suggest appropriate measures to avoid more pessimistic population-level outcomes.

The uncertain role of substandard and falsified medicines in the emergence and spread of antimicrobial resistance

Approximately 10% of antimicrobials used by humans in low- and middle-income countries are estimated to be substandard or falsified. In addition to their negative impact on morbidity and mortality, they may also be important drivers of antimicrobial resistance. Despite such concerns, our understanding of this relationship remains rudimentary. Substandard and falsified medicines have the potential to either increase or decrease levels of resistance, and here we discuss a range of mechanisms that could drive these changes. Understanding these effects and their relative importance will require an improved understanding of how different drug exposures affect the emergence and spread of resistance and of how the percentage of active pharmaceutical ingredients in substandard and falsified medicines is temporally and spatially distributed.

Phylodynamic approaches to studying avian influenza virus

Avian influenza viruses can cause severe disease in domestic and wild birds and are a pandemic threat. Phylodynamics is the study of how epidemiological, evolutionary, and immunological processes can interact to shape viral phylogenies. This review summarizes how phylodynamic methods have and could contribute to the study of avian influenza viruses. Specifically, we assess how phylodynamics can be used to examine viral spread within and between wild or domestic bird populations at various geographical scales, identify factors associated with virus dispersal, and determine the order and timing of virus lineage movement between geographic regions or poultry production systems. We discuss factors that can complicate the interpretation of phylodynamic results and identify how future methodological developments could contribute to improved control of the virus.

Eco-evolutionary dynamics in finite network-structured populations with migration

We consider the effect of network structure on the evolution of a population. Models of this kind typically consider a population of fixed size and distribution. Here we consider eco-evolutionary dynamics where population size and distribution can change through birth, death and migration, all of which are separate processes. This allows complex interaction and migration behaviours that are dependent on competition. For migration, we assume that the response of individuals to competition is governed by tolerance to their group members, such that less tolerant individuals are more likely to move away due to competition. We look at the success of a mutant in the rare mutation limit for the complete, cycle and star networks. Unlike models with fixed population size and distribution, the distribution of the individuals per site is explicitly modelled by considering the dynamics of the population. This in turn determines the mutant appearance distribution for each network. Where a mutant appears impacts its success as it determines the competition it faces. For low and high migration rates the complete and cycle networks have similar mutant appearance distributions resulting in similar success levels for an invading mutant. A higher migration rate in the star network is detrimental for mutant success because migration results in a crowded central site where a mutant is more likely to appear.

Genetic Characteristics and Phylogeographic Dynamics of Echovirus

Echoviruses belong to the genus Enterovirus in the Picornaviridae family, forming a large group of Enterovirus B (EV-B) within the Enteroviruses. Previously, Echoviruses were classified based on the coding sequence of VP1. In this study, we performed a reliable phylogenetic classification of 277 sequences isolated from 1992 to 2019 based on the full-length genomes of Echovirus. In this report, phylogenetic, phylogeographic, recombination, and amino acid variability landscape analyses were performed to reveal the evolutional characteristics of Echovirus worldwide. Echoviruses were clustered into nine major clades, e.g., G1-G9. Phylogeographic analysis showed that branches G2-G9 were linked to common strains, while the branch G1 was only linked to G5. In contrast, strains E12, E14, and E16 clustered separately from their G3 and G7 clades respectively, and became a separate branch. In addition, we identified a total of 93 recombination events, where most of the events occurred within the VP1-VP4 coding regions. Analysis of amino acid variation showed high variability in the a positions of VP2, VP1, and VP3. This study updates the phylogenetic and phylogeographic information of Echovirus and indicates that extensive recombination and significant amino acid variation in the capsid proteins drove the emergence of new strains.

Vaccine hesitancy promotes emergence of new SARS-CoV-2 variants

The successive emergence of SARS-CoV-2 mutations has led to an unprecedented increase in COVID-19 incidence worldwide. Currently, vaccination is considered to be the best available solution to control the ongoing COVID-19 pandemic. However, public opposition to vaccination persists in many countries, which can lead to increased COVID-19 caseloads and hence greater opportunities for vaccine-evasive mutant strains to arise. To determine the extent that public opinion regarding vaccination can induce or hamper the emergence of new variants, we develop a model that couples a compartmental disease transmission framework featuring two strains of SARS-CoV-2 with game theoretical dynamics on whether or not to vaccinate. We combine semi-stochastic and deterministic simulations to explore the effect of mutation probability, perceived cost of receiving vaccines, and perceived risks of infection on the emergence and spread of mutant SARS-CoV-2 strains. We find that decreasing the perceived costs of being vaccinated and increasing the perceived risks of infection (that is, decreasing vaccine hesitation) will decrease the possibility of vaccine-resistant mutant strains becoming established by about fourfold for intermediate mutation rates. Conversely, we find increasing vaccine hesitation to cause both higher probability of mutant strains emerging and more wild-type cases after the mutant strain has appeared. We also find that once a new variant has emerged, perceived risk of being infected by the original variant plays a much larger role than perceptions of the new variant in determining future outbreak characteristics. Furthermore, we find that rapid vaccination under non-pharmaceutical interventions is a highly effective strategy for preventing new variant emergence, due to interaction effects between non-pharmaceutical interventions and public support for vaccination. Our findings indicate that policies that combine combating vaccine-related misinformation with non-pharmaceutical interventions (such as reducing social contact) will be the most effective for avoiding the establishment of harmful new variants.

Host adaptation drives genetic diversity in a vector-borne disease system

The range of hosts a pathogen can infect is a key trait, influencing human disease risk and reservoir host infection dynamics. Borrelia burgdorferi sensu stricto (Bb), an emerging zoonotic pathogen, causes Lyme disease and is widely considered a host generalist, commonly infecting mammals and birds. Yet the extent of intraspecific variation in Bb host breadth, its role in determining host competence, and potential implications for human infection remain unclear. We conducted a long-term study of Bb diversity, defined by the polymorphic ospC locus, across white-footed mice, passerine birds, and tick vectors, leveraging long-read amplicon sequencing. Our results reveal strong variation in host breadth across Bb genotypes, exposing a spectrum of genotype-specific host-adapted phenotypes. We found support for multiple niche polymorphism, maintaining Bb diversity in nature and little evidence of temporal shifts in genotype dominance, as would be expected under negative frequency-dependent selection. Passerine birds support the circulation of several human-invasive strains (HISs) in the local tick population and harbor greater Bb genotypic diversity compared with white-footed mice. Mouse-adapted Bb genotypes exhibited longer persistence in individual mice compared with nonadapted genotypes. Genotype communities infecting individual mice preferentially became dominated by mouse-adapted genotypes over time. We posit that intraspecific variation in Bb host breadth and adaptation helps maintain overall species fitness in response to transmission by a generalist vector.

The importance of utilizing travel history metadata for informative phylogeographical inferences: a case study of early SARS-CoV-2 introductions into Australia

Inferring the spatiotemporal spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) via Bayesian phylogeography has been complicated by the overwhelming sampling bias present in the global genomic dataset. Previous work has demonstrated the utility of metadata in addressing this bias. Specifically, the inclusion of recent travel history of SARS-CoV-2-positive individuals into extended phylogeographical models has demonstrated increased accuracy of estimates, along with proposing alternative hypotheses that were not apparent using only genomic and geographical data. However, as the availability of comprehensive epidemiological metadata is limited, many of the current estimates rely on sequence data and basic metadata (i.e. sample date and location). As the bias within the SARS-CoV-2 sequence dataset is extensive, the degree to which we can rely on results drawn from standard phylogeographical models (i.e. discrete trait analysis) that lack integrated metadata is of great concern. This is particularly important when estimates influence and inform public health policy. We compared results generated from the same dataset, using two discrete phylogeographical models: one including travel history metadata and one without. We utilized sequences from Victoria, Australia, in this case study for two unique properties. Firstly, the high proportion of cases sequenced throughout 2020 within Victoria and the rest of Australia. Secondly, individual travel history was collected from returning travellers in Victoria during the first wave (January to May) of the coronavirus disease 2019 (COVID-19) pandemic. We found that the implementation of individual travel history was essential for the estimation of SARS-CoV-2 movement via discrete phylogeography models. Without the additional information provided by the travel history metadata, the discrete trait analysis could not be fit to the data due to numerical instability. We also suggest that during the first wave of the COVID-19 pandemic in Australia, the primary driving force behind the spread of SARS-CoV-2 was viral importation from international locations. This case study demonstrates the necessity of robust genomic datasets supplemented with epidemiological metadata for generating accurate estimates from phylogeographical models in datasets that have significant sampling bias. For future work, we recommend the collection of metadata in conjunction with genomic data. Furthermore, we highlight the risk of applying phylogeographical models to biased datasets without incorporating appropriate metadata, especially when estimates influence public health policy decision making.

Importation of Alpha and Delta variants during the SARS-CoV-2 epidemic in Switzerland: Phylogenetic analysis and intervention scenarios

The SARS-CoV-2 pandemic has led to the emergence of various variants of concern (VoCs) that are associated with increased transmissibility, immune evasion, or differences in disease severity. The emergence of VoCs fueled interest in understanding the potential impact of travel restrictions and surveillance strategies to prevent or delay the early spread of VoCs. We performed phylogenetic analyses and mathematical modeling to study the importation and spread of the VoCs Alpha and Delta in Switzerland in 2020 and 2021. Using a phylogenetic approach, we estimated between 383-1,038 imports of Alpha and 455-1,347 imports of Delta into Switzerland. We then used the results from the phylogenetic analysis to parameterize a dynamic transmission model that accurately described the subsequent spread of Alpha and Delta. We modeled different counterfactual intervention scenarios to quantify the potential impact of border closures and surveillance of travelers on the spread of Alpha and Delta. We found that implementing border closures after the announcement of VoCs would have been of limited impact to mitigate the spread of VoCs. In contrast, increased surveillance of travelers could prove to be an effective measure for delaying the spread of VoCs in situations where their severity remains unclear. Our study shows how phylogenetic analysis in combination with dynamic transmission models can be used to estimate the number of imported SARS-CoV-2 variants and the potential impact of different intervention scenarios to inform the public health response during the pandemic.

Temporal Pattern of the Emergence of a Mutant Virus Escaping Cross-Immunity and Stochastic Extinction Within a Host

A high mutation rate of the RNA virus results in the emergence of novel mutants that may escape the immunity activated by the original (wild-type) strain. However, many of them go extinct because of the stochasticity due to the small initial number of infected cells. In a previous paper, we studied the probability of escaping stochastic extinction when the novel mutant has a faster rate of infection and when it is resistant to a drug that suppresses the wild-type virus. In this study, we examine the effect of escaping the immune reaction of the host. Based on a continuous-time branching process with time-dependent rates, we conclude the chance for a mutant strain to be established [Formula: see text] decreases with time [Formula: see text] since the wild-type infection when the mutant is produced. The number of novel mutants that can escape extinction risk has a peak soon after the wild-type infection. The number of novel escape mutations produced per patient in the early phase of host infection is small both for very strong and very weak immune responses, and it attains its maximum value when immune activity is of an intermediate strength.