E3SIM: epidemiological-ecological-evolutionary simulation framework for genomic epidemiology

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.

Estimating the impact of sarcoptic mange epidemic on the population size of wild raccoon dogs (Nyctereutes procyonoides) from wildlife rescue data

Pathogens are ubiquitous in insect populations and yet few studies examine their dynamics and impacts on host populations. We discuss four lepidopteran systems and explore their contributions to disease ecology and evolution. More specifically, we elucidate the role of pathogens in insect population dynamics. For three species, western tent caterpillars, African armyworm and introduced populations of gypsy moth, infection by nucleopolyhedrovirus (NPV) clearly regulates host populations or reduces their outbreaks. Transmission of NPV is largely horizontal although low levels of vertical transmission occur, and high levels of covert infection in some cases suggest that the virus can persist in a nonsymptomatic form. The prevalence of a mostly vertically transmitted protozoan parasite, Ophryocystis elektroscirrha, in monarch butterflies is intimately related to their migratory behaviour that culls highly infected individuals. Virulence and transmission are positively related among genotypes of this parasite. These systems clearly demonstrate that the interactions between insects and pathogens are highly context dependent. Not only is the outcome a consequence of changes in density and genetic diversity: environmental factors, particularly diet, can have strong impacts on virulence, transmission and host resistance or tolerance. What maintains the high level of host and pathogen diversity in these systems, however, remains a question.

ESA HISTORICAL RECORDS COMMITTEE (Established 1944) Newsletter

The spatial structure of a host population has a profound effect on the dynamics of infectious diseases. The basic reproduction number, a central quantity in the study of epidemic dynamics, is affected by host clustering as well as host density. Several authors have developed methods to quantify the basic reproduction number in a spatially structured host population. The methods used and the expressions derived are however difficult to apply to real life spatial host structures. In this paper we introduce an explicit expression for the basic reproduction number using the O‐ring statistic, developed in spatial statistics, that quantifies the host density as a function of the distance from a randomly selected host individual. The O‐ring statistic is frequently used in the study of the ecology of spatially structured plant populations, being a convenient summary of the properties of a landscape by way of a single function. The connection we develop between spatial statistics and epidemic dynamics can be used to study the effect of host spatial pattern on the basic reproduction number of infectious diseases. As well as showing how explicit expressions for the basic reproduction number can be derived for landscapes with standard structures, our expression for the basic reproduction number is tested against a simulation model. The model structure in our simulation is motivated by the spread of a plant disease epidemic, although it is applicable more broadly. The agreement between our analytic expression for the basic reproduction number and the corresponding numeric quantity extracted from simulations is close to perfect across a wide range of landscape structures and model parameterisations, and including cases in which more than one species of host is at risk of infection.

Models of Infectious Diseases in Spatially Heterogeneous Environments

BackgroundMonoculture, multi-cropping and wider use of highly resistant cultivars have been proposed as mechanisms to explain the elevated rate of evolution of plant pathogens in agricultural ecosystems. We used a mark-release-recapture experiment with the wheat pathogen Phaeosphaeria nodorum to evaluate the impact of two of these mechanisms on the evolution of a pathogen population. Nine P. nodorum isolates marked with ten microsatellite markers and one minisatellite were released onto five replicated host populations to initiate epidemics of Stagonospora nodorum leaf blotch. The experiment was carried out over two consecutive host growing seasons and two pathogen collections were made during each season.ResultsA total of 637 pathogen isolates matching the marked inoculants were recovered from inoculated plots over two years. Genetic diversity in the host populations affected the evolution of the corresponding P. nodorum populations. In the cultivar mixture the relative frequencies of inoculants did not change over the course of the experiment and the pathogen exhibited a low variation in selection coefficients.ConclusionsOur results support the hypothesis that increasing genetic heterogeneity in host populations may retard the rate of evolution in associated pathogen populations. Our experiment also provides indirect evidence of fitness costs associated with host specialization in P. nodorum as indicated by differential selection during the pathogenic and saprophytic phases.

Evolutionary suicide is a riveting phenomenon in which adaptive evolution drives a viable population to extinction. Gyllenberg and Parvinen (Bull Math Biol 63(5):981-993, 2001) showed that, in a wide class of deterministic population models, a discontinuous transition to extinction is a necessary condition for evolutionary suicide. An implicit assumption of their proof is that the invasion fitness of a rare strategy is well-defined also in the extinction state of the population. Epidemic models with frequency-dependent incidence, which are often used to model the spread of sexually transmitted infections or the dynamics of infectious diseases within herds, violate this assumption. In these models, evolutionary suicide can occur through a non-catastrophic bifurcation whereby pathogen adaptation leads to a continuous decline of host (and consequently pathogen) population size to zero. Evolutionary suicide of pathogens with frequency-dependent transmission can occur in two ways, with pathogen strains evolving either higher or lower virulence.

Patterns of diversity and turnover in macroorganism communities can often be predicted from differences in habitat, phylogenetic relationships among species and the geographical scale of comparisons. In this study, we asked whether these factors also predict diversity and turnover in parasite communities. We studied communities of avian malaria in two sympatric, ecologically similar, congeneric host species at three different sites. We asked whether parasite prevalence and community structure varied with host population, host phylogeography or geographical distance. We used PCR to screen birds for infections and then used Bayesian methods to determine phylogenetic relationships among malaria strains. Metrics of both community and phylogenetic beta diversity were used to examine patterns of malaria strain turnover between host populations, and partial Mantel tests were used determine the correlation between malaria beta diversity and geographical distance. Finally, we developed microsatellite markers to describe the genetic structure of host populations and assess the relationship between host phylogeography and parasite beta diversity. We found that different genera of malaria parasites infect the two hosts at different rates. Within hosts, parasite communities in one population were phylogenetically clustered, but there was otherwise no correlation between metrics of parasite beta diversity and geographical or genetic distance between host populations. Patterns of parasite turnover among host populations are consistent with malaria transmission occurring in the winter rather than on the breeding grounds. Our results indicate greater turnover in parasite communities between different hosts than between different study sites. Differences in host species, as well as transmission location and vector ecology, seem to be more important in structuring malaria communities than the distance-decay relationships frequently found in macroorganisms. Determining the factors affecting parasite community diversity and turnover has wide-ranging implications for understanding the selective pressures shaping host ecology and ecosystem structure. This study shows that metrics of community and phylogenetic beta diversity can be useful tools for disentangling the ecological and evolutionary processes that underlie geographical variation in parasite communities.

Many micro-organisms employ a parasitic lifestyle and, through their antagonistic interactions with host populations, have major impacts on human, agricultural and natural ecosystems. Most pathogens are likely to host parasites of their own, that is, hyperparasites, but how nested chains of parasites impact on disease dynamics is grossly neglected in the ecological and evolutionary literature. In this minireview we argue that the diversity and dynamics of micro-hyperparasites are an important component of natural host–pathogen systems. We use the current literature from a handful of key systems to show that observed patterns of pathogen virulence and disease dynamics may well be influenced by hyperparasites. Exploring these factors will shed light on many aspects of microbial ecology and disease biology, including resistance–virulence evolution, apparent competition, epidemiology and ecosystem stability. Considering the importance of hyperparasites in natural populations will have applied consequences for the field of biological control and therapeutic science, where hyperparastism is employed as a control mechanism but not necessarily ecologically understood.

Beyond Gene Discovery in Inflammatory Bowel Disease: The Emerging Role of Epigenetics

1. Methods for measuring animal movement are critical for understanding numerous ecological and evolutionary processes. However, few methods are available for small organisms, and even fewer methods offer consistent individual-level resolution while remaining affordable, scalable and operable in the field. 2. We describe a low-cost animal movement tracking method with a user-friendly graphical interface, called GRAPHITE. Our automated software can quantify motions of insects by offline video analysis of inexpensive and lightweight human-readable tags attached to individual insects. The integrated graphical editor provides a full-featured environment for users to review the generated tracking data and make individual- or group-level edits. 3. GRAPHITE is a novel video analysis and graphical editing software (MATLAB v.9.0.0+) that identifies tags in image frames with a minimal false negative rate, links sequences of corresponding tags into "tracks" for each individual insect, infers the tag identifier, and provides a user-friendly graphical environment for editing tracking data. Users can either batch process raw video data using the full analysis pipeline or execute GRAPHITE modules independently for a tailored analysis. 4. We demonstrate the efficacy of the developed software with a specific application to the movement of honey bees at the entrance of hives. However, this system can be easily modified to track individually marked insects of 3 mm and larger. A notable advantage of this method is its ability to provide easy access to individual-level tracking data using human-readable tags.

Blood-feeding mosquitoes locate humans spatially by detecting a combination of human-derived chemical signals, including carbon dioxide, lactic acid, and other volatile organic compounds. Mosquitoes use these signals to differentiate humans from other animals. Spatial abiotic factors (e.g. humidity, heat) are also used by mosquitoes to find a host. Mosquitoes cause discomfort and harm to humans, being vectors of many pathogens. However, not all humans suffer from mosquito bites with the same frequency or intensity. Some individuals are more attractive to mosquitoes than others, and this has an important impact on the risk of infection by pathogens transmitted by these vectors, such as arboviruses and malaria parasites. Variability in human attractiveness to mosquitoes is partially due to individual characteristics in the composition and intensity in the release of mosquito attractants. The factors that determine these particularities are diverse, modestly understood and still quite controversial. Thus, this review discusses the role of pregnancy, infection with malaria parasites (Plasmodium spp.), skin microbiota, diet, and genetics in human attractiveness to mosquitoes. In brief, pregnancy and Plasmodium infection increase the host attractiveness to mosquitoes. Skin microbiota and human genetics (especially HLA alleles) modulate the production of mosquito attractants and therefore influence individual susceptibility to these insects. There is evidence pointing to a role of diet on human susceptibility to mosquitoes, with some dietary components having a bigger influence than others. In the last part of the review, other factors affecting human-mosquito interactions are debated, with a special focus on the role of mosquito genetics, pathogens and environmental factors (e.g. wind, environmental disturbances). This work highlights that individual susceptibility to mosquitoes is composed of interactions of different human-associated components, environmental factors, and mosquito characteristics. Understanding the importance of these factors, and how they interact with each other, is essential for the development of better mosquito control strategies and studies focused on infectious disease dynamics.

TEMPERATURE CAN BE AN EFFECTIVE PREDICTOR FOR DENGUE FEVER OUTBREAK

Salmonella is a zoonotic disease that is transmitted from animal products by contact with sick animals or the environments where these animals are living. The mathematical model of transmission disease improve the students' understanding of pathogen dynamics, the role of factors that influence the transmission and control of a specific pathogen and the trend of antimicrobial resistance for this pathogen. The tendency to increase of resistance of Salmonella to antibiotic and combination of antibiotics suggest that models are useful in simulation of different scenarios for dynamic of transmission of this disease. There are two main Salmonella types: Typhimurium serotype and Enteritidis serotype. Both types are included in the software toolbox in a tutorial and interactive manners. The three models of Salmonella compartmental are presented in friendly manner to user with possibility to automatically generation of system equations using built-in templates. Tools that calculate the R0 number and stability analysis are provided as modules in order to evaluate how the experimental data are fit to model or to evaluate the influence of constant coefficients over mathematical model. Because Salmonella typhi bacteria is responsible for a communicable disease, Typhoid fever, an optional module is append to main software in order to give to student the possibility to improve the knowledge with mathematical model of this disease as direct result of a particular bacteria from a larger group of bacteria. The educational software has a friendly GUI (Graphic User Interface) that help student to understand better the dynamic of a specific pathogen modeled by class of larger mathematical models, the compartmental models.