EvoLaps: a web interface to visualize continuous phylogeographic reconstructions

Infectious disease dynamics result from the complex interplay of epidemiological, ecological and evolutionary (epi-eco-evo) processes. Accurately modelling these coupled processes is crucial for understanding pathogen spread and informing public health strategies. However, existing genomic epidemiology simulators typically assume conditional independence among these processes: generating transmission trees independently of pathogen evolution, and then superimposing neutral mutations onto fixed genealogies without ecological feedback. This simplification fails to capture how pathogen evolution dynamically reshapes epidemic trajectories.We introduce e3SIM, an open-source, agent-based, forward-time simulator for macOS and Linux that explicitly integrates pathogen transmission dynamics, molecular evolution and environmental factors. e3SIM incorporates configurable compartmental models, user-defined host contact networks, customizable pathogen genetic architectures and optional eco-evolutionary features (e.g. within-host dynamics, multi-strain infections). This integration enables realistic modelling of pathogen spread and evolution. Key features include modularity, flexible epidemiological and population-genetic modelling, time-varying environmental factors and a user-friendly graphical interface.We demonstrated e3SIM’s capabilities by simulating SARS-CoV-2 and Mycobacterium tuberculosis outbreaks. e3SIM captured the emergence and spread of drug-resistant variants under sequential treatments, highlighting how pathogen evolution and environmental variations dynamically reshape epidemic trajectories. We also illustrated how interactions between pathogen transmissibility and host population structures, particularly those involving socially active “superspreaders”, strongly influence pathogen lineage expansion and transmission clusters. Runtime profiling demonstrated computational efficiency and scalability.e3SIM provides a powerful tool for simulating infectious disease dynamics through the explicit integration of epi-eco-evo processes, substantially enhancing realism and predictive accuracy in genomic epidemiology. Its modular, user-friendly design supports broad applications across diverse host-pathogen systems, enabling rigorous exploration of scenarios critical to public health.

Infectious disease dynamics are driven by the complex interplay of epidemiological, ecological, and evolutionary processes. Accurately modeling these interactions is crucial for understanding pathogen spread and informing public health strategies. However, existing simulators often fail to capture the dynamic interplay between these processes, resulting in oversimplified models that do not fully reflect real-world complexities in which the pathogen’s genetic evolution dynamically influences disease transmission. We introduce the epidemiological-ecological-evolutionary simulator (e3SIM), an open-source framework that concurrently models the transmission dynamics and molecular evolution of pathogens within a host population while integrating environmental factors. Using an agent-based, discrete-generation, forward-in-time approach, e3SIM incorporates compartmental models, host-population contact networks, and quantitative-trait models for pathogens. This integration allows for realistic simulations of disease spread and pathogen evolution. Key features include a modular and scalable design, flexibility in modeling various epidemiological and population-genetic complexities, incorporation of time-varying environmental factors, and a user-friendly graphical interface. We demonstrate e3SIM’s capabilities through simulations of realistic outbreak scenarios with SARS-CoV-2 and Mycobacterium tuberculosis, illustrating its flexibility for studying the genomic epidemiology of diverse pathogen types.

Host individuals and populations often vary in their responses to infection, with direct consequences for pathogen spread and evolution. While considerable work has focused on the mechanisms underlying differences in resistance-the ability to kill pathogens-we know little about the mechanisms underlying tolerance-the ability to minimize fitness losses per unit pathogen. Here, we examine patterns and mechanisms of tolerance between two populations of house finches (Haemorhous [formerly Carpodacus] mexicanus) with different histories with the bacterial pathogen Mycoplasma gallisepticum (MG). After infection in a common environment, we assessed two metrics of pathology, mass loss and eye lesion severity, as proxies for fitness. We calculated tolerance using two methods, one based on pathology and pathogen load at the peak of infection (point tolerance) and the other based on the integrals of these metrics over time (range tolerance). Alabama birds, which have a significantly longer history of exposure to MG, showed more pronounced point tolerance than Arizona birds, while range tolerance did not differ between populations. Alabama birds also displayed lower inflammatory cytokine signaling and lower fever early in infection. These results suggest that differences in inflammatory processes, which can significantly damage host tissues, may contribute to variation in tolerance among house finch individuals and populations. Such variation can affect pathogen spread and evolution in ways not predictable by resistance alone and sheds light on the costs and benefits of inflammation in wild animals.

How infectious disease spreads both from individual to individual and across a landscape will depend upon many inter-related factors, including the genetic composition of host and pathogen populations, the pathogen transmission rate, host density, population connectivity, and the evolutionary response of both host and pathogen over time. As such, the study of infectious disease straddles a number of fields and approaches. A key advance in the field has come from incorporation of spatial structure into both theoretical and empirical studies of the epidemiology, evolution, and ecology of disease. Researchers taking this approach were recently brought together for a workshop entitled ‘Spatial evolutionary epidemiology’ in Montpellier, France organized by Sebastien Lion and Sylvain Gandon from the Centre d’Ecologie Fonctionnelle et Evolutive (CEFE). The workshop spanned topics from infection genetics in Daphnia, to cooperation and conflict in microbial populations, to the utility of spatial epidemiological models for designing better crop planting strategies, and emphasized the broader need to think about the importance of spatial heterogeneity in order to predict the spread and evolution of pathogens. The recent literature includes a number of studies at the cutting edge of this field. For example, a model based on interactions between bacteria and their bacteriophage parasites by Ben Ashby and collaborators has demonstrated the importance of spatial structure in shaping the breadth of host resistance. They find that hosts and parasites from spatially structured populations should be less constrained by costs associated with ‘generalism’ than those from well-mixed populations, and therefore, that spatial structure is likely to increases the breadth of host resistance/parasite infectivity, especially when this increased breadth carries a significant fitness cost (Ashby et al. 2014). Another potential effect of spatial structure on host-parasite interactions is through changing rates of infection, as opposed to infection range. A recent empirical study of bacteria and phages by Pavitra Roychoudhury and coauthors experimentally evolved phages on spatially structured agar plates for 550 phage generations and found increased phage fitness was associated with a mutation conferring slower phage adsorption rate to bacterial host cells. This is in line with theoretical work suggesting spatial structure may lead to reduced parasite infectivity (e.g., Boots and Sasaki 1999) and builds upon previous empirical work from nonmicrobial systems in support of this theory. Furthermore, the authors develop a system-specific, spatially explicit model to explain how low attachment probability might lead to increased phage fitness through higher plaque density as a result of trade-offs between phage diffusion and adsorption (Roychoudhury et al. 2014). Studying spatial structure in natural populations is of course a more challenging goal, as one loses the ability to control for the type of heterogeneity across which a study is performed. Despite these potential limitations, recent work by Jussi Jousimo, Ayco Tack, and collaborators examined the impact of connectivity among over 4000 populations of the plant, Plantago lanceolata, on resistance to a co-occurring fungal pathogen, Podosphaera plantaginis, over a 12-year period. Using this large spatial dataset, the authors were able to explain the unexpected observation that highly connected populations typically showed lower pathogen prevalence and higher pathogen extinction than more isolated populations. They demonstrate that hosts from highly connected populations are in fact more resistant to the pathogen than those from more isolated populations, most likely as a result of stronger parasite-mediated selection over time (Jousimo et al. 2014). This work highlights the importance of incorporating host and pathogen evolution into models predicting the spread of disease across space, and emphasizes the potential for differential parasite-mediated selection across a host metapopulation. Finally, work by David Rasmussen and colleagues has demonstrated the strength of incorporating spatial structure into phylodynamic (coalescent) approaches to inferring past disease dynamics from genealogies (Rasmussen et al. 2014). Motivated to explain the common discrepancy between phylodynamic inferences and those made from hospital records, the authors apply their new method to a case study of mosquito-borne dengue virus in southern Vietnam. They demonstrate that a spatially structured susceptible-infected-recovered (SIR) model resulted in similar patterns to those seen from the hospitalization data, including large seasonal fluctuations in disease, and that the incorporation of ecological complexity into coalescent models increases the accuracy of inferences to disease demography. Overall, these recent studies demonstrate the added value of incorporating spatial structure into epidemiological, phylodynamic, and evolutionary/ecological models of infectious disease. Although the current body of theory on the topic offers clear predictions for how spatial structure might influence disease evolution and spread, there remains a paucity of empirical and observational studies testing these key ideas. Moving forward, the more we understand about these eco-evolutionary feedbacks, the better we will be able to manage emerging disease in natural, agricultural, and human populations.

Whole genome sequencing has revolutionized infectious disease surveillance for tracking and monitoring the spread and evolution of pathogens. However, using a linear reference genome for genomic analyses may introduce biases, especially when studies are conducted on highly variable bacterial genomes of the same species. Pangenome graphs provide an efficient model for representing and analyzing multiple genomes and their variants as a graph structure that includes all types of variations. In this study, we present a practical bioinformatics pipeline that employs the PanGenome Graph Builder and the Variation Graph toolkit to build pangenomes from assembled genomes, align whole genome sequencing data and call variants against a graph reference. The pangenome graph enables the identification of structural variants, rearrangements, and small variants (e.g., single nucleotide polymorphisms and insertions/deletions) simultaneously. We demonstrate that using a pangenome graph, instead of a single linear reference genome, improves mapping rates and variant calling for both simulated and real datasets of the pathogen Neisseria meningitidis. Overall, pangenome graphs offer a promising approach for comparative genomics and comprehensive genetic variation analysis in infectious disease. Moreover, this innovative pipeline, leveraging pangenome graphs, can bridge variant analysis, genome assembly, population genetics, and evolutionary biology, expanding the reach of genomic understanding and applications.

The evolution of viral pathogens is shaped by strong selective forces that are exerted during jumps to new hosts, confrontations with host immune responses and antiviral drugs, and numerous other processes. However, while undeniably strong and frequent, adaptive evolution is largely confined to small parts of information-packed viral genomes, and the majority of observed variation is effectively neutral. The predictions and implications of the neutral theory have proven immensely useful in this context, with applications spanning understanding within-host population structure, tracing the origins and spread of viral pathogens, predicting evolutionary dynamics, and modeling the emergence of drug resistance. We highlight the multiple ways in which the neutral theory has had an impact, which has been accelerated in the age of high-throughput, high-resolution genomics.

Since the first complete sequencing of a free-living organism, Haemophilus influenzae, genomics has been used to probe both the biology of bacterial pathogens and their evolution. Single-genome approaches provided information on the repertoire of virulence determinants and host-interaction factors, and, along with comparative analyses, allowed the proposal of hypotheses to explain the evolution of many of these traits. These analyses suggested many bacterial pathogens to be of relatively recent origin and identified genome degradation as a key aspect of host adaptation. The advent of very-high-throughput sequencing has allowed for detailed phylogenetic analysis of many important pathogens, revealing patterns of global and local spread, and recent evolution in response to pressure from therapeutics and the human immune system. Such analyses have shown that bacteria can evolve and transmit very rapidly, with emerging clones showing adaptation and global spread over years or decades. The resolution achieved with whole-genome sequencing has shown considerable benefits in clinical microbiology, enabling accurate outbreak tracking within hospitals and across continents. Continued large-scale sequencing promises many further insights into genetic determinants of drug resistance, virulence and transmission in bacterial pathogens.

Bacterial leaf streak of corn, caused by Xanthomonas vasicola pv. vasculorum, has been present in South Africa for over 70 years, but is an emerging disease of corn in North and South America. The only scientific information pertaining to this disease on corn came from work done in South Africa, which primarily investigated host range on other African crops, such as sugarcane and banana. As a result, when the disease was first reported in the United States in 2016, there was very limited information on where this pathogen came from, how it infects its host, what plant tissue(s) it is capable of infecting, where initial inoculum comes from at the beginning of each crop season, how the bacterium spreads from plant to plant and long distance, what meteorological variables and agronomic practices favor disease development and spread, how many other plant species X. vasicola pv. vasculorum is capable of infecting or using as alternate hosts, and if the bacterium will be able to persist in all corn growing regions of the United States. There were also no rapid diagnostic assays available which initially hindered prompt identification prior to the development of molecular diagnostic tools. The goal of this synthesis is to review the history of X. vasicola pv. vasculorum and bacterial leaf streak in South Africa and its movement to North and South America, and highlight the recent research that has been done in response to the emergence of this bacterial disease.

Bats are unique mammals, exhibit distinctive life history traits and have unique immunological approaches to suppression of viral diseases upon infection. High-throughput next-generation sequencing has been used in characterizing the virome of different bat species. The cave nectar bat, Eonycteris spelaea, has a broad geographical range across Southeast Asia, India and southern China, however, little is known about their involvement in virus transmission. Here we investigate the diversity and abundance of viral communities from a colony of Eonycteris spelaea residing in Singapore. Our results detected 47 and 22 different virus families from bat fecal and urine samples, respectively. Among these, we identify a large number of virus families including Adenoviridae, Flaviviridae, Reoviridae, Papillomaviridae, Paramyxoviridae, Parvoviridae, Picornaviridae, and Polyomaviridae. In most cases, viral sequences from Eonycteris spelaea are genetically related to a group of bat viruses from other bat genera (e.g., Eidolon, Miniopterus, Rhinolophus and Rousettus). The results of this study improve our knowledge of the host range, spread and evolution of several important viral pathogens. More significantly, our findings provide a baseline to study the temporal patterns of virus shedding and how they correlate with bat phenological trends.

The One Health approach integrates health investigations across the tree of life, including, but not limited to, wildlife, livestock, crops, and humans. It redresses an epistemological alienation at the heart of much modern population health, which has long segregated studies by species. Up to this point, however, One Health research has also omitted addressing fundamental structural causes underlying collapsing health ecologies. In this critical review we unpack the relationship between One Health science and its political economy, particularly the conceptual and methodological trajectories by which it fails to incorporate social determinants of epizootic spillover. We also introduce a Structural One Health that addresses the research gap. The new science, open to incorporating developments across the social sciences, addresses foundational processes underlying multispecies health, including the place-specific deep-time histories, cultural infrastructure, and economic geographies driving disease emergence. We introduce an ongoing project on avian influenza to illustrate Structural One Health's scope and ambition. For the first time researchers are quantifying the relationships among transnational circuits of capital, associated shifts in agroecological landscapes, and the genetic evolution and spatial spread of a xenospecific pathogen.

The increasing prevalence of multidrug-resistant (MDR) and extensively drug-resistant (XDR) Acinetobacter baumannii presents a major global health threat, particularly in hospital settings. Understanding the genomic landscape of A. baumannii is essential to elucidate its virulence mechanisms, resistance profiles, and evolutionary adaptations, which are critical for developing novel therapeutic strategies. This study aims to comprehensively analyze the pan-genome, antimicrobial resistance (AMR) genes, virulence factors, and clonal relationships of A. baumannii, with the goal of uncovering insights into its pathogenicity and genomic evolution. A total of 27,884 A. baumannii genomes were retrieved from GenBank for analysis. Genome annotation was carried out using Prokka, and pan-genome analysis was performed with Roary. AMR genes and virulence factors were identified through AMRFinderPlus and the Virulence Factor Database via Abricate. Temporal trends in AMR and virulence were analyzed statistically to assess changes over time. The study found that A. baumannii possesses a stable core genome and a highly diverse accessory genome, suggesting an open pan-genome structure. Temporal analysis revealed a significant increase in AMR genes, including blaOXA-23, blaNDM-1, and pmrCAB. Virulence genes were widely distributed across strains, with the sequence types (ST) like ST2Pas clone exhibiting global dissemination, highlighting the strain's potential for widespread pathogenicity. This comprehensive genomic analysis of A. baumannii reveals its significant genomic diversity and adaptability, underscoring the critical role of both core and accessory genomes in shaping its pathogenicity and resistance mechanisms. The increasing prevalence of key AMR genes, alongside the widespread dissemination of virulent clones, highlights the urgent need for enhanced surveillance and novel therapeutic strategies to control the spread of this global pathogen. Future research should focus on the functional characterization of resistance and virulence factors to better understand their roles in pathogenicity and to facilitate the development of targeted interventions against A. baumannii.

The spread and adaptation of fungal plant pathogens in agroecosystems are facilitated by environmental homogeneity. Metagenomic sequencing of infected tissues allowed us to monitor eco-evolutionary dynamics and interactions between host, pathogen and plant microbiome. Exserohilum turcicum, the causal agent of northern corn leaf blight (NCLB) in maize, is distributed in multiple clonal lineages throughout Europe. To characterize regional pathogen diversity, we conducted metagenomic DNA sequencing on 241 infected leaf samples from the highly susceptible Swiss maize landrace Rheintaler Ribelmais, collected over 3 years (2016-2018) from an average of 14 agricultural farms within the Swiss Rhine Valley. All major European clonal lineages of E. turcicum were identified. Lineages differ by their mating types which indicates potential for sexual recombination and rapid evolution of new pathogen strains, although we found no evidence of recent recombination. The associated eukaryotic and prokaryotic leaf microbiome exhibited variation in taxonomic diversity between years and locations and is likely influenced by local weather conditions. A network analysis revealed distinct clusters of eukaryotic and prokaryotic taxa that correlates with the frequency of E. turcicum sequencing reads, suggesting causal interactions. Notably, the yeast genus Metschnikowia exhibited a strongly negative association with E. turcicum, supporting its known potential as biological control agent against fungal pathogens. Our findings show that metagenomic sequencing is a useful tool for analysing the role of environmental factors and potential pathogen-microbiome interactions in shaping pathogen dynamics and evolution, suggesting their potential for effective pathogen management strategies.

Currently, there is great interest in detecting complex trait rare variant associations using next-generation sequence data. On a monthly basis, new rare variant association methods are published. It is difficult to evaluate these methods because there is no standard to generate data and often comparisons are biased. In order to fairly compare rare variant association methods, it is necessary to generate data using realistic population demographic and phenotypic models. SimRare is an interactive program that integrates generation of rare variant genotype/phenotype data and evaluation of association methods using a unified platform. Variant data are generated for gene regions using forward-time simulation that incorporates realistic population demographic and evolutionary scenarios. Phenotype data can be obtained for both case-control and quantitative traits. SimRare has a user-friendly interface that allows for easy entry of genetic and phenotypic parameters. Novel rare variant association methods implemented in R can also be imported into SimRare, to evaluate their performance and compare results, e.g. power and Type I error, with other currently available methods both numerically and graphically.

In recent years, the demand for intelligent and efficient energy management systems has increased significantly. Traditional electricity meters only provide total energy consumption data and fail to offer appliance-wise monitoring, resulting in a lack of transparency and control over energy usage. This paper presents PowerVision, an AI-powered Smart Energy Tracker and Bill Predictor designed to monitor real-time, appliance-wise power consumption and predict future electricity bills using Artificial Intelligence (AI) and Machine Learning (ML) techniques. The proposed system bridges the gap between Electrical Engineering and InformationTechnology by integrating hardware sensors with a Java Full Stack web application. Real-time data from household appliances such as PC, fan, and bulb are collected using sensors interfaced with a microcontroller (ESP32) and stored in a MySQL database. The web-based dashboard, built using HTML, CSS, and Bootstrap, provides a responsive and user-friendly interface with graphical visualization of power usage and AI-generated bill prediction. Additionally, the system supports voice control features for enhanced interactivity. The implementation demonstrates accurate power tracking, intelligent bill forecasting, and an intuitive visualization platform that can be extended for residential and industrial applications. The project thus contributes to efficient energy The system’s web interface, developed using HTML, CSS, and Bootstrap, displays detailed energy utilization, cost optimization, and smart energy management for the future.

Ion mobility coupled to mass spectrometry informs on the shape and size of protein structures in the form of a collision cross section (CCSIM). Although there are several computational methods for predicting CCSIM based on protein structures, including our previously developed projection approximation using rough circular shapes (PARCS), the process usually requires prior experience with the command-line interface. To overcome this challenge, here we present a web application on the Rosetta Online Server that Includes Everyone (ROSIE) webserver to predict CCSIM from protein structure using projection approximation with PARCS. In this web interface, the user is only required to provide one or more PDB files as input. Results from our case studies suggest that CCSIM predictions (with ROSIE-PARCS) are highly accurate with an average error of 6.12%. Furthermore, the absolute difference between CCSIM and CCSPARCS can help in distinguishing accurate from inaccurate AlphaFold2 protein structure predictions. ROSIE-PARCS is designed with a user-friendly interface, is available publicly and is free to use. The ROSIE-PARCS web interface is supported by all major web browsers and can be accessed via this link (https://rosie.graylab.jhu.edu).